Reviewer #3 (Public Review):

A mechanical model of C. elegans, embedded in a resistive force environment, is used to calculate input torque patterns required to generate output curvature patterns and coordinates, corresponding to a number of different locomotion behaviors in C. elegans.

Strengths:

The use of a mechanical model to study a variety of locomotor sequences and the grounding in empirical data are strengths. The matching of speeds (though requiring adjusted drag coefficients) is a strength.

Weaknesses:

The paper lacks evidence of numerical validation or comparison with the results and tools in the literature. E.g. is it surprising that the uniform torque distribution yields maximal speed? What is the relation between input and output data? How does the input-output relation depend on the parameters of the model? What novel model predictions are made?

In particular, if validated, the breakdown of drag forces and torque distributions during forward locomotion and turning behaviors may be interesting to compare to predictions by other tools, and to empirical measurement. One caveat is that the worm touches itself during such turns, and even crosses over itself in delta turns, and so the estimated drag coefficients and the resultant mechanical forces are likely incorrect.

eLife assessment

This paper reports a valuable set of new results. The main result is that the projection from adult-born granule cells in the dentate gyrus to the hippocampal subfield CA2 is necessary for the retrieval of a social memory formed during development, and solid evidence is provided to support this conclusion.

Reviewer #2 (Public Review):

Summary:

Laham et al. investigate how the projection from adult born granule cells into CA2 affects the retrieval of social memories at various developmental points. They use chemogenetic manipulations and electrophysiological recordings to test how this projection affects hippocampal network properties during behavior. The study is of relevant interest for the neuroscience community and the results are important for our understanding of how social memories of different nature (remote or immediate) are encoded and supported by the hippocampal circuitry. The behavioral experiments after abGC projections to CA2 are compelling as they show clearly distinct behavioral readout. While the electrophysiological experiments are difficult to interpret without more single cell responses quantifications, they clearly show that more than one region in the hippocampus is involved in the formation of social memories.

Reviewer #3 (Public Review):

Laham et al. present a manuscript investigating the function of adult-born granule cells (abGCs) projecting to the CA2 region of the hippocampus during social memory. It should be noted that no function for the general DG to CA2 projection has been proposed yet. The authors use targeted ablation, chemogenetic silencing and in vivo ephys to demonstrate that the abGCs to CA2 projection is necessary for the retrieval of a remote social memories such as the memory of one's mother. They also use in vivo ephys to show that abGCs are necessary for differential CA2 network activity, including theta-gamma coupling and sharp wave-ripples, in response to novel versus familiar social stimuli.

The question investigated is important since the function of DG to CA2 projection remained elusive a decade after its discovery. Overall, the results are interesting but focused to the social memory of the mother and their description in the manuscript and figures is too cursory. For example, raw interaction times must be shown before their difference. The assumption that mice exhibit social preference between familiar or novel individuals such as mother and non-mother based on social memory formation, consolidation and retrieval should be better explained throughout the manuscript. Thus, when describing the results, the authors should comment on changes in preference and how this can be interpreted as a change in social memory retrieval. Several critical experimental details such as the total time of presentation to the mother and non-mother stimulus mice are also lacking from the manuscript. The in vivo e-phys results are interesting as well but even more succinct with no proposed mechanism as to how abGCs could regulate SWR and PAC in CA2.

The manuscript is well-written with the appropriate references. The choice of behavioral test is somewhat debatable however. It is surprising the authors chose to use a direct presentation test (presentation of the mother and non-mother in alternance) instead of the classical 3-chamber test which is particularly appropriate to investigate social preference. Since the authors focused exclusively on this preference, the 3-chamber test would have been more adequate in my opinion. It would greatly strengthened the results if the authors could repeat a key experiment from their investigation using such test. In addition, the authors only impaired the mother's memory. An additional experiment showing that disruption of the abGCs to CA2 circuit impairs social memory retrieval in general would allow to generalize the findings to social memories in general. As the manuscript stands, the authors can only conclude as to the importance of this circuit for the memory of the mother. Developmental memory implies the memory of familiar kin as well.

The in vivo ephys section (Figure 3) is interesting but even more minimalistic and it is unclear how abGCs projection to CA2 can contribute to SWR and theta-gamma PAC. In figure 1, the authors suggest that abGCs project preferentially to PV+ neurons in CA2. At minima, the authors should discuss how the abGCs to PV+ neurons to CA2 pyramidal neurons circuit can facilitate SWR and theta-gamma PAC.

Finally, proposing a function for 4-6-week-old abGCs projecting to CA2 begs two questions: What are abGCs doing once they mature further and more generally, what is the function of the DG to CA2 projection? It would be interesting for the authors to comment on these questions in the discussion.

Revision:

The authors have followed my recommendations except for the ones suggesting new experiments. As a result, the clarity of the manuscript and the links between evidence and claims have improved by the message is quite reduced. Many important questions remain open such as: What makes mother's memories so special they require the abGC projection to CA2 unlike other types of social memories? Do abGCs truly connect CA2 PV+ interneurons and how does this connection shape sharp-wave ripples in CA2?

eLife assessment

In their study, Diana et al. introduce a novel method for spike inference from calcium imaging data using a Monte Carlo-based approach, emphasizing the quantification of uncertainties in spike time estimates through a Bayesian framework. This method employs particle Gibbs sampling for estimating model parameter probabilities, offering accuracy comparable to existing methods with the added benefit of directly assessing uncertainties. Although the paper provides a solid methodological explanation, it lacks a thorough comparison with other inference methods. Nevertheless, it presents a valuable advancement for neuroscientists interested in new approaches for parameter estimation from calcium imaging data.

Reviewer #1 (Public Review):

Summary:

In this study, Diana et al. present a Monte Carlo-based method to perform spike inference from calcium imaging data. A particular strength of their approach is that they can estimate not only averages but also uncertainties of the modeled process. The authors then focus on the quantification of spike time uncertainties in simulated data and in data recorded with a high sampling rate in cerebellar slices with GCaMP8f.

Strengths:

- The authors provide a solid groundwork for sequential Monte Carlo-based spike inference, which extends previous work of Pnevmatikakis et al., Greenberg et al., and others.

- The integration of two states (silence vs. burst firing) seems to improve the performance of the model.

- The acquisition of a GCaMP8f dataset in the cerebellum is useful and helps make the point that high spike time inference precision is possible under certain conditions.

Weaknesses:

- The algorithm is designed to predict single spike times. Currently, it is not benchmarked against other algorithms in terms of single spike precision and spike time errors. A benchmarking with the most recent other SMC model and another good model focused on single spike outputs (e.g., MLSpike) would be useful to have.

- Some of the analyses and benchmarks seem too cursory, and the reporting simply consists of a visual impression of results instead of proper analysis and quantification. For example, the authors write "The spike patterns obtained using our method are very similar across trials, showing that PGBAR can reliably detect single-trial action potential-evoked GCaMP8f fluorescence transients." This is a highly qualitative statement, just based on the (subjective) visual impression of a plot. Similarly, the authors write "we could reliably identify the two spikes in each trial", but this claim is not supported by quantification or a figure, as far as I can see. The authors write "but the trade-off between temporal accuracy, SNR and sampling frequency must be considered", but they don't discuss these trade-offs systematically.

- It has been shown several times from experimental data that spike inference with single spike resolution does not work well (Huang et al. eLife, 2021; Rupprecht et al., Nature Neuroscience, 2021) in general. This limitation should be discussed with respect to the applicability of the proposed algorithm for standard population calcium imaging data.

- Several analyses are based on artificial, simulated data with simplifying assumptions. Ever since Theis et al., Neuron, 2016, it has been known that artificially generated ground truth data should not be used as the primary means to evaluate spike inference algorithms. It would have been informative if the authors had used either the CASCADE dataset or their cerebellum dataset for more detailed analyses, in particular of single spike time precision.

- In its current state, the sum of the current weaknesses makes the suggested method, while interesting for experts, rather unattractive for experimentalists who want to perform spike inference on their recorded calcium imaging data.

Other comments:

- One of the key features of the SMC model is the assumption of two states (bursting vs. non-bursting). However, while it seems clear that this approach is helpful, it is not clear where this idea comes from, from an observation of the data or another concept.

- Another SMC algorithm (Greenberg et al., 2018) stated that the fitted parameters showed some degeneracy, resulting in ambiguous fitting parameters. It would be good to know if this problem was avoided by the authors.

Reviewer #2 (Public Review):

Summary:

Methods to infer action potentials from fluorescence-based measurements of intracellular calcium dynamics are important for optical measurements of activity across large populations of neurons. The variety of existing methods can be separated into two broad classes: a) model-independent approaches that are trained on ground truth datasets (e.g., deep networks), and b) approaches based on a model of the processes that link action potentials to calcium signals. Models usually contain parameters describing biophysical variables, such as rate constants of the calcium dynamics and features of the calcium indicator. The method presented here, PGBAR, is model-based and uses a Bayesian approach. A novelty of PGBAR is that static parameters and state variables are jointly estimated using particle Gibbs sampling, a sequential Monte Carlo technique that can efficiently sample the latent embedding space.

Strengths:

A main strength of PGBAR is that it provides probability distributions rather than point estimates of spike times. This is different from most other methods and may be an important feature in cases when estimates of uncertainty are desired. Another important feature of PGBAR is that it estimates not only the state variable representing spiking activity but also other variables such as baseline fluctuations and stationary model variables, in a joint process. PGBAR can therefore provide more information than various other methods. The information in the GitHub repository is well-organized.

Weaknesses:

On the other hand, the accuracy of spike train reconstructions is not higher than that of other model-based approaches, and clearly lower than the accuracy of a model-independent approach based on a deep network. The authors demonstrate convincingly that PGBAR can resolve inter-spike intervals in the range of 5 ms using fluorescence data obtained with a very fast genetically encoded calcium indicator at very high sampling rates (line scans at >= 1 kHz). It would be interesting to more systematically compare the performance of PGBAR to other methods in this regime of high temporal resolution, which has not been explored much.

eLife assessment

This study explores the role of one the most abundant circRNAs, circHIPK3, in bladder cancer cells, providing convincing data that circHIPK3 depletion affects thousands of genes and that those downregulated (including STAT3) share an 11-mer motif with circHIPK3, corresponding to a binding site for IGF2BP2. The experiments demonstrate that circHIPK3 can compete with the downregulated mRNAs targets for IGF2BP2 binding and that IGF2BP2 depletion antagonizes the effect of circHIPK3 depletion by upregulating the genes containing the 11-mer motif. These valuable findings contribute to the growing recognition of the complexity of cancer signaling regulation and highlight the intricate interplay between circRNAs and protein-coding genes in tumorigenesis.

Reviewer #1 (Public Review):

Short Assessment

In this work the authors propose a new regulatory role for one the most abundant circRNAs, circHIPK3. They demonstrate that circHIPK3 interacts with an RNA binding protein (IGF2BP2), sequestering it away from its target mRNAs. This interaction is shown to regulates the expression of hundreds of genes that share a specific sequence motif (11-mer motif) in their untranslated regions (3'-UTR), identical to one present in circHIPK3 where IGF2BP2 binds. The study further focuses on the specific case of STAT3 gene, whose mRNA product is found to be downregulated upon circHIPK3 depletion. This suggests that circHIPK3 sequesters IGF2BP2, preventing it from binding to and destabilizing STAT3 mRNA. The study presents evidence supporting this mechanism and discusses its potential role in tumor cell progression. These findings contribute to the growing complexity of understanding cancer regulation and highlight the intricate interplay between circRNAs and protein-coding genes in tumorigenesis.

Strengths:<br /> The authors show mechanistic insight into a proposed novel "sponging" function of circHIPK3 which is not mediated by sequestering miRNAs but rather a specific RNA binding protein (IGF2BP2). They address the stoichiometry of the molecules involved in the interaction, which is a critical aspect that is frequently overlooked in this type of studies. They provide both genome-wide analysis and a specific case (STAT3) which is relevant for cancer progression. Overall, the authors have significantly improved their manuscript in their revised version.

Weaknesses:<br /> While the authors have performed northern blots to measure circRNA levels, an estimation of the circRNA overexpression efficiency, namely the circular-to-linear expression ratio, would be desired. The seemingly contradictory effects of circHIPK3 and STAT3 depletion in cancer progression, are now addressed by the authors in their revised manuscript, incorporating potential reasons that might explain such complexity.

Major points about revised manuscript

(1) In Supplementary Figure S5H, the membrane may have been trimmed too closely to the circRNA band, potentially resulting in the absence of the linear RNA band. Could the authors provide a full image of the membrane that includes the loading points? Having access to the complete image would allow for a more comprehensive evaluation of the results, including the presence or absence of expected linear and circular RNA bands.

Reviewer #2 (Public Review):

Summary:

The authors have diligently addressed most of the points raised during the review process (except the important point of "additional in vitro experiments [...] needed to investigate the implication of circHIPK3 in bladder cancer cell phenotype" for which no additional experiments were performed), resulting in an improvement in the study. The data are now described with clarity and conciseness, enhancing the overall quality of the manuscript.

Strengths:

New, well-defined molecular mechanism of circRNAs involvement in bladder cancer.

Weaknesses:

Lack of solid translational significance data.

Reviewer #3 (Public Review):

In Okholm et al., the authors evaluate the functional impact of circHIPK3 in bladder cancer cells. By knocking down circHIPK3 and performing an RNA-seq analysis, the authors found thousands of deregulated genes which look unaffected by miRNAs sponging function and that are, instead, enriched for a 11-mer motif. Further investigations showed that the 11-mer motif is shared with the circHIPK3 and able to bind the IGF2BP2 protein. The authors validated the binding of IGF2BP2 and demonstrated that IGF2BP2 KD antagonizes the effect of circHIPK3 KD and leads to the upregulation of genes containing the 11-mer. Among the genes affected by circHIPK3 KD and IGF2BP2 KD, resulting in downregulation and upregulation respectively, the authors found the STAT3 gene, which also consistently has concomitant upregulation of one of its targets TP53. The authors propose a mechanism of competition between circHIPK3 and IGF2BP2 triggered by IGF2BP2 nucleation, potentially via phase separation.

Strengths:

Although the number of circRNAs continues to grow, this field lacks many instances of detailed molecular investigations. The presented work critically addresses some of the major pitfalls in the field of circRNAs, and there has been a careful analysis of aspects frequently poorly investigated. Experiments involving use of time-point knockdown followed by RNA-seq, investigation of miRNA-sponge function of circHIPK3, identification of 11-mer motif, identification and validation of IGF2BP2, and the analysis of copy number ratio between circHIPK3 and IGF2BP2 in assessing the potential ceRNA mode of action are thorough and convincing.

Weaknesses:

It is unclear why the authors used certain bladder cancer cells versus non-bladder cells in some experiments. The efficacy of certain experiments (specifically rescue experiments) and some control conditions is still questionable. Overall, the presented study adds some further knowledge in describing circHIPK3 function, its capability to regulate some downstream genes, and its interaction and competition for IGF2BP2.

eLife assessment

This important study represents a follow-up of previous papers by Huff et al. (2023) in which the authors further investigate a specific medullary region named the Postinspiratory Complex (PiCo) involved in the control of swallow behaviour and its coordination with breathing. In the present work, they tested the impact of chronic intermittent hypoxia on the swallow motor pattern evoked by optogenetic stimulation of the same medullary area in transgenic mice. These solid results indicate that in chronic intermittent hypoxia-exposed mice PiCo stimulation triggers atypical swallow motor patterns. The experimental procedures are rigorous and technically remarkable. The work will be of interest in the field of respiratory physiology and pathophysiology since a disruption of swallowing and possibly discoordination with breathing may be involved in diseases characterized by the presence of hypoxic conditions such as obstructive sleep apnea.

Reviewer #1 (Public Review):

Summary:

Authors were attempting to determine the extent that CIH altered swallowing motor function; specifically, the timing and probability of the activation of the larygneal and submental motor pools. The paper describes a variety of different motor patterns elicited by optogenetic activation of individual neuronal phenotypes within PiCo in a group of mice exposed to CIH. They show that there are a variety of motor patterns that emerge in CIH mice; this is apparently different than the more consistent motor patterns elicited by PiCo activation in normoxic mice (previously published)

Strengths:

The preparation is technically challenging and gives valuable information related to the role of PiCo in the pattern of motor activation involved in swallowing and its timing with phrenic activity. Genetic manipulations allow for the independent activation of the individual neuronal phenotypes of PiCo (glutamatergic, cholinergic) which is a strength.

Weaknesses:

(1) Comparisons made between experimental data acquired currently with those previously published are extremely problematic, with the potential confounding influence of changing environments, genetics and litter effects. For example, were the current mice tested at the same time as those exposed to normoxia? Are they littermates (or at least from the same colony) as those previously examined? If they were tested at the same time and age, then the authors should explicitly state this in the methods. The authors have provided no statistical analyses to determine whether there is an effect of CIH on the motor patterns. In short, how can they be sure that the phenomena they observe with respect to motor patterns is due to CIH?

(2) The data are descriptive in nature, reporting only differences (diversity) of motor patterns in this cohort of animals exposed to CIH. There is limited mechanistic insight into how PiCo manipulation alters the pattern and probability of motor activation. Can they utilize Fos or marker of activation within the nTS or other regions to provide initial insight? Or in another nucleus that contributes as part of the circuit.

(3) The differences between the genotypes (ChaTcre; Vglut2Cre; ChatCre:Vglut2FlpO) with regard to the probability of generating a swallow are not sufficiently discussed, in my view. If, as the authors state, it is "reasonable to suggest that CIH differentially affects" these populations, then what are some viable reasons? What are the known differences in these populations of neurons that could lead to variable responses? Do they project to different places?

(4) The Results section is difficult to follow and interpret. It would be beneficial to have a couple of sentences after each sub-section stating what the data actually mean. As of now it reads like a statistical report of the data with little "basic" interpretation of the data.

(5) I have a hard time understanding the functional significance of calculating and plotting the degree of correlation between shifting/delaying the following inspiratory burst and triggering a swallow.

Reviewer #2 (Public Review):

The manuscript has been revised according to Reviewer's suggestions. Recommendations for the Authors have been almost entirely followed. However, there are some points where the authors state that they have made changes, but the text does not show this. The revised version would have gained in clarity if it was with track changes and numbered rows. In particular, I cannot see the following changes:

Lines 104-105: Did you mean: "We confirmed that optogenetic stimulation of PiCo neurons in ChATcre:Vglut2FlpO:ChR2 mice exposed to CIH triggers swallow and laryngeal activation similar to the control mice exposed to room air (Huff et al., 2023)." Otherwise, the sentence is not clear.<br /> Thank you, this has been changed

Lines 228-232: "PiCo-triggered swallows are characterized by a significant decrease in duration compared to swallows evoked by water in ChATcre:Ai32 mice (265 {plus minus} 132ms vs 144 {plus minus} 101ms; paired t-test: p= 0.0001, t= 5.21, df= 8), Vglut2cre:Ai32 mice (308 {plus minus} 184ms vs 125 {plus minus} 44ms; paired t-test: p= 0.0003, t= 6.46, df= 7), and ChATcre:Vglut2FlpO:ChR2 mice (230 {plus minus} 67ms vs 130 {plus minus} 35ms; paired t-test: p= 0.0005, t= 5.62, df= 8) exposed to CIH (Table S1).".<br /> Thank you, this has been changed

Lines 283-290: "Thus, CIH does not alter PiCo's ability to coordinate the timing for swallowing and breathing. Rather, our data reveals that CIH disrupts the swallow motor sequence likely due to changes in the interaction between PiCo and the SPG, presumably the cNTS.

While it has previously been demonstrated that PiCo is an important region in swallow-breathing coordination (Huff et al., 2023), previous studies did not demonstrate that PiCo is involved in swallow pattern generation itself. Thus, here we show for the first time that CIH resulted in the instability of the swallow motor pattern activated by stimulating PiCo, suggesting PiCo plays a role in its modulation.".<br /> Thank you, this has been changed

Line 437: Mice of the ChATcre:Ai32, Vglut2cre:Ai32 and ChATcre:Vglut2FlpO:ChR2 lines were kept in collective cages with food and water ad libitum placed inside custom-built chambers.<br /> Thank you, this has been changed.

Overall, the manuscript has been improved.

eLife assessment

This valuable study examines the activity and function of dorsomedial striatal neurons in estimating time. The authors examine striatal activity as a function of time and the impact of optogenetic striatal manipulation on the animal's ability to estimate a time interval. However, the task's design and methodology present several confounding factors that mean the evidence in support of the authors' claims is incomplete. With these limitations addressed, the work would be of interest to neuroscientists examining how striatum contributes to behavior.

Reviewer #1 (Public Review):

Summary:

In this work, the authors examine the activity and function of D1 and D2 MSNs in dorsomedial striatum (DMS) during an interval timing task. In this task, animals must first nose poke into a cued port on the left or right; if not rewarded after 6 seconds, they must switch to the other port. Critically, this task thus requires animals to estimate if at least 6 seconds have passed after the first nose poke - this is the key aspect of the task focused on here. After verifying that animals reliably estimate the passage of 6 seconds by leaving on average after 9 seconds, the authors examine striatal activity during this interval. They report that D1-MSNs tend to decrease activity, while D2-MSNs increase activity, throughout this interval. They suggest that this activity follows a drift-diffusion model, in which activity increases (or decreases) to a threshold after which a decision (to leave) is made. The authors next report that optogenetically inhibiting D1 or D2 MSNs, or pharmacologically blocking D1 and D2 receptors, increased the average wait time of the animals to 10 seconds on average. This suggests that both D1 and D2 neurons contribute to the estimate of time, with a decrease in their activity corresponding to a decrease in the rate of 'drift' in their drift-diffusion model. Lastly, the authors examine MSN activity while pharmacologically inhibiting D1 or D2 receptors. The authors observe most recorded MSNs neurons decrease their activity over the interval, with the rate decreasing with D1/D2 receptor inhibition.

Major strengths:

The study employs a wide range of techniques - including animal behavioral training, electrophysiology, optogenetic manipulation, pharmacological manipulations, and computational modeling. The behavioral task used by the authors is quite interesting and a nice way to probe interval timing in rodents. The question posed by the authors - how striatal activity contributes to interval timing - is of importance to the field and has been the focus of many studies and labs; thus, this paper can meaningfully contribute to that conversation. The data within the paper is presented very clearly, and the authors have done a nice job presenting the data in a transparent manner (e.g., showing individual cells and animals). Overall, the manuscript is relatively easy to read and clear, with sufficient detail given in most places regarding the experimental paradigm or analyses used.

Major weaknesses:

I perceive two major weaknesses. The first is the impact or contextualization of their results in terms of the results of the field more broadly. More specifically, it was not clear to me how the authors are interpreting the striatal activity in the context of what others have observed during interval timing tasks. In other words - what was the hypothesis going into this experiment? Does observing increasing/decreasing activity in D2 versus D1 support one model of interval timing over another, or does it further support a more specific idea of how DMS contributes to interval timing? Or was the main question that we didn't know if D2 or D1 neurons had differential activity during interval timing?

In the second, I felt that some of the conclusions suggested by the authors don't seem entirely supported by the data they present, or the data presented suggests a slightly more complicated story. Below I provide additional detail on some of these instances.

Regarding the results presented in Figures 2 and 3:

I am not sure the PC analysis adds much to the interpretation, and potentially unnecessarily complicates things. In particular, running PCA on a matrix of noisy data that is smoothed with a Gaussian will often return PCs similar to what is observed by the authors, with the first PC being a line up/down, the 2nd PC being a parabola that is up/down, etc. Thus, I'm not sure that there is much to be interpreted by the specific shape of the PCs here. I think an alternative analysis that might be both easier and more informative is to compute the slope of the activity of each neuron across the 6 seconds. This would allow the authors to quantify how many neurons increase or decrease their activity much like what is shown in Figure 2.

Relatedly, it seems that the data shown in Figure 2D *doesn't* support the authors' main claim regarding D2/D1 MSNs increasing/decreasing their activity, as the trial-by-trial slope is near 0 for both cell types.

Regarding the results in Figure 4:

The authors suggest that their data is consistent with a drift-diffusion model. However, it is unclear how well the output from the model fits the activity from neurons the authors recorded. Relatedly, it is unclear how the parameters were chosen for the D1/D2 versions of this model. I think that an alternate approach that would answer these questions is to fit the model to each cell, and then examine the best-fit parameters, as well as the ability of the model to predict activity on trials held out from the fitting process. This would provide a more rigorous method to identify the best parameters and would directly quantify how well the model captures the data.

Relatedly, looking at the raw data in Figure 2, it seems that many neurons either fire at the beginning or end of the interval, with more neurons firing at the end, and more firing at the beginning, for D2/D1 neurons respectively. Thus, it's not clear to me whether the drift-diffusion model is a good model of activity. Or, perhaps the model is supposed to be related to the aggregate activity of all D1/D2 neurons? (If so, this should be made more explicit. The comment about fitting the model directly to the data also still stands).

Further, it's unclear to me how, or why, the authors changed the specific parameters they used to model the optogenetic manipulation. Were these parameters chosen because they fit the manipulation data? This I don't think is in itself an issue, but perhaps should be clearly stated, because otherwise it sounds a bit odd given the parameter changes are so specific. It is also not clear to me why the noise in the diffusion process would be expected to change with increased inhibition.

Regarding the results in Figure 6:

My comments regarding the interpretation of PCs in Figure 2 apply here as well. In addition, I am not sure that examining PC2 adds much here, given that the authors didn't examine such nonlinear changes earlier in the paper.

A larger concern though that seems potentially at odds with the authors' interpretation is that there seems to be very little change in the firing pattern after D1 or D2 blockade. I see that in Figure 6F the authors suggest that many cells slope down (and thus, presumably, they are recoding more D1 cells), and that this change in slope is decreased, but this effect is not apparent in Figure 6C, and Figure 6B shows an example of a cell that seems to fire in the opposite direction (increase activity). I think it would help to show some (more) individual examples that demonstrate the summary effect shown by the authors, and perhaps the authors can comment on the robustness (or the variability) of this result.

Also, it seems that if the authors want to claim that this manipulation lowers the drift rate. I think to make this claim, they could fit the DDM model and examine whether D is significantly lower.

Regarding the results in Figure 7:

I am overall a bit confused about what the authors are trying to claim here. In Figure 7, they present data suggesting that D1 or D2 blockade disrupts their ability to decode time in the interval of interest (0-6 seconds). However, in the final paragraph of the results, the authors seem to say that by using another technique, they didn't see any significant change in decoding accuracy after D1 or D2 blockade. What do the authors make of this?

Impact:

The task and data presented by the authors are very intriguing, and there are many groups interested in how striatal activity contributes to the neural perception of time. The authors perform a wide variety of experiments and analysis to examine how DMS activity influences time perception during an interval-timing task, allowing for insight into this process. However, the significance of the key finding - that D2/D1 activity increases/ decreases with time - remains somewhat ambiguous to me. This arises from a lack of clarity regarding the initial hypothesis and the implications of this finding for advancing our understanding of striatal functions.

Reviewer #2 (Public Review):

Summary:

In the present study, the authors investigated the neural coding mechanisms for D1- and D2-expressing striatal direct and indirect pathway MSNs in interval timing by using multiple strategies. They concluded that D2-MSNs and D1-MSNs have opposing temporal dynamics yet disrupting either type produced similar effects on behavior, indicating the complementary roles of D1- and D2- MSNs in cognitive processing. However, the data was incomplete to fully support this major finding. One major reason is the heterogenetic responses within the D1-or D2-MSN populations. In addition, there are additional concerns about the statistical methods used. For example, the majority of the statistical tests are based on the number of neurons, but not the number of mice. It appears that the statistical difference was due to the large sample size they used (n=32 D2-MSNs and n=41 D1-MSNs), but different neurons recorded in the same mouse cannot be treated as independent samples; they should use independent mouse-based statistical analysis.

Strengths:

The authors used multiple approaches including awake mice behavior training, optogenetic-assistant cell-type specific recording, optogenetic or pharmacological manipulation, neural computation, and modeling to study neuronal coding for interval timing.

Weaknesses:

(1) More detailed behavior results should be shown, including the rate of the success switches, and how long it takes to wait in the second nose poke to get a reward. For line 512 and the Figure 1 legend, the reviewer is not clear about the reward delivery. The methods appear to state that the mouse had to wait for 18s, then make nose pokes at the second port to get the reward. What happens if the mouse made the second nose poke before 18 seconds, but then exited? Would the mouse still get the reward at 18 seconds? Similarly, what happens if the mice made the third or more nosepokes within 18 seconds? It is important to clarify because, according to the method described, if the mice made a second nose poke before 18 seconds, this already counted as the mouse making the "switch." Lastly, what if the mice exited before 6s in the first nosepoke?

(2) There are a lot of time parameters in this behavior task, the description of those time parameters is mentioned in several parts, in the figure legend, supplementary figure legend, and methods, but was not defined clearly in the main text. It is inconvenient, sometimes, confusing for the readers. The authors should make a schematic diagram to illustrate the major parameters and describe them clearly in the main text.

(3) In Line 508, the reviewer suggests the authors pay attention to those trials without "switch". It would be valuable to compare the MSN activity between those trials with or without a "switch".

(4) The definition of interval is not very clear. It appears that the authors used a 6-second interval in analyzing the data in Figure 2 and Figure 3. But from my understanding, the interval should be the time from time "0" to the "switch", when the mice start to exit from the first nose poke.

(5) For Figure 2 C-F, the authors only recorded 32 D2-MSNs in 4 mice, and 41 D1-MSNs in 5 mice. The sample size is too small compared to the sample size usually used in the field. In addition to the small sample size, the single-cell activity exhibited heterogeneity, which created potential issues. For both D1 and D2 MSNs, the authors tried to make conclusions on the "trend" of increasing in D2-MSNs and decreasing in D1-MSNs populations, respectively, during the interval. However, such a conclusion is not sufficiently supported by the data presented. It looks like the single-cell activity patterns can be separated into groups: one is a decreasing activity group, one is an increasing activity group and a small group for on and off response. Because of the small sample size, the author should pay attention to the variance across different mice (which needs to be clearly presented in the manuscript), instead of pooling data together and analyzing the mean activity.

(6) For Figure 2, from the activity in E and F, it seems that the activity already rose before the trial started, the authors should add some longer baseline data before time zero for clarification and comparison, and show the timing of the actual start of the activity with the corresponding behavior. What behavior states are the mice in when initiating the activity?

(7) The authors were focused on the "switch " behavior in the task, but they used an arbitrary 6s time window to analyze the activity, and tried to correlate the decreasing or increasing activities of MSNs to the neural coding for time. A better way to analyze is to sort the activity according to the "switch" time, from short to long intervals. This way, the authors could see and analyze whether the activity of D1 or D2 MSNs really codes for the different length of interval, instead of finding a correlation between average activity trends and the arbitrary 6s time window.

Reviewer #3 (Public Review):

Summary:

The cognitive striatum, also known as the dorsomedial striatum, receives input from brain regions involved in high-level cognition and plays a crucial role in processing cognitive information. However, despite its importance, the extent to which different projection pathways of the striatum contribute to this information processing remains unclear. In this paper, Bruce et al. conducted a study using a range of causal and correlational techniques to investigate how these pathways collectively contribute to interval timing in mice. Their results were consistent with previous research, showing that the direct and indirect striatal pathways perform opposing roles in processing elapsed time. Based on their findings, the authors proposed a revised computational model in which two separate accumulators track evidence for elapsed time in opposing directions. These results have significant implications for understanding the neural mechanisms underlying cognitive impairment in neurological and psychiatric disorders, as disruptions in the balance between direct and indirect pathway activity are commonly observed in such conditions.

Strengths:

The authors employed a well-established approach to study interval timing and employed optogenetic tagging to observe the behavior of specific cell types in the striatum. Additionally, the authors utilized two complementary techniques to assess the impact of manipulating the activity of these pathways on behavior. Finally, the authors utilized their experimental findings to enhance the theoretical comprehension of interval timing using a computational model.

Weaknesses:

The behavioral task used in this study is best suited for investigating elapsed time perception, rather than interval timing. Timing bisection tasks are often employed to study interval timing in humans and animals. The main results from unit recording (opposing slopes of D1/D2 cell firing rate, as shown in Figure 3D) appear to be very sensitive to a couple of outlier cells, and the predictive power of ensemble recording seems to be only slightly above chance levels. In the optogenetic experiment, the laser was kept on for too long (18 seconds) at high power (12 mW). This has been shown to cause adverse effects on population activity (for example, through heating the tissue) that are not necessarily related to their function during the task epochs. Given the systemic delivery of pharmacological interventions, it is difficult to conclude that the effects are specific to the dorsomedial striatum. Future studies should use the local infusion of drugs into the dorsomedial striatum.

Author Response

We would like to thank the three reviewers and the eLife editors for their careful analysis of our work, and for their constructive feedback and positive evaluation. We are especially pleased to see echoed in the reviews and in the editorial assessment that our results underline the importance of taking into account glycosylation in viral evolution, immune surveillance, and in the interpretation of complex epistatic interactions. With this provisional response we would like to communicate to the editors, reviewers and to the eLife readership our intention to integrate in the paper a detailed description of the GM1os and GM2os binding site on the RBD with details on the computational approach we used. We agree that this addition will strengthen the work by making it more self-contained. Also, as suggested by the editorial team, we will provide a comprehensive discussion of published data, as a firmer foundation for our findings.

eLife assessment

This study presents a valuable finding on the structural role of glycosylation at position N343 of the SARS-CoV-2 spike protein's receptor-binding domain in maintaining its stability, with implications across different variants of concern. The evidence supporting the claims of the authors is solid, although a more complete discussion of published data would have strengthened the study by providing a foundation for the new findings. The work will be of interest to evolutionary virologists.

Reviewer #1 (Public Review):

Summary:

The authors seek to elucidate the structural role of N-glycosylation at the N343 position of the SARS-CoV-2 Spike protein's Receptor Binding Domain (RBD) and its evolution across different variants of concern (VoCs). Specifically, they aim to understand the impact of this glycosylation on the RBD's stability and function, which could have implications for the virus's infectivity and, eventually, the effectiveness of vaccines.

Strengths:

The major strength of the study stems from the molecular-level picture emerging from the use of over 45 μs of cumulative molecular dynamics (MD) simulations, including both conventional and enhanced sampling schemes, which provide detailed insights into the structural role of N343 glycosylation. The combination of these simulations with experimental assays, such as electron-spray ionization mass spectrometry (ESI-MS) for affinity measurements, bolsters the reliability of the findings. At the same time, one potential weakness is the inherent limitation of the current computational models to fully capture the complexities of in vivo systems. While the authors acknowledge the difficulty in completely gauging the N343 glycosylation's impact on RBD folding due to the dynamic nature of glycan structures, their computational/experimental approach lends support to their claims.

Weaknesses:

One potential weakness is the inherent limitation of computational models to fully capture the complexities of in vivo systems. While the authors acknowledge the difficulty in completely gauging the N343 glycosylation's impact on RBD folding due to the dynamic nature of glycan structures, their multi-faceted approach lends solid support to their claims.

Other Comments:

The study shows that N343 glycosylation plays a structural role in stabilizing the RBD across various SARS-CoV-2 strains. The removal of this glycan led to conformational changes that could affect the virus's infectivity. The results correlate with a reported reduction in viral infectivity upon deletion of glycosylation sites, supporting the authors' conclusion that N343 glycosylation is functionally essential for viral infection.

By providing molecular insights into the spike protein's architectural changes, the work could influence the design of more effective vaccines and therapeutic agents. The data and methods used could serve as a valuable resource for researchers looking into viral evolution, protein-glycan interactions, and the development of glycan-based interventions.

Reviewer #2 (Public Review):

The authors sought to establish the role played by N343 glycosylation on the SARS-CoV-2 S receptor binding domain structure and binding affinity to the human host receptor ACE2 across several variants of concern. The work includes both computational analysis in the form of molecular dynamics simulations and experimental binding assays between the RBD and ganglioside receptors.

The work extensively samples the conformational space of the RBD beginning with atomic coordinates representing both the bound and unbound states and computes molecular dynamics trajectories until equilibrium is achieved with and without removing N343 glycosylation. Through comparison of these simulated structures, the authors are able to demonstrate that N343 glycosylation stabilizes the RBD. Prior work had demonstrated that glycosylation at this site plays an important role in shielding the RBD core and in this work, the authors demonstrate that removal of this glycan can trigger a conformational change to reduce water access to the core without it. This response is variant-dependent and variants containing interface substitutions that increase RBD stability, including Delta substitution L452R, do not experience the same conformational change when the glycan is removed. The authors also explore structures corresponding to Alpha and Beta in which no structure-reinforcing substitutions were identified and two Omicron variants in which other substitutions with an analogous effect to L452R are present.

The authors experimentally assessed these inferred structural changes by measuring the binding affinity of the RBD for the oligosaccharides of the mono-sialylated gangliosides GM1os and GM2os with and without the glycan at N343. While GM1os and GM2os binding is influenced by additional factors in the Beta and Omicron variants, the comparison between Delta and Wuhan-hu-1 is clear: removal of the glycan abrogated binding for Wuhan-hu-1 and minimally affected Delta as predicted by structural simulations.

In summary, these findings suggest, in the words of the authors, that SARS-CoV-2 has evolved to render the N-glycosylation site at N343 "structurally dispensable". This study emphasizes how glycosylation impacts both viral immune evasion and structural stability which may in turn impact receptor binding affinity and infectivity. Mutations that stabilize the antigen may relax the structural constraints on glycosylation opening up avenues for subsequent mutations that remove glycans and improve immune evasion. This interplay between immune evasion and receptor stability may support complex epistatic interactions which may in turn substantially expand the predicted mutational repertoire of the virus relative to expectations that do not take into account glycosylation.

Reviewer #3 (Public Review):

Summary:

The receptor binding domain of SARS-Cov-2 spike protein contains two N-glycans which have been conserved by the variants observed in these last 4 years. Through the use of extensive molecular dynamics, the authors demonstrate that even if glycosylation is conserved, the stabilization role of glycans at N343 differs among the strains. They also investigate the effect of this glycosylation on the binding of RBD towards sialylated gangliosides, as a function of evolution.

Strengths:

The molecular dynamics characterization is well performed and demonstrates differences in the effect of glycosylation as a factor of evolution. The binding of different strains to human gangliosides shows variations of strong interest. Analyzing the structure function of glycans on SARS-Cov-2 surface as a function of evolution is important for the surveillance of novel variants since it can influence their virulence.

Weaknesses:

The article is difficult to read, with no sufficient efforts of clarification for non-glycobiology audiences. The presentation of previous knowledge about RBD glycosylation and its effect on structure is very difficult to follow and should be reorganized. The choice of the nature of the biantennary glycan at N343 is not rationalized. A major weakness is the absence of data supporting the proposed binding site for ganglioside.

Author Response

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

eLife assessment

In this study, the authors develop a useful strategy for fluorophore-tagging endogenous proteins in human induced pluripotent stem cells (iPSCs) using a split mNeonGreen approach. Experimentally, the methods are solid, and the data presented support the author's conclusions. Overall, these methodologies should be useful to a wide audience of cell biologists who want to study protein localization and dynamics at endogenous levels in iPSCs.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors have applied an asymmetric split mNeonGreen2 (mNG2) system to human iPSCs. Integrating a constitutively expressed long fragment of mNG2 at the AAVS1 locus, allows other proteins to be tagged through the use of available ssODN donors. This removes the need to generate long AAV donors for tagging, thus greatly facilitating high-throughput tagging efforts. The authors then demonstrate the feasibility of the method by successfully tagging 9 markers expressed in iPSC at various, and one expressed upon endoderm differentiation. Several additional differentiation markers were also successfully tagged but not subsequently tested for expression/visibility. As one might expect for high-throughput tagging, a few proteins, while successfully tagged at the genomic level, failed to be visible. Finally, to demonstrate the utility of the tagged cells, the authors isolated clones with genes relevant to cytokinesis tagged, and together with an AI to enhance signal-to-noise ratios, monitored their localization over cell division.

Strengths:

Characterization of the mNG2 tagged parental iPSC line was well and carefully done including validation of a single integration, the presence of markers for continued pluripotency, selected offtarget analysis, and G-banding-based structural rearrangement detection.

The ability to tag proteins with simple ssODNs in iPSC capable of multi-lineage differentiation will undoubtedly be useful for localization tracking and reporter line generation.

Validation of clone genotypes was carefully performed and highlights the continued need for caution with regard to editing outcomes.

Weaknesses:

IF and flow cytometry figures lack quantification and information on replication. How consistent is the brightness and localization of the markers? How representative are the specific images? Stability is mentioned in the text but data on the stability of expression/brightness is not shown.

To address this comment, we have quantified the mean fluorescence intensity of the tagged cell populations in Fig. S3B-T. This data correlates well with the expected expression levels of each gene relative to the others (Fig. S3A), apart from CDH1 and RACGAP1, which are described in the discussion.

The images in Fig. 2 show tagged populations enriched by FACS so they are non-clonal and are representative of the diversity of the population of tagged cells.

The images shown in Fig. 3 are representative of the clonal tagged populations. The stability of the tag was not quantified directly. However, the fluorescence intensity was very stable across cells in clonal populations. Since these populations were recovered from a single cell and grown for several weeks, this low variability across cells in a population suggests that these tags are stable.

The localization of markers, while consistent with expectations, is not validated by a second technique such as antibody staining, and in many cases not even with Hoechst to show nuclear vs cytoplasmic.

We find that the localization of each protein is distinct and consistent with previous studies. To address this comment, we have added an overlay of the green fluorescence images with brightfield images to better show the location of the tagged protein relative to the nuclei and cytoplasm. We have also added references to other studies that showed the same localization patterns for these proteins in iPSCs and other relevant cell lines.

For the multi-germ layer differentiation validation, NCAM is also expressed by ectoderm, so isn't a good solo marker for mesoderm as it was used. Indeed, the kit used for the differentiation suggests Brachyury combined with either NCAM or CXCR4, not NCAM alone.

Since Brachyury is the most common mesodermal marker, we first tested differentiation using anti-Brachyury antibodies, but they did not work well for flow cytometry. We then switched to anti-NCAM antibodies. Since we used a kit for directed differentiation of iPSCs into the mesodermal lineage, NCAM staining should still report for successful differentiation. In the context of mixed differentiation experiments (embryoid body formation or teratoma assay), NCAM would not differentiate between ectoderm and mesoderm. The parental cells (201B7) have also been edited at the AAVS1 locus in multiple other studies, with no effect on their differentiation potential.

Only a single female parental line has been generated and characterized. It would have been useful to have several lines and both male and female to allow sex differences to be explored.

We agree that it would be interesting (and important) to study differences in protein localization between female and male cell types, and from different individuals with different genetic backgrounds. We see our tool as opening a door for cell biology to move away from randomly collected, transformed, differentiated cell types to more directed comparative studies of distinct normal cell types. Since few studies of cell biological processes have been done in normal cells, a first step is to understand how processes compare in an isogenic background, then future studies can reveal how they compare with other individuals and sexes. We hope that either our group or others will continue to build similar lines so that these studies can be done.

The AI-based signal-to-noise enhancement needs more details and testing. Such models can introduce strong assumptions and thus artefacts into the resolved data. Was the model trained on all markers or were multiple models trained on a single marker each? For example, if trained to enhance a single marker (or co-localized group of markers), it could introduce artefacts where it forces signal localization to those areas even for others. What happens if you feed in images with scrambled pixel locations, does it still say the structures are where the training data says they should be? What about markers with different localization from the training set? If you feed those in, does it force them to the location expected by the training data or does it retain their differential true localization and simply enhance the signal?

The image restoration neural network was used as in Weigert et al., 2018. The model was trained independently for each marker. Each trained model was used only on the corresponding marker and with the same imaging conditions as the training images. From visual inspection, the fluorescent signal in the restored images was consistent with the signal in the raw images, both for interphase and mitotic cells. We found very few artefacts of the restoration (small bright or dark areas) that were discarded. We did not try to restore scrambled images or images of mismatched markers.

Reviewer #2 (Public Review):

Summary:

The authors have generated human iPSC cells constitutively expressing the mNG21-10 and tested them by endogenous tagging multiple genes with mNG211 (several tagged iPS cell lines clones were isolated). With this tool, they have explored several weakly expressed cytokinesis genes and gained insights into how cytokinesis occurs.

Strengths:

Human iPSC cells are used.

Weaknesses:

i) The manuscript is extremely incremental, no improvements are present in the split-fluorescent (split-FP) protein variant used nor in the approach for endogenous tagging with split-FPs (both of them are already very well established and used in literature as well as in different cell types).

Although split fluorescent proteins and the endogenous tagging methodology had been developed previously, their use in human stem cells has not been explored. We argue that human iPSCs are a valuable model for cell biologists to study cellular processes in differentiating cells in an isogenic context for proper comparison. Many normal human cell types have not been studied at the cellular/subcellular level, and this tool will enable those studies. Importantly, other existing cell lines required transformation to persist in culture and represent a single, differentiated cell type that is not normal. Moreover, the protocols that we developed along with this methodology (e.g. workflows for iPSC clonal isolation that include automated colony screening and Nanopore sequencing) will be useful to other groups undertaking gene editing in human cells. Therefore, we argue that our work opens new doors for future cell biology studies.

ii) The fluorescence intensity of the split mNeonGreen appears rather low, for example in Figure 2C the H2BC11, ANLN, SOX2, and TUBB3 signals are very noisy (differences between the structures observed are almost absent). For low-expression targets, this is an important limitation. This is also stated by the authors but image restoration could not be the best solution since a lot of biologically relevant information will be lost anyway.

The split mNeonGreen tag is one of the brighter fluorescent proteins that is available. The low expression that the reviewer refers to for H2BC11, ANLN, TUBB3 and SOX2 is expected based on their predicted expression levels. Further, these images were taken with cells in dishes using lower resolution imaging and were not intended to be used for quantification. As shown in the images in Figures 3H, when using a different microscope with different optical settings and higher magnification, the localization is very clear and quantifiable without needing to use restoration (e.g., compare H2BC11 and ANLN). Using microscopes with high NA objectives, lasers and EMCCD or sCMOS cameras with high sensitivity can sufficiently detect levels of very weakly expressing proteins that can be quantified above background and compared across cells. It is worth noting that each tag may be studied in very different contexts. For example, ANLN will be useful for studies of cytokinesis, while the loss of SOX2 expression and gain of TUBB3 expression may be used to screen for differentiation rather than for localization per se. The reason for endogenous tagging is to study proteins at their native levels rather than using over-expression or fixation with antibodies where artefacts can be introduced. Endogenous tags tag will also enable studies of dynamic changes in localization during differentiation in an isogenic background as described previously.

Importantly, image restoration is not required to image any of these probes! We use it to demonstrate how a researcher can increase the temporal resolution of imaging weakly-expressed proteins for extended periods of time. This data can be used to compare patterns of localization and reveal how patterns change with time and during differentiation. Imaging with fewer timepoints and altered optical settings will still permit researchers to extract quantifiable information from the raw data without requiring image restoration.

iii) There is no comparison with other existing split-FP variants, methods, or imaging and it is unclear what the advantages of the system are.

We are not sure what the reviewer means by this comment. In the future, we plan to incorporate an additional split-FP variant (e.g., split sfCherry) in this iPSC line to enable the imaging of more than one protein in the same cell. However, the split mNeonGreen system is still amenable for use with dyes with different fluorescence spectra that can mark other cellular components, especially for imaging over shorter timespans. In addition to tagging efficiency, the main advantage of split FPs is its scale, as demonstrated by the OpenCell project by tagging 1,310 proteins endogenously (Cho et al., 2022). We developed protocols that facilitate the identification of edited cell lines with high throughput. We also used multiple imaging methods throughout the study that relied on the use of different microscopes and flow cytometry, demonstrating the flexibility of this tagging system. Even for more weakly expressing proteins, the probe could be sufficiently visualized by multiple systems. Such endogenous tags can be used for everything from simply knowing when cells have differentiated (e.g., loss of SOX2 expression, gain of differentiation markers), to studying biological processes over a range of timescales.

Reviewer #3 (Public Review):

The authors report on the engineering of an induced Pluripotent Stem Cell (iPSC) line that harbours a single copy of a split mNeonGreen, mNG2(1-10). This cell line is subsequently used to take endogenous protein with a smaller part of mNeonGreen, mNG2(11), enabling the complementation of mNG into a fluorescent protein that is then used to visualize the protein. The parental cell is validated and used to construct several iPSC lines with endogenously tagged proteins. These are used to visualize and quantify endogenous protein localisation during mitosis.

I see the advantage of tagging endogenous loci with small fragments, but the complementation strategy has disadvantages that deserve some attention. One potential issue is the level of the mNG2(1-10). Is it clear that the current level is saturating? Based on the data in Figure S3, the expression levels and fluorescence intensity levels show a similar dose-dependency which is reassuring, but not definitive proof that all the mNG2(11)-tagged protein is detected.

We have not quantified the levels of mNG21-10 expression directly. However, the increase in fluorescence observed with highly expressed proteins (e.g., ACTB) supports that mNG21-10 levels must be sufficiently high to permit differences among endogenous proteins with vastly different expression levels. To ensure high expression, we used a previously validated expression system comprised of the CAG promoter integrated at the AAVS1 locus, which has previously been used to provide high and stable transgene expression (e.g. Oceguera-Yanez et al., 2016). We acknowledge that it is difficult to confirm that all of the endogenous mNG211-tagged protein is ‘detectable’.

Do the authors see a difference in fluorescence intensity for homo- and heterozygous cell lines that have the same protein tagged with mNG2(11)? One would expect two-fold differences, or not?

To answer this question, we measured the fluorescence intensity of homozygous and heterozygous clones carrying smNG2-anillin and smNG2-RhoA. We found homozygous clones that were approximately twice as bright as the corresponding heterozygous clones (Fig. S4H and I). This suggests that the complementation between mNG21-10 and mNG211 occurs efficiently over a range of mNG211 expression, since anillin is expressed weakly and RhoA is expressed more strongly in iPSCs. However, we also observed some homozygous clones that were not brighter than the corresponding heterozygous clones, which could be due to undetected byproducts of CRISPR or clonal variation in protein expression.

Related to this, would it be favourable to have a homozygous line for expressing mNG2(1-10)?

Our heterozygous cell line leaves the other AAVS1 allele available for integrations of other transgenes for future experiments. While a homozygous line could express more mNG2(1-10), it does not seem to be rate-limiting even with a highly-expressed protein like beta-actin, and we are not sure that it is necessary. The value gained by having the free allele could outweigh the difference in mNG2(1-10) levels.

The complementation seems to work well for the proteins that are tested. Would this also work for secreted (or other organelle-resident) proteins, for which the mNG2(11) tag is localised in a membrane-enclosed compartment?

The interaction between the 1-10 and 11 fragments is strong and should be retained when proteins are secreted. It was recently shown that secreted proteins tagged with GFP11 can be detected when interacting with GFP1-10 in the extracellular space, albeit using over-expression (Minegishi et al., 2023). However, in our work, the mNG21-10 fragment is cytosolic and we have only explored proteins localized to the nucleus or the cytoplasm similar to Cho et al., (2022). By GO annotation, 75% of human proteins are present in the cytoplasm and/or nucleus, which still covers a wide range of proteins of interest. Future versions of our line could include incorporating organelle-targeting peptides to drive the large fragment to specific, non-cytosolic locations.

The authors present a technological advance and it would be great if others could benefit from this as well by having access to the cell lines.

As discussed below, some of the resources are already available, and we are working to make the mNG21-10 cell line available for distribution.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

The manuscript is methodological, the main achievement is the generation of a stable iPSC with the split Neon system available for the scientific community. Although it is technically solid, the judgement of this reviewer is that the manuscript should be considered for a more specialised/methodological/resource-based journal.

Indeed, we have submitted this article under the “tools and resources” category of eLife, which publishes methodology-centered papers of high technical quality. We felt this was a good venue for the audience that it can reach compared to more specialized journals that may be more limited in scope. For example, our system will be a useful resource for cell biologists and they are more likely to see it in eLife compared to more specialized journals.

Reviewer #3 (Recommendations For The Authors):

(1) The authors present a technological advance and it would be great if others can benefit from this as well. Therefore access to the materials (and data) would be valuable (the authors do a great job by listing all the repair templates and primers).

We have added several pieces of data and information to the supplementary materials, as described below.

For instance:

What is the (complete/plasmid) sequence of the AAVS1-mNG2(1-10) repair plasmid? Will it be deposited at Addgene?

The plasmids used in this paper are now available on Addgene, along with their sequences [ID 206042 for pAAVS1-Puro-CAG-mNG2(1-10) and 206043 for pH2B-mNG2(11)].

The ImageJ code for the detection of colonies is interesting and potentially valuable. Will the code be shared (e.g. at Github, or as supplemental text)?

The ImageJ macro has been uploaded to the CMCI Github page (https://github.com/CMCI/colony_screening). The parameters are optimized to perform segmentation on images obtained using a Cytation5 microscope with our specific settings, but they can be tweaked for any other sets of images. The following text has been added to the methods section: “The code for this macro is available on Github (https://github.com/CMCI/colony_screening)”.

The cell line with the mNG2(1-10) as well as other cell lines can be of interest to others. Will the cell lines be made available? If so, can the authors indicate how?

We are in the process of depositing our cell line in a public repository. This process may take some time for quality control. For now, the cells can be made available by requesting them from the corresponding authors.

(2) How well does the ImageJ macro for detection of the colonies in the well work? Is there any comparison of analysis by a human vs. the macro?

In our most recent experiment, the colony screening macro correctly identified 99.5% of wells compared to manual annotation (83/84 positive wells and 108/108 negative wells). For each 96-well plate, imaging takes 25 minutes, and it takes 7 minutes for analysis. Despite a few false negatives, we expect this macro to be useful for large-scale experiments where multiple 96-well plates need to be screened, which would take hours manually.

(3) The CDH labeling was not readily detected by FACS, but was visible by microscopy. Is the labeling potentially disturbed by the procedure (low extracellular calcium + trypsin?) to prepare the cell for FACS?

It is not clear why the CDH labelling was not detected by FACS. As the reviewer suggests, there could be several reasons: E-cadherin could be broken down by the dissociation reagent (Accutase), or recycled into the cell following the loss of adhesion and the low extracellular calcium in PBS. However, the C-terminal intracellular tail of E-cadherin was tagged, which should not be affected by Accutase. Moreover, recycling into the cell should still result in a detectable fluorescent signal. Notably, the flow cytometry experiments were done as quickly as possible after dissociation to minimize the time that E-cadherin could be degraded or recycled. We also resuspended the cells in MTeSR Plus media instead of PBS, and compared cells grown on iMatrix511 to those grown on Matrigel in case differences in the extracellular matrix affected Ecadherin expression. Another possibility is that the microscopy used for detection of E-cadherin in cells involved using a sweptfield livescan confocal microscope with high NA objective, 100mW 488nm laser and an EMCCD camera with high sensitivity, and perhaps this combination permitted detection better than the detector on the BD FACSMelody used for FACs.

(4) The authors write that the "Tubulin was cytosolic during interphase" which is surprising (and see also figure 3H), as I was expecting it to be incorporated in microtubules. May this be an issue of insufficient resolution (if I'm right this was imaged with 20x, NA=0.35 and so the resolution could be improved by imaging at higher NA)?

Indeed, as the reviewer points out, our terminology (cytosol vs. microtubule) reflects the low resolution of the imaging for the cell populations in dishes and the individual alpha-tubulin monomers being labelled with the mNG211 tag, which are present as cytoplasmic monomers as well as polymers on microtubules. However, even in this image (Fig. 2C), the mitotic spindle microtubules are visible as they are so robust compared to the interphase microtubules. Notably, when we imaged cells from the cloned tagged cell line using a microscope designed for live imaging with a higher NA objective (see above), endogenous tagged TUBA1B was even more clearly visible in spindle microtubules, and was weakly observed in some microtubules in interphase cells, although they are slightly out of focus (Fig. 3H). If we had focused on a lower focal plane where the interphase cells are located and altered the optical settings, we would see more microtubules.

(5) It would be nice to have access to the Timelapse data as supplemental movies (.e.g from the experiments shown in Figure 4).

We have added the movies corresponding to the timeplase images as supplementary movies (Movies S1-6), with the raw and restored movies shown side-by-side.

(6) In Figure 3B, the order of the colors in the bar is reversed relative to the order of the legend. Would it be possible to use the same order? That makes it easier for me (as a colorblind person) to match the colors in the figure with that of the legend.

We have modified the legend in Fig 2B and 3B to be in the same order as the bars.

Reviewer #1 (Public Review):

Summary:

In this manuscript the authors have applied an asymmetric split mNeonGreen2 (mNG2) system to human iPSCs. By integrating a constitutively expressed long fragment of mNG2 at the AAVS1 locus, this allows other proteins to be tagged through the use of available ssODN donors. This removes the need to generate long AAV donors for tagging, thus greatly facilitating high-throughput tagging efforts. The authors then demonstrate the feasibility of the method by successfully tagging 9 markers expressed in iPSC at various, and one expressed upon endoderm differentiation. Several additional differentiation markers were also successfully tagged but not subsequently tested for expression/visibility. As one might expect for high-throughput tagging, a few proteins, while successfully tagged at the genomic level, failed to be visible. Finally, to demonstrate the utility of the tagged cells, the authors isolated clones with genes relevant to cytokinesis tagged, and together with an AI to enhance signal to noise ratios, monitored their localization over cell division.

Strengths

Reviewer Comment: Characterization of the mNG2 tagged parental iPSC line was well and carefully done including validation of a single integration, the presence of markers for continued pluripotency, selected off-target analysis and G-banding-based structural rearrangement detection.<br /> The ability to tag proteins with simple ssODNs in iPSC capable of multi-lineage differentiation will undoubtedly be useful for localization tracking and reporter line generation.<br /> Validation of clone genotypes was carefully performed and highlights the continued need for caution with regards to editing outcomes.

Weaknesses

Reviewer Comment: IF and flow cytometry figures lack quantification and information on replication. How consistent is the brightness and localization of the markers? How representative are the specific images? Stability is mentioned in the text but data on the stability of expression/brightness is not shown.

Author Response: To address this comment, we have quantified the mean fluorescence intensity of the tagged cell populations in Fig. S3B-T. This data correlates well with the expected expression levels of each gene relative to the others (Fig. S3A), apart from CDH1 and RACGAP1, which are described in the discussion.

Reviewer Reply: Great, thanks.

Reviewer Comment: The localization of markers, while consistent with expectations, is not validated by a second technique such as antibody staining, and in many cases not even with Hoechst to show nuclear vs cytoplasmic.

Author Response: We find that the localization of each protein is distinct and consistent with previous studies. To address this comment, we have added an overlay of the green fluorescence images with brightfield images to better show the location of the tagged protein relative to the nuclei and cytoplasm. We have also added references to other studies that showed the same localization patterns for these proteins in iPSCs and other relevant cell lines.

Reviewer Reply: There was no question that the localization fit with expectations, however, this still doesn't show that in the same cell the tag is in the same spot. It would have been fairly simple to do for at least a handful of markers, image, fix and stain to demonstrate unequivocally the tag and protein are co-localized. Of course, this isn't damning by any means, it just would have been nice.

Reviewer Comment: For the multi-germ layer differentiation validation, NCAM is also expressed by ectoderm, so isn't a good solo marker for mesoderm as it was used. Indeed, the kit used for the differentiation suggests Brachyury combined with either NCAM or CXCR4, not NCAM alone.

Author Response: Since Brachyury is the most common mesodermal marker, we first tested differentiation using anti-Brachyury antibodies, but they did not work well for flow cytometry. We then switched to anti-NCAM antibodies. Since we used a kit for directed differentiation of iPSCs into the mesodermal lineage, NCAM staining should still report for successful differentiation. In the context of mixed differentiation experiments (embryoid body formation or teratoma assay), NCAM would not differentiate between ectoderm and mesoderm. The parental cells (201B7) have also been edited at the AAVS1 locus in multiple other studies, with no effect on their differentiation potential.

Reviewer Reply: This is placing a lot of trust in the kit that it only makes what it says it makes. It could have been measured by options other than flow such as qPCR, Western blot, or imaging, but fine.

Reviewer Comment: Only a single female parental line has been generated and characterized. It would have been useful to have several lines and both male and female to allow sex differences to be explored.

Author Response: We agree that it would be interesting (and important) to study differences in protein localization between female and male cell types, and from different individuals with different genetic backgrounds. We see our tool as opening a door for cell biology to move away from randomly collected, transformed, differentiated cell types to more directed comparative studies of distinct normal cell types. Since few studies of cell biological processes have been done in normal cells, a first step is to understand how processes compare in an isogenic background, then future studies can reveal how they compare with other individuals and sexes. We hope that either our group or others will continue to build similar lines so that these studies can be done.

Reviewer Reply: Fair enough.

Reviewer Comment: The AI-based signal to noise enhancement needs more details and testing. Such models can introduce strong assumptions and thus artefacts into the resolved data. Was the model trained on all markers or were multiple models trained on a single marker each? For example, if trained to enhance a single marker (or co-localized group of markers), it could introduce artefacts where it forces signal localization to those areas even for others. What happens if you feed in images with scrambled pixel locations, does it still say the structures are where the training data says they should be? What about markers with different localization from the training set. If you feed those in, does it force them to the location expected by the training data or does it retain their differential true localization and simply enhance the signal?

Author Response: The image restoration neural network was used as in Weigert et al., 2018. The model was trained independently for each marker. Each trained model was used only on the corresponding marker and with the same imaging conditions as the training images. From visual inspection, the fluorescent signal in the restored images was consistent with the signal in the raw images, both for interphase and mitotic cells. We found very few artefacts of the restoration (small bright or dark areas) that were discarded. We did not try to restore scrambled images or images of mismatched markers.

Reviewer Reply: I understand. What I'm saying is that for the restoration technique to be useful you need to know that it won't introduce artefacts if you have an unexpected localization. Think of it this way, if you already know the localization, then there's no point measuring it. If you don't, or there's a possibility that it is somewhere unexpected, then you need to know with confidence that your algorithm will be able to accurately detect that unexpected localization. As such, it would be extremely important to validate that your restoration algorithm will not bias the results to the expected localization if the true localization is unexpected/not seen in the training dataset. It would have been extremely trivial to run this analysis and I do not feel this comment has been in any way adequately addressed.

Reviewer #2 (Public Review):

Summary:

The authors have generated human iPSC cells constitutively expressing the mNG21-10 and tested them by endogenous tagging multiple genes with mNG211 (several tagged iPS cell lines clones were isolated). With this tool they have explored several weakly expressed cytokinesis genes gained insights into how cytokinesis occurs.

Strengths:

(i) Human iPSC cells are used

Weaknesses:

(i) The manuscript is extremely incremental, no improvements are present in the split-Fluorescent (split-FP) protein variant used nor in the approach for endogenous tagging with split-FPs (both of them are already very well established and used in literature as well as in different cell types).

(ii) The fluorescence intensity of the split mNeonGreen appears rather low, for example in Figure 2C the H2BC11, ANLN, SOX2 and TUBB3 signals are very noisy (differences between the structures observed are almost absent). For low expression targets this is an important limitation. This is also stated by the authors but image restoration could not be the best solution since a lot of biologically relevant information will be lost anyway.

(iii) there is no comparison with other existing split-FP variants, methods, or imaging and it is unclear what the advantages of the system are.

Reviewer #3 (Public Review):

The authors report on the engineering of an induced Pluripotent Stem Cell (iPSC) line that harbours a single copy of a split mNeonGreen, mNG2(1-10). This cell line is subsequently used to take endogenous protein with a smaller part of mNeonGreen, mNG2(11), enabling complementation of mNG into a fluorescent protein that is then used to visualize the protein. The parental cell is validated and used to construct several iPSC line with endogenously tagged proteins. These are used to visualize and quantify endogenous protein localisation during mitosis.

I see the advantage of tagging endogenous loci with small fragments, but the complementation strategy has disadvantages that deserve some attention. One potential issue is the level of the mNG2(1-10). In addition, this may probably not work for organelle-resident proteins, where the mNG2(11) tag is localised in a membrane enclosed compartment.

Overall the tools and resources reported in this paper will be valuable for the community that aims to study proteins at endogenous levels.

Author Response

We are deeply grateful for the highly professional analysis of our work by the Journal Editor and Reviewers. Here is our provisional response to some of the reviewer comments. In our response, we would like to address two comments that were common to all Reviewers' responses. We will thoroughly address all of the Reviewers' comments in the final version of the paper.

Incomplete analysis of maturational changes of striato-nigral connections.

In the initial study, we showed that chronic inhibition of striosomal neurons with the DREADD approach during early postnatal development leads to decreased functional innervation of dopaminergic cells by striosomes in adulthood. We have shown that by two approaches: (1) analysis of miniature inhibitory post-synaptic currents (mIPSCs) and (2) analysis of GFP and gephyrin puncta densities around dopaminergic cells. The results from these experiments strongly suggest a decrease in inhibitory drive to dopaminergic neurons of substantia nigra pars compacta, yet we agree that only GFP puncta density can be considered as a direct evidence for weakened striatonigral connections. Reviewers indicated that additional direct measurements of striatonigral synaptic efficacy would be needed to strengthen our conclusions. We completely agree with this statement and will evaluate the possibility of doing the suggested experiments, using optogenetic stimulation of striosomal inputs to dopaminergic neurons.

Inconsistent description of Ca2+ imaging experiments.

Unfortunately, there was a general misunderstanding in interpreting the Ca2+ imaging methods description. All our experiments were done so that baseline Ca2+ oscillations and oscillations in the presence of a drug were recorded in the usual ACSF (containing 3 mM KCl) at the patch-clamp setup chamber. So, conditions were exactly the same as for cell-attached and whole-cell recordings. At the end of each experiment, ACSF containing 8 mM KCl was applied. This high-KCl condition was used to calculate the total number of viable cells reacting to elevated potassium concentrations, and this number was taken as 100 %. Therefore, the percents displayed in the paper represent the actively oscillating cells in common ACSF (3 mM KCl), counted as a percent of the total number of cells that responded to the following high potassium stimulation (8 mM KCl). The formula was: (Number of active cells in 3 mM KCl / number of viable cells active at 8 mM KCl)*100.

eLife assessment

This valuable study describes early postnatal compartmental differences in the functional maturation of striatal projection neurons. It explores how the postnatal activity of these neurons may determine the GABAergic innervation of dopaminergic neurons in the adult substantia nigra pars compacta. While the functional characterization of striatal neuron development is solid, analysis of how early postnatal activity of striatal projection neurons shapes their functional innervation of dopaminergic neurons is incomplete.

Reviewer #1 (Public Review):

Summary:

This study offers a comprehensive examination of the early postnatal development of the patch and matrix compartments within the striatum. These are segregated circuits within the striatum circuits with distinct embryonic origins and functional roles in mature brain physiology. Despite the recognized significance of these circuits, a comprehensive understanding of their postnatal maturation remains elusive.

Strengths:

The authors undertake a thorough investigation, characterizing the intrinsic properties of direct pathway spiny projection neurons (dSPNs) and indirect pathway spiny projection neurons (iSPNs) across both matrix and striosome compartments throughout development. The authors identify the regulatory role of M1 receptors in modulating spontaneous activity in SPNs, and demonstrate the impact of chemogenetic inhibition of MOR-positive neurons during development on GABAergic synapses in substantia nigra pars compacta (SNc) dopamine (DA) neurons. These findings significantly advance our understanding of striatal development and function.

Weaknesses:

Certain methodological considerations warrant attention. Notably, the reliance on TdTomato expression for the identification of striosomes raises concerns, particularly regarding the substantial difference in slice thickness between the immunohistochemistry (IHC) images (50um) shown in Figure 2 and those utilized for whole-cell recordings (300um).

Enhanced clarification regarding the identification of cell patches is possible in the electrophysiology rig conditions. Using a widefield microscope rather than a confocal would strengthen the reliability of this methodology.

In the Ca2+ imaging experiments of Figure 2, striosomes were defined as the regions of brighter GCaMP fluorescence. This presents a potential limitation because it presupposes higher activity levels within patch cells, which is what the experiment is designed to test. Based on this criteria, neurons of this region will necessarily have more activity than in others.

There is also no information on how Ca2+ imaging traces were analyzed. In the examples provided, putative matrix neurons seem to exhibit different Ca2+ dynamics compared to striosome neurons. The plateau responses might reflect even higher activity than the transient signals observed in striosome neurons. It'll be important to know how the data was quantified. For example, calculations of F0 based on rolling functions tend to underestimate dF/F in traces like this. Calculations of the area under the curve can also provide valuable information in these cases.

There is no description of the 8mM KCl treatment in the methods. Was this only used for the Ca2+ imaging experiments? The percentage of active cells in Figures 2C-D is similar to or lower than that described in Figure 2B, which is confusing. Were recordings always performed in 8mM KCl?

Lastly, while the findings of Figure 6 suggest a deficit in striosomal inputs to SNc DA neurons, they do not conclusively demonstrate this point (DA neurons receive many sources of inhibition, and local interneurons in SNc are highly plastic). Given the availability of Opmr1-Cre mice and the utilization of multiple viruses in Figure 6 experiments, the inclusion of experiments employing ChR2 to directly assess striatal/striosome inputs would substantially strengthen this claim. This is the main claim stated in the manuscript title, so it is important to provide evidence of specific striatonigral deficits.

Reviewer #2 (Public Review):

Summary:

The manuscript by Kokinovic et al. presents evidence that a significant portion of striatal projection neurons (SPNs) are spontaneously active early in development. This spontaneous activity (as measured in ex vivo brain slices) is due to intrinsic mechanisms, and subsides over the course of the first few postnatal weeks in a cell-type specific way: striosome direct and indirect pathway SPNs (dSPNs and iSPNs, respectively) remain spontaneously active until postnatal days 10-14, by which time matrix dSPNs and iSPNs have become entirely silent. The authors suggest that this early spontaneous activity may be in part due to M1 muscarinic receptor signaling. Through chemogenetic inhibition of striosome SPNs (of which dSPNs target dopaminergic neurons of the SNc), the authors present evidence that critical postnatal windows of SPN activity shape the strength of GABAergic innervation of the SNc (measured in adults). This study provides a useful and solid characterization of the functional, postnatal compartmental development of the striatum. However, some weaknesses in the experimental design should be addressed before definitively concluding that postnatal striosome SPN activity determines its functional innervation of dopaminergic SNc neurons.

Specific Comments:

(1) While certainly interesting and possibly true, evidence for the necessity of early striosome dSPN activity in shaping their functional innervation of dopaminergic SNc neurons is not entirely convincing. The functional measure of GABAergic innervation of dopamine neurons is inferred from mIPSCs. As the authors state, dopaminergic neurons have numerous other sources of GABAergic inputs in addition to striosome dSPNs. So while manipulating striosome activity may ultimately alter the overall GABAergic innervation of SNc dopamine neurons, the specificity of this to striosome dSPN inputs is not known. Optogenetic stimulation of striosome->SNc neurons after chemogenetic silencing would help support the authors' interpretation. Related to this point, while striatonigral projections form embryonically, is there evidence that striosome->SNc synapses are indeed functional by P6-14 when CNO is delivered?

(2) One big caveat that needs to be addressed is that all measures of early postnatal spontaneous SPN activity were performed in ex vivo slices. Are SPNs active (in pathway/compartmental specific ways) in vivo during this time? If it is unknown, is there other evidence (e.g. immediate early gene expression, etc...) that may suggest this is indeed the case in vivo?

(3) It appears that 8mM KCl (external) was only used while measuring spontaneous calcium oscillations, not spontaneous spiking (Figure 2). Was there any evidence of spontaneous calcium activity in the lower KCl concentration (3mM?) used for cell-attached recordings? One caveat is that experiments demonstrating that SPNs fire spontaneously in the presence of AMPA receptor blockers (Figure S1) were presumably performed in 3mM KCl. Does elevated KCl increases spontaneous EPSPs during the ages examined? If so, are the calcium oscillations shown in Figure 2 synaptically driven or intrinsically generated? Somewhat related, speculation on why M1 receptor blockade reduces calcium oscillations but not spontaneous spikes in striosome dSPNs would be useful.

(4) Several statements in the introduction could use references.

Reviewer #3 (Public Review):

Summary:

Kokinovic et al. presents an interesting paper that addresses an important gap in knowledge about the differences in the development of direct and indirect pathway striatal neurons in the striosome and matrix compartments. The division of the striatum into 4 distinct populations, striosome-dSPNs, striosome-iSPNs, matrix-dSPNs, and matrix-iSPNs is important, but rarely done. This study records all four populations across early development and shows differences in action potential characteristics and intrinsic properties. They also suppress striosome activity during postnatal development and evaluate the characteristics of adult dopaminergic neurons in control and previously striosome-quieted conditions.

Strengths:

The striatal electrophysiology is beautifully and carefully done and shows important developmental differences between neural subtypes.

The idea to test the striatonigral connection is a good idea.

Weaknesses:

The authors didn't actually test the striatonigral connection. The experiments they do instead don't convincingly show that the striosomal or even striatal connection to the dopaminergic neurons is altered after postnatal striosome suppression.

Major concerns:

(1) mIPSCs are measured and are reduced after chemogenetic suppression of striosomal neurons during development. This is an interesting finding, but these mIPSCs could be coming from any inhibitory input onto the SNc neurons. It is unlikely that most of the mIPSCs are coming from the striosomal inputs. The GPe is much more likely to be the source of these mIPSCs than the striatum because the GPe inputs form synapses nearer the soma and have a higher probability of release (Evans et al., 2020). dSPNs inhibit GPe neurons through a non-canonical pathway (Cui et al., 2021; Spix et al., 2021) and striosomes also inhibit the SNr (McGregor et al., 2019). The striatum has the potential to disinhibit SNc neurons through both the SNr or the GPe (Evans, 2022), and modification of the striosome-SNr or striosome-GPe connections during development could be what is causing the mIPSC changes. To claim that the striosome-SNc connection is altered, a direct test of this connection is necessary.

(2) The dopaminergic neurons recorded seem to be randomly selected, but the striosomes do not inhibit all SNc dopamine neurons. They selectively inhibit the ventral tier SNc neurons (Evans et al., 2020). In the present manuscript, it is impossible to know which subpopulation of SNc neurons was recorded, so it is impossible to tell whether the dopaminergic neurons recorded are the ones expected to receive striosomal input.

(3) Very similarly, the striosomes selectively wrap around the "SNr dendrite" of SNc neurons that participate in striosome-dendron bouquets (Crittenden et al., 2016). However, not all SNc neurons have prominent SNr dendrites (Henny et al., 2012). In the morphological images of Supplemental Figure 3, it looks like the recorded cells sometimes have an SNr dendrite and sometimes don't (but it is hard to tell because the medial-lateral rostral-caudal axis is not labeled in the images). The presence or absence of the "SNr dendrite" is a strong determinant of whether an individual dopaminergic neuron receives striosomal inhibition or not (Evans et al., 2020). As above, not knowing whether the neurons recorded have SNr dendrites makes it impossible to know whether they should be receiving striosomal input at all.

(4) It's quite interesting that the dendron-bouquet structure is intact even after striosomal activity suppression, as cannabinoid receptor knockout greatly disrupts the structural integrity of bouquets (Crittenden et al., 2022). However, going along with point 3, the gephyrin puncta analysis only at the somas is very limiting. The striosome-SNc relevant puncta would be primarily on the SNr dendrite. Gephyrin density on the SNr dendrites or in bouquets would be much more informative than density on the soma.

(5) The authors claim that "CNO didn't affect the shape of the DA neuron dendritic tree", but more information about the morphological analysis should be added. It is not clear how the sholl analysis was conducted or whether a full 3D reconstruction was made. This claim seems to be based on only one dendritic measurement (sholl analysis), but many other dendritic or morphological features could be altered.

Crittenden, J.R., Tillberg, P.W., Riad, M.H., Shima, Y., Gerfen, C.R., Curry, J., Housman, D.E., Nelson, S.B., Boyden, E.S., & Graybiel, A.M. (2016) Striosome-dendron bouquets highlight a unique striatonigral circuit targeting dopamine-containing neurons. Proc. Natl. Acad. Sci. U.S.A., 113, 11318-11323.<br /> Crittenden, J.R., Yoshida, T., Venu, S., Mahar, A., & Graybiel, A.M. (2022) Cannabinoid Receptor 1 Is Required for Neurodevelopment of Striosome-Dendron Bouquets. eNeuro, 9, ENEURO.0318-21.2022.<br /> Cui, Q., Du, X., Chang, I.Y.M., Pamukcu, A., Lilascharoen, V., Berceau, B.L., García, D., Hong, D., Chon, U., Narayanan, A., Kim, Y., Lim, B.K., & Chan, C.S. (2021) Striatal Direct Pathway Targets Npas1+ Pallidal Neurons. J Neurosci, 41, 3966-3987.<br /> Evans, R.C. (2022) Dendritic involvement in inhibition and disinhibition of vulnerable dopaminergic neurons in healthy and pathological conditions. Neurobiol Dis, 172, 105815.<br /> Evans, R.C., Twedell, E.L., Zhu, M., Ascencio, J., Zhang, R., & Khaliq, Z.M. (2020) Functional Dissection of Basal Ganglia Inhibitory Inputs onto Substantia Nigra Dopaminergic Neurons. Cell Rep, 32, 108156.<br /> Henny, P., Brown, M.T.C., Northrop, A., Faunes, M., Ungless, M.A., Magill, P.J., & Bolam, J.P. (2012) Structural correlates of heterogeneous in vivo activity of midbrain dopaminergic neurons. Nat. Neurosci., 15, 613-619.<br /> McGregor, M.M., McKinsey, G.L., Girasole, A.E., Bair-Marshall, C.J., Rubenstein, J.L.R., & Nelson, A.B. (2019) Functionally Distinct Connectivity of Developmentally Targeted Striosome Neurons. Cell Rep, 29, 1419-1428.e5.<br /> Spix, T.A., Nanivadekar, S., Toong, N., Kaplow, I.M., Isett, B.R., Goksen, Y., Pfenning, A.R., & Gittis, A.H. (2021) Population-specific neuromodulation prolongs therapeutic benefits of deep brain stimulation. Science, 374, 201-206.

Author Response

We appreciate your constructive feedback on our manuscript entitled “Deletion of sulfate transporter SUL1 extends yeast replicative lifespan via reduced PKA signaling instead of decreased sulfate uptake” (ID: eLife-RP-RA-2023-94609). Your comments/suggestions are very helpful for improving our manuscript. In particular, we feel additional experiments and analysis suggested by the reviewers will help strengthen our argument that Sul1 deletion mutant extends lifespan via decreased PKA signaling, instead of via decreased sulfate uptake. Below we outline our response to the reviewer's comments/suggestions and the plans for additional experiments and analysis.

(1) Our current model is that lifespan extension following SUL1 knockout depends on the PKA signaling pathway but not sulfate transport. To further substantiate this, we plan to conduct further transcriptome sequencing and dynamic sulfate uptake experiments using WT, Sul1D and Sul1E427Q strains. If our model is correct, we expect that PKA signaling pathway will be more repressed in Sul1D strain than in Sul1E427Q strain, but the sulfate transport will be similar in both strains. This will add strong evidences supporting the model in addition to the lifespan data.

(2) The reviewer mentioned the disparities observed between the lifespan of WT in Figure 1B and other experimental assays. Although it is known that lifespan for WT varies considerably from experiment to experiment (thus the need for WT control for every lifespan measurement), we agree it is important to make a solid conclusion that Sul1E427Q does not extend lifespan. We plan to measure the lifespan of more cells for the mutant strains illustrated in Figure 1B and update the data and charts.

(3) Other issues, for example, the small images of Msn2/4 in the nucleus, grammar and formatting errors, and the lifespan data of double (Sul1/Msn4) mutants will be addressed in the revised version of the manuscript after we performed the additional experiments/analysis.

Reviewer #2 (Public Review):

Summary:

In this study, the authors find that the deletion of a sulfate transporter in yeast, Sul1, leads to the extension of replicative lifespan. They investigate mechanisms underlying this extension and claim that the effects on longevity can be separated from sulfate transport, and are instead linked to a previously proposed transceptor function of the Sul1 transporter. Through RNA sequencing analysis, the authors find that Sul1 loss triggers activation of several stress response pathways, and conclude that deletion of two pathways, autophagy or Msn2/4, partially prevents lifespan extension in cells lacking Sul1. Overall, while it is well-appreciated that activation of Msn2/4 or autophagy is beneficial for lifespan extension in yeast, the results of this study would add an important new mechanism by which this could achieved, through perceived sulfate starvation. However, as described below, several of the experiments utilized to support the authors' conclusion are not experimentally sound, and significant additional experimentation is required to support the authors' claims throughout the manuscript.

Strengths:

The major strength of the study is the robust RNA-seq data that identified differentially expressed genes in cells lacking Sul1. This facilitated the authors' focus on two of these pathways, autophagy and the Msn2/4 stress response pathway.

Weaknesses:

Several critical experimental flaws need to be addressed by the authors to more rigorously test their hypothesis.

(1) The lifespan assays throughout the manuscript contain inconsistencies in the mean lifespan of the wild-type strain, BY4741. For example, in Figure 1A, the lifespan of BY4741 is 24.3, and the extended lifespan of the sul1 mutant is 31. However, although all mutants tested in Figure 1B also have lifespans close to 30 cell divisions, the wild-type control is also at 30 divisions in those experiments as well. This is problematic, as it makes it impossible to conclude anything about the lifespan extension of various mutants with inconsistencies in the wild-type lifespan. Additionally, the mutants analyzed in 1B are what the authors use to claim that loss of the transporter does not extend lifespan through sulfate limitation, but instead through a signaling function. Thus, it remains unclear whether loss of sul1 extends lifespan at all, and if it does, whether this is separable from cellular sulfate levels.

(2) While the authors use mutants in Figure 1 that should have differential effects on sulfate levels in cells, the authors need to include experiments to measure sulfate levels in their various mutant cells to draw any conclusions about their data.

3) Similar to point 2, the authors focused their RNA sequencing analysis on the deletion of sul1 and did not include important RNA seq analysis of the specific Sul1 mutation or other mutants in Figure 1B that do not exhibit lifespan extension. The prediction is that they should not see the activation of stress response pathways in these mutants as they do not see lifespan extension, but this needs to be tested.

(4) While the RNA-seq data is robust in Figure 2 as well as the follow-up quantitative PCR and trehalose/glycogen assays in 2A-B, the follow-up imaging assays for Msn2/4 localization in Figure 2 are not robust and are difficult to interpret. The authors need to include more high-resolution imaging or at least a close-up of the cells in Figure 3C.

(5) The autophagy assays utilized in Figure 4 appear to all be done with a C-terminal GFP-tagged Atg8 protein. As C-terminal GFP is removed from Atg8 prior to conjugation to phosphatidylethanolamine, microscopy assays of this reporter cannot be utilized to report on autophagy activity or flux. Instead, the authors need to utilize N-terminally tagged Atg8, which they can monitor for vacuole uptake as an appropriate readout of autophagy levels. As it stands, the authors cannot draw any conclusions about autophagy activity in their studies.

eLife assessment

The study by Long et al. presents valuable findings on the role of the SUL1 gene in yeast longevity, proposing that lifespan extension can occur through signaling pathways independent of its sulfate transport function, offering new insights into aging mechanisms with potential implications beyond yeast biology. However, the evidence supporting the uncoupling of SUL1's transport and signaling functions is inadequate, relying on limited lifespan analysis without measurements for nutrients and nutrient signaling status. This research is of particular interest to the aging research community, although additional experiments are needed to fully substantiate the claims.

Reviewer #1 (Public Review):

The manuscript by Long et al. focused on SUL1, a gene encoding a sulfate transporter with signaling roles in yeast. The authors claim that the deletion of SUL1, rather than SUL2 (encoding a similar transporter), extended yeast replicative lifespan independent of sulfate transport. They also show that SUL1 loss-of-function mutants display decreased PKA activity, indicated by stress-protective carbohydrate accumulation, relevant transcription factor relocalization (measured during aging in single cells), and changes in gene expression. Finally, they show that loss of SUL1 increases autophagy, which is consistent with the longer lifespan of these cells. Overall, this is an interesting paper, but additional work should strengthen several conclusions, especially for the role of sulfate transport. Specific points include the following:

- What prompted the authors to measure the RLS of sul1 mutants? Prior systematic surveys of RLS in the same strain background (which included the same sul1 deletion strain they used) did not report lifespan extension in sul1 cells (PMID: 26456335).

- Cells carrying a mutant Sul1 (E427Q), which was reported to be disrupted in sulfate transport, did not have a longer lifespan (Figure 1), leading them to conclude that "lifespan extension by SUL1 deletion is not caused by decreased sulfate uptake". They would need to measure sulfate uptake in the mutants they test to draw that conclusion firmly.

- Related to my previous point, another simple experiment would be to repeat the assays in Figure 1 with exogenous sulfur added to see if the lifespan extension is suppressed.

- There needs to be more information in the text or the methods about how they did the enrichment analysis in Figure 2B. P-values are typically insufficient, and adjusted FDR values are reported from standard gene ontology platforms (e.g., PANTHER).

- It is somewhat puzzling that relocalization of Msn2 was not seen in very old cells (past the 17th generation), but it was evident in younger cells. The authors could consider another possibility, that it was early and midlife experiences that made those cells live longer. Past that window, loss of Sul1 may have no impact on longevity. A conditional shutoff system to regulate SUL1 expression would be needed to test the above, albeit this is probably beyond the scope of this report.

- The connections between glucose restriction, autophagy, and sul1 (Figure 4) could be further tested by measuring the RLS of sul1 cells in glucose-restricted cells. If RLS is further extended by glucose restriction, then whatever effects they see should be independent of glucose restriction.

- They made and tested the double (sul1, msn2) mutants, but they should also test the sul1, msn4 combination since Msn4 functions similarly to Msn2.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Long et al. demonstrated that the deletion of SUL1, which encodes a sulfate transporter localized on the plasma membrane, extends the replicative lifespan in S. cerevisiae. The authors further investigated the mechanism underlying this lifespan extension. They found that, unlike sul1∆ mutants, other mutants that have been shown to have a deficiency in sulfate transport cannot extend lifespan, from which they concluded that it is unlikely that SUL1 deletion extends lifespan by impairing sulfate intake. The authors then performed a series of characterizations on sul1∆ mutants and found that consistent with previous studies, PKA activity is downregulated when SUL1 is deleted. The authors demonstrated that SUL1 deletion promotes the nuclear localization of Msn2, as well as autophagy, which are known downstream signals of the PKA pathway. In addition, the authors show that MSN2 and ATG8 are indispensable for the lifespan extension in sul1∆ cells. Altogether, this manuscript suggests that SUL1 deletion extends lifespan by affecting PKA activity.

Strengths:

This study reported an interesting phenotype that the deletion of SUL1, but not SUL2, promotes lifespan extension in budding yeast. The authors performed some characterizations on sul1∆ mutants and epistatic studies to demonstrate that this lifespan extension requires MSN2 and ATG8, which further support the importance of the PKA pathway in regulating lifespan.

Weaknesses:

However, one of the major findings in this paper that SUL1 deletion extends lifespan independently of its role in sulfate uptake was merely based on lifespan measurements on sul2∆, SUL1E427Q, and met3∆ mutants, which cannot exclude the possibility that yeast lifespan is affected by sulfate intake. In addition, the strength of evidence for whether SUL1 deletion extends lifespan through affecting PKA activity is incomplete. It has been shown that Sul1 and Sul2 have redundant functions in both sulfate transport and PKA activation (Kankipati et al. 2015). However, in this manuscript, as shown by the authors, the deletion of SUL2 does not extend the lifespan compared with sul1∆ mutants. Without a further characterization on why deletion of SUL1, but not SUL2, extends lifespan, it is likely that SUL1 deletion extends lifespan independently of either sulfate transport or PKA activation.

Author Response

Reviewer #1 (Public Review):

(1) It is unclear whether the authors took into consideration the contribution of nuclear blebs for nuclear volume measurements. This would be particularly relevant in situations of very strong confinement. Blebs were previously shown to affect volume (Mistriotis et al., JCB 2019). One could argue that the decreased nuclear volume was due to the increased blebbing observed in very strong confinements.

As stated in the main text: “[Nuclear Blebs] had a limited contribution to the increase in nuclearprojected area, as the increase remained significantly different even if protrusions were dismissed to compute the projected area (Fig S3C)”. In addition, a decrease in the nuclear volume was also observed for slight and intermediate confinement (height = 7 and 9 µm), while in these two conditions, no blebs are observed.

(2) From their experimental setup, it is unclear whether the reduced nuclear volume observed after confined cell division arises from a geometrical constraint or is due to an intrinsic nuclear feature. One could argue that cells exiting mitosis under confinement have clustered chromosomes and, therefore, will have decreased volume. This would imply that the nucleus is not "reset" but rather that a geometrical constraint is forcing nuclei to be smaller. One way to test this would be to follow individual cells under confinement, let them enter mitosis, and then release the confinement. If, under these conditions, the daughter nuclei are smaller, then it supports their model. If daughter nuclei recover to their initial value, then it´s simply due to a geometrical constraint that forces the clustering of chromosomes and the reassembly of the NE in a confined space.

We agree with the reviewer. As stated in the discussion, “For now, the mechanisms involved remain elusive”, and “Our results call for an in-depth analysis of the molecular pathways at play”. The experiments suggested by the reviewer are definitely important experiments that we plan to carry out. Indeed, it is important to know if cells that were ‘born’ under confinement will retain smaller nuclei in the next generation if confinement is released, or whether the next generation will recover their initial larger nuclei.

(3) The authors claim that the nucleus adapts to confinement based on evidence that the nucleus no longer shrinks in the second division following the first division. I would argue no further decrease is possible because the DNA is already compacted in the smallest possible volume. If indeed nuclei are in a new homeostatic state as the authors claim, then one would expect nuclei to remain smaller even after confinement is removed. This analysis is missing.

As mentioned above, we agree that “deconfinement experiments” are indeed important. Nevertheless, we respectfully want to point out that the DNA is not compacted to its maximum level during confinement.

First, we observed that the nuclei of the second generation of cells born in confinement no longer shrink for all investigated confinement conditions, including for slight confinement (height of 9 µm, corresponding to an initial nuclear deformation of 41%), where DNA is less confined compared to the very strong confinement condition (height of 3 µm, corresponding to an initial nuclear deformation of 70%).

Second, the total uncompressible volumetric fraction of a cell is smaller than 30% (Roffay et al. PMID: 34785592, Cell Biology by the Numbers ISBN: 9780815345374) this allows a nucleus to be compressed to over 70% of its size, as we observed in the extreme scenario.

(4) Also, if the authors want to claim that this is a mechanism used for cancer cells to adapt to confined situations as the title says, they need to show that normal, near-diploid cells do not behave in the same way. This analysis is missing.

We agree with the reviewer. For the revised version, we have planned to analyze cell response to confinement using the RPE-1 cell line, as a model of a diploid and untransformed cell line. This will be important experiments to know if the nuclear mechanism identified in the HT-29 cell line is also at stake for normal cells.

(5) Authors state that "Loss of nuclear blebs is clearly linked to mitosis, suggesting that nuclear volume and nuclear envelope tension are tightly coupled, and supports the hypothesis that mitosis is a key regulator of nuclear envelope tension". I have a few issues with the way this sentence is written. Firstly, one could say that all nuclear structures (and not only blebs) are lost during mitosis because the nucleus disassembles. Hence, the new homeostatic state could be determined by envelope reassembly after mitosis and not mitosis itself. Thirdly, how can mitosis be a key regulator of nuclear envelope tension when the nucleus is disassembled during the process? These require clarification.

We agree with the reviewer that the formulation used required clarification that will be made in the revised version: for now, we only have evidence that nuclear volume regulation is at stake at mitosis. The most probable hypothesis is that confinement perturbed NE reassembly after mitosis, and that this perturbed reassembly leads to a change in nuclear volume. Complementary experiments are needed to test such a hypothesis, using cell lines stably expressing LAP2/LAP2b-GFP for instance. It is however delicate experiments that will require a dedicated study on its own.

Secondly, I don´t understand why the loss of nuclear blebs suggests that volume and tension are tightly coupled.

Nuclear Blebs appear once nuclei have reached a critical NE tension (Srivastava, et al PMID: 33662810). The fact that cells “born” under confinement have no nuclear blebs means that their nuclei are no longer under tension. This is a direct consequence of the decrease in nuclear volume, implying a coupling between volume and tension.

(6) The authors claim that, unlike previous studies (Lomakin et al), this work shows a "gradual nuclear adaptation". From their results, this is difficult to conclude simply because they do not analyse cPLA2 levels. This is solely based on indirect evidence obtained from cPLA2 inhibition. A gradual adaptation would mean that based on the level of confinement we would expect to have increasingly higher levels of cPLA2 (and therefore nuclear tension).

We thank the reviewer for his/her comment. Indeed, we have no direct evidence of gradual cPLA2 recruitment in our study, as we did not analyze cPLA2 levels.

However, of note, in our study, nuclear volume and tension adaptation occur in the entire range of confinement height (from 3 to 9 µm), with a decrease in nuclear volume inversely correlated with the imposed initial nuclear deformation (fig S2C). On the contrary, in Lomakin et al., for HeLa cells, a threshold of 5 µm confinement is needed to trigger a cell motility response mediated by cPLA2. Such a difference suggests that other parameters are used as a confinement readout by cells during the reassembly of the NE after mitosis.

(7) The authors should refrain from saying that the mechanism behind DNA repair is coupled to the nuclear adaptation they show. There are several points regarding this statement. Firstly, increased DNA damage could be due to nuclear ruptures imposed by confinement at 2h. In fact, the authors show leakage of NLS from the nucleus after confinement (Figure S3A). Secondly, the decrease in DNA damage at 24h could be because these nuclei did not rupture. How can they ensure that cells with low DNA damage at 24h had increased DNA damage at 2h? Finally, one needs to confirm if the nuclei they are analysing at 24h did undergo a round of cell division previously. From the evidence provided, the authors cannot conclude that DNA damage regulation is occurring in confined cells. Moreover, cell cycle arrest is a known effect of DNA damage. Cells with high damage at 2h most likely are arrested or will present with increased mitotic errors (which the authors exclude from their analyses).

We need to clarify our analysis workflow: it was only in live experiments that we excluded cells with abnormal cell division, as cell division was visible in the timelapse. For immuno-staining analysis on fixed samples, all non-apoptotic cells were taken into account in the analysis. The decrease in DNA damage observed at 24h thus applies to all cells under confinement. There is a clear difference between 2h and 24h in the 2AX immunostaining (that is used as a proxy for DNA damage): whereas at 2h almost all cells have several foci (10-15 foci per cells on average fig. 3H), the number of foci in the entire cell population decreases to 1-2 foci per cell at 24h. The population at 24h mainly includes cells that have undergone a round of cell division, with >80 % of normal cells, as quantified in Fig. 3 E. In the revised version, we will include as a supplementary figure, a quantification of the percentage of cells having more than 5 foci at 2h and 24h, as well as large field of views for -2AX immunostaining to illustrate the distribution.

Reviewer #2 (Public Review)

One major limitation is that all experiments are performed in a single cell line, HT-29 human colorectal cancer cells, which has an unusual nuclear envelope composition as it has no lamin B2, low lamin B1 levels, and contains a p53 mutation. Because lamins B1 and B2 play important functions in protecting the nuclear envelope from blebs and confinement-induced rupture, and p53 is crucial in the cellular DNA damage response, it remains unclear whether other cell lines exhibit similar adaptation behavior.

We agree that including other cell lines would help generalize our findings. It would be interesting in the future to analyze if a similar regulation exists for other cell types. In particular, as stated in the discussion, it would be very interesting to investigate whether this nuclear adaptation is universal, or if it is a consequence of a dysregulation in a specific cancer pathway. Our current manuscript is relevant as it uncovers the existence of this highly interesting phenomenon.

Investigating if other cell types have the same capacity to adapt would provide insights into the molecular mechanisms involved. In the revised version, we specifically plan to analyze nuclear response under prolonged confinement in 2 types of cells :(1) normal cells with near diploid characteristics (RPE-1 cell line, as a model of a diploid and untransformed cell line); (2) other colorectal cancer cell lines presenting higher levels of lamin B2 and B1, and no P53 mutation (HCT-116).

Furthermore, although the time-lapse experiments suggest that reduction in nuclear volume occurs primarily during mitosis, the authors do not address whether prolonged confinement, even in the absence of apoptosis, could also result in cells adjusting their nuclear volume, or alternatively normalizing nuclear envelope tension by recruiting additional membrane from the endoplasmic reticulum, which is continuous with the nuclear membranes.

Even if we cannot completely ruin the hypothesis raised by the reviewer, we respectfully want to stress that if additional membrane from the endoplasmic reticulum were recruited, we should observe an increase in nuclear volume at S/G2, which is the case only for the strongest imposed confinment (h=3 µm, corresponding to an initial nuclear deformation of 70 % Figure S2E). It should be however very interesting in the future to directly assess nuclear envelope tension and to follow with high resolution live experiments the eventual recruitment of additional membrane.

Regarding the proposed role of cPLA2, previous studies have shown that cPLA2 recruitment to the nuclear membrane, which is essential to mediate its nuclear mechanotransduction function, requires both an increase in nuclear membrane tension and intracellular calcium. However, the current study does not include any data showing the recruitment of cPLA2 to the nuclear membrane upon confinement, or the disappearance of nuclear membrane-associated cPLA2 during prolonged confinement, leaving unclear the precise function and dynamics of cPLA2 in the process.

We agree with the reviewer that it would be very informative to analyze the recruitment of cPLA2 in live experiments. We plan to do this in future experiments using cPLA2 immunostaining at different time points or the cPLA2-mKate construct. This will be the subject of a dedicated study, together with possible changes in nuclear pores size and organization, as well as nuclear tension analysis. For this article, we plan to add the analysis of the effect of cPLA2 inhibition in live experiments.

Lastly, it remains unclear (1) whether the reduction in nuclear volume is caused by a reduction in nuclear water content, by chromatin compaction, e.g. associated with an increase in heterochromatin, or through other mechanisms, (2) whether the change in nuclear volume is reversible, and if so, how quickly,

We thank the reviewer for his/her comment. This point was also mentioned by Reviewer #1. It is important to know if cells that were ‘born’ under confinement will retain smaller nuclei in the next generation if confinement is released, or whether the next generation will recover their initial larger nuclei. We plan to perform such “deconfinement” experiments and add the results in the revised version. In addition, we also plan to investigate in more detail the DNA compaction state during confinement.

and (3) what functional consequences the substantial reduction in nuclear volume has on nuclear function, as one would expect that this reduction would be associated with a substantial increase in nuclear crowding, affecting numerous nuclear processes.

We agree with the reviewer that such a reduction in nuclear volume would most probably affect numerous nuclear processes that would be highly interesting to decipher in the future. Especially, as pointed out in the discussion, “the regulation of nuclear size identified in this study could have important consequences on resistance to classical chemotherapeutic treatments that target proliferation”. This question merits an entire study and is outside the scope of our current manuscript.

Reviewer #3 (Public Review)

(1) One essential consideration that goes unaddressed is whether the nuclear volume alone is changing under compression (resulting in a higher nuclear to cytoplasmic ratio) or if the cell volume is changing and the nuclear volume is following suit (no change in the N:C ratio). Depending on which of these is the case, the overall model would likely shift. In particular, interpreting the effect of disrupting myosin II activity given its different distribution at the cortex in response to the higher confinement would be influenced by which of these conditions are at play.

We agree with the reviewer. As stated in the discussion, “the nuclear to cytoplasmic volume ratio, which is constant within a given population, is most likely to be impacted by confinement and changes in nuclear envelope tension (24, 45, 46), and might be at play in the regulation we describe herein”.

As mentioned in the results section, “the distance between the cell membrane and the nuclear envelope was significantly reduced with confinement (Fig. 1D, Fig. S1B) and accompanied by the relocalization of the contractility machinery (Phosphorylated Myosin Light Chain (p-MLC) staining) from above the nucleus to the side, indicating a cortex rearrangement (Fig. S1C)”. For the revised version, we plan to investigate if such relocalization is accompanied by a change in the nuclear to cytoplasmic ratio using the p-MLC and nuclei immunostaining performed at 2h and 24h under the entire range of confinement investigated.

(2) -A key approach used and interpreted by the investigators is an assessment of the folding of the "inner lamin envelope", which they derive from an image analysis routine of lamin staining that they developed and argue reflects "nuclear envelope tension". I am not convinced of the robustness of this approach or what it mechanistically reveals. It may or may not reflect the contour of the inner nuclear membrane, which (perhaps) is the most relevant to the authors' interpretation of nuclear envelope tension. Given the major contribution of this data to the model, which is based on the "unfolding" of the nuclear envelope, an orthogonal approach (e.g. electron microscopy - which one needs to truly address the high-frequency undulations of the nuclear envelope) is needed to support the larger conclusions.

We agree with the reviewer that the precise measurement of NE surface area is challenging because of the NE folds, and that our approach is provides semi-quantitative information. Higher-resolution approaches would be necessary to investigate that point in more details, using 3D super-resolution. However, we want to point out that even with our limited resolution, the differences observed in lamin A/C staining are striking (Fig. 3A): while lamin folds are completely absent at 2h under strong confinement, inner lamin folds are massively observed at 24h, showing a pattern very similar to the control condition. In the revised version, we will add more representative images to strengthen that our analysis is representative of our observations.

(3) The authors argue that nuclear tension is lost after mitosis in the confined devices because nuclear volume has decreased. While a smaller nuclear volume might indeed translate to less compressive force from the device on the nucleus, one would imagine that the chromosomes still have to be accommodated and that confining them in a smaller volume could increase the tension. Although arguable, the potential alternative possibilities suggest that actual measurements of nuclear envelope tension are needed to robustly test the model. The authors cite the observation that blebs are less prevalent after mitosis as additional support for this model, but this is expected as nuclear envelope breakdown and reformation will "reset" the nuclear contour while the appearance of blebs at mitotic entry is essential a "memory" of all blebs and ruptures over the entire preceding cell cycle.

We agree with the reviewer that assessing the nuclear envelope tension would enable a better description of the underlying process. It will be the subject of a dedicated study, together with possible changes in nuclear pore size and organization, as well as the analysis of cPLA2 recruitment.

The proposed model in the current study is for the moment simply a geometrical model. Given the simplicity of the model, the fit with our experimental points is striking.

(4) Representative images for the pharmacological perturbations other than blebbistatin are notably absent - only the analyzed data are presented in the manuscript or the supplemental material. How these perturbations (e.g. to cPLA2) also affect the cortex is important to interpret the data given the point raised above. Orthogonal approaches would also strengthen the conclusions (for example, the statement that "nuclear adaptation observed during mitosis requires nuclear tension sensing through cPLA2" requires more evidence to be convincing - it is not sufficiently supported by the data presented). Even if this is the case, the authors acknowledge that cPLA2 is likely not the answer to the adaption observed under the lower degrees of confinement. Thus, the mechanisms underlying the adaptive changes to nuclear volume remain enigmatic.

We thank the reviewer for this insightful comment, and we plan to add representative images for the pharmacological perturbation in the revised version of the manuscript.

(5) One more consideration that seems to go without comment is that the cells under confinement do not appear to successfully complete cytokinesis (Fig. 5b). At a minimum this seems like a major perturbation to cell physiology and needs to be more fully discussed by the authors as playing a role in the observed changes in nuclear volume.

We agree that in the image chosen for Fig. 5b, cytokinesis does not seem to be complete. This is not representative of the entire cell population as 80% of the cell population showed a normal phenotype under very strong confinement with no drug (Fig. 5C and 3E, as well as fig S3D for a representative large field of view). Live experiments using the FUCCI cell lines also show that cells are capable of making several complete divisions under confinement (Fig. 2). Complementary experiments under pharmacological treatments and confinement are planned to extend our analysis of such processes.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors discover that nuclear volume decreases after mitotic exit following cell confinement in a manner that scales with the extent of confinement. This adaptation appears to protect the cells from adverse outcomes of critical confinement such as nuclear blebs and DNA damage. The evidence to support these claims is strong.

The authors also provide a model in which argue that what they call the "apparent nuclear surface area" is modulated by confinement through a mechanism regulated by cPLA2 and myosin II activities. Here there are weaknesses in that the manuscript relies on a single approach, measurements are indirect, and alternative models are not explored. Similarly, additional considerations need to be addressed so that the reader can interpret the data presented - for example whether cell volume is also changing coincident with nuclear volume changes, and whether other aspects of cell physiology such as cytokinesis are altered.

Considerations that could support the manuscript further:

One essential consideration that goes unaddressed is whether the nuclear volume alone is changing under compression (resulting in a higher nuclear to cytoplasmic ratio) or if the cell volume is changing and the nuclear volume is following suit (no change in the N:C ratio). Depending on which of these is the case, the overall model would likely shift. In particular, interpreting the effect of disrupting myosin II activity given its different distribution at the cortex in response to the higher confinement would be influenced by which of these conditions are at play.

A key approach used and interpreted by the investigators is an assessment of the folding of the "inner lamin envelope", which they derive from an image analysis routine of lamin staining that they developed and argue reflects "nuclear envelope tension". I am not convinced of the robustness of this approach or what it mechanistically reveals. It may or may not reflect the contour of the inner nuclear membrane, which (perhaps) is the most relevant to the authors' interpretation of nuclear envelope tension. Given the major contribution of this data to the model, which is based on the "unfolding" of the nuclear envelope, an orthogonal approach (e.g. electron microscopy - which one needs to truly address the high-frequency undulations of the nuclear envelope) is needed to support the larger conclusions.

The authors argue that nuclear tension is lost after mitosis in the confined devices because nuclear volume has decreased. While a smaller nuclear volume might indeed translate to less compressive force from the device on the nucleus, one would imagine that the chromosomes still have to be accommodated and that confining them in a smaller volume could increase the tension. Although arguable, the potential alternative possibilities suggest that actual measurements of nuclear envelope tension are needed to robustly test the model. The authors cite the observation that blebs are less prevalent after mitosis as additional support for this model, but this is expected as nuclear envelope breakdown and reformation will "reset" the nuclear contour while the appearance of blebs at mitotic entry is essential a "memory" of all blebs and ruptures over the entire preceding cell cycle.

Representative images for the pharmacological perturbations other than blebbistatin are notably absent - only the analyzed data are presented in the manuscript or the supplemental material. How these perturbations (e.g. to cPLA2) also affect the cortex is important to interpret the data given the point raised above. Orthogonal approaches would also strengthen the conclusions (for example, the statement that "nuclear adaptation observed during mitosis requires nuclear tension sensing through cPLA2" requires more evidence to be convincing - it is not sufficiently supported by the data presented). Even if this is the case, the authors acknowledge that cPLA2 is likely not the answer to the adaption observed under the lower degrees of confinement. Thus, the mechanisms underlying the adaptive changes to nuclear volume remain enigmatic.

One more consideration that seems to go without comment is that the cells under confinement do not appear to successfully complete cytokinesis (Fig. 5b). At a minimum this seems like a major perturbation to cell physiology and needs to be more fully discussed by the authors as playing a role in the observed changes in nuclear volume.

Reviewer #1 (Public Review):

Summary<br /> In this work, Mouelhi et al investigated how the nucleus responds to long term confinement. They find that short-term confinement does not affect nuclear volume, whereas long-term confinement leads to a decrease in volume. The authors propose this decrease occurs after mitosis and relies on cPLA2 and myosin contractility.

Strengths

The ability to accurately control cell confinement allows authors to determine its effects on cellular function with high resolution. This provides a good addition to the existing collection of tools used for cellular micromanipulation. The results provided are relevant and timely and could help understand how cancer cells adapt to conditions of confinement.

Weaknesses

I have a few concerns which I believe should be addressed:

(1) It is unclear whether the authors took into consideration the contribution of nuclear blebs for nuclear volume measurements. This would be particularly relevant in situations of very strong confinement. Blebs were previously shown to affect volume (Mistriotis et al., JCB 2019). One could argue that the decreased nuclear volume was due to the increased blebbing observed in very strong confinements.

(2) From their experimental setup, it is unclear whether the reduced nuclear volume observed after confined cell division arises from a geometrical constraint or is due to an intrinsic nuclear feature. One could argue that cells exiting mitosis under confinement have clustered chromosomes and, therefore, will have decreased volume. This would imply that the nucleus is not "reset" but rather that a geometrical constraint is forcing nuclei to be smaller. One way to test this would be to follow individual cells under confinement, let them enter mitosis, and then release the confinement. If, under these conditions, the daughter nuclei are smaller, then it supports their model. If daughter nuclei recover to their initial value, then it´s simply due to a geometrical constraint that forces the clustering of chromosomes and the reassembly of the NE in a confined space.

(3) The authors claim that the nucleus adapts to confinement based on evidence that the nucleus no longer shrinks in the second division following the first division. I would argue no further decrease is possible because the DNA is already compacted in the smallest possible volume. If indeed nuclei are in a new homeostatic state as the authors claim, then one would expect nuclei to remain smaller even after confinement is removed. This analysis is missing.

(4) Also, if the authors want to claim that this is a mechanism used for cancer cells to adapt to confined situations as the title says, they need to show that normal, near-diploid cells do not behave in the same way. This analysis is missing.

(5) Authors state that "Loss of nuclear blebs is clearly linked to mitosis, suggesting that nuclear volume and nuclear envelope tension are tightly coupled, and supports the hypothesis that mitosis is a key regulator of nuclear envelope tension". I have a few issues with the way this sentence is written. Firstly, one could say that all nuclear structures (and not only blebs) are lost during mitosis because the nucleus disassembles. Hence, the new homeostatic state could be determined by envelope reassembly after mitosis and not mitosis itself. Secondly, I don´t understand why the loss of nuclear blebs suggests that volume and tension are tightly coupled. Thirdly, how can mitosis be a key regulator of nuclear envelope tension when the nucleus is disassembled during the process? These require clarification.

(6) The authors claim that, unlike previous studies (Lomakin et al), this work shows a "gradual nuclear adaptation". From their results, this is difficult to conclude simply because they do not analyse cPLA2 levels. This is solely based on indirect evidence obtained from cPLA2 inhibition. A gradual adaptation would mean that based on the level of confinement we would expect to have increasingly higher levels of cPLA2 (and therefore nuclear tension).

(7) The authors should refrain from saying that the mechanism behind DNA repair is coupled to the nuclear adaptation they show. There are several points regarding this statement. Firstly, increased DNA damage could be due to nuclear ruptures imposed by confinement at 2h. In fact, the authors show leakage of NLS from the nucleus after confinement (Figure S3A). Secondly, the decrease in DNA damage at 24h could be because these nuclei did not rupture. How can they ensure that cells with low DNA damage at 24h had increased DNA damage at 2h? Finally, one needs to confirm if the nuclei they are analysing at 24h did undergo a round of cell division previously. From the evidence provided, the authors cannot conclude that DNA damage regulation is occurring in confined cells. Moreover, cell cycle arrest is a known effect of DNA damage. Cells with high damage at 2h most likely are arrested or will present with increased mitotic errors (which the authors exclude from their analyses).

Reviewer #2 (Public Review):

Summary:

Extensive previous research has shown that cell confinement, e.g., vertical compression of cells to a height smaller than the height of the unconfined cells, results in the unfolding of nuclear membrane invaginations, calcium and membrane tension mediated recruitment of cPLA2 to the nuclear membrane (which triggers increased cortical myosin accumulation and activity, among other effects), nuclear blebbing, and DNA damage. However, the long-term effects of confinement, and how cells adapt to such confined conditions, have remained largely unexplored.

In this work, the authors use custom-built cell confinement devices that enable precise control of confinement for prolonged periods of time (up to several days), along with live cell and fixed cell imaging to compare short-term (2 hours) and long-term (24+ hours) effects of confinement on nuclear structure. The authors report that while vertical confinement results in a short-term increase in nuclear cross-sectional area, associated with an increase in nuclear surface area due to unfolding of nuclear envelope invaginations while maintaining nuclear volume, long-term confinement results in a decrease in nuclear volume, reduced cross-sectional area, and re-appearance of nuclear envelope invaginations. Using time-lapse imaging, the authors demonstrate that these effects are associated with a reduction in nuclear volume upon completion of the first mitosis under confinement. Pharmacological inhibition experiments indicate a requirement of cPLA2, calcium signaling, and actomyosin contractility in this process. Although it is not surprising that nuclear blebs disappear following mitosis, as the nuclear envelope breaks down at the onset of mitosis and subsequently reforms as the chromatin decondenses, the observed change in nuclear volume upon prolonged confinement is intriguing. Notably, the nuclear adaptation following prolonged confinement was also associated with a reduction in DNA damage when comparing cells at 2h and 24h of confinements, measured by the presence of gamma-H2AX foci in the nucleus. By fitting their experimental data of nuclear surface area measurements, the authors arrive at the conclusion that cells have an intrinsic nuclear envelope tension set-point and that completing mitosis enables cells to reset nuclear envelope tension to this set-point.

Strengths:

The use of an agarose confinement system with precise control over vertical confinement enables the authors to apply long-term confinement without depriving cells of nutrients while performing live cell imaging or immunofluorescence analysis following fixation. The live cell imaging is a powerful tool to assess the effect of confinement not only on nuclear morphology, but also on cell cycle progression (using the FUCCI fluorescent reporter) and to compare nuclear volume between mother and daughter cells. The data presented by the authors to demonstrate changes in nuclear volume and surface area are convincing and supported by several independent measurements. The model comparing total and apparent nuclear surface area nicely complements the experimental measurements and helps to make the point that cells have a nuclear envelope tension set-point, even though the authors were unable to directly measure nuclear envelope tension. The inhibitor experiments targeting cPLA2 (using AACOCF3), intracellular calcium (using BAPTA-Amand 2APB), and myosin contractility (using blebbistatin) identify key players in the underlying cellular mechanism.

Weaknesses:

Although the findings by the authors will be of interest to a broad community, several weaknesses limit the mechanistic insights gained from this study. One major limitation is that all experiments are performed in a single cell line, H-29 human colorectal cancer cells, which has an unusual nuclear envelope composition as it has no lamin B2, low lamin B1 levels, and contains a p53 mutation. Because lamins B1 and B2 play important functions in protecting the nuclear envelope from blebs and confinement-induced rupture, and p53 is crucial in the cellular DNA damage response, it remains unclear whether other cell lines exhibit similar adaptation behavior.

Furthermore, although the time-lapse experiments suggest that reduction in nuclear volume occurs primarily during mitosis, the authors do not address whether prolonged confinement, even in the absence of apoptosis, could also result in cells adjusting their nuclear volume, or alternatively normalizing nuclear envelope tension by recruiting additional membrane from the endoplasmic reticulum, which is continuous with the nuclear membranes.

Additionally, the molecular mechanisms underlying the observed loss in nuclear volume and the regulation of this process remain to be identified. The pharmacological studies implicate cPLA2, intracellular calcium, and actomyosin contractility in this process, but do not include validation to confirm the efficiency of the drug treatment or to rule out off-target effects. Regarding the proposed role of cPLA2, previous studies have shown that cPLA2 recruitment to the nuclear membrane, which is essential to mediate its nuclear mechanotransduction function, requires both an increase in nuclear membrane tension and intracellular calcium. However, the current study does not include any data showing the recruitment of cPLA2 to the nuclear membrane upon confinement, or the disappearance of nuclear membrane-associated cPLA2 during prolonged confinement, leaving unclear the precise function and dynamics of cPLA2 in the process.

Lastly, it remains unclear (1) whether the reduction in nuclear volume is caused by a reduction in nuclear water content, by chromatin compaction, e.g. associated with an increase in heterochromatin, or through other mechanisms, (2) whether the change in nuclear volume is reversible, and if so, how quickly, and (3) what functional consequences the substantial reduction in nuclear volume has on nuclear function, as one would expect that this reduction would be associated with a substantial increase in nuclear crowding, affecting numerous nuclear processes.

Author Response

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

eLife assessment:

This study presents a valuable finding on the possible use of vilazodone in the management of thrombocytopenia through regulating 5-HT1A receptor signaling. The evidence supporting the claims of the authors is solid, with the combined use of computational methods and biochemical assays. The work will be of broad interest to scientists working in the field of thrombocytopenia.

Public Review:

Reviewer #1 (Public Review):

Summary:

This is well-performed research with solid results and thorough controls. The authors did a good job of finding the relationship between the 5-HT1A receptor and megakaryocytopoiesis, which demonstrated the potential of vilazodone in the management of thrombocytopenia. The paper emphasizes the regulatory mechanism of 5-HT1A receptor signaling on hematopoietic lineages, which could further advance the field of thrombocytopenia for therapeutic purposes.

Strengths:

This is comprehensive and detailed research using multiple methods and model systems to determine the pharmacological effects and molecular mechanisms of vilazodone. The authors conducted in vitro experiments using HEL and Meg-01 cells and in vivo experiments using Zebrafish and Kunming-irradiated mice. The experiments and bioinformatics analysis have been performed with a high degree of technical proficiency. The authors demonstrated how vilazodone binds to 5-HTR1A and regulates the SRC/MAPK pathway, which is inhibited by particular 5-HTR1A inhibitors. The authors determined this to be the mechanistic underpinning for the effects of vilazodone in promoting megakaryocyte differentiation and thrombopoiesis.

Weaknesses:

(1) Which database are the drug test sets and training sets for the creation of drug screening models obtained from? What criteria are used to grade the results?

Response: Thank you for your thoughtful comment. The database is built by our laboratory. Firstly, we collected 39 small molecule compounds that can promote MK differentiation or platelet formation and 691 small molecule compounds that have no obvious effect on MK differentiation or platelet formation to buiid the datbase. Then, the data of the remaining 713 types of small molecule compounds were utilized as the Training set, and the Molecular Descriptors of 2 types of active and 15 types of inactive small molecule compounds were randomly picked as the Validation set. With regard to the activity evaluation criteria, the prediction score for each molecule was between 0 and 1, and the model decision was made with a threshold of 0.5. The molecule with a score above the 0.5 threshold was identified as a megakaryopoiesis inducer (1).

Reference：

(1) Mo Q, Zhang T, Wu J, et al. Identification of thrombopoiesis inducer based on a hybrid deep neural network model. Thromb Res. 2023;226:36-50. doi:10.1016/j.thromres.2023.04.011

(2) What is the base of each group in Figure 3b for the survival screening of zebrafish? The positivity rate of GFP-labeled platelets is too low, as indicated by the quantity of eGFP+ cells. What gating technique was used in Figure 3e?

Response: We are deeply grateful for the insightful feedback you have provided regarding Figure 3 and the assessment of zebrafish model. We used 50 zebrafish embryos per group to evaluate VLZ toxicity, and we think this is a suitable and fair baseline. Our gating procedure is clearly depicted in the resulting diagram. Since our goal was to evaluate the fluorescence intensity quantitatively, we isolated the entire zebrafish cell. Since the amount of eGFP+ in various zebrafish tissues found in other literature is likewise quite low and we are unsure of the typical eGFP+ threshold for zebrafish (1, 2), we think this finding should be fair given that each group's activities in the experiment were conducted in parallel.

Reference：

(1) Yang L, Wu L, Meng P, et al. Generation of a thrombopoietin-deficient thrombocytopenia model in zebrafish. J Thromb Haemost. 2022; 20(8): 1900-1909. doi:10.1111/jth.15772

(2) Fallatah W, De Silva IW, Verbeck GF, Jagadeeswaran P. Generation of transgenic zebrafish with 2 populations of RFP- and GFP-labeled thrombocytes: analysis of their lipids. Blood Adv. 2019;3(9):1406-1415. doi:10.1182/bloodadvances.2018023960

(3) In Figure 4C, the MPV values of each group of mice did not show significant downregulation or upregulation. The possible reasons for this should be explained.

Response: Thank you for your thoughtful comment. Megakaryocytes build pseudopodia, which form extensions that release proplatelets into the bone marrow sinusoids. Proplatelets convert into barbell-shaped proplatelets to form platelets in an integrin αIIbβIII mediated process (1-2). Platelet size is established by microtubule and actin-myosin-sceptrin cortical forces which determine platelet size during the vascular formation of barbell proplatelets (3). Conversion is regulated by the diameter and thickness of the peripheral microtubule coil. Proplatelets can also be formed from proplatelets in the circulation (4). Megakaryocyte ploidy correlates with platelet volume following a direct nonlinear relationship to mean platelet volumes (5). Usually there is an equilibrium between platelet generation and clearance from the circulation (normal turnover) controlled by thrombopoietin. When healthy humans receive thrombopoietin, their platelet size decreases (6). Proplatelet formation is dynamic and influenced by platelet turnover (7) which increases upon increased platelet consumption and/or sequestration. In our study, the MPV values of each group of mice did not show significant downregulation or upregulation, from our point of view, there are several possible reasons for these results.

(1) Mice in a radiation-damaged state may result in a decrease in platelet count, but at the same time stimulate the bone marrow to release young and larger platelets, thus keeping the MPV relatively stable.

(2) After radiation injury, bone marrow cells were suppressed, resulting in a decrease in the number of platelets produced, but MPV remained unchanged, possibly because the direct effects of radiation on the bone marrow caused thrombocytopenia, but not necessarily the average platelet size.

Reference：

(1) Thon JN, Italiano JE. Platelet formation. Semin Hematol. 2010(3):220-226. doi: 10.1053/j.seminhematol.2010.03.005.

(2) Larson MK, Watson SP. Regulation of proplatelet formation and platelet release by integrin alpha IIb beta3. Blood. 2006(5):1509-1514. doi: 10.1182/blood-2005-11-011957.

(3) Thon JN, Macleod H, Begonja AJ, et al., Microtubule and cortical forces determine platelet size during vascular platelet production. Nat. Commun. 2012(3):852. doi: 10.1038/ncomms1838.

(4) Machlus KR, Thon JN, Italiano JE Jr. Interpreting the developmental dance of the megakaryocyte: a review of the cellular and molecular processes mediating platelet formation. Br. J. Haematol. 2014(2):227-36. doi: 10.1111/bjh.12758.

(5) Bessman JD. The relation of megakaryocyte ploidy to platelet volume. Am. J. Hematol. 1984(2):161-170. doi: 10.1002/ajh.2830160208.

(6) Harker LA, Roskos LK, Marzec UM, et al., Effects of megakaryocyte growth and development factor on platelet production, platelet life span, and platelet function in healthy human volunteers. Blood. 2000(8):2514-2522. doi: 10.1182/blood.V95.8.2514.

(7) Kowata S, Isogai S, Murai K, et al., Platelet demand modulates the type of intravascular protrusion of megakaryocytes in bone marrow. Thromb. Haemost. 2014(4):743-756. doi: 10.1160/TH14-02-0123.

(4) The PPI diagram and the KEGG diagram in Figure 6 both provide a possible mechanism pathway for the anti-thrombocytopenia effect of vilazodone. How can the authors analyze the differences in their results?

Response: We are appreciated your valuable comments. PPI (Protein-Protein Interaction) refers to the interaction between proteins. Inside cells, proteins interact with each other to perform various biological functions, influencing cell signaling, metabolic pathways, cell cycle, and more. KEGG (Kyoto Encyclopedia of Genes and Genomes) is a database that integrates information on genomes, chemicals, and biological systems. In pharmacoinformatic, KEGG pathways are often used to understand the molecular mechanisms of specific diseases or biological processes. KEGG contains the interrelationships between genes, proteins, and metabolites, helping to reveal key nodes in biological processes. PPI information can be integrated with data from KEGG pathways, such as metabolic and signaling pathways, to gain a more comprehensive understanding of the role of protein-protein interactions in cellular processes and biological functions. For example, by analyzing nodes in the PPI network, proteins associated with a specific disease can be identified, and further examination of these proteins' locations in KEGG pathways can reveal molecular mechanisms underlying the onset and development of the disease. However, this method also has some limitations：

Uncertainty (1): The construction of protein-protein interaction networks and drug interaction networks involves many assumptions and speculations. The edges of these networks may be based on experimental data but can also rely on bioinformatics predictions. Therefore, the accuracy of predictions is limited by the quality and reliability of the data used during network construction.

Insufficient data (2): Despite the availability of a large amount of bioinformatics data for network construction, interactions between some proteins and drugs may still lack sufficient experimental data. This data insufficiency can result in inaccuracies in network predictions.

Dynamics and temporal-spatial changes (3): The dynamics and temporal-spatial changes in biological systems are crucial for drug effects. Pharmacoinformatic may struggle to capture these changes as it often relies on static network representations, overlooking the temporal and dynamic nature of biological systems.

Reference：

(1) Fernando PC, Mabee PM, Zeng E. Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities. BMC Bioinformatics. 2020(1):442. doi: 10.1186/s12859-020-03773-2.

(2) Zhang S, Zhao H, Ng MK. Functional module analysis for gene coexpression networks with network integration. IEEE/ACM Trans. Comput. Biol. Bioinform. 2015(5):1146-1160. doi: 10.1109/TCBB.2015.2396073.

(3) Cinaglia P, Cannataro M. A method based on temporal embedding for the pairwise alignment of dynamic networks. Entropy (Basel). 2023(4):665. doi: 10.3390/e25040665.

(5)-HTR1A protein expression is measured only in the Meg-01 cells assay. Similar quantitation through western blot is not shown in other cell models.

Response: Your insightful criticism and recommendation to use different cell models in order to obtain a more accurate depiction of 5-HTR1A protein expression are greatly appreciated. We completely concur that using this strategy would greatly increase the validity of our research. However, establishing a primary megakaryocyte model requires specialized expertise and technical resources, which unfortunately are not readily available to us within the given timeframe. Nevertheless, we acknowledge the limitations of Meg-01 cells, which may exhibit distinct properties compared to true megakaryocytes. To mitigate this concern, we have ensured robust experimental design and rigorous data analysis to interpret our findings within the context of these model cell lines. We believe our results still provide valuable insights into megakaryocyte differentiation and address an important biological question.

Reviewer #2 (Public Review):

Summary:

The authors tried to understand the mechanism of how a drug candidate, VLZ, works on a receptor, 5-HTR1A, by activating the SRC/MAPK pathway to promote the formation of platelets.

Strengths:

The authors used both computational and experimental methods. This definitely saves time and funds to find a useful drug candidate and its therapeutic marker in the subfield of platelets reduction in cancer patients. The authors achieved the aim of explaining the mechanism of VLZ in improving thrombocytopenia by using two cell lines and two animal models.

Weaknesses:

Only two cell lines, HEL and Meg-01 cells, were evaluated in this study. However, using more cell lines is really depending on the workflow and the grant situations of the current research team.

Response: We deeply appreciate your insightful feedback and valuable suggestions regarding the use of more suitable models for studying the role of VLZ in megakaryocyte differentiation and platelet production. We fully agree that CD34+ hematopoietic stem/progenitor cells or primary megakaryocytes would provide a more accurate representation of in vitro megakaryopoiesis compared to HEL and Meg-01 cells, which possess limited potential for this process. We acknowledge that our current study did not include experiments with these preferred cell models. This is because our laboratory is still actively developing the technical expertise and resources required for establishing and maintaining primary megakaryocyte and CD34+ cell cultures. Despite the limitations of the current study, we believe the results using HEL and Meg-01 cells provide valuable preliminary insights into the potential effects of VLZ on megakaryocyte differentiation. We are actively working to overcome these limitations and plan to incorporate these more advanced models in our future investigations.

Reviewer #1 (Recommendations For The Authors):

I think the authors can enhance the mechanism study by developing more reliable models and methodologies. The connection to clinical research should be strengthened at the same time.

Response: We deeply appreciate your insightful feedback and valuable suggestions regarding the use of more suitable models for studying the role of VLZ in megakaryocyte differentiation and platelet production. Despite the limitations, we are committed to expanding our research in the future by incorporating your suggestion and establishing a primary megakaryocyte model to further validate our findings and strengthen our conclusions. At the same time, we wholeheartedly concur with your suggestion to combine clinical research. Unfortunately, VLZ is not a first-line treatment for depression in China, and getting blood samples from the matching number of patients for analysis is a challenge. To give additional experimental support for the medication, we have attempted to improve the data in vivo as much as feasible, including by implementing the intervention in normal mice. Our findings should also contribute to the theoretical underpinnings of this medication and aid in its practical application.

Reviewer #2 (Recommendations For The Authors):

Issues the authors need to address:

Figure 7: Why the band intensity of GAPDH in b or e is much greater than that in f, g, or h?

Response: Thank you for your careful observation and insightful comment regarding Figure 7. Because the concentration of each batch of protein samples is different, sometimes the GAPDH band strength is increased by the large loading volume. Other factors that may influence the GAPDH band strength include the instrument's contrast adjustment during exposure and the use of different numbers of holes for electrophoresis. Meanwhile, the original three replicate results of all WB results will be provided in the supplementary materials.

Finally, we sincerely thank you for providing us with this opportunity to make a further revision and modification of our manuscript, and your valuable and scientific comments are useful for the great improvement of our manuscript!

Reviewer #1 (Public Review):

Summary:

This is well-performed research with solid results and thorough control. The authors did a good job of finding the relationship between the 5-HT1A receptor and megakaryocytopoiesis, which demonstrated the potential of vilazodone in the management of thrombocytopenia. It emphasizes the regulatory mechanism of 5-HT1A receptor signaling on hematopoietic lineages, which could further advance the field of thrombocytopenia for therapeutic purposes.

Strengths:

This is a comprehensive and detailed research using multiple methods and model systems to determine the pharmacological effects and molecular mechanisms of vilazodone. The authors conducted in vitro experiments using HEL and Meg-01 cells and in vivo experiments using Zebrafish and Kunming-irradiated mice. The experiments and bioinformatics analysis have been performed with a high degree of technical proficiency. The authors demonstrated how vilazodone binds to 5-HTR1A and regulates the SRC/MAPK pathway, which is inhibited by particular 5-HTR1A inhibitors. The authors determined this to be the mechanistic underpinning for the effects of vilazodone in promoting megakaryocyte differentiation and thrombopoiesis.

Weaknesses:

(1) Which database are the drug test sets and training sets for the creation of drug screening models obtained from? What criteria are used to grade the results?<br /> (2) What is the base of each group in Figure 3b for the survival screening of zebrafish? The positivity rate of GFP-labeled platelets is too low, as indicated by the quantity of eGFP+ cells. What gating technique was used in Figure 3e?<br /> (3) In Figure 4C, the MPV values of each group of mice did not show significant downregulation or upregulation. Please explain the possible reasons.<br /> (4) The PPI diagram and the KEGG diagram in Figure 6 both provide a possible mechanism pathway for the anti-thrombocytopenia effect of vilazodone. How can the author analyze the differences in their results?<br /> (5) 5-HTR1A protein expression is measured only in the Meg-01 cells assay. Similar quantitation through western blot is not shown in other cell models.

eLife assessment

This study presents a rather valuable finding that vilazodone can restore the normal platelet level through regulating 5-HT1A receptor. The evidence supporting the claims of the authors is solid, although inclusion of more cell lines and more detailed analysis of the results would have strengthened the study. The work will be of interest to scientists working in the field of thrombocytopenia.

Reviewer #2 (Public Review):

Summary:

The authors tried to understand the mechanism on how a drug candidate, VLZ, works on a receptor, 5-HTR1A, by activating the SRC/MAPK pathway to promote the formation of platelets.

Strengths:

The authors used both computational and experimental methods. This definitely saves time and funds to find a useful drug candidate and its therapeutic marker in the subfield of platelets reduction in cancer patients. The authors achieved the aim to explain the mechanism of VLZ on improving thrombocytopenia by using two cell lines and two animal models.

Weaknesses:

Only two cell lines, HEL and Meg-01 cells, were evaluated in this study. However, using more cell lines is really depending on the work flow and the grant situations of the current research team.

Author Response

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

Response to reviewers

We wish to thank the reviewers for the time taken to appraise the manuscript and the helpful feedback to improve it. We have taken onboard the suggested feedback and incorporated it into the revision. The findings of the revised manuscript are unchanged. Below is a point-by-point response to specific comments.

Public reviews

Reviewer 1

Thank you to reviewer 1 for the thorough and insightful review of our manuscript. We are pleased that the strengths of our research, particularly the use of whole-genome bisulfite sequencing, the combination of animal and human data, and the investigation of a potential dietary intervention were recognized. We are confident that these aspects contribute significantly to the value and originality of our work.

We acknowledge the concerns regarding the statistical rigor of the study, particularly the sample size and data analysis methods. We would like to address these points in more detail:

Sample size: While we agree that a larger sample size would be ideal, the chosen sample size (n=4 per group) is consistent with other murine whole-genome bisulfite sequencing experiments in the field. We have carefully considered the cost-benefit trade-off in selecting this approach. In the revision we discuss the potential limitations of this sample size.

Data analysis: We acknowledge the inconsistencies in the study reporting and have committed to improving the clarity in the revision. We carefully reviewed the concerns regarding the use of causal language and the interpretation of differences in our results. In some cases, the use of causal language is justified by the intervention study design. We also believe other explanations like stochastic variation affecting the same genomic regions in different tissues, are exceedingly unlikely from a statistical viewpoint. In the revision we have adopted a balanced approach to the language.

Confounders: We acknowledge the importance of accounting for potential confounders such as birthweight, alcohol exposure and sex. The pups selected for genome analysis were matched for sex and on litter size as a proxy for in utero alcohol exposure. This careful selection of mice for genome analysis was intentionally guided to mitigate potential confounding.

Statistical rigour: We acknowledge the importance of multiple testing correction in the genome-wide analysis. We used the DSS method of Feng et al (PMID: 2456180) which employs a two-step procedure for assessing significance of a region. Instead of a single p-value for the whole DMR, DSS uses the area statistic to rank candidate regions and control the false discovery rate through shrinkage estimation methods. This approach reduces the risk of reporting false positives due to multiple testing across numerous CpG sites. It is similar in respects to employing local FDR correction at 0.05 level, with an additional minimum effect size threshold applied, and particularly suited to experiments where the number of replicates is low. In the revision we have committed to improving the clarity of the reporting of statistical methods.

Reviewer 2

Thank you to reviewer 2 for the comprehensive and valuable feedback on our manuscript. We take your concerns about the generalizability of our findings and the interpretation of certain results seriously. We would like to address your specific criticisms in detail:

Generalizability and Human Data: We agree that the generalizability of mouse models to human conditions has limitations. However, our study focused on understanding the early molecular alterations caused by moderate PAE, which can be more effectively modelled in a controlled environment like mice. To clarify this, we have strengthened the manuscript by emphasizing the focus on moderate PAE in the title and throughout the paper.

Transcriptome Analysis: We recognize the importance of investigating the functional consequences of PAE-induced DMRs and agree that transcriptome analysis would be highly valuable. We are currently planning to conduct future transcriptomic studies to understand the link between DMRs and gene expression.

Species-Specificity and DMR Enrichment: We acknowledge the likelihood of species-specific PAE effects. Our finding of enrichment of DMRs in non-coding regions was consistent with observations from the Lussier study of FASD. We agree there is further work to do and now highlight this in the discussion.

Tissue Sample Locations: Due to technical restrictions of processing newborn mouse tissue, we are unable to enhance the manuscript with specific tissue regions sampled.

Interpretation of Shared Genomic Regions: We appreciate your point about the alternative explanation for the shared genomic regions between brain and liver. Our interpretation is that regions identified in the alcohol group only affected equally in both tissues are likely established stochastically (as a result of the exposure) in the early embryo and then maintained in the germ layers. We have revised to suggest this is the most likely explanation and we acknowledge a more detailed examination in more tissues would be warranted for proof.

Additional Feedback

Reviewer 1

Introduction

• Line 65 - alcohol consumption is not always preventable and these statements further increase the stigma associated with FASD. A better way to say this would be "a leading cause of neurodevelopmental impairments".

We have implemented this suggestion in revised manuscript.

• The studies cited in lines 87-89 are somewhat outdated, as several more recent studies with better sample sizes have been published in recent years. I would recommend citing more recent publications in addition to these studies. Similarly, the authors should also cite Portales-Casamar et al., 2016 (Epigenetic & Chromatin) for the validation in humans, as it was the original study for those data.

We have added a citation for the study mentioned by Portales-Casamar et al. (2016) in the revised manuscript.

• Lines 95-95 - the authors should elaborate further on the "encouraging results" from choline supplementation studies, as these details may help interpret the findings from their own study.

In the revised manuscript, we replaced “encouraging results” with “results suggesting a high methyl donor diet (HMD) could at least partially mitigate the adverse effects of PAE on various behavioural outcomes”.

• Minor point: DNA methylation is preferable to "methylation" alone when not referring to specific CpGs or sites, as methylation can also refer to protein or RNA methylation.

“Methylation” has been replaced with “DNA methylation” in revised manuscript

Results

• Line 118 - HMD should be defined here.

HMD defined in revised manuscript

• The figures in the main manuscript and supplemental materials are not in the same order as they are presented in the text.

We apologise for this and thank the reviwer for their attendtion to detail. In the revision we have corrected the order of figures to match the text.

• It is concerning that the H20-HMD group had lower baseline weights, which could impact the findings from these analyses. Please discuss how these differences were accounted for in the study design and analyses.

We appreciate the reviewer's concern about the lower baseline weight in the H20-HMD group. We agree that this difference could potentially affect our findings. However, we want to emphasize that total weight gain during pregnancy was statistically similar across all groups by linear mixed effect model. Additionally, all dams were within the healthy weight range for their strain. While we cannot completely rule out any potential influence of baseline weight, we believe the similarity in weight gain and the healthy range of all dams suggest that the in-utero experience of pups regarding weight-related factors was likely comparable across groups.

• I have some concerns regarding the cutoffs used to identify the DMRs, particularly given the small N and number of tests. The authors should report the number of DMRs that meet a multiple testing threshold; if none, they should use a more stringent threshold than p<0.05, as one would expect 950,000 CpGs to meet that threshold by chance (19,000,000 CpGs x 0.05). The authors should also report the number of DMRs tested, as this will be a more appropriate benchmark for their analyses than the number of CpGs (they should also report the specific number here).

We appreciate the reviewer's concerns regarding the DMR cut-offs. We agree that clarifying the methods and justifying our choices is crucial. Our implementation of the DSS method for defining DMRs employs a local FDR p<0.05 cut-off, with additional delta beta threshold of 5%. We have clarified this in the methods section of the revised manuscript . We want to emphasize that the local FDR approach effectively mitigates the concern of chance findings by adjusting for multiple comparisons across the genome. Line 414-420 in the revised methods contains the following amended text

“Differentially methylated regions (DMRs) were identified within each tissue using a Bayesian hierarchical model comparing average DNA methylation ratios in each CpG site between PAE and non-PAE mice using the Wald test with smoothing, implemented in the R package DSS (46). False-discovery rate control was achieved through shrinkage estimation methods. We declared DMRs as those with a local FDR P-value < 0.05 based on the p-values of each individual CpG site in the DMR, and minimum mean effect size (delta) of 5%”

• I also have concerns about the delta cutoff for their DMRs. First, it is not clear if this cutoff is set for a single CpG or across the DMR (even then, it is not clear if this is a mean, median, max, min, etc.) Second, since the authors analyzed CpGs with 10X coverage, they can only reliably detect a delta of 0.1 (1/10 reads).

Thank you for raising this important point. In the revision we have clarified the effect size cutoff reflects the mean effect across CpGs within the DMR as follows (line 418)

“We declared DMRs as those with a local FDR P-value < 0.05 based on the p-values of each individual CpG site in the DMR, and minimum mean effect size (delta) of 5%”

We chose the mean as it provides a comprehensive representation of the overall methylation change within the region, while ensuring all individual CpGs used in the analysis had at least 10x coverage. It is not true that we can only detect a delta of 1/10 reads, the mean effect is the relative difference in means between groups and is not dependent on the underlying sequencing depth.

• Prenatal alcohol exposure is known to impact cell type proportions in the brain, which could lead to differences in DNAm patterns. The authors should address this possibility in the discussion, as well as examine their list of DMRs to determine if they are associated with specific brain cell types. The possibility of cell type differences in the liver should also be discussed.

We agree with the reviewer that PAE-induced alterations in cell type proportions can influence DNA methylation patterns. While isolating specific cell types in our current study's brain and liver samples was not achievable due to tissue limitations, we acknowledge this as a limitation and recognize the need for further investigations incorporating single-cell or cell type-specific approaches in the discussion.

• It is interesting, but maybe not surprising, that more DMRs were identified in the liver compared to the brain. This finding would warrant some additional interpretation in the discussion.

We appreciate and agree that this finding indeed warrants further interpretation. We have added the following sentence into the discussion section of the revised manuscript that provides some potential factors behind this observation.

Lines 263 “Indeed, most of the observed effects were tissue-specific, with more perturbations to the epigenome observable in liver tissue, which may reflect the liver’s specific role in metabolic detoxification of alcohol. Alternatively, cell type composition differences between brain and liver might explain differential sensitivity to alcohols effects”.

• Lines 148-149 - I disagree about the enrichment of decreased DNAm in brain DMRs, as 52.6% is essentially random chance. The authors should also include a statistical test here, such as a chi-squared test, to support this statement.

We agree that a revised interpretation is warranted. The updated manuscript has been amended as follows: “Lower DNA methylation with early moderate PAE in NC mice was more frequently observed in liver DMRs (93.5% of liver DMRs), while brain DMRs were almost equally divided between lower and higher DNA methylation with early moderate PAE (52.6% of brain DMRs had lower DNA methylation with early moderate PAE).”

• Similarly, I would recommend the authors use increased/decreased DNAm, rather than hypermethylated/hypomethylation, as the latter terms are better suited to DNAm values near 100% or 0%.

The use of hyper/hypo methylation is still considered common and well understood even for moderate changes. We agree the use of increased/decreased is more inclusive for a broader audience, so we have amended all references accordingly in the main text.

• Lines 153-155 - please report the statistics to support these enrichment results. A permutation test would be well suited to this analysis.

The reporting of statistics related to the enrichment test has now been amended to read “Overlap permutation tests showed liver DMRs were enriched in inter-CpG regions and non-coding intergenic regions (p < 0.05), while being depleted in all CpG regions and genic regions except 1to5kb, 3UTR and 5UTR regions, where there was no significant difference (Figure 2f).”

• Line 156 - "overwhelming enrichment" is a very strong statement considering the numbers themselves.

Omitted “overwhelming” in revised manuscript. Revised manuscript states: “Using open chromatin assay and histone modification datasets from the ENCODE project, we found enrichment (p < 0.05) of DMRs in open chromatin regions (ATAC-seq), enhancer regions (H3K4me1), and active gene promoter regions (H3K27ac), in mouse fetal forebrain tissue and fetal liver (Table 2).”

• Lines 165-167 - Please describe the analyses and metrics used to determine if the DNAm differences were mitigated in the HMD groups. As it stands, it is not clear if they are simply not significant, or if the delta was decreased. In terms of a figure, a scatter plot of the deltas for these DMRs would be better suited to visualizing these changes.

To determine whether DMRs were mitigated we simply applied the same statistical testing procedure on the subset of PAE DMRs in the group of mice exposed to the HM diet. The sample size is the same, and the burden on multiple testing is reduced as we did not test the entire genome. We believe our interpretation stands although we have urged caution in the discussion as follows (line 319)

“Another key finding from this study was that HMD mitigated some of the effects of PAE on DNA methylation. Although a plausible alternative explanation is that some of the PAE regions were not reproduced in the set of mice given the folate diet, our data are consistent with preclinical studies of choline supplementation in rodent models (34, 35) (36). Moreover, a subset of PAE regions were statistically replicated in subjects with FASD, suggestive or robust associations. Although our findings should be interpreted with caution, they collectively support the notion that alcohol induced perturbation of epigenetic regulation may occur, at least in part, through disruption of the one-carbon metabolism.”

• Given the lenient threshold to identify DMRs, it is possible that PAE-associated DMRs are simply false positives and do not "replicate" in a different subset of animals. One way to check this would be to determine whether there are any differences between mitigated/unmitigated DMRs and the strength of their initial associations. Should the mitigated DMRs skew towards higher p-values and lower deltas, one might consider that these findings could be false positives.

We appreciate the reviewer's concern about potential false positives due to the chosen DMR identification threshold. We reiterate the DMR calling thresholds were adjusted for local FDR; however, we acknowledge the need for further validation. We haven't observed this trend of mitigated DMRs having higher p-values and lower deltas, but we have replicated some PAE DMRs in independent human datasets and found support for their biological plausibility in the context of PAE.

• Related to the HMD analyses, I am concerned that the EtOH-HMD group consumed less alcohol, which could manifest in the PAE-induced DMRs disappearing, unrelated to the HMD exposure. The authors should comment on whether the pups were matched for ethanol exposure and include sensitivity analyses that include ethanol level as a covariate to confirm that their results are not simply due to decreased alcohol exposure.

We appreciate the reviewer's concern regarding the lower alcohol consumption by Dams in the EtOH-HMD group and its potential impact on DMRs. We agree that consistent in utero exposure is crucial for reliable results. Our pup selection for genomic analysis involved matching litter size as a proxy for in utero exposure, so even through the average alcohol consumption was lower for the EtOH-HMD group, we matched pups across treatment groups based on litter size as a proxy for alcohol intake levels, excluding pups with significantly different exposure levels. We agree more robust methods including direct measurement of blood alcohol content would improve the study. We have now incorporated this into the discussion of the revised manuscript on lines 351: “Additionally, we employed an ad-libitum alcohol exposure model rather than direct dosing of dams. Although the trajectories of alcohol consumption were not statistically different between groups, this introduces more variability into alcohol exposure patterns, and might might impact offspring methylation data”

• Lines 172 - please be more specific about the neurocognitive domains tested.

In the revision we have included more detail about the neurocognitive domains tested (originally mentioned in the results) in the methods as follows:

“These tests included the open field test (locomotor activity, anxiety) (38), object recognition test (locomotor activity, spatial recognition) (39), object in place test (locomotor activity, spatial recognition) (40), elevated plus maze test (locomotor activity, anxiety) (41), and two trials of the rotarod test (motor coordination, balance) (42)”

• Line 191 - please report the tissue type used in the human study, as well as the method used to estimate cell type proportions.

We stated in the results section that buccal swabs were used in both human cohorts.

We added to the revised manuscript that cell type proportions were estimated using the EpiDISH R package.

• Related to validation, it is unclear whether the human-identified DMRs were also validated in mice, or if the authors are showing their own DMRs. Please also discuss why DMRs might not have been replicated in AQUA.

We used human data sets to validate observations from our murine model, focusing on regions identified in our early moderate PAE model. This is now explicitly state on line 209 of the revision:

“We undertook validation studies by examining PAE sensitive regions identified in our murine model using existing DNA methylation data from human cohorts to address the generalizability of our findings.”

“In the section entitled ‘Candidate Gene Analysis..’ we used our murine data sets to reproduce previously published associations that included regions identified in both animal and human studies. We posit the lack of replication of our early moderate PAE regions in AQUA is explained in part by species-specific differences and considering the striking differences in effect size seen in regions that did replicate in FASD subjects, the exposure may need to be of sufficient magnitude and duration for the effects seen in brain and liver to survive reprogramming in the blood. The AQUA cohort is largely enriched for low to moderate patterns of alcohol consumption.

• Line 197 - please provide a citation for the ethanol-sensitive regions. There are also several existing DNAm analyses in brain tissues from animal models that should be included as part of these analyses, as several have shown brain-region and sex-specific DMRs related to prenatal alcohol exposure. These contrasts might help the authors further delineate the effects of prenatal alcohol in their model and expand on current literature to explain the deficits caused by alcohol exposure.

Our candidate gene/region selection was informed by a systematic review of previously published human and animal studies reporting associations between in utero exposure to PAE and offspring DNA methylation. We synthesized evidence across several models, tissues and methylation platforms to arrive at a core set of reproducible associations. Line 481 of the methods now includes a citation to our systematic review which details our selection criteria.

Discussion

• Line 211 - This is a strong statement for one hypothesis. It is also possible that different cell types have similar responses to prenatal alcohol exposure. In this scenario, perturbations need not arise before germ layer separation. The authors should soften this causal statement.

We appreciate this point although given the genome size relative to the size of the DMRs we have detected, the chance that different cell types would respond similarly in exactly the same regions seems exceedingly rare. We posit a more likely explanation is early perturbations in the embryo are established stochastically as a result of the exposure (supported by the interventional design) and maintained in the differentiating tissues. We agree further work is needed to prove this, specifically in a wider set of tissues from multiple germ layers so we have amended the discussion as follows:

“These perturbations may have been established stochastically because of alcohol exposure in the early embryo and maintained in the differentiating tissue. Further analysis in different germ layer tissues is required to formally establish this.”

• Lines 222-224 - I completely agree with this statement. However, the authors had the opportunity to examine dosage effects in their model as they measured alcohol-levels from the dams. At the very least, I would recommend sensitivity analyses in their DMRs to assess whether alcohol level/dosage influences their results.

Although a great suggestion to improve the manuscript, we did not have opportunity to examine dosages by design as we selected mice for genome analysis with matched exposure patterns. It would be fascinating to conduct a sensitivity analysis.

Methods:

• Please include the lysis protocol.

Thank you for picking up this error in our reporting. We have now included the following details in the methods which improve the reproducibility of this study: “Ten milligrams of tissue were collected from each liver and brain and lysed in Chemagic RNA Tissue10 Kit special H96 extraction buffer”.

• Please include the total reads for each sample and details of the QC pipeline, including filtering flags, quality metrics, and genome build.

Thank you for suggesting improvements to our reporting which improve the reproducibility of this study. We have included a new supplementary tableTab of sequencing statistics and details of the quality metrics. Please note the genome build is explicitly stated in the methods already.

• Please make your code publicly available to ensure that these analyses can be replicated.

Thank you for this suggestion. A data availability statement has now been included in the revision and code will be made available upon request

• Why were Y chromosome reads included in the dataset?

Y chromosomal reads were not included in the DMR analysis. Amended “We filtered the X chromosomal reads” to “We filtered the sex chromosomal reads” in revised manuscript.

• Please provide the number of total CpGs available for analysis.

Added sentence into results section of revised manuscript: “A total of 21,842,961 CpG sites were initially available for analysis.” We also clarified that the ~19,000,000 CpGs were analysed following coverage filtering.

• Please provide the parameters for the DMR analysis and report how the p-values and deltas were calculated.

We have addressed this in previous comments

• The supplemental materials for the human data are missing.

Thank you for picking up this oversight. The revision now includes an additional data supplement which details the analysis of the human data sets for interested readers.

Tables and figures

• Table 1. It is not clear how the DMRs for this table were selected. The exact p-values and FDR should also be reported in this table. The number of CpGs in these DMRS should also be reported.

Table 1 includes select DMRs that were consistently detected in both brain and liver tissue. These are particularly of interest as they represent regions highly sensitive to alcohol exposure. We agree that exact reporting of p-values would be ideal. Instead of a single p-value for the whole DMR, DSS uses the area statistic to rank candidate regions and control the false discovery rate (FDR) through shrinkage estimation methods. In the revision we have now included region size and number of CpGs in table 1.

• Table 3. Please include p-values for the DMR analyses.

As above we report the area-statistic which is an equivalent measure to assess evidence for differential methylation.

• Figure 2 (Figure 4 in revised manuscript). Please report the N for these analyses. It also seems that the pairwise t-tests were only compared to the H20-NC, which does not provide much insight into the PAE group. The relevance of the sexP analysis to the present manuscript is also unclear.

Figure 2 is now Figure 4 in the revision and the sample size has been included in figure legend. We compared all groups to the control group (H20-NC) as we aimed to determine any differences in intervention groups from the control.

We apologies for lack of clarity around the ‘sex P’ terminology. This refers to the p-value for the main effect of sex on the behavioural outcome. We agree it lacks relevance since the regression models were adjusted for sex. In the revision we have updated the methods as follows (line426) and removed references to sex P

“To examine the effect of alcohol exposure on behavioural outcomes we used linear regression with alcohol group (binary) as the main predictor adjusted for diet and sex.”

• Figure 3ef (Figure 2ef in revised manuscript). It is unclear how the regions random regions were generated. A permutation test would be relevant to determine whether there are any actual enrichment differences.

As stated in methods section: “DMRs were then tested for enrichment within specific genic and CpG regions of the mouse genome, compared to a randomly generated set of regions in the mouse genome generated with resampleRegions in regioneR, with equivalent means and standard deviations.”

• Figure 5. Please include the gene names for these DMRs, as well as their genomic locations. It would also be relevant to annotate these plots with the max, min, and mean delta between groups.

Thank you, we considered this however the DMRs are not in genes so we cannot apply a gene label. The locations are reported on the x-axis and the statistics are shown in Table 3.

• Figure S1b and S2c- It is quite worrisome that the PAE-HMD group drank less throughout pregnancy than their PAE counterparts. Please discuss how this was addressed in the analyses.

We appreciate the reviewer's concern regarding the lower alcohol consumption in the PAE-HMD group and its potential impact on DMRs. We agree that consistent in-utero exposure is crucial for reliable results. Although the total amount of liquid consumed over pregnancy was lower in this group, they started with a lower baseline and the trajectory was not statistically different compared to other groups.

We have now incorporated this into the discussion section of the revised manuscript on lines 336: “Additionally, we employed an ad-libitum alcohol exposure model rather than direct dosing of dams. Although the trajectories of alcohol consumption were not statistically different between groups, this introduces more variability into alcohol exposure patterns, and might might impact offspring methylation data.”

• Figure S1cd. See my comments about Figure 2.

Suggested changes have been incorporated.

• Figure S2d. it is not clear to what the statistics presented in this panel refer. Please clarify and discuss the implications of dietary intake differences on your findings.

Added sentence to caption in revised manuscript: “Statistical analysis involved linear mixed-effects regression comparing trajectories of treatment groups to H2O-NC baseline control group.”

• Figure S3. See my comments about Figure 2.

Suggested changes have been incorporated

• Figure S4. I am confused by the color legend, as it seems both colors are PAE. I also do not see how any regions show increased or decreased DNAm in PAE based on this plot (also no statistics are presented to support these conclusions).

The plot is intended to show there are no gross changes in methylation when averaged across all CpGs within different regulatory genomic contexts. Statistics are not included as it is intuitive from the plot that the means are the same. We have updated the figure legend which now reads

“Figure S4. No evidence for global disruption of methylation by PAE. The figure shows methylation levels averaged across CpGs in different regulatory genomic contexts. Neither brain tissue (A & B), nor liver tissue (C & D) were grossly affected by PAE exposure (blue bars). Bars represent means and standard deviation.”

Reviewer #2 (Public Review):

Summary:

Bestry et al. investigated the effects of prenatal alcohol exposure (PAE) and high methyl donor diet (HMD) on offspring DNA methylation and behavioral outcomes using a mouse model that mimics common patterns of alcohol consumption in pregnancy in humans. The researchers employed whole-genome bisulfite sequencing (WGBS) for unbiased assessment of the epigenome in the newborn brain and liver, two organs affected by ethanol, to explore tissue-specific effects and to determine any "tissue-agnostic" effects that may have arisen prior to the germ-layer commitment during early gastrulation. The authors found that PAE induces measurable changes in offspring DNA methylation. DNA methylation changes induced by PAE coincide with non-coding regions, including enhancers and promoters, with the potential to regulate gene expression. Though the majority of the alcohol-sensitive differentially methylated regions (DMRs) were not conserved in humans, the ones that were conserved were associated with clinically relevant traits such as facial morphology, educational attainment, intelligence, autism, and schizophrenia Finally, the study provides evidence that maternal dietary support with methyl donors alleviates the effects of PAE on DNA methylation, suggesting a potential prenatal care option.

Strengths:

The strengths of the study include the use of a mouse model where confounding factors such as genetic background and diet can be well controlled. The study performed whole-genome bisulfite sequencing, which allows a comprehensive analysis of the effects of PAE on DNA methylation.

Weaknesses:

Transcriptome analysis to test if the identified DMRs indeed affect gene expression would help determine the potential function of the identified methylation changes.

eLife assessment

This important study unveils the significant impact of prenatal alcohol exposure on epigenetic patterns, offering new insights into its adverse health outcomes through solid evidence from both mouse models and human data. The findings, which reveal how a high-methyl diet can mitigate these epigenetic alterations, present a promising prenatal care strategy. Despite its solid data overall, the study's small sample size and unaccounted confounders suggest the need for further research to confirm these findings and explore their practical implications.

Reviewer #1 (Public Review):

Summary:

This manuscript examined the impact of prenatal alcohol exposure on genome-wide DNA methylation in the brain and liver, comparing ethanol-exposed mice to unexposed controls. They also investigated whether a high-methyl diet (HMD) could prevent the DNA methylation alterations caused by alcohol. Using bisulfite sequencing (n=4 per group), they identified 78 alcohol-associated differentially methylated regions (DMRs) in the brain and 759 DMRs in the liver, of which 85% and 84% were mitigated by the HMD group, respectively. The authors further validated 7 DMRs in humans using previously published data from a Canadian cohort of children with FASD.

Overall, the findings from this study provide new insight into the impact of prenatal alcohol exposure, while also showing evidence for methyl-rich diets as an intervention to prevent the effects of alcohol on the epigenome. Some methodological concerns and confounders limit the robustness of these results, and should be addressed in future studies to further strengthen the conclusions of this study and its applicability to broader settings.

Strengths:

- The use of whole genome bisulfite sequencing allowed for the interrogation of the entire DNA methylome and DMR analysis, rather than a subset of CpGs.<br /> - The combination of data from animal models and humans allowed the authors to make stronger inferences regarding their findings<br /> - The authors investigated a potential mechanism (high methyl diet) to buffer against the effects of prenatal alcohol exposure, which increases the relevance and applicability of this research.

Weaknesses:

- The sample size was small for the epigenetic analyses, which limits the robustness of the findings.<br /> - The authors could not account for potential confounders in their analyses, including birthweight, alcohol levels, and sex. This is a particular problem for the high-methyl diet analyses, in which the alcohol-exposed mice consumed less alcohol than their non-diet counterparts.

Author Response

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

We greatly appreciate the editor and reviewers’ careful and professional assessment of this manuscript. We are delighted with the reviewers’ instructive comments and suggestions. We have tried to address the raised points comprehensively. The reviewers’ scrutiny has helped us immensely to discuss and present our work extensively and properly. We are grateful for the reviewers’ efforts and insights. The detailed responses are listed here.

Recommendations for the authors

(1) The intuition behind the model is not properly explained, i.e., the derivation of Eqs. 1-2 and the biological meaning of the AA/OO logic modes. A different notation could be helpful.

We thank the reviewers for this comment, and agree that the interpretation of our model in manuscript was indeed in need of improvement. We have incorporated this suggestion into the manuscript. For clarity, we have substituted AND-AND/OR-OR for original expression of AA/OO, and hope that new notations are helpful for interpreting our work.

In general, considering the diverse audience including those with experimental background, we feel that it is essential to present this manuscript in a more digestible manner. We therefore retain the entire derivation of Eqs. 1-2 in the supplementary method. We have added a qualitative introduction to model derivation and molecular biological significance underlying different logic motifs (AND-AND/OR-OR) in the revised manuscript. Please refer to Page 5 of the revised manuscript, lines 161-167 (see below).

“X and Y are TFs in the CIS network. n1 and n2 are the coefficients of molecular cooperation. k1-k3 in Eq1 and k4-k6 in Ep2 represent the relative probabilities for possible configurations of binding of TFs and CREs. (Fig2.A). d1 and d2 are degradation rates of X and Y, respectively. Here, we considered a total of four CRE’s configurations as shown in Figure 2A (i.e., TFs bind to the corresponding CREs or not, 22=4). Accordingly, depending on the transcription rates (i.e., r0x, r1, r2, r3 in Eq1, similarly in Eq2) of each configuration, we can model the dynamics of TFs in the Shea-Ackers formalism[1, 2].

Thus, the distinct logic operations (AND/OR) of two inputs (e.g., activation by X itself and inhibition by Y) can be further implemented by assigning corresponding profile of transcription rates in four configurations (Fig2.A). From the perspective of molecular biology, the regulatory logics embody the complicated nature of TF regulation that TFs function in a context-dependent manner. Considering the CIS network, when X and Y bind respective CREs concurrently, whether the expression of target gene is turned on or off depends on the different regulatory logics (specifically, off in the AND logic and on in the OR logic; Fig2.A). Notably, instead of exploring the different logics of one certain gene[3, 4], we focus on different combinations of regulatory logics due to dynamics in cell fate decisions is generally orchestrated by GRN with multiple TFs.”

(2) More clearly specify the used parameters and how these are chosen. This would be helpful to get a more quantitative grasp of the conditions that they compare.

We appreciate the reviewers pointing out unspecified parts in the main text. We have now included related discussion in the revised manuscript. Please refer to Page 5 of the revised manuscript, lines 179-181 (“Benchmarking the Boolean models with different logic motifs (Fig2.B), we reproduced the geometry of the attractor basin in the continuous models resembling those represented by corresponding Boolean models (Fig2.C; see Methods).”).

We would like to highlight that the Boolean models with different logic motifs (Fig. 2B) explicitly display the difference of state spaces (i.e., attractor basin). Moreover, as the focus of this work is on the role of regulatory logics in cell fate decisions, we ponder that it is rational to specify the geometry of the landscape based on the hint from Boolean models. Therefore, we reason that it is intuitive and reliable to assign values to used parameters by mapping our ODE models (Eqs. 1-2) to corresponding Boolean models qualitatively (refer to the statement in our original manuscript, Page 5, lines 162-163, “With appropriate parameters, we are able to reproduce the Boolean-like attractor basin in the continuous models”). In producing Figure 2-5, setting of parameters was performed in a heuristic way without particular searching. However, to draw general conclusions, like the "trade-offs between progression and accuracy" and the presence of the fully-connected stage, we sampled a substantial number of sets parameters to ensure statistically robust findings.

(3) Include the explanation of how the nullclines and basins shown in the figures (e.g., Fig. 2C, Fig. 4C, Fig. 4F, etc.) are calculated.

We thank the reviewers for this suggestion. We have incorporated this into the legend of corresponding figures when first mentioned in the main text. Please refer to Page 7 of the revised manuscript, lines 217-223 (see below).

“Fig2.C:

(C) State spaces of the AND-AND (top panel) and OR-OR (bottom panel) motifs in ODE models. Dark and red lines represent nullclines of respectively. Stable steady states (SSS) are denoted as orange dots. Unstable Steady States (USSs) are denoted as white dots. Each axis represents the concentration of each transcription factor, which units are arbitrary. Blue, green and purple areas in state spaces indicate attractor basins representing LX, S and LY, respectively. Color of each point in state space was assigned by the attractors they finally enter according to the deterministic models (Eq1, Eq2). These annotations were used for the following Figure 3-7.”

(4) Clarity on the decisions in the work is needed. For example, the "introduction" of asymmetry of the noise levels (as stated in line 215) appears completely arbitrary. The reason behind it can be guessed in the following paragraph, but the reader shouldn't have to guess.

We agree entirely with the reviewers’ comment. Indeed, this should have been stated more explicitly. The motivation for incorporating asymmetry in the noise levels stems from our endeavor to mimic the inherent biological variability in gene expression within a cell population. We have adjusted the manuscript to better convey the motivation for investigating asymmetric noise level. Please refer to Page 8 of the revised manuscript, lines 237-238 (“In biological systems, it is unlikely that the noise level of different genes is kept perfectly the same.”).

(5) Arbitrary and/or out-of-context jargon is used throughout the manuscript, making it hard to read and follow what the authors mean in some cases. For example, "temporal fully-connected stage" is used for the first time in line 290, and the term is not explained either in the main text or in the manuscript. Similarly, the reference to a Boolean-like and Boolean model (line 163 and Figure 1) without clarifying if this is just an analogy or if a formal model is built, nor the utility and implications of this comparison. Another problem related to jargon occurs on line 291, where the authors talk about "parameter sensibility", but such analysis (as it is normally understood in the field) is never performed; the authors perform a parameter exploration and make some general conclusions about the parameter space, but that is different than a parameter sensitivity analysis.

We thank the reviewers for this comment, as it has prompted us to better clarify our manuscript. We have reviewed the manuscript and made the necessary adjustments to improve its clarity. We do hope that this revision meets the reviewers’ expectations on the clarity and comprehensiveness of our analysis.

Regarding the jargon of "temporal fully-connected stage", we realized that this term was slightly vague and in need of improvement. Instead, we now employ “transitory fully-connected stage” in the revised manuscript to underline the short emergence of this particular stage. Please refer to Page 11 of the revised manuscript, lines 323.

We thank the reviewers for pointing out the lack of clarity concerning the Boolean models. We have now amended the manuscript to make this implicit expression explicit. Please refer to Page 5 of the revised manuscript, lines 179-181 (“Benchmarking the Boolean models with different logic motifs (Fig2.B; see Methods), we reproduced the geometry of the attractor basin in the continuous models resembling those represented by corresponding Boolean models (Fig2.C; see Methods).”). Specifically, we employed the Boolean models (Fig.2B) as the reference to assist us to heuristically evaluate the applicability of used parameters in the ODE models. Therefore, the Boolean models are built formally, and corresponding updated rules are listed in Fig.2A (refer to the middle row in the table called “Logic Function”, now also noted in the legend of Fig.2B, Page 7, lines 213-214). Nevertheless, we do utilize the analogy between the attractor basins from Boolean models and ODE models (refer to Fig.2B-C). Accordingly, we used the term “Boolean-like” to describe the landscape presented by the continuous models (Eqs. 1-2; refer to the statement in our original manuscript, Page 5, lines 162-163, “With appropriate parameters, we are able to reproduce the Boolean-like attractor basin in the continuous models”).

We appreciate the reviewers for this valuable comment, and agree that the usage of “parameter sensibility” was in need of adjustment. We have now amended the manuscript. Please refer to Page 10 of the revised manuscript, lines 318-321 (see below).

“To manifest the generality, we globally screened 6,213 groups of parameter sets under the AND-AND motif, and this logic-dependent intermediated stage can be observed for 82.7% of them (see Methods; Table S1), indicating little dependence on particular parameter setting (1.8% in the OR-OR motif).”

(6) Probably related just to the language clarity (i.e., the abuse of jargon), but we don't understand the conclusion on lines 296-298.

We thank the reviewers for this comment. We have adjusted the manuscript accordingly. Please refer to Page 11 of the revised manuscript, lines 323-327 (see below). And we hope that the reviewers agree with our attempt at mapping into the particular stage in cell fate decisions from the point of landscape.

“Furthermore, this transitory fully-connected stage locates between the fate-undetermined stage (Fig4.C top panel) and fate-determined stage (Fig4.C 3rd panel), comparable to the initiation (or activation) stage before the lineage commitment in experimental observations [5-7]. Therefore, we suspected that the robust fully-connected stage in the AND-AND motif may correspond to a specific period in cell fate decisions.”

(7) The so-called "solution landscape" in Figure 4E needs to be better explained.

We thank the reviewers for this comment. We have introduced the concept of solution landscape, which is a pathway map consisting of all stationary points and their connections, in lines 196-198 of the revised manuscript (see below).

“Furthermore, we introduced the solution landscape method. Solution landscape is a pathway map consisting of all stationary points and their connections, which can describe different cell states and transfer paths of them [82-84].”

In Figure 4E, we added detailed explanation of the solution landscape for the AND-AND motif. Specifically, it describes a hierarchical structure including one 2-saddle (yellow triangle), three 1-saddles (crimson X-cross sign), and three attractors (green dot). The layer of 1-saddles is represented by a blue translucent plane, and the bottom layer is the flow field diagram. The connections from 2-saddle to 1-saddles and from 1-saddles to the attractors are represented by red and blue lines, respectively. The arrow and color of the heatmap correspond to the flow direction and the length of the acceleration at each point in the state space.

(8) Table S1 is not properly annotated, and then it is impossible to interpret how it supports the observations in the paragraph in lines 342-342.

We appreciate the reviewers’ useful feedback. We have refined the annotations of all tables in our manuscript (Table S1-3). Please refer to “Supplementary Table” in resubmitted files.

Specifically, we randomly collected 6,231 sets of parameters for the AND-AND motif and 6,682 sets for the OR-OR motif (k1-k6 in Eq1 and Eq2; refer to Page 6 of the revised supplementary method, see below).

“First, to collect parameter sets with 3 SSSs, we used Latin hypercube sampling (LHS) to screen k-series parameters symmetrically (i.e., k1 = k4, k2 = k5, k3 = k6) ranging from 0.001 to 5 both in the AND-AND and OR-OR motifs. We ultimately collected 6,231 sets for the AND-AND motif and 6,682 sets for the OR-OR motifs (Table S1).”

To analyze the sequence of vanishing SSSs, we further filtered parameter sets with 2 SSSs remained as increasing ux (corresponding to Eq3 in the revised manuscript, Page 10, lines 293). We then got a collection of 6,207 sets for the AND-AND motif and 6,634 sets for the OR-OR motif. Based on these parameter settings, we checked if the observations (refer to Page 13, lines 377-378, “The distinct sequences of attractor basin disappearance as ux increasing can be viewed as a trade-off between progression and accuracy.”) are artifacts of particular parameter choice.

(9) The flow in Section 5 needs to be reorganised. For instance, it is not clear which question the authors are addressing in line 395, or how the proposed approach answers the question stated in lines 381-382.

We greatly thank the reviewers for pointing this out, and acknowledge that the Section 5 was definitely in need of improvement. We have now amended the manuscript to make this implicit understanding explicit. Please refer to Page 15 of the revised manuscript, lines 426-430 (see below).

“In prior sections, we systematically investigated two logic motifs under the noise- and signal-driven modes in silico. With various combinations of logic motifs and driving forces, features about fate-decision behaviors were characterized by computational models. Next, we questioned whether observations in computation can be mapped into real biological systems. And how to discern different logic motifs and driving modes is a prerequisite for answering this question.

To end this, we first evaluated the performance of different models, specifically in simulating the process of stem cells differentiating towards LX (Fig6.A).”

(10) There are two important weak points for the successful classification of the regulatory logic of real gene expression data as presented in the manuscript: (1) the small number of time-points in the datasets and clear peaks in gene expression heterogeneity cannot be identified, and (2) it is not always clear whether cell differentiation really exclusively relies on a CIS network, and which genes constitute it. These limitations should be solved or at least discussed in the manuscript.

We thank the reviewer for this comment. First, we agree entirely that analysis of datasets with more time points will be more amenable to identifying the trends of gene expression variation. We have made a concerted effort towards searching for such datasets, but unfortunately, there are not many such datasets publicly available. Specifically, to apply our computational framework, the datasets of our interest need to fulfill the following three characteristics: (i) sampling at multiple time points (as many as possible); (ii) to illustrate/validate our findings clearly and representatively, we would like the cell fate decisions in the biological systems to follow the classical binary tree-like pattern. i.e., there is one stem cell fate (or progenitor) and two downstream cell fates in the systems; (iii) the core GRN circuits for orchestrating the fate-decision processes have been experimentally confirmed (at least clearly supported). We have also extended the discussion to include above points to explicitly note the limitations regarding the used datasets. Please refer to Page 25 of the revised manuscript, lines 762-766 (see below).

“The gene expression datasets analyzed here are only available for a limited number of time points. Though they meet the need for discerning trends, it is evident that the application to the datasets with more time points will yield clearer and less ambiguous changing trends to support the conclusions of this paper more generally.”

In regards to second point, we do acknowledge that the CIS network may not always be the core module for every fate-decision case (but to our knowledge, this can be assumed in many cases, especially in binary tree-like pattern). For applicability and potential relevance to our intended readership, we developed the models and draw our conclusions primarily based on the CIS topology for its representativeness. We intend to incorporate diverse topologies (like mutual activation with self-activation, Feed-Forward Loop, etc.) in our computational framework presented here in near future. Additionally, we have incorporated this point into the discussion in the revised manuscript. Please refer to Page 25 of the revised manuscript, lines 766-769 (see below).

“Notwithstanding the fact that the CIS network is prevalent in fate-decision programs, there are other topologies of networks that serve important roles in the cell-state transitions, like feed-forward loop, etc. The framework presented in this work should further incorporate diverse network motifs in the future.”

As referred by the reviewers, even if given the CIS network, we may not sure about which genes constitute it in some cases. We agree that further extension of our framework to mining key regulators is an interesting question. We also note that we have become very enthusiastic about recent work that shows how to nominate core factors from high-throughput data[8, 9]. Of note, in the last section of our manuscript titled “The chemical-induced reprogramming of human erythroblasts (EBs) to induced megakaryocytes (iMKs) is the signal-driven fate decisions with an OR-OR-like motif”, we leveraged patterns of temporal expression variance to filter out key regulators (Fig7.F and H). We thus underline the potential of mining genes comprising core GRN circuits through expression variance. Nevertheless, as the focus of the present paper is on the role of regulatory logic in cell fate decisions, we feel it is beyond the scope of the present article to continue the development of our results on this point. Instead, we have included discussion of case that genes comprising the CIS network are not defined. Please refer to Page 23 of the revised manuscript, lines 685-687 (see below).

“Notably, if the genes that constituting the CIS network are not specified, we can conversely leverage the patterns of temporal expression variance to nominate key regulators in a model-guided manner.”

(11) The models used in Figure S5 are never clearly described.

We thank the reviewers for pointing this out. We have now introduced the settings of the models used in Figure S5 more clearly in the legend (see below).

Two logic motifs with the noise-driven mode (FigS5.A, see below):

Author response image 1.

“Initial values were identical with attractor of S fate in Figure 2C (SSSs in green attractor basins). Simulation was preformed 1000 times for each pseudo-time point, with each temporal state (from left to right) recorded as a dot on the plot. Top panel: Noise level of X (σx) is set to 0.21, and σy is 0.09. Bottom panel: Noise level of Y (σy) is set to 0.21, and σx is 0.09. Red arrow represents the direction of fate transitions of S to LX. Other than adding a white noise, parameters were identical with those in Figure 2C.”

Two logic motifs with the signal-driven mode (FigS5.B, see below):

Author response image 2.

“Initial values were identical with attractor of S fate in Figure 2C (SSSs in green attractor basins). Top panel: Noise level of X (σx) and Y (σy) are both set to 0.06. Simulation was preformed 1000 times, with each final state recorded as a dot on the plot. Parameter ux switched from 0 to 0.09 (0, 0.045, 0.09, from left to right). Bottom panel: Noise level of X (σx) and Y (σy) are both set to 0.05. Simulation was preformed 1000 times, with each final state recorded as a dot on the plot. Parameter ux switched from 0 to 0.24 (0, 0.12, 0.24, from left to right). Red arrow represents the direction of fate transitions of S to LX. Other model’s parameters were identical with those in Figure 2C.”

(12) Up until Section 5, "noise levels" have been used to refer to an input/parameter in the model. Here it is assumed as an emergent property. Are the authors talking about the variance in expression (e.g., see line 398)? Is it defined as the coefficient of variation? Clarity is essential to interpret the observations in this section, e.g., "different driving modes change in the patterns of noise rather than expression levels" (lines 399-400).

We greatly appreciate the reviewers pointing this ambiguity out. The term of “noise level” was indeed used to refer the strength of the noise in the models in Section 1-4. For classifying different logic motifs with two driving forces, we needed a practical metric that can be quantified from data, and we found population-level gene expression variance (i.e., “noise level” in line 398) is useful which defined as the coefficient of variation. For clarity, we carefully decide to substitute “expression variance” for “noise level” presented in Section 5-6. We have amended the manuscript accordingly, and hope this revision will be helpful for interpreting our result. Please refer to Page 15 of the revised manuscript.

(13) "Pulse-like behaviour" is used in an arbitrary way, not as it is normally used in the field. Moreover, we consider this jargon expression does not contribute to the understanding of the paper. (The authors probably meant "discrete transitions" vs "gradual transitions".)

We appreciate the reviewers’ valuable feedback regarding our use of the term “Pulse-like behavior”. We agree with the reviewers’ statement, and acknowledge that terminology of noise level’s patterns between different driving modes (noise-driven vs signal-driven; refer to Section 5 in our manuscript) was in need of improvement.

Upon comprehensive consideration, we primarily decided to adopt the terms “monotonic transitions” and “nonmonotonic transitions” to recapitulate the trends of noise level, underlining the distinct temporal noise’s patterns in cell fate decisions brought by two driving forces in a more contrastive way. We anticipate that current jargon expressions will be beneficial for interpreting our work. Please refer to Page 15 of the revised manuscript.

(14) The temporal resolution of the scRNAseq datasets that the authors used is too low to unambiguously distinguish a discrete pattern of gene expression heterogeneity from a rising profile. This limitation needs to be at least acknowledged in the text. Alternatively, the authors might want to identify more recent datasets with higher time resolution.

We appreciate the reviewers’ insightful suggestions. We agree that analysis of datasets with higher time resolution will be more unambiguous to identifying the trends of gene expression variation. We have made a concerted effort towards searching for such datasets, but unfortunately, there are not many such datasets publicly available. Specifically, to apply our computational framework, the datasets of our interest need to fulfill the following three characteristics: (i) sampling at multiple time points (as many as possible); (ii) to illustrate/validate our findings clearly and representatively, we would like the cell fate decisions in the biological systems to follow the classical binary tree-like pattern. i.e., there is one stem cell fate (or progenitor) and two downstream cell fates in the systems; (iii) the core GRN circuits for orchestrating the fate-decision processes have been experimentally confirmed (at least clearly supported). Nevertheless, we recognize this limitation should be mentioned in the paper. So, we have also extended the discussion to include above points. Please refer to Page 25 of the revised manuscript, lines 762-766 (see below).

“The gene expression datasets analyzed here are only available for a limited number of time points. Though they meet the need for discerning trends, it is evident that the application to the datasets with more time points will yield clearer and less ambiguous changing trends to support the conclusions of this paper more generally.”

(15) In the case of embryonic stem cell differentiation, an additional complication is that this protocol yields heterogeneous cell type mixtures, whereas the authors' simulations usually are designed to give differentiation towards a single cell type. This difference makes it difficult to compare measures of gene expression heterogeneity between simulations and the experimental system to infer regulatory logic questionable.

We thank the reviewers for this valuable comment and realize that we were not clear enough in the manuscript regarding the case of embryogenesis. In the biological system devised by Semrau et al[10], mouse embryonic stem cells (mESCs) differentiates into two lineages simultaneously, just as mentioned by the reviewers. We noticed this additional complication and performed other simulations in two logic motifs with increasing noise level of gene X and Y, as presented in Fig.S6E (see below).

Author response image 3.

“(E) Time courses on the coefficient of variation in expression levels of X and Y genes in silico during differentiation under the noise-driven mode. Initial values were set to the attractors of S fate in Figure 2C (SSSs in green attractor basins). Top panel: Noise level of X (σx) and Y (σy) are both set to 0.14. Bottom panel: Noise level of X (σx) and Y (σy) are both set to 0.1. Stochastic simulation was preformed 1000 times for each pseudo-time point.”

Given the noise-driven mode, we further employed the expression pattern of Gbx2-Tbx3 circuit to heuristically infer the logic motif.

(16) In contrast to the hematopoiesis example, the authors do not focus on a specific gene regulatory circuit with the ESC dataset. How their approach is possible on genome-wide data needs to be discussed.

We thank the reviewers for this comment. Indeed, the core GRN orchestrating the fate-decision process reported by Semrau et al[10] is not fully elucidated. We here focus on the Gbx2-Tbx3 circuit (Fig.6H, Fig.S6D). These two TFs were filtered out from 22 candidate TFs and suggested as potential key regulators in the original paper[10]. Accordingly, at this point we followed the original paper’s statement.

In regards to extension into biological systems without specific gene regulatory circuits, we have included discussions about the possibility that genes comprising the CIS network are not defined. Please refer to Page 23 of the revised manuscript, lines 685-687 (see below).

“Notably, if the genes that constituting the CIS network are not specified, we can conversely leverage the patterns of temporal expression variance to nominate key regulators in a model-guided manner.”

(17) [In supplemental material, pp.1] Possible typo: "In our word, we considered a GRN comprised...".

Thanks for spotting this typo. We have amended it in the revised supplemental method (refer to Page 1 of the revised supplementary method).

(18) [In supplemental material, pp.1] In Eqs. (1), the notation for the function HX([X]) implies that HX only depends on X, leaving the combinatorial regulation out. HX([X],[Y]) would be more general and accurate.

Thanks for pointing this out. We have incorporated this suggestion into the manuscript. Please refer to Page 1 of the revised supplementary method.

(19) [In supplemental material, pp.1] There are several works that have shown that the Hill coefficient is rarely representative of the number of binding elements. The model can be more general. See, for example, «Santillán, Moisés. "On the Use of the Hill Functions in Mathematical Models of Gene Regulatory Networks." Mathematical Modelling of Natural Phenomena 3, no. 2 (October 22, 2008): 85-97. https://doi.org/10.1051/mmnp:2008056.» and «Nam, Kee-Myoung, Rosa Martinez-Corral, and Jeremy Gunawardena. "The Linear Framework: Using Graph Theory to Reveal the Algebra and Thermodynamics of Biomolecular Systems." Interface Focus 12, no. 4 (June 10, 2022): 20220013. https://doi.org/10.1098/rsfs.2022.0013.»;

We thank the reviewer for drawing our attention to this and highlighting the above works. Indeed, this is important information to include in the manuscript. We have incorporated this suggestion into the revised supplemental method (refer to Page 1 of the revised supplementary method). These references have now been included in the revised supplemental method (refer to references [2]-[3]).

(20) [Minor] The configuration labels can be confusing, especially the AA, which is rather an AND NOT gate.

We thank the reviewers for this comment. For clarity, we have substituted AND-AND/OR-OR for original expression of AA/OO, and hope that new notations are helpful for interpreting our work.

(21) [Minor] Very low printing quality in Figure 1.

Thanks for the feedback regarding the printing quality of Figure 1. We have made the necessary adjustments to improve its quality. We have also ensured that all other figures in the manuscript meet the required standards.

(22) [Minor] We suggest including a quantitative scale for the bias in Fig. 3E.

Thanks, we have incorporated this suggestion into the manuscript.

(23) [Recommendation] Authors could also evaluate the cell fate decision processes as mutations or other perturbations affect a regulatory network.

We appreciate the reviewers for this valuable recommendation. We agree with the reviewers that further involving new cases would be helpful, especially those mutation-driven disease-related fate-decision processes, such as neutropenia in chemotherapy. However, given the considerable effort towards searching for appropriate datasets, we carefully decide not to make this change.

(24) [Recommendation] The authors could include some discussion of the likely impact of the work on the field and the utility of the methods and data to the community. For example, understanding the fluidity of the epigenetic landscape and the regulatory forces behind cell fate decisions can be of great importance in designing synthetic gene regulatory circuits.

We greatly appreciate the reviewers pointing this out. In the original manuscript, we intentionally limited the length of the discussion to make the whole story more focus. We thank the reviewers for their insightful suggestions regarding the content of discussion. We have incorporated this suggestion into the revised manuscript. Please refer to Page 25, lines 751-757 (see below).

“Recently, synthetic biology has realized the insertion of the CIS network in mammalian cells. One of the prerequisites for recapitulating the complex dynamics of fate transitions in synthetic biology is systematical understanding of the role of GRNs and driving forces in differentiation. And the logic motifs are the essential and indispensable elements in GRNs. Our work also provides a blueprint for designing logic motifs with particular functions. We are also interested in validating the conclusions drawn from our models in a synthetic biology system.”

In addition, a longstanding question of our interest in cell fate decisions is what contributes the distinctive development cross species, like human, mice and so on forth. However, in addition to protein coding sequences, regulatory interactions between genes (i.e., activation and inhibition) also exhibit conservation as reported in recent work of multi-species cell atlas [11], and it is generally acknowledged that gene regulatory networks (GRNs) orchestrate fate-decision procedures. Namely, conserved regulatory programs further bring us a conserved topology of core GRNs. Thus, the logics of regulation, as another vital element in GRNs, is naturally under the spot light (related to the introduction, lines 99-120 of the revised manuscript). Nevertheless, to our knowledge, regulatory logic in cell fate decisions has received only scant attention. We hope that our elucidation of the role of logic motifs in cell fate decisions will attract more inquiries in community into GRN’s regulatory logic.

Public reviews

In this manuscript, Xue and colleagues investigate the fundamental aspects of cellular fate decisions and differentiation, focusing on the dynamic behaviour of gene regulatory networks. It explores the debate between static (noise-driven) and dynamic (signal-driven) perspectives within Waddington's epigenetic landscape, highlighting the essential role of gene regulatory networks in this process. The authors propose an integrated analysis of fate-decision modes and gene regulatory networks, using the Cross-Inhibition with Self-activation (CIS) network as a model. Through mathematical modelling, they differentiate two logic modes and their effect on cell fate decisions: requires both the presence of an activator and absence of a repressor (AA configuration) with one where transcription occurs as long the repressor is not the only species on the promoter (OO configuration).

The authors establish a relationship between noise profiles, logic-motifs, and fate-decision modes, showing that defining any two of these properties allows the inference of the third. They also identify, under the signal-driven mode, two fundamental patterns of cell fate decisions: either prioritising progression or accuracy in the differentiation process. The authors apply this analysis to available high-throughput datasets of cell fate decisions in hematopoiesis and embryogenesis, proposing the underlying driving force in each case and utilising the observed noise patterns to nominate key regulators.

The paper makes a substantial contribution by rigorously evaluating assumptions in gene regulatory network modelling. Notably, it extensively compares two model configurations based on different integration logic, illuminating the consequences of these assumptions in a clear, understandable manner. The practical simulation results effectively bridge theoretical models with real biological systems, adding relevance to the study's insights. With its potential to enhance our understanding of gene regulatory networks across biological processes, the paper holds promise. Its implications extend practically to synthetic circuit design, impacting biotechnology. The conclusions stand out, addressing cell fate decisions and noise's role in gene networks, contributing significantly to our understanding. Moreover, the adaptable approach proposed offers versatility for broader applications in diverse scenarios, solidifying its relevance beyond its current scope.

We thank the reviewers for their enthusiasm for our work, and appreciate the professional, insightful and encouraging assessment.

However, the manuscript in its current form also has some important weaknesses, including the lack of clarity in the text and the questionable generality of specific observations.

We thank the reviewers for this comment. We have reviewed the manuscript and made the necessary adjustments to improve its clarity. We do hope that this revision meets the reviewers’ expectations on the clarity and comprehensiveness of our analysis.

For instance, even when focusing on the CIS network, the effect of alternative model implementations is not discussed. Notably, the input signals are only considered as an additive effect over the differential equations, while signals can potentially affect each of the individual processes.

We agree with the reviewers’ comment that signals may affect at each level of the central dogma, including transcription, translation, etc. Further, we have also included additional section titled “limitation of this study” on this point in the revised manuscript, and explicitly point to the potential limitations of our models. Please refer to Page 25 of the revised manuscript, lines 769-771 (see below).

“In addition, for simplicity and intuition, we here considered signals as uncoupled and additive effects in ODE models, due to feasible mapping in real biological systems, such as ectopic overexpression.”

The proposed model allows for a continuum of interactions/competition between transcription factors, yet only very restrictive scenarios are explored (strict AND/OR logic operations).

We thank the reviewers for this comment, and appreciate them sharing the potential for further generalization of our framework. Indeed, in addition to logic operations, our framework is able to be applied to all two-node circuits (34=81 in total), including mutual activation with self-activation. As the focus of this work is to illustrate the role of logic motifs in cell fate decisions, we mainly concentrated on two classical, intuitive and representative (at least to us) logic operations AND/OR in the context of the CIS network. Nonetheless, we already have four combinations to consider (two logic motifs and two driving forces). And we feel that the currently involved scenarios have properly fulfilled our need to manifest the role of logic motifs. Hence, we carefully decided not to further explore more logic operations in this work. Instead, we have included additional section titled “limitation of this study” in the revised manuscript. Please refer to Page 25 of the revised manuscript, lines 760-762.

“Although our framework enables the investigation of more logic motifs, we chose two classical and symmetrical logic combinations for our analysis. Future work should involve more logic gates like XOR and explore asymmetrical logic motifs like AND-OR.”

Moreover, how the model parameters are chosen throughout the paper is not clear. Similarly, the concentration and times are not clearly specified, making their comparison to experimental data troublesome.

We thank the reviewers for this comment. Regarding how to specify parameters in our model, we have now revised the manuscript. Please refer to Page 5 of the revised manuscript, lines 179-181 (“Benchmarking the Boolean models with different logic motifs (Fig2.B; see Methods), we reproduced the geometry of the attractor basin in the continuous models resembling those represented by corresponding Boolean models (Fig2.C; see Methods).”). In terms of concentration and time, we acknowledge that their units are arbitrary compared to a real experimental system. We now have noted this point in the legend of corresponding figures (Fig2.C, Fig3.B&D, Fig6.B-C, Fig7.E).

We would like to highlight that our entire work is organized in a model-driven fashion (also called top-down). We did not fine-tune the sets of parameters used in our model to specifically match the experimental data. Actually, it is also a longstanding challenge in computational biology since experimental datasets are usually insufficient to specify the parameters in a dynamical model. So, in general, it is inevitable to involve more assumptions such as non-Markov process[12, 13] and may lead to artifacts. Thus, we decided to draw qualitative conclusions (e.g., trends over time) from a quantitative model with sampling of parameter sets. Hence, we did not intentionally tailor our models to fit different datasets (i.e., all models used in our work share same basic setting of parameters), mapping into real biological systems in a top-down manner.

Regarding clarity, how the general model (equations 1-2) transforms into the specific cases evaluated in the paper is not clearly stated in the main text, nor are the positive and negative effects of individual transcription factors adequately explained. Similarly, in the main text and Figure 2, the authors refer to a Boolean model. However, they do not clearly explain how this relates to the differential equation model, nor its relevance to understanding the paper.

We thank the reviewers for this comment, as it has prompted us to better clarify our manuscript. We have adjusted the manuscript accordingly and made the necessary adjustments to improve its clarity.

Additionally, the term "noise levels" is generally used to refer to noise introduced in the "noise-driven" analysis (i.e., as an input or parameter in the models). Nonetheless, it is later claimed to be evaluated as an intrinsic property of the network (likely referring to expression level variability measured by the coefficient of variation).

We greatly appreciate the reviewers pointing this ambiguity out. The term of “noise level” was indeed used to refer the strength of the noise in the models in Section 1-4. For classifying different logic motifs with two driving forces, we needed a practical metric that can be quantified from data, and we found population-level gene expression variance (i.e., “noise level” in line 398) is useful which defined as the coefficient of variation.

For clarity, we carefully decide to substitute “expression variance” for “noise level” presented in Section 5-6. We have amended the manuscript accordingly.

Finally, some jargon is introduced without sufficient context about its meaning (e.g., "temporal fully-connected stage").

Regarding the jargon of "temporal fully-connected stage", we have realized that this term was slightly vague and in need of improvement. Instead, we now employ “transitory fully-connected stage” in the revised manuscript to underline the short emergence of this particular stage. Please refer to Page 10-11 of the revised manuscript, lines 316-327 (see below).

“Notably, in the AND-AND motif we observed a brief intermediated stage before S attractor disappears, where all three fates are directly interconnected (Fig4.C 2nd panel and D 2nd panel, Fig.4E). To manifest the generality, we globally screened 6,213 groups of parameter sets under the AND-AND motif, and this logic-dependent intermediated stage can be observed for 82.7% of them (see Methods; Table S1), indicating little dependence on particular parameter setting (1.8% in the OR-OR motif). Unlike the indirect attractor adjacency structure mediated by S attractor (Fig2.D), the solution landscape with fully-connected structure facilitates transitions between any two pairs of fates. Furthermore, this transitory fully-connected stage locates between the fate-undetermined stage (Fig4.C top panel) and fate-determined stage (Fig4.C 3rd panel), comparable to the initiation (or activation) stage before the lineage commitment in experimental observations [5-7]. Therefore, we suspected that the robust fully-connected stage in the AND-AND motif may correspond to a specific period in cell fate decisions.”

Additionally, proper discussion of previous work is also missing. For instance, the dynamics of the CIS network investigated by the authors have been extensively characterised (see e.g., Huang et al., Dev Biol, 2007), and how the author's results compare to this previous work should be discussed. In particular, the central assumptions behind the derivation of the model proposed in the manuscript must be assessed in the context of previous work.

Thanks for pointing this out. We have extended the discussion to include above points. We have also discussed and cited the work of Huang mentioned above. Please refer to Page 22, lines 644-647 in the revised manuscript (see below).

“One of the most representative work is that Huang et al. [14] modeled the bifurcation in hematopoiesis to reveal the lineage commitment quantitatively. Compared to simply modularizing activation or inhibition effect by employing Hill function in previous work, our models reconsidered the multiple regulations from the level of TF-CRE binding.”

References

(1) Ackers, G.K., A.D. Johnson, and M.A. Shea, Quantitative model for gene regulation by lambda phage repressor. Proc Natl Acad Sci U S A, 1982. 79(4): p. 1129.

(2) Shea, M.A. and G.K. Ackers, The OR control system of bacteriophage lambda: A physical-chemical model for gene regulation. Journal of Molecular Biology, 1985. 181(2): p. 211-230.

(3) Hunziker, A., et al., Genetic flexibility of regulatory networks. Proc Natl Acad Sci U S A, 2010. 107(29): p. 12998-3003.

(4) Kittisopikul, M. and G.M. Suel, Biological role of noise encoded in a genetic network motif. Proc Natl Acad Sci U S A, 2010. 107(30): p. 13300-5.

(5) Brand, M. and E. Morrissey, Single-cell fate decisions of bipotential hematopoietic progenitors. Curr Opin Hematol, 2020. 27(4): p. 232-240.

(6) Zhang, Y., et al., Hematopoietic Hierarchy - An Updated Roadmap. Trends Cell Biol, 2018. 28(12): p. 976-986.

(7) Arinobu, Y., et al., Reciprocal activation of GATA-1 and PU.1 marks initial specification of hematopoietic stem cells into myeloerythroid and myelolymphoid lineages. Cell Stem Cell, 2007. 1(4): p. 416-27.

(8)Kamimoto, K., et al., Dissecting cell identity via network inference and in silico gene perturbation. Nature, 2023. 614(7949): p. 742-751.

(9) Hammelman, J., et al., Ranking reprogramming factors for cell differentiation. Nat Methods, 2022. 19(7): p. 812-822.

(10) Semrau, S., et al., Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. Nat Commun, 2017. 8(1): p. 1096.

(11) Li, J., et al., Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types. Nature Genetics, 2022. 54(11): p. 1711-1720.

(12) Stumpf, P.S., F. Arai, and B.D. MacArthur, Modeling Stem Cell Fates using Non-Markov Processes. Cell Stem Cell, 2021. 28(2): p. 187-190.

(13) Stumpf, P.S., et al., Stem Cell Differentiation as a Non-Markov Stochastic Process. Cell Syst, 2017. 5(3): p. 268-282 e7.

(14) Huang, S., et al., Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev Biol, 2007. 305(2): p. 695-713.

eLife assessment

The study presented in this manuscript makes important contributions to our understanding of cell fate decisions and the role of noise in gene regulatory networks. Through computational and theoretical analysis, the authors provide solid support for distinguishing distinct driving forces behind fate decisions based on noise profiles and reprogramming trajectories. While acknowledging the potential limitations of small gene regulatory networks in capturing the richness of whole-transcriptome sequencing datasets, this study offers a creative approach for formulating hypotheses about gene regulation during stem cell differentiation using single-cell sequencing data.

Joint Public Review:

In this manuscript, Xue and colleagues investigate the fundamental aspects of cellular fate decisions and differentiation, focusing on the dynamic behaviour of gene regulatory networks. It explores the debate between static (noise-driven) and dynamic (signal-driven) perspectives within Waddington's epigenetic landscape, highlighting the essential role of gene regulatory networks in this process. The authors propose an integrated analysis of fate-decision modes and gene regulatory networks, using the Cross-Inhibition with Self-activation (CIS) network as a model. Through mathematical modelling, they differentiate two logic modes and their effect on cell fate decisions: requires both the presence of an activator and absence of a repressor (AA configuration) with one where transcription occurs as long the repressor is not the only species on the promoter (OO configuration).

The authors establish a relationship between noise profiles, logic-motifs, and fate-decision modes, showing that defining any two of these properties allows the inference of the third. They also identify, under the signal-driven mode, two fundamental patterns of cell fate decisions: either prioritising progression or accuracy in the differentiation process. The authors apply this analysis to available high-throughput datasets of cell fate decisions in hematopoiesis and embryogenesis, proposing the underlying driving force in each case and utilising the observed noise patterns to nominate key regulators.

The paper significantly advances our understanding of gene regulatory networks through a well-described computational study, where the authors rigorously evaluate assumptions in modelling. Particularly commendable is their introduction of the concept of combinatorial logic, exemplified by the double 'and' and double 'or' (AA/OO) logic motifs, which they successfully map to previously described cell fate decision processes. This theoretical and computational exploration sheds light on the dynamic landscape of epigenetic cell fate decisions, emphasising the role of combinatorial logic in coordinating noise and signal-driven processes. The thorough comparison of two model configurations underscores the importance of integration logic, contributing to a clearer understanding of gene regulatory network dynamics. Importantly, the results of the simulations are presented clearly, enhancing accessibility and intuitive understanding. The paper's strength also lies in its predictive power, as the authors use simulations to make insightful predictions about the regulatory organisation of stem cell differentiation systems. While the exploration is restricted to specific scenarios, these limitations serve to highlight areas for future research rather than detract from the paper's strengths.

While the paper presents an intriguing framework for understanding gene regulatory networks and cell fate decisions, there are some weaknesses that warrant attention. Firstly, the framework would benefit from validation with more experimental data and application to diverse systems beyond those explored in the study, such as de-differentiation in adult tissues and regeneration processes. Additionally, while the authors successfully make predictions about the regulatory organisation of stem cell differentiation systems, there is a lack of discussion regarding how perturbations in the regulatory network could affect cell fate decisions. Furthermore, the paper could be strengthened by addressing the effects of mutations and other perturbations that may significantly influence cell fate decision-making processes, thus enhancing the robustness of the findings. Finally, there are instances where the clarity of the writing could be improved to enhance understanding and accessibility for readers.

Author Response

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

eLife assessment

This work presents some valuable information regarding the molecular mechanisms controlling the regeneration of pancreatic beta cells following induced cell ablation. However, the study lacks the critical lineage tracing result to support the conclusion about the origin of the regenerated beta cells. The results of the pharmacological manipulation of CaN signaling are also incomplete. In particular, these manipulation are not cell-specific, making it difficult to interpret and thus genetic approach is recommended.

Public Reviews:

Reviewer #1 (Public Review):

Induction of beta cell regeneration is a promising approach for the treatment of diabetes. In this study, Massoz et.al., identified calcineurin (CaN) as a new potential modulator of beta cell regeneration by using zebrafish as model. They also showed that calcineurin (CaN) works together with Notch signaling calcineurin (CaN) to promote the beta cell regeneration. Overall, the paper is well organized, and technically sound. However, some evidence seems weak to get the conclusion.

Reviewer #2 (Public Review):

This work started with transcriptomic profiling of ductal cells to identify the upregulation of calcineurin in the zebrafish after beta-cell ablation. By suppressing calcineurin with its chemical inhibitor cyclosporin A and expressing a constitutively active form of calcineurin ubiquitously or specifically in ductal cells, the authors found that inhibited calcineurin activity promoted beta-cell regeneration transiently while ectopic calcineurin activity hindered beta-cell regeneration in the pancreatic tail. They also showed similar effects in the basal state but only when it was within a particular permissive window of Notch activity. To further investigate the roles of calcineurin in the ductal cells, the authors demonstrated that calcineurin inhibition additionally induced the proliferation of the ductal cells in the regenerative context or under a limited level of Notch activity. Interestingly, the enhanced proliferation was followed by a depletion of ductal cells, suggesting that calcineurin inhibition would exhaust the ductal cells. Based on the data, the authors proposed a very attractive and intriguing model of the role of calcineurin in maintaining the balance of the progenitor proliferation and the endocrine differentiation. However, the conclusions of this paper are only partially supported by the data as some evidence from the data remains suggestive.

(1) In the transcriptomic profiling, genes differentially regulated in the ablated adults could be solely due to the chemical effects of metronidazole instead of the beta-cell ablation. A control group without ins:NTR-mCherry but treated with metronidazole is necessary to exclude the side effects of metronidazole.

We believe that it is unlikely that the differential regulation observed is due to metronidazole rather than the beta cell loss. This experimental strategy as proven successful in well-published studies to identify regulators of beta cell regeneration in the zebrafish larvae. Importantly, the candidates identified in these studies were subsequently functionally validated in mammalian models (Lu et al. 2016, Karampelias 2021). Moreover, in our study, we also used another chemical compound, the nifurpirinol (Bergemann et al., 2018), to ablate the beta cells. Regardless of whether we employed metronidazole or nifurpirinol for beta cell ablation, our results consistently indicate a notable involvement of calcineurin. Of note, the nifurpirinol molecule is commonly used in fishkeeping without toxicity reported on the global health of the fish.

(2) Although it has been shown that the pancreatic duct is a major source of the secondary islets in the pancreatic tail in previous studies, there is no direct evidence showing the cyclosporin A-induced cells share the source in this manuscript. Without any proper lineage tracing work, the origin of those cyclosporin A-induced cells cannot be concluded.

Our experimental setting is similar to the one described in Ninov et al. 2013, where lineage tracing experiments demonstrate an increase of beta cell formation in the pancreatic tail that originate from the pancreatic ducts. In our study, we performed the same experiment with the addition of CsA and showed more ductal cell proliferation (Figure 5G) followed by a 19% increase of beta cell regeneration compared to nonregenerative conditions (Figure 2B). It is unlikely that the additional 19% of regenerated beta cells under CaN inhibition come from another source than the 68% first.

On the other hand, the acinar cells cannot be consider as another source of regenerated beta cell as they are not able to form beta cells unless they are artificially reprogrammed (Maddison et al., 2012). Therefore the only other potential source of regenerated beta cell is the endocrine compartment. However at the stage where we performed beta cell ablation, there are no endocrine cell in the pancreatic tail. Moreover, there are no evidence that secondary islets could come from the principal islet, they are tightly associated with the ducts and differentiate form ductal cell (Mi et al., 2023).

Importantly, we demonstrated that overexpression of CaN specifically in the pancreatic ducts prevents beta cell regeneration. CaN effect is therefore intrinsic to the ducts. Moreover, we showed that CsA increase beta cells formation when Notch signalling is repressed. Given that Notch signalling is known to act on the ductal cell population, this strongly suggests again that CsA exacerbate beta cells formation from the ducts.

All of these compelling evidences strongly support the notion that the cyclosporininduced beta cells originate from the ductal cells.

(3) It is interesting to see an increase of beta cells in the primary islet after cyclosporin A treatment (Supplemental Fig 2B). However, it remains unclear if their formation shares the same mechanism with the newly formed beta cells in the pancreatic tail.

There are indeed several source of beta cell regeneration in the primary islet. However, a recent study showed that the contribution of alpha cell to regeneration is minor and the main contributors are ductal and sst1.1 cells (Mi et al., 2023). In our previous publication, we indeed showed that a major source of beta cell in the principal islet is the delta 1.1 cell population. Those sst1.1 cells begin to express insulin and therefore are named ‘bihormonal’ (Carril et al., 2022). We tested if this population is impacted by CsA treatment and we showed below that CsA does not affect bi-hormonal cell formation (Figure 2D supplemental). These new results suggest that the CsA mediated increase of beta cells in the principal islet arise from the ductal cells as observed in the tail. These results were added in the manuscript as Figure 2D supplemental.

Author response image 1.

Tg (sst1.1:GFP); Tg (ins:NTR*-mCherry) larvae were treated at 3dpf with NFP 4µM to induce beta cell ablation. Then larvae were treated with CsA 1µM from 4 to 6 dpf (or ctl with DMSO); prior fixation and analysis of bi-hormonal cells in the principal islet at 6dpf.

(4) The conclusion of the effect of cyclosporin A on the endocrine progenitors (Line 175) is not convincing because the data cannot distinguish the endocrine progenitors from the insulin-expressing cells. Indeed, Figure 2E shows that neurod1+ cells are fewer than ins+ cells (Figure 2D) in the pancreatic tail at 10 dpt, suggesting that all or at least the majority of neurod1+ cells are already ins+.

The neurod1+ cells population indeed included both endocrine progenitor cells and differentiated endocrine cells. However, we would like to point out that the timing of the analysis is essential to reach our conclusion. When we treat with CsA, we show an increase of neurod1+ cells already at 4dpt. At this time point, no hormone- producing cell can yet be detected (Figure 2E). Those additional neurod1+ cell are therefore endocrine progenitors and not beta cells. This result shows that CaN inhibition induces pro-endocrine cell formation in regenerative conditions.

At 10dpt, the neurod1+ cells population includes beta cells as well as endocrine progenitor cell. We agree that the way the data are presented in figure 2D and 2E can be confusing. Those 2 figures come form 2 separated experiments, the number of beta cell in figure 2D can therefore not be compared to the number of Neurod1+ cell in figure 2E. Indeed, from one experiment to another the efficiency and rate of regeneration can vary, independently of calcineurin. To clarify, we added the number of beta cells regenerated in the experiment of figure 2E (see Author response image 2 in red). As you can see in this experiment, regeneration was a bit slower than usual.

Author response image 2.

Tg (neurod1:GFP); Tg (ins:NTR*-mCherry) larvae were treated at 3dpf with NFP 4µM to induce beta cell ablation. Then larvae were treated with CsA 1µM from 4 to 6 dpf (or ctl with DMSO); prior fixation and analysis of GFP+ cells (in grey, pink, dark grey and green), and mCherry+ cells for the condition ablated + CsA in red from 2 to 10 dpf.

(5) Figure 5D shows a significant loss of nkx6.1+ cells in the combined treatment group but there is no direct evidence showing this was a result of differentiation as the authors suggested. This cell loss also outnumbered the increase in ins+ cells (Figure 4D). The cell fates of these lost cells are still undetermined, and the authors did not demonstrate if apoptosis could be a reason of the cell loss.

Firstly, as you can notice on the graphs, we encountered a very high variability between individuals within the same condition. We decided to show this variability by presenting the raw data. This high variability could partially explain the differences that you underline. Moreover, we would like to point out that independently of CaN inhibition the progenitor loss (nkx6.1+ cell) outnumber the gain of beta cells. Indeed, in average there is a loss of 29% (41 GFP+) of the nkx6.1+ cells and a gain of only 6 beta cells after Notch inhibitory treatment. The other progenitors cells being differentiated into other endocrine cell types (pro-endocrine, alpha, delta). In the combined treatment (Notch and CaN inhibitors), we decreased the number of progenitors cell by 50%, i.e 21% (20 cells) more than without CaN inhibitor. However, we increased the number of regenerated beta cells by two fold (6 cell to 12 cells). In brief, the important progenitors cell loss could be explained by precocious differentiation in the pro-endocrine and endocrine cells type. It is therefore normal than the number of beta cells regenerated do not match the progenitors cell number loss and this in presence or absence of CaN inhibition.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major concerns:

(1) The evidence to indicate the proliferating ductal cell differentiate into beta cell is weak. They should use linkage tracing, or other marker genes immunostaining to confirm that.

The experiment from the Figure 5 A-D is a short term tracing experiment and should have been presented as such in the manuscript. After LY411575 (Notch inhibitor) and CsA treatments at 3dpf, we exposed the larvae to EdU at 4dpf during 8 hours (Figure 5A). We showed that EdU is incorporated in dividing ductal cells at 4dpf (Figure 5C) ant that 2 days later there are newly form beta cells that are EdU+.(see Author response image 3) To reinforce our conclusion, the image below will be added to the manuscript.

Author response image 3.

Tg (nkx6.1:GFP); Tg (ins:NTR*-mCherry) larvae were treated at 3dpf with both CsA 1µM and LY411575 5µM. At 4dpf, the larvae were exposed to EdU 4mM during 8 hours, before analysis at 6 dpf.

(2) To inhibition of CaN and Notch pathway, they just used the pharmacological approaches, genetical approaches should be used to get stronger evidence.

We employed two distinct inhibitors specifically targeting calcineurin (CsA and FK506) for CaN inhibition. While these inhibitors have distinct chemical structures and potential non-specific effects, they both yield the same result of increased beta cell formation under Notch repression (see Figure 4D and Figure 4B in the supplementary data). This convergence of outcomes strongly suggests that the observed effect is primarily attributable to the specific inhibition of calcineurin.

Furthermore, we complemented our inhibitor-based approach with a genetic strategy involving CaN overexpression (see Figure 3). Notably, the overactivation of CaN resulted in a reduction of beta cell regeneration. Given that this genetic approach generated an effect contrary to that achieved with the inhibitors, it provides robust support for our model, which postulates that calcineurin plays a critical role in the regulation of beta cell regeneration (see Figure 3, panels C-E).

As for Notch inhibition, previous published data from our laboratory compared the effects of Notch inhibitor (LY411575) and genetic approaches (mib mutant and transgenic line) on pro-endocrine cell (ascl1b+) and ductal cell (nkx6.1+) formation. This study showed that both Notch inhibitor (LY411575) and Notch repression using genetic approaches recapitulate the same effect: an induction of pro-endocrine cells formation. The specificity of this inhibitor being validated (Ghaye et al., 2015), we did not consider the need of a genetic approach.

(3) The most enriched pathways among the up-regulated genes were DNA replication and cell cycle, which suggested that these genes are more important for the duct cell proliferation, how is Calcineurin related to these pathways, such as regulating the genes important for proliferation?

The transcriptomic data presented in this manuscript suggest that the ductal cells undergo a strong proliferative response after beta cell ablation. This is in accordance with our experimental data showing activation of ductal proliferation after beta cell ablation (Ghaye at al., 2015) and data from this manuscript (Figure 1 I-J).

Calcineurin is a well-known regulator of the cell cycle, and can either promote or repress the cell cycle depending on the cell type. For example, stressing the cell provokes an entry of calcium and subsequently a CaN activation which result in cell cycle arrest (Leech et al. 2020). Nevertheless, depending the cell type, CaN can be either necessary or deleterious to cell proliferation (Goshima et al. 2019; Masaki and Shimada 2022). The intriguing dual role of CaN in cell cycle is well illustrated in β cell regeneration. While CaN should be repressed to enable ductal progenitor amplification and subsequent endocrine differentiation, CaN is then necessary for β cell function and for their replication (Dai et al. 2017; Heit et al. 2006). Moreover, CaN is related to cellular senescence and CaN function is important for proper fin regeneration in zebrafish.

(4) It is hard to understand why they pick up the pathway of cellular senescence signature for the duct cell progenitor neogenesis? Moreover, among these senescence genes, many genes are cell cycle regulators.

In response to beta cell ablation, the ductal cells undergo a strong proliferative response, as shown in our previous data (Ghaye 2015). It was therefore not surprising that many differentially expressed genes are cell cycle regulators. On the other hand, the cellular senescence signature was surprising. Indeed, senescence is usually associated with cell cycle arrest and aging. However, recent studies showed that cellular senescence is required for proper development and regeneration. We therefore wanted to investigate this pathway and more particularly the function of calcineurin, which can either promote or repress the cell cycle in different cell types (see comment above).

(5) The RNA-seq data obtained from adult fish, while the authors use larvae to explore the CaN functions, it may have different conclusion using adult fish. Moreover, it is unclear whether the CaN increased when the beta cell ablated in young larvae.

We decided to first perform functional experiment in the larvae as this model unable the quantification of beta cell regeneration from the ducts in the pancreatic tail. However, to validate our results in non-developmental stages, we perform experiments in juveniles (2 months old) and adults. CsA treatments in juveniles zebrafish recapitulated the same results that in larvae (Figure 2B and Figure 6A-C). Moreover, we showed that CaN overactivation delayed glycemia recovery after ablation adults (Figure 6D-E), which is in accordance with an impaired regeneration. Altogether, these results strongly suggest that CaN act as regulator of beta cell regeneration both in the juvenile/adult and larval stages.

Concerning the expression of CaN in the zebrafish larvae, we tried to detect the level of CaN in the different experimental conditions by in situ hybridization. However, we were not able to detect it using this technique. We also tried immunostaining with antiphospho-nfact3 ser165 polyclonal antibody (Invitrogen) but this antibody does not seem to work in zebrafish. Finally, we tried to sort ductal cell at larval stage to perform a transcriptomic analysis but we were unable to collect enough ductal cells to proceed further. Indeed our staining experiment showed that there are only around 150 ductal cells (nkx6.1+, Figure 5D) at this stage.

(6) The beta cell regeneration in the young larvae usually recovers within ~ 5 days in principle islet. Please also show the beta cell number (PI) during the beta cell recovery after ablation.

We did show beta cell regeneration in the principal islet in Figure 2A-B supplemental. While new beta cells appears quickly in this islet (Carril, Massoz, Dupont et al., 2023), the principal islet has not yet fully recover at 5dpt.

(7) Since the studies did not show the CaN level in Fig.3, it is hard to know that the CaN is exactly expressed.

In the figure 3B, using Tg(hsp70:GFP-CaNCA), it is indeed not possible to see CaN expression at 10 dpt as the heat shocks induce only transiently CaNCA overexpression. However, the transient expression was detected in live shortly after the heat shocks. On the other hand, with the transgenic line Tg(UAS:GFP-CaNCA); Tg(cftr:Gal4), in which GFPCaNCA is continuously expressed allowing us to show CaNCA expression in the pancreatic ducts (Figure 3).

(8) In Fig.6 D and 6E, did these drug treatments change the glucose level in nonablated fish?

As you can see below, the CaN inhibitor, CsA does not affect the glycemia of the fish in non-regenerative conditions.

Author response image 4.

Glycemia of non-ablated fish, 3 days after drug treatment.

(9) The logic of writing in Results is very hard to understand.

We proofed read the paper in an effort to clarify it.

Minor concerns,

(1) Make a scheme for ablation and RNA-seq, and indicate the age of the fish used in Fig. 1.

We added the scheme in Figure 1 supplemental.

(2) In Fig. 1G, two arrows indicated mCherry+ cells is hard to see in the non-ablated fish.

One arrow was indeed mislocated, we moved the arrow and try to improve the intensity of red. However, the only cells are indeed small and can be difficult to see.

(3) In Fig.6, it is hard to know that the arrows indicated islets are small islets (up to 5 cells), how they compared with big islets and defined as small islet. Moreover, some of these islets are almost invisible.

We now show a close up of a portion of the pancreatic tail and show the beta cells with arrows only in this picture, to enhance clarity.

Reviewer #2 (Recommendations For The Authors):

(1) This manuscript needs more proofreading and polishing to increase its readability.

We proofread the manuscript and change some paragraph for more clarity.

(2) The extensive use of words like "modulate" or "regulate" sometimes makes the text ambiguous as the effect is not stated directly and clearly.

We re-wrote some parts of the text and try to avoid using “regulate” as often.

However, as we used both repression and over-activation of CaN, we still use words as regulate to stipulate general conclusions on the function of CaN.

(3) The list of individual differentially regulated genes after the beta-cell ablation in the RNAseq seems missing. This list could be interesting and helpful for other researchers. We added it.

(4) In Figure 1D, "modulated" genes are shown but were they all upregulated like those in Figure 1A? The modulation should be indicated more clearly (e.g. up- or down-regulated) in the figure. The authors can use different colours to illustrate that.

Done.

(5) Is Figure 2D showing the same data extracted from Figure 2B? Does Figure 2D add any information to the data?

No, it does not add data. We actually add the Figure 2D for a better visualisation of the increase at 10dpt.

(6) In the y-axis of Figure 3E, it should be "mCherry".

It already is. We did check all the axis again to be sure it is correct.

(7) Line 219, "Figure 4E supplemental" instead of "Figure 4D supplemental"

Done.

(8) Line 266, "ablated juveniles" instead of "ablated larvae"

Done. Thank you for noticing these mistakes.

(9) In Figure 6A, many mCherry+ cells are hardly visible and there are some greyish white signals in the images that are supposed to show the mCherry channel only. What are those grey signals?

There is no channel showing grey on the picture, I improved the overall quality of this pictures and show close up to improve the figure.

(10) In Figure 6D and 6E, CaNCA overexpression had a significant effect on the glycemia. But did the overexpression affect the beta cell formation or regeneration? We showed that CaNCA overexpression did not affect beta cell formation in absence of regeneration in the larvae (Figure 3E). Moreover, it does not affect the glycemia of the fish in non-regenerative conditions (Author response image 5). As for regenerative conditions, CaN overexpression decreased the regeneration in the larvae (Figure 3E).

Author response image 5.

Glycemia of Tg(UAS:GFP-CaNCA); Tg(cftr:Gal4) fish, overexpressing CaNCA, compared to controls fish, in non-regenerative conditions.

(11) The role of calcineurin seems transient (e.g. Figure 2B and 4E) and does not play a significant role in long term. It would be interesting to see if long-term/repeated treatments of calcineurin inhibitors and overexpression/knockout of important members of calcineurin signaling would affect the pool of progenitors in long term.

We were also interested in the consequences of CaN overexpression on the long term. Our overexpression tool Tg(UAS:CaNCA) allow to address this question, as CaN is overexpress permanently. We assessed the structure of the ducts and the number of beta cells in transgenic larvae and did not see any defects of the ducts whether in regenerative context or not. On the other hand, we showed in this manuscript that CaN effect is specific to regenerative conditions. As a consequence, it is not likely that repeated treatments long after the ablation would continue to affect beta cell formation and the progenitors pool.

Reviewer #2 (Public Review):

This work started with transcriptomic profiling of ductal cells to identify the upregulation of calcineurin in the zebrafish after beta-cell ablation. By suppressing calcineurin with its chemical inhibitor cyclosporin A and expressing a constitutively active form of calcineurin ubiquitously or specifically in ductal cells, the authors found that inhibited calcineurin activity promoted beta-cell regeneration transiently while ectopic calcineurin activity hindered beta-cell regeneration in the pancreatic tail. They also showed similar effects in the basal state but only when it was within a particular permissive window of Notch activity. To further investigate the roles of calcineurin in the ductal cells, the authors demonstrated that calcineurin inhibition additionally induced the proliferation of the ductal cells in the regenerative context or under a limited level of Notch activity. Interestingly, the enhanced proliferation was followed by a depletion of ductal cells, suggesting that calcineurin inhibition would exhaust the ductal cells. Based on the data, the authors proposed a very attractive and intriguing model of the role of calcineurin in maintaining the balance of the progenitor proliferation and the endocrine differentiation. However, the conclusions of this paper are only partially supported by the data as some evidence of the lineage between ductal cells and beta cells remains suggestive.

eLife assessment

This work presents some valuable information regarding the molecular mechanisms controlling the regeneration of pancreatic beta cells following induced cell ablation. However, the study lacks the critical lineage tracing result to support the conclusion about the origin of the regenerated beta cells. The results of the pharmacological manipulation of CaN signaling are also incomplete. In particular, these manipulation are not cell-specific, making it difficult to interpret and thus a genetic approach is recommended.

Reviewer #1 (Public Review):

Induction of beta cell regeneration is a promising approach for the treatment of diabetes. In this study, Massoz et.al., identified calcineurin (CaN) as a new potential modulator of beta cell regeneration by using zebrafish as model. They also showed that calcineurin (CaN) works together with Notch signaling to promote the beta cell regeneration. Overall, the paper is well organized, and technically sound. However, some evidences seem weak to get the conclusion.

Author Response

eLife assessment

We appreciate the assessment carried out by the editorial team at eLife. Therefore, we plan to review the methods section in order to make the statistical analysis more comprehensible for each of the displayed figures.

Public reviews

Reviewer 1

We would like to express our gratitude to Reviewer 1 for providing a thorough summary of our work and highlighting its strengths. With regards to the weaknesses, we are committed to improve the manuscript by performing the necessary changes. First, we will specify the exact p-value in all cases.

Regarding the discussion section, we acknowledge the feedback regarding its potential confusion. In line with the reviewer's suggestion, we will reduce the literature review and highlight our findings.

Finally, for the preprint we did not include cofounders such as HIV infection and ethnicity as our study population did not exhibit viral infections and comprised only Hispanic individuals. We will make a more thorough description of the population of study and address these characteristics explicitly in both the methods section and the initial part of the results.

Reviewer 2

We appreciate and thank reviewer 2 for the commentaries. Although it is true that several papers have described the role of microbiome in COVID-19 severity, we firmly believe that our current work stands out.

There is not much information related to this association in mediterranean countries, especially in the south of Spain. In addition, most of the studies only describe microbiota composition in stool or nasopharyngeal samples separately, without investigating any potential relationships between them as we do.

(1) We agree with the reviewer idea of a limited sample size. We faced the challenge of collecting the samples during the peak of COVID-19 pandemia. Thus, doctors and nurses were overwhelmed and not always available for carrying out patient recruitment following the inclusion criteria. Despite these constraints, we ensured that all included samples met our specified inclusion criteria and were from subjects with confirmed symptomatology.

In addition, our main goal was to identify whether severity of the disease could be assessed through microbiota composition. Therefore we did not include a healthy group. Despite not having a large N, our results should be reproducible as they are supported by statistical analysis.

(2) We thank reviewer commentary, and since our original sentence may have lacked clarity, we intend to modify it to ensure it conveys the intended meaning more effectively.

Nonetheless, we remain confident in the significance of our findings. Not only have we found correlation between microbiota and COVID severity, but we have also described how specific bacteria from each condition is associated with key biochemical parameters of clinical COVID infection.

(3) We appreciate the feedback provided by the reviewer. In this case, we have performed 16S analysis due to its cost-effectiveness compared to metagenomic approaches. Furthermore, 16S analysis has undergone refinements that ensure comprehensive coverage and depth, along with standardized analysis protocols. Unlike 16S, metagenomic approaches lack software tools such as QIIME that facilitate standardization of analysis and, thus, reduce reproducibility of results.

(4) We sincerely appreciate this insightful suggestion. simply listing associations between both microbiomes and COVID-19 severity could not be enough, we intend to discuss how microbiota composition may be linked to the mechanisms underlying COVID-19 pathogenesis in our discussion.

(5) We are grateful for the constructive criticism and intend to rewrite our abstract to enhance clarity. Additionally, we will thoroughly review all figures and their descriptions to ensure accuracy and comprehensibility.

Reviewer 3

We acknowledge the annotations made by reviewer 3 and are committed to addressing all identified weaknesses to enhance the quality of our work. Our idea is to modify the methods section and figures to make them easier to understand.

Specifically, in the case of Figure 1, we recognize an error in the description of the Bray-Curtis test. We appreciate the commentary and we will make the necessary changes. Moreover, there is another observation related to Figure 1 description. We are going to modify it in order to gain accuracy.

For figure 2 we are planning to add a supplementary table showing the abundance of detected genus. Nevermind, we will also update the manuscript text to provide clarification on how we obtained this result.Regarding the clarification about "1% abundance," we want to emphasize that we are referring to relative abundance, where 1 represents 100%. To avoid confusion, we will explicitly state this in both the methods section and figure descriptions. Besides, it is true that the statistical test employed for the analysis is not mentioned in the figure description and we recognize that the image may be difficult to interpret. Therefore, we will modify the text and a supplementary table displaying the abundance and p values is going to be added.

Furthermore, we agree with the reviewer's suggestion to investigate whether the bacteria identified as potential biomarkers for each condition are specific to their respective severity index or if there is a threshold. Thus, we will reanalyze the data and include a supplementary table with the abundance of each biomarker for each condition. We will also place greater emphasis on these results in our discussion.

Finally, in response to the reviewer's suggestion, we are going to go through the nasopharyngeal-fecal axis part in the discussion. It is well described that COVID-19 induces a dysbiosis in both microbiomes.

Consequently, we understand that the ratio we have described could be an interesting tool for assessing COVID severity development as it considers alterations in both environments. However, we acknowledge that there may be room for improvement in clarifying the significance of this intriguing finding and its implications.

eLife assessment

This potentially useful work characterizes the changes in microbial composition of the nasal and fecal microbiomes of COVID-19 patients according to the severity of disease. However, the description of methods and statistics used for several figures is incomplete.

Reviewer #1 (Public Review):

Summary:

The research study under review investigated the relationship between the gut and identified potential biomarkers derived from the nasopharyngeal and gut microbiota-based that could aid in predicting COVID-19 severity. The study reported significant changes in the richness and Shannon diversity index in nasopharyngeal microbiome associated with severe symptoms. The study showed a high abundance of Bacillota and Pesudomonadota in patients exhibiting severe symptomatology. Positive correlations were also found between Corynebacterium, Acinetobacter, Staphylococcus, and Veillonella, with the severity of SARS-CoV-2 infection.

Strengths:

The study successfully identified differences in the microbiome diversity that could indicate or predict disease severity. Furthermore, the authors demonstrated a link between individual nasopharyngeal organisms and the severity of SARS-CoV-2 infection. The density of the nasopharyngeal organism was shown to be a potential predictor of the severity of COVID-19.

Weaknesses:

The authors claimed an association between nasopharyngeal organisms and severity of SARS-CoV-2 infection but omitted essential data on the statistical significance of these associations between groups. The authors frequently referred to a p-value < 0.05 without presenting the actual p-values and percentages to show the significance of their results. The discussion is hard to understand (lacked clarity), as it contained an extensive literature review without discussing the study findings. A more focused discussion and results section on the main findings could have improved the overall readability of the paper. The role of potential confounders, such as HIV infection, and ethnicity which impacts the nasopharyngeal microbiome composition, was not included in the paper. Addressing the potential confounders would contribute to a more comprehensive understanding of the study's implications, specifically the role of the nasopharyngeal microbiome as a predictor of COVID-19 severity.

Reviewer #2 (Public Review):

The study conducted by Benita et al studied the gut and nasopharyngeal microbiome in covid-19 severity. There are a lot of studies on this topic, and this study therefore cannot stand out from a pool of such similar studies. Beyond that, I have a number of major concerns:

(1) The sample size is limited. There were 3 cohorts, but only ~100 subjects in total. This indicates that there were only a small number of subjects in each cohort (the authors did not list this information), and beyond that, there was a lack of healthy individuals as controls. A cohort-specific effect should usually exist, I believe with such a small number of patients (they were further divided into 3 groups), the authors cannot find reproducible data between cohorts.

(2) The study did not meet the study goal. The authors say "Many factors have been described to be correlated with its severity but no specific determinants of infection outcome have been identified yet". However, numerous studies have shown the relationship between microbiome and covid. The present study only again showed a correlation between microbiome and covid severity and did not provide further insights, nor did they find specific determinants.

(3) This study only studied 16s-seq for microbiome profiling, which made this study lack depth and resolution. Many peer papers have used metagenomics sequencing for in-depth interrogation.

(4) Since there are fecal and nasopharyngeal microbiome data, the authors only listed their respective associations with covid severity yet did not provide further insights into whether and how these two microbiome types are linked to covid, or into whether there is a microbiome priority, resistance or transmission.

(5) The abstract is amiss where each sentence lacks a key message - I don't understand each of the sentences or the underlying meanings. One example of an unclear expression is "this ratio" - what ratio?

(6) The figures are all unclear and need significant improvement

Reviewer #3 (Public Review):

Summary:

How the microbial composition of the human body is influenced by and influences disease progression is an important topic. For people with COVID-19, symptomatic progression and deterioration can be difficult to predict. This manuscript attempts to associate the nasal and fecal microbiomes of COVID-19 patients with the severity of disease symptoms, with the goal of identifying microbial markers that can predict disease outcomes. However, the value of this work is held back by unclear methods and data presentation.

Strengths:

Analysis of microbiomes from two distinct anatomical locations and across three distinct patient groups is a substantial undertaking. How these microbiomes influence and are influenced by COVID-19 disease progression is an important question. In particular, the putative biomarker identified here could be of clinical value with additional research.

Weaknesses:

The methods and statistics used for several figures and comparisons are unclear or used in non-standard ways. For instance: the description of the Bray-Curtis test for Figure 1 is inaccurate and conflicts between the text and figure legend; the method used to compare the relative abundance of genera in Figure 2 is not clear; and it is not stated how the "total amount" of detected bacteria is inferred from the data presented in Figures 2C and 2D.

The description of results for Figure 1 is overstated or unclear for both the alpha diversity among disease groups and the overlap for nasal samples.

The most abundant phyla from nasal samples cumulatively account for less than 1% of abundance and it is unclear why this would be expected or how it compares to other work. Relatedly, the potential biological relevance of the very small proportional changes among phyla in the nasal samples is also not clear.

There is no real discussion of how the identified biomarkers might work in practice. While some microbes are detected in one condition but not others, it is unclear whether these organisms are expected to already exist below the detection threshold and then increase in abundance along with disease severity, or if they are picked up from the environment. For instance, would the presence of these 'severe' - associated microbes in patients with mild or moderate disease justify additional treatment to prevent disease progression?

The authors use the term "nasopharyngeal-faecal axis", but there is no substantial discussion of how these two microbiomes interact to influence disease progression, or how they are jointly affected to yield useful biomarkers. With one exception, correlation values between nasal and fecal microbes range from negligible to modest. It is unclear, then, how much parallel influence disease has on these microbiomes.

Author Response

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

eLife assessment

This comprehensive study provides valuable information on the cooperation of Ikaros with Foxp3 to establish and regulate a major portion of the epigenome and transcriptome of T-regulatory cells. However, the characterization is incomplete in that incontrovertible evidence that these are intrinsic features regulating biological function and not outcomes of the inflammatory micro-environment of the genetically manipulated mice is missing.

Public Reviews:

This study investigates the role of Ikaros, a zinc finger family transcription factor related to Helios and Eos, in T-regulatory (Treg) cell functionality in mice. Through genome-wide association studies and chromatin accessibility studies, the authors find that Ikaros shares similar binding sites to Foxp3. Ikaros cooperates with Foxp3 to establish a major portion of the Treg epigenome and transcriptome. Ikaros-deficient Treg exhibits Th1-like gene expression with abnormal expression of IL-2, IFNg, TNFa, and factors involved in Wnt and Notch signaling. Further, two models of inflammatory/ autoimmune diseases - Inflammatory Bowel Disease (IBD) and organ transplantation - are employed to examine the functional role of Ikaros in Treg-mediated immune suppression. The authors provide a detailed analysis of the epigenome and transcriptome of Ikaros-deficient Treg cells.

These studies establish Ikaros as a factor required in Treg for tolerance and the control of inflammatory immune responses. The data are of high quality. Overall, the study is well organized, and reports new data consolidating mechanistic aspects of Foxp3 mediated gene expression program in Treg cells.

Strengths:

The authors have performed biochemical studies focusing on mechanistic aspects of molecular functions of the Foxp3-mediated gene expression program and complemented these with functional experiments using two models of autoimmune diseases, thereby strengthening the study. The studies are comprehensive at both the cellular and molecular levels. The manuscript is well organized and presents a plethora of data regarding the transcriptomic landscape of these cells.

Response: We thank the reviewers for their careful review and feedback on our manuscript. We appreciate that the reviewers and editors recognize the strength and comprehensive nature of our in vivo, cellular, biochemical, and genome-wide molecular studies, which are well-organized in the manuscript. The acknowledgment of the complementary functional experiments in two models of inflammatory disease is also encouraging.

Weakness:

The authors claim that the mice have no pathologic signs of autoimmune disease even at a relatively old age, yet mice have an increased number of activated CD4+ T cells and T-follicular helper cells (even at the age of 6 weeks) as well as reduced naïve T-cells. Thus, immune homeostasis is perturbed in these mice even at a young age and the eXect of inflammatory microenvironments on cellular functions cannot be ruled out. Further, clear conclusions from the genome-wide studies are lacking.

Response: We agree with the reviewers' comment regarding the absence of overt autoimmune pathologies in Ikzf1-fl/fl-Foxp3-Cre+ mice, despite the increased frequency of activated CD4+ T cells, TFH cells, and apparent perturbation of lymphocyte homeostasis, even at a young age. It is noteworthy that while Ikaros is implicated in various autoimmune diseases, our specific mouse model in which Ikaros expression is lost only in Tregs, may not lead to a strong autoimmune phenotype in part due to the controlled environment of an extra-clean, pathogen-free animal facility. This aligns with a related study by Ana et al (2019, J. Immunol: doi:10.4049/jimmunol.1801270) in Ikzf1-fl/fl-dLck-Cre+ mice with loss of Ikaros expression in all mature CD4+ T cells, including Tregs, that exhibit no overt signs of overt autoimmune disease. Moreover, our transcriptomic studies reveal that increased expression of inflammatory genes in Ikzf1-deficient Treg is coupled with the simultaneous upregulation of genes with positive roles in Treg function. This balance suggests a compensatory mechanism within Ikaros-deficient Tregs that maintains their suppressive function until encountering an inflammatory immune challenge, which eventually leads to loss of Treg suppressive function in Treg-specific Ikaros-deficient mice. Our studies clearly show that Ikaros has cell-intrinsic eXects in Treg that also lead to cell-extrinsic eXects mediated by secreted factors that are likewise regulated by Ikaros. This can be said about the function of any transcription factor in any cell type. Our data clearly support the conclusion from the genome-wide studies that Ikaros plays a major role in establishing the active chromatin landscape, gene expression profile, and function of regulatory T cells in mice.

The following recommendations consolidate the views of the three reviewers of the manuscript.

The experiments suggested and, in some instances, fresh analysis, are thought necessary, so that the evidence of Ikaros-Foxp3 interactions regulating T-regulatory cell biology is comprehensive and solid. We hope the comments are useful to strengthen the comprehensive analysis reported in this submission.

The primary concern is that the indications of inflammation in the mice (see points 1 & 2 below) do not reflect in the experiments or consequent conclusions. The gap in the data should be addressed by testing these interactions in an appropriate context for which suggestions are included.

Please note that the title of the manuscript may be modified to reflect the use of mice as the system of study for this work.

(1) The evidence of inflammation (increased CD4 and T follicular cells) reported in the work requires new experiments to rigorously examine the relationship between Ikaros and Foxp3 to rule out the possible impact of the (inflammatory) microenvironment of the mice (Please see: Zemmour et al., Nat. Immunology 22, 607, 2021). Two possible experimental systems in mice are suggested.

a) The use of heterozygous female mice, which should be phenotypically normal due to the presence of 50% normal Treg. Or,

b) The generation of bone chimeras between wild-type and deficient mice using congenic markers.

Response: We agree that immune dysregulation that develops in the mice with age or during an inflammatory insult due to loss of Ikaros function in the Treg lineage is an important part of the phenotype of the animals. Our studies show that loss of Ikaros function in Treg influences the gene expression program such that Treg now produce inflammatory cytokines and ligands capable of engaging receptors expressed on Treg and other cells. This likely results in autocrine and paracrine signaling that induces further metabolic and gene expression diXerences not observed in wild-type mice. Indeed, we report in the manuscript that a sizable fraction of the diXerentially expressed genes do not appear to be direct Ikaros targets, but rather are downstream of Ikaros target genes such as Il2, Ifng, Notch, and Wnt. The mosaic experiments suggested will be a useful topic of future studies. Importantly, we argue that no gene expression study involving modulation of transcription factor activity in an organism- or cell-based system can be designed to measure only the direct eXects of that transcription factor in a manner isolated from any indirect, downstream eXects on the expression of other genes. We suggest that our current data remain highly valuable, as they reveal real and relevant biology in physiologic in vivo systems that do not depend upon the use of heterologous models. The fact that loss of Ikaros has an eXect not only on its direct targets, but on gene programs driven in turn by the indirect eXects of Ikaros-regulated factors, has been acknowledged in the manuscript.

(2) Figs. 7 and S5 show accumulation of CD4 cells (activated, memory, Tfh, Tfr) in LNs and spleens of the Ikaros KO over time. This is accompanied by elevated Igs but without overt autoimmune disease. KO Tregs had equivalent suppressive activity as WT Tregs against WT TeX in vitro. However, TeX from KO mice were resistant to the suppressive eXects of WT or KO Tregs. The authors interpret this as due to the increased percentage of memory cells within the KO TeXs, although they did not formally prove this point. Figs. 9 and S6 show that Ikaros KO mice are unable to be tolerized for cardiac allograft survival using two diXerent standard tolerogenic regiments. The rejecting allografts are accompanied by increased T-cell infiltration and upregulation of inflammatory genes. The authors suggest there is increased alloantibody, but alloantibody does not seem to have been measured.

Response: We are currently exploring in more detail the dysregulation of humoral immunity in the Ikzf1-deficient Treg model and plan to report these results in a future study.

(3) Linked to the above, a comparison of the chromatin occupancy of Ikaros in resting and activated Tregs would inform on whether and how Ikaros occupancy changes with the activation status of Tregs. Since the authors use in vitro stimulation for RNAseq and ATAC seq, ChIP seq analyses under these matching conditions will greatly add to the quality of the study. Since "Foxp3-dependent", ie. diXerential gene expression in the Foxp3GFPKO cells (PMID: 17220874) gene expression has been shown to be not entirely the same as Treg signature (i.e. gene expression or Tregs compared to Tnv), it will be worth correlating Ikaros, Foxp3 co-occupied genes and the corresponding fate of their expression with Foxp3-dependent and independent Treg signature gene sets.

Response: The prior study by Gavin et al. referred to above used duplicate samples instead of the standard three or more replicates required for a robust diXerential analysis of gene expression. The two samples in this study are variable, and no statistically significant diXerential gene expression was found between the experimental groups when we subjected these data to current analysis methods. For this reason, we have elected not to compare these prior data with our current data, which are robust, reproducible, and analyzed using current statistical methods. Furthermore, the mice used for the prior study develop a fatal inflammatory disease (scurfy) and therefore the Treg examined in this study would be subject to a much stronger extrinsic inflammatory environment than the Treg in our study, as our mice show no overt disease even with age.

Further, the consequence of the cooperation between the two transcription factors that can be inferred from the experiments in the study remains unclear. It is suggested that the authors could first consider the ChIP seq data from Foxp3, Ikaros co- and diXerentially occupied genes, and then correlate with the ATAC seq and gene expression data to comment on the consequence of this cooperation.

Response: We find that Ikaros binding at a given region has a strong eXect on accessibility, as reported in the manuscript, but that Foxp3 occupancy has less consequence, consistent with a prior study suggesting that Foxp3 largely utilizes the open chromatin landscape already present in the conventional CD4 T cell lineage (PMID:23021222). Our data suggest that the dominant eXect of Ikaros on Foxp3 is at the level of chromatin occupancy.

(4) In the comparative analyses of Ikaros and Foxp3 co-occupied regions and gene expression outcome, the authors mention "A total of 4423 Foxp3 binding sites were detected in the open chromatin landscape of wild-type Treg (Supplementary Table 9), and this ChIP-seq signal was enriched at accessible Foxp3 motifs." It is unclear whether the authors focused on the ATAC seq data and only examined the open chromatin regions for this analysis. In that case, it is unclear why. More so because the Ikaros footprint is more apparent in regions where accessibility is reduced upon deletion of Ikaros.

Response: Foxp3 has been shown to bind primarily at open chromatin shared between Tconv and Treg, unlike the pioneer activity of other Fox family members (PMID: 23021222, biorXiv https://www.biorxiv.org/content/10.1101/2023.10.06.561228v2.full.pdf). Consistent with this, we found the majority of peaks were in open chromatin. The motif analysis is quantitative, not binary, and takes into account Foxp3 binding sites at regions considered open in either condition, which is why we can see enrichment of Foxp3 motifs at sites going from more open to less open in the absence of Ikaros.

(5) Comments on figures:

The authors use MFI repeatedly in many of the figures for quantitation of antigen expression. This is misleading as several of the target antigens are normally expressed on a subpopulation of cells, e.g., Eos. Percent positive and MFI would be more relevant. Cytokine production should be presented by intracellular staining (e.g., IL-2, IFNg) as Elisa data does not allow one to determine the percentage of abnormally producing cells.

Response: We show both ICS and ELISA in this paper, preferring ELISA because it is much more quantitative than ICS.

Suppl. Fig. 1c - the panels do not correspond precisely to the legend or the text. At least one panel is missing. In Supp fig 1c, the authors plotted eXector Tregs, which are by definition CD62LloCD44hi, but the Y axis says CD44hiCD62Lhi. Is this a typo? Also on page 4, describing this data the authors mentioned Tfr, but the data is not shown in the Supp fig 1c.

Response: We thank the reviewer for catching these mistakes. We have corrected the typo in the figure panel for Supplementary Figure 1c. Follicular Treg data are indeed presented in Figure 7h, not Supplementary Figure 1, and we have corrected the text.

Fig. 2, which lists the diXerent categories of diXerentially expressed genes, it will be helpful if the authors add two columns indicating fold change and FDR values.

Response: These values are included in Table S1

Fig. 3c, the resolution of the histograms in the inset should be enhanced.

Fig. 3d, a histogram of representative CTV dilution plots, and an explanation of how the quantifications were done may be included.

Fig. 3e - not well labeled. Are these fold changes? Enrichments? Number of gene elements within the GO term that are aXected? Something else?

Fig. 3f - presented out of sequence. The data are a little hard to understand as the color scale is so subtle and the colors so close to one another that it is not entirely clear which gene expressions are increased vs decreased. Other than the simple statement that the Ikaros KO causes numerous changes, there does not seem to be a more consistent message from this data panel.

Fig. 4a, in addition to the bar graphs, it will be better to show the plots in a histogram, gated on Foxp3+ Tregs in WT and KO groups, with representative MFI indicated on top. The resolution of the scatter plots in this figure, as well as some others throughout the manuscript, may be improved. Please increase the resolution wherever necessary.

Fig. 4b should include representative plots for cytokine production gated in Tconv (CD4+Foxp3-) cells.

Figs. 5a-h, S2-3a-d, and Suppl. Tables S4-8 show a comprehensive ATAC-seq and ChIP-seq analysis of genes and chromatin occupied or regulated by Ikaros, comparing Tconv vs Treg, stimulated vs naïve, and WT vs KO cells. It is a comprehensive tour-de-force analysis, again showing the major eXects of Ikaros on the entire Treg landscape of gene regulation.

Fig. S5h-j should be explained or labeled in more detail. The fonts are too small to read, even at 200% magnification; and the cell and gene comparisons are not entirely clear.

Supp. Fig. S3e is not referred to in the text.

Fig. S4a is very diXicult to read; the font and plotted points are too small.

Response: We have improved the clarity of the figures where necessary. We also indicate in the figure legends that full gene lists are to be found in the supplementary tables.

Page 8, "Regions that exhibit reduced accessibility in Ikzf1 cko compared to wild-type Treg are enriched for the binding motif for Ikaros and the motif for TCF1 (Figure 5g).... ". Is this Fig. 5i or 5g?

Response: This statement is correct and is referring to data depicted in Figure 5g.

In Fig 6e, Flag-Ik7 is not visible in any of the inputs. The co-IP between Foxp3 and Runx1 (presumably a positive control) is not eXicient in this experimental condition. Co-IP experiments performed in primary cells upon retroviral transduction of the tagged proteins to confirm observations in cell lines are suggested.

Response: Runx1 is shown to co-precipitate with Foxp3 as expected, although the band is not intense, and the data depicted are representative of 3 experiments. Ik7 was included in this transient transfection experiment as a redundant control, and the referee is correct that Ik7 did not express well in this experiment and cannot be seen in this exposure. We showed these blots intact in the spirit of not digitally altering the data, and because the low Ik7 expression did not impact our ability to demonstrate specific co-precipitation of Foxp3 with full length Ikaros (Ik1). The images include nearly the entire mini-blots, and we have added molecular weight markers for clarity. As indicated in the legend, the cytokine and ChIP data in 6f are from a separate model of retrovirally Foxp3/Ik7transduced T cells that we and others have used in multiple prior studies (e.g. Thomas JI 2007, Thomas JI 2010). The interpretability of these experiments is not impacted by the transient transfection data from figure 6e. It should be noted that a prior study by Rudra et al. that is cited and referred to in the manuscript used a similar approach to also establish that Foxp3 and Ikaros form a complex in cells.

In Fig 6f, the authors state that Foxp3 overexpression in CD4 cells results in promoter occupancy of both IL2 and IFNg, however, data shows only IL2. Also in 6f, Foxp3 overexpression reduces IL2 and IFNg secretion, measured by ELISA, which is recovered by IkDN. However, the eXect of Foxp3 along with WT Ikaros (which should not modulate, and if anything, further repress IL2, IFNg production) is not shown.

Response: The reviewer is correct that ectopic expression of Ikaros leads to repression of cytokine gene expression, which we and others have shown in prior studies. Because the focus of this study was on loss of Ikaros function in Treg, we did not elect to overexpress full-length Ikaros. However, we completely agree that Ikaros GOF in Treg is an important topic for future studies.

Fig. 7e-g, how is %suppression calculated? Can representative CTV dilution plots for the suppression assays be shown?

Response: Cell division was quantified as described previously (see ref 50), and percent suppression represents the reduction in cell division measured by Tconv in the presence of Treg compared to in the absence of Treg. This has been clarified in the methods section.

In Fig 8 and the supplementary figures the representative colon pictures (Fig. S6a-c) do not show convincing diXerences in colon morphology even though all the other histology and clinical parameters are clear. Are the figures mislabeled?

In Fig 8c-e and other histology figures scale bars should be shown.

Fig. 8c-e, the Alcian blue staining among the groups appears similar; perhaps this is due to the low power magnification.

Response: We have edited this figure for clarity

Additional comments:

Fig 10 is explained in the discussion section for the first time. The authors may want to consider including this when introducing Ikzf1 ChIPseq data for the first time in the study.

Response: The reviewer raises a valid point but we have elected to retain the current organizational structure of the manuscript.

A more complete characterization of the activated conventional cells including both CD4+ and CD8+ T cells for cytokine production during aging may be considered, as it is highly likely that abnormalities in cytokine production will be observed.

Response: We agree and are planning additional such experiments in future studies focusing on in vivo models of tolerance.

The failure of suppression of T cell proliferation which the authors claim is due to the presence of activated memory T cells can be better documented by using naive responder cells from the cKO mice.

Response: We agree and are planning additional such experiments in a future study focusing on further aspects of cellular immunobiology impacted by Ikaros, but we will give preference to in vivo models of tolerance in such studies.

eLife assessment

This comprehensive study provides valuable information on the cooperation of Ikaros with Foxp3 to establish and regulate a major portion of the epigenome and transcriptome of T-regulatory cells. While the data are compelling, the evidence that these features are solely intrinsic, independent of the micro-environment, could be strengthened.

Joint Public Review:

This study investigates the role of Ikaros, a zinc finger family transcription factor related to Helios and Eos, in T-regulatory (Treg) cell functionality in mice. Through genome-wide association studies and chromatin accessibility studies, the authors find that Ikaros shares similar binding sites to Foxp3. Ikaros cooperates with Foxp3 to establish a major portion of the Treg epigenome and transcriptome. Ikaros-deficient Treg exhibits Th1-like gene expression with abnormal expression of IL-2, IFNg, TNFa, and factors involved in Wnt and Notch signalling. Further, two models of inflammatory/ autoimmune diseases - Inflammatory Bowel Disease (IBD) and organ transplantation - are employed to examine the functional role of Ikaros in Treg-mediated immune suppression. The authors provide a detailed analysis of the epigenome and transcriptome of Ikaros-deficient Treg cells.

These studies establish Ikaros as a factor required in Treg for tolerance and the control of inflammatory immune responses. The data are of high quality. Overall, the study is well organized, and reports new data consolidating mechanistic aspects of Foxp3 mediated gene expression program in Treg cells.

Strengths:

The authors have performed biochemical studies focusing on mechanistic aspects of molecular functions of the Foxp3-mediated gene expression program and complemented these with functional experiments using two models of autoimmune diseases, thereby strengthening the study. The studies are comprehensive at both the cellular and molecular levels. The manuscript is well organized and presents a plethora of data regarding the transcriptomic landscape of these cells.

Weakness:

The findings of markedly increased percentages of activated conventional T cells (CD44hi), major increases in TFH cells, and elevated serum Ig levels indicate disrupted immune homeostasis even in the absence of overt autoimmune manifestations seen in histopathology. Thus, some of the observed genetic changes observed by the authors are likely Treg cell extrinsic. Further, clear conclusions from the genome-wide studies are lacking.

eLife assessment

This study is a useful showcase of a workflow to perform large-scale characterization of drug mechanisms of action using proteomics. The work is backed by solid evidence, however, more statistical analyses and a user-friendly interface to enhance data mining by the readers are recommended. The strengths of this study include the large number of compounds tested within a common workflow and well-described experimental protocols. This will be of broad interest to medicinal chemists, toxicologists, and biochemists.

Reviewer #1 (Public Review):

Summary:

This is an interesting and potentially important paper, which however has some deficiencies.

Strengths:

A significant amount of potentially useful data.

Weaknesses:

One issue is a confusion of thermal stability with solubility. While thermal stability of a protein is a thermodynamic parameter that can be described by the Gibbs-Helmholtz equation, which relates the free energy difference between the folded and unfolded states as a function of temperature, as well as the entropy of unfolding. What is actually measured in PISA is a change in protein solubility, which is an empirical parameter affected by a great many variables, including the presence and concentration of other ambient proteins and other molecules. One might possibly argue that in TPP, where one measures the melting temperature change ∆Tm, thermal stability plays a decisive or at least an important role, but no such assertion can be made in PISA analysis that measures the solubility shift.

Another important issue is that the authors claim to have discovered for the first time a number of effects well described in prior literature, sometimes a decade ago. For instance, they marvel at the differences between the solubility changes observed in lysate versus intact cells, while this difference has been investigated in a number of prior studies. No reference to these studies is given during the relevant discussion.

The validity of statistical analysis raises concern. In fact, no calculation of statistical power is provided. As only two replicates were used in most cases, the statistical power must have been pretty limited. Also, there seems to be an absence of the multiple-hypothesis correction.

Also, the authors forgot that whatever results PISA produces, even at high statistical significance, represent just a prediction that needs to be validated by orthogonal means. In the absolute majority of cases such validation is missing.

Finally, to be a community-useful resource the paper needs to provide the dataset with a user interface so that the users can data-mine on their own.

Reviewer #2 (Public Review):

Summary:

Using K562 (Leukemia) cells as an experimental model, Van Vracken et. al. use Thermal Proteome Profiling (TPP) to investigate changes in protein stability after exposing either live cells or crude cell lysates to a library of anti-cancer drugs. This was a large-scale and highly ambitious study, involving thousands of hours of mass spectrometry instrument time. The authors used an innovative combination of TPP together with Proteome Integral Solubility Alternation (PISA) assays to reduce the amount of instrument time needed, without compromising on the amount of data obtained.

The paper is very well written, the relevance of this work is immediately apparent, and the results are well-explained and easy to follow even for a non-expert. The figures are well-presented. The methods appear to be explained in sufficient detail to allow others to reproduce the work.

Strengths:

Using CDK4/6 inhibitors, the authors observe strong changes in protein stability upon exposure to the drug. This is expected and shows their methodology is robust. Further, it adds confidence when the authors report changes in protein stability for drugs whose targets are not well-known. Many of the drugs used in this study - even those whose protein targets are already known - display numerous off-target effects. Although many of these are not rigorously followed up in this current study, the authors rightly highlight this point as a focus for future work.

Weaknesses:

While the off-target effects of several drugs could've been more rigorously investigated, it is clear the authors have already put a tremendous amount of time and effort into this study. The authors have made their entire dataset available to the scientific community - this will be a valuable resource to others working in the fields of cancer biology/drug discovery.

Reviewer #3 (Public Review):

Summary:

This work aims to demonstrate how recent advances in thermal stability assays can be utilised to screen chemical libraries and determine the compound mechanism of action. Focusing on 96 compounds with known mechanisms of action, they use the PISA assay to measure changes in protein stability upon treatment with a high dose (10uM) in live K562 cells and whole cell lysates from K562 or HCT116. They intend this work to showcase a robust workflow that can serve as a roadmap for future studies.

Strengths:

The major strength of this study is the combination of live and whole cell lysates experiments. This allows the authors to compare the results from these two approaches to identify novel ligand-induced changes in thermal stability with greater confidence. More usefully, this also enables the authors to separate the primary and secondary effects of the compounds within the live cell assay.

The study also benefits from the number of compounds tested within the same framework, which allows the authors to make direct comparisons between compounds.

These two strengths are combined when they compare CHEK1 inhibitors and suggest that AZD-7762 likely induces secondary destabilisation of CRKL through off-target engagement with tyrosine kinases.

Weaknesses:

One of the stated benefits of PISA compared to the TPP in the original publication (Gaetani et al 2019) was that the reduced number of samples required allows more replicate experiments to be performed. Despite this, the authors of this study performed only duplicate experiments. They acknowledge this precludes the use of frequentist statistical tests to identify significant changes in protein stability. Instead, they apply an 'empirically derived framework' in which they apply two thresholds to the fold change vs DMSO: absolute z-score (calculated from all compounds for a protein) > 3.5 and absolute log2 fold-change > 0.2. They state that the fold-change threshold was necessary to exclude non-specific interactors. While the thresholds appear relatively stringent, this approach will likely reduce the robustness of their findings in comparison to an experimental design incorporating more replicates. Firstly, the magnitude of the effect size should not be taken as a proxy for the importance of the effect. They acknowledge this and demonstrate it using their data for PIK3CB and p38α inhibitors (Figures 2B-C). They have thus likely missed many small, but biologically relevant changes in thermal stability due to the fold-change threshold. Secondly, this approach relies upon the fold-changes between DMSO and compound for each protein being comparable, despite them being drawn from samples spread across 16 TMT multiplexes. Each multiplex necessitates a separate MS run and the quantification of a distinct set of peptides, from which the protein-level abundances are estimated. Thus, it is unlikely the fold changes for unaffected proteins are drawn from the same distribution, which is an unstated assumption of their thresholding approach. The authors could alleviate the second concern by demonstrating that there is very little or no batch effect across the TMT multiplexes. However, the first concern would remain. The limitations of their approach could have been avoided with more replicates and the use of an appropriate statistical test. It would be helpful if the authors could clarify if any of the missed targets passed the z-score threshold but fell below the fold-change threshold.

The authors use a single, high, concentration of 10uM for all compounds. Given that many of the compounds likely have low nM IC50s, this concentration will often be multiple orders of magnitude above the one at which they inhibit their target. This makes it difficult to assess the relevance of the off-target effects identified to clinical applications of the compounds or biological experiments. The authors acknowledge this and use ranges of concentrations for follow-up studies (e.g. Figure 2E-F). Nonetheless, this weakness is present for the vast bulk of the data presented.

The authors claim that combining cell-based and lysate-based assays increases coverage (Figure 3F) is not supported by their data. The '% targets' presented in Figure 3F have a different denominator for each bar. As it stands, all 49 targets quantified in both assays which have a significant change in thermal stability may be significant in the cell-based assay. If so, the apparent increase in % targets when combining reflects only the subsetting of the data. To alleviate this lack of clarity, the authors could update Figure 3F so that all three bars present the % targets figure for just the 60 compounds present in both assays.

Aims achieved, impact and utility:

The authors have achieved their main aim of presenting a workflow that serves to demonstrate the potential value of this approach. However, by using a single high dose of each compound and failing to adequately replicate their experiments and instead applying heuristic thresholds, they have limited the impact of their findings. Their results will be a useful resource for researchers wishing to explore potential off-target interactions and/or mechanisms of action for these 96 compounds, but are expected to be superseded by more robust datasets in the near future. The most valuable aspect of the study is the demonstration that combining live cell and whole cell lysate PISA assays across multiple related compounds can help to elucidate the mechanisms of action.

eLife assessment

This is a very strong, well-written, and interesting paper analyzing in an original way how tension pattern dynamics can reveal the contribution of active versus passive intercalation during tissue elongation. The authors apply a new concept of isogonal tension decomposition to extract a global map of tissue mechanics that will be extremely valuable in the field of biomechanics. The model is convincing to explain the authors' data but could be strengthened further by analyzing data from mutant backgrounds that could serve as a test.

Joint Public Review:

Summary:

Brauns et al. work to decipher the respective contribution of active versus passive contributions to cell shape changes during germ band elongation. Using a novel quantification tool of local tension, their results suggest that epithelial convergent extension results from internal forces.

Strengths:

The approach developed here, tension isogonal decomposition, is original and the authors made the demonstration that we can extract comprehensive data on tissue mechanics from this type of analysis.

They present an elegant diagram that quantifies how active and passive forces interact to drive cell intercalations.

The model qualitatively recapitulates the features of passive and active intercalation for a T1 event.

Regions of high isogonal strains are consistent with the proximity of known active regions.

They define a parameter (the LTC parameter) which encompasses the geometry of the tension triangles and allows the authors to define a criterium for T1s to occur.

The data are clearly presented, going from cellular scale to tissue scale, and integrating modeling approach to complement the thoughtful description of tension patterns.

Weaknesses:

The modeling is interesting, with the integration of tension through tension triangulation around vertices and thus integrating force inference directly in the vertex model. However, the authors are not using it to test their hypothesis and support their analysis at the tissue level. Thus, although interesting, the analysis at the tissue level stays mainly descriptive.

Major points:

(1) The authors mention that from their analysis, they can predict what is the tension threshold required for intercalations in different conditions and predict that in Snail and Twist mutants the T1 tension threshold would be around √2. Since movies of these mutants are most probably available, it would be nice to confirm these predictions.

(2) While the formalism is very elegant and convincing, and also convincingly allows making sense of the data presented in the paper, it is not all that clear whether the claims are compatible with previous experimental observations. In particular, it has been reported in different papers (including Collinet et al NCB 2015, Clement et al Curr Biol 2017) that affecting the initial Myosin polarity or the rate of T1s does not affect tissue-scale convergent extension. Analysis/discussion of the Tor phenotype (no extension with myosin anisotropy) and the Eve/Runt phenotype (extension without Myosin anisotropy), which seem in contradiction with an extension mostly driven by myosin anisotropy.

eLife assessment

This paper makes a valuable contribution by implicating S-acylation of Cys-130 in recruitment of the inflammasome receptor NLRP3 to the Golgi. Enzymes are identified as candidates for mediating S-acylation and de-acylation of NLRP3, and evidence is presented that S-acylation plays a role in response to the stress induced by nigericin treatment. Although it seems likely that Cys-130 does indeed contribute to membrane association of NLRP3, the mechanistic analyses are incomplete and the interpretations about the effects of nigericin are not fully conclusive.

Reviewer #1 (Public Review):

This is an interesting study investigating the mechanisms underlying membrane targeting of the NLRP3 inflammasome and reporting a key role for the palmitoylation-depalmitoylation cycle of cys130 in NRLP3. The authors identify ZDHHC3 and APT2 as the specific ZDHHC and APT/ABHD enzymes that are responsible for the s-acylation and de-acylation of NLRP3, respectively. They show that the levels of ZDHHC3 and APT2, both localized at the Golgi, control the level of palmitoylation of NLRP3. The S-acylation-mediated membrane targeting of NLRP3 cooperates with polybasic domain (PBD)-mediated PI4P-binding to target NLRP3 to the TGN under steady-state conditions and to the disassembled TGN induced by the NLRP3 activator nigericin.

However, the study has several weaknesses in its current form as outlined below.

(1) The novelty of the findings concerning cys130 palmitoylation in NLRP3 is unfortunately compromised by recent reports on the acylation of different cysteines in NLRP3 (PMID: 38092000), including palmitoylation of the very same cys130 in NLRP3 (Yu et al https://doi.org/10.1101/2023.11.07.566005), which was shown to be relevant for NLRP3 activation in cell and animal models. What remains novel and intriguing is the finding that NLRP3 activators induce an imbalance in the acylation-deacylation cycle by segregating NLRP3 in late Golgi/endosomes from de-acylating enzymes confined in the Golgi. The interesting hypothesis put forward by the authors is that the increased palmitoylation of cys130 would finally contribute to the activation of NLRP3. However, the authors should clarify the trafficking pathway of acylated-NLRP3. This pathway should, in principle, coincide with that of TGN46 which constitutively recycles from the TGN to the plasma membrane and is trapped in endosomes upon treatment with nigericin.

(2) To affect the S-acylation, the authors used 16 hrs treatment with 2-bromopalmitate (2-BP). In Figure 1f, it is quite clear that NLRP3 in 2-BP treated cells completely redistributed in spots dispersed throughout the cells upon nigericin treatment. What is the Golgi like in those cells? In other words, does 2-BP alter/affect Golgi morphology? What about PI4P levels after 2-BP treatment? These are important missing pieces of data since both the localization of many proteins and the activity of one key PI4K in the Golgi (i.e. PI4KIIalpha) are regulated by palmitoylation.

(3) The authors argue that the spots observed with NLRP-GFP result from non-specific effects mediated by the addition of the GFP tag to the NLRP3 protein. However, puncta are visible upon nigericin treatment, as a hallmark of endosomal activation. How do the authors reconcile these data? Along the same lines, the NLRP3-C130S mutant behaves similarly to wt NLRP3 upon 2-BP treatment (Figure 1h). Are those NLRP3-C130S puncta positive for endosomal markers? Are they still positive for TGN46? Are they positive for PI4P?

(4) The authors expressed the minimal NLRP3 region to identify the domain required for NLRP3 Golgi localization. These experiments were performed in control cells. It might be informative to perform the same experiments upon nigericin treatment to investigate the ability of NLRP3 to recognize activating signals. It has been reported that PI4P increases on Golgi and endosomes upon NG treatment. Hence, all the differences between the domains may be lost or preserved. In parallel, also the timing of such recruitment upon nigericin treatment (early or late event) may be informative for the dynamics of the process and of the contribution of the single protein domains.

(5) As noted above for the chemical inhibitors (1) the authors should check the impact of altering the balance between acyl transferase and de-acylases on the Golgi organization and PI4P levels. What is the effect of overexpressing PATs on Golgi functions?

Reviewer #2 (Public Review):

Summary:

This paper examines the recruitment of the inflammasome seeding pattern recognition receptor NLRP3 to the Golgi. Previously, electrostatic interactions between the polybasic region of NLRP3 and negatively charged lipids were implicated in membrane association. The current study reports that reversible S-acylation of the conserved Cys-130 residue, in conjunction with upstream hydrophobic residues plus the polybasic region, act together to promote Golgi localization of NLRP3, although additional parts of the protein are needed for full Golgi localization. Treatment with the bacterial ionophore nigericin inhibits membrane traffic and prevents Golgi-associated thioesterases from removing the acyl chain, causing NLRP3 to become immobilized at the Golgi. This mechanism is put forth as an explanation for how NLRP3 is activated in response to nigericin.

Strengths:

The experiments are generally well presented. It seems likely that Cys-130 does indeed play a previously unappreciated role in the membrane association of NLRP3.

Weaknesses:

The interpretations about the effects of nigericin are less convincing. Specific comments follow.

(1) The experiments of Figure 4 bring into question whether Cys-130 is S-acylated. For Cys-130, S-acylation was seen only upon expression of a severely truncated piece of the protein in conjunction with overexpression of ZDHHC3. How do the authors reconcile this result with the rest of the story?

(2) Nigericin seems to cause fragmentation and vesiculation of the Golgi. That effect complicates the interpretations. For example, the FRAP experiment of Figure 5 is problematic because the authors neglected to show that the FRAP recovery kinetics of non-acylated resident Golgi proteins are unaffected by nigericin. Similarly, the colocalization analysis in Figure 6 is less than persuasive when considering that nigericin significantly alters Golgi structure and could indirectly affect colocalization.

eLife assessment

The authors introduce a valuable machine-learning model for predicting binding sites of diverse ligands, including DNA, RNA, peptides, proteins, ATP, HEM, and metal ions, on proteins. The method is freely accessible and user-friendly. The authors have conducted thorough benchmarking and ablation studies, providing convincing evidence of the model's overall performance, despite some imperfections of the comparisons to other methods that arise from intrinsic differences between training methods and data.

Reviewer #1 (Public Review):

Summary:

The authors aim to address a critical challenge in the field of bioinformatics: the accurate and efficient identification of protein binding sites from sequences. Their work seeks to overcome the limitations of current methods, which largely depend on multiple sequence alignments or experimental protein structures, by introducing GPSite, a multi-task network designed to predict binding residues of various molecules on proteins using ESMFold.

Strengths:

(1) Benchmarking. The authors provide a comprehensive benchmark against multiple methods, showcasing the performances of a large number of methods in various scenarios.

(2) Accessibility and Ease of Use. GPSite is highlighted as a freely accessible tool with user-friendly features on their website, enhancing its potential for widespread adoption in the research community.

Weaknesses:

(1) Lack of significant insights. The paper reproduces results and analyses already presented in previous literature, without providing significant novel analysis or interpretation. However, they show a novel method with an original approach.

The work is useful for the field, especially in disease mechanism elucidation and novel drug design. The availability of genome-scale binding residue annotations GPSite offers is a significant advancement.

Reviewer #2 (Public Review):

Summary:

This work provides a new framework, "GPsite" to predict DNA, RNA, peptide, protein, ATP, HEM, and metal ions binding sites on proteins. This framework comes with a webserver and a database of annotations. The core of the model is a Geometric featurizer neural network that predicts the binding sites of a protein. One major contribution of the authors is the fact that they feed this neural network with predicted structure from ESMFold for training and prediction (instead of native structure in similar works) and a high-quality protein Language Model representation. The other major contribution is that it provides the public with a new light framework to predict protein-ligand interactions for a broad range of ligands. It is a convincing outcome of previous efforts to Geometric Deep Learning approaches to model protein-ligand interactions. The authors have demonstrated the interest of their framework with comprehensive ablation studies and benchmarks.

Strengths:

- The performance of this framework as well as the provided dataset and web server make it useful to conduct studies.<br /> - The ablations of some core elements of the method, such as the protein Language Model part, the use of multiple ligands in the same model, the input structure, or the use of predicted structure to complement native structure are very insightful. They can help convince the reader that every part of the framework is necessary. This could also guide further developments in the field. As such, the presentation of this part of the work holds a critical place in this work.

Weaknesses:

- The authors made an important effort to compare their work to other similar frameworks. Yet, the lack of homogeneity of training methods and data from one work to the other makes the comparison slightly unconvincing, as the authors pointed out. Ablations performed by the authors were able to compensate for this general weakness, as well as the focus on several example structures.

Reviewer #3 (Public Review):

Summary

The authors of this work aim to address the challenge of accurately and efficiently identifying protein binding sites from sequences. They recognize that the limitations of current methods, including reliance on multiple sequence alignments or experimental protein structure, and the under-explored geometry of the structure, which limit the performance and genome-scale applications. The authors have developed a multi-task network, GPSite, that predicts binding residues for a range of biologically relevant molecules, including DNA, RNA, peptides, proteins, ATP, HEM, and metal ions, using sequence embeddings from protein language models and ESMFold-predicted structures. The reported results showed to be superior to current sequence-based and structure-based methods in terms of accuracy and efficiency.

Strengths<br /> (1) The GPSite model's ability to predict binding sites for a wide variety of molecules, including DNA, RNA, peptides, and various metal ions.<br /> (2) Based on the presented results, GPSite outperforms state-of-the-art methods in several benchmark datasets in terms of accuracy and efficiency.<br /> (3) GPSite adopts predicted structure instead of native structures as input, enabling the model to be applied to a wider range of scenarios where native structures are rare.<br /> (4) The low computational cost of GPSite is beneficial, which enables rapid genome-scale binding residue annotations, indicating the model's potential for large-scale downstream applications and discoveries.

Weaknesses

There are no major weaknesses after the revision.

eLife assessment

The development of this mouse model is an important step to establish the role of the FSH Receptor in tissues beyond the reproductive system, and the data provided in this paper are convincing for a role for the FSH receptor in cell systems well beyond the classic reproductive tissues. Such model(s) have long been needed in this field and will provide expanded opportunities to better define FSH biology in vivo in these important target tissues. Ultimately, this model could shed light on FSH biology in women after menopause, when endogenous FSH levels rise dramatically, or in men with hypogonadism when FSH levels are high.

Reviewer #1 (Public Review):

The manuscript describes the development of a mouse model that co-expresses a fluorescent protein ZsGreen) marker in gene fusion with the FSHR gene.

The authors are correct in that there is a lack of reliable antibodies against many of the GPCR family members. The approach is novel and interesting, with the potential to help understand the expression pattern of gonadotropin receptors. There has been a very long debate about the expression of gonadotropin receptors in other tissues other than gonads. While their expression of the FSHR in some of those tissues has been detected by a variety of methods, their physiological, or pathophysiological, function(s) remain elusive.

The authors in this manuscript assume that the expression of ZsGren and the FSHR are equal. While this is correct genetically (transcription->translation) it does not go hand in hand with other posttranslational processes.

(1) One of the shocking observations in this manuscript is the expression of FSHR in Leydig cells. Other observations are in the osteoblasts and endothelial cells as well as epithelial cells in different organs. The expression of ZsGreen in these tissues seems high and one shall start questioning if there are other mechanisms at play here.

First, the turnover of fluorescent proteins is long, longer than 48h, which means that they accumulate at a different speed than the endogenous FSHR This means that ZsGreen will accumulate in time while the FSHR receptor might be degraded almost immediately. This correlated with mRNA expression (by the authors) but does not with the results of other studies in single-cell sequencing (see below).

The expression of ZsGreen in Leydig cells seems much higher than in Sertoli cells, this is "disturbing" to put it mildly. This is visible in both the ZsGreen expression and the FISH assay (Figure 2 B-D).

(2) The expression in WAT and BAT is also questionable as the expression of ZsGreen is high everywhere. That makes it difficult to believe that the images are truly informative. For example, the stainings of aorta show the ZsGreen expression where elastin and collagen fibres are - these are not "cells" and therefore are not expressing ZsGreen.

(3) FISH expression (for FSHR) in WT mice is missing.

Also, the tissue sections were stained with the IgG only (neg control) but in practice both the KI and the WT tissues should be stained with the primary and secondary antibodies. The only control that I could think of to truly get a sense of this would be a tagged receptor (N-terminal) that could then be analysed by immunohistochemistry.

(4) The authors also claim:<br /> To functionally prove the presence of FSHR in osteoblasts/osteocytes, we also deleted FSHR in osteocytes using an inducible model. The conditional knockout of FSHR triggered a much more profound increase in bone mass and decrease in fat mass than blockade by FSHR antibodies (unpublished data).

This would be a good control for all their images. I think it is necessary to make the large claim of extragonadal expression, as well as intragonadal such as Leydig cells.

(5) Claiming that the under-developed Leydig cells in FSHR KO animals are due to a direct effect of the FSHR, and not via a cross-talk between Sertoli and Leydig cells, is too much of a claim. It might be speculated to some degree but as written at the moment it suggests this is "proven".

(6) We also do not know if this FSHR expressed is a spliced form that would also result in the expression of ZsGreen but in a non-functional FSHR, or whether the FSHR is immediately degraded after expression. The insertion of the ZsGreen might have disturbed the epigenetics, transcription, or biosynthesis of the mRNA regulation.

(7) The authors should go through single-cell data of WT mice to show the existence of the FSHR transcript(s).<br /> For example here:<br /> https://www.nature.com/articles/sdata2018192

Reviewer #2 (Public Review):

The authors developed an original knock-in reporter mice line expressing ZSGreen under the control of endogenous FSHR promoter. The existence of FSHR in various extra-gonadal tissues and the physio-pathological consequences indeed remains a debated question and could potentially have an important impact on many high-incidence diseases occurring in menopausal women. Unfortunately, the provided data set lacks crucial controls and therefore does not provide a robust/convincing answer to the above-mentioned question.

Summary:<br /> The authors investigated the expression pattern of the FSHR in the gonads, where its expression has been demonstrated for decades, but also in many extra-gonadal tissues. The question is important since the expression of FSHR outside of the gonads has been increasingly reported and associated with the dramatic increase of circulating FSH after menopause, and has been suggested to play an important role in the advent of multiple diseases occurring with high incidence in post-menopausal women. However, the reality of such extra-gonadal expression of FSHR remains debated, mainly because this receptor is expressed at a low level and because the specificity/affinity of the available anti-FSHR antibodies is questionable.

Strengths:<br /> The development of reporter mice expressing ZsGreen fluorescent protein under the control of endogenous FSHR promoter is an original and potentially powerful approach to tackle the problem.

Weaknesses:<br /> The data provided are provocative since the FSHR seems to be expressed in all tested tissues. In the testis, for instance, the authors report very high levels of FSHR in interstitial cells and germ cells. In the ovary, there seems to be no difference in FSHR expression between granulosa cells and the other cell types. These findings alone contradict all the knowledge on FSH expression patterns in the gonads that have been accumulated over decades by many independent labs. In view of such results, the validity of the reporter mice line should be questioned thoroughly:

(1) Is the FSHR expression pattern affected by the knockin mice (no side-by-side comparison between wt and GSGreen mice, using in situ hybridization and ddRTPCR, at least in the gonads, is provided)?

(2) Is the splicing pattern of the FSHR affected in the knockin compared to wt mice, at least in the gonads?

(3) Are there any additional off-target insertions of GSGreen in these mice?

(4) Are similar results observed in separate founder mice?

(5) How long is GSGreen half-life? Could a very long half-life be a major reason for the extremely large expression pattern observed?

In the absence of answers to these questions, the data produced in extra-gonadal tissues using the same reporter mice, are not convincing and do not support the authors' claims.

eLife assessment

This study on scRNA-seq of allergic contact dermatitis (ACD) is important in that it presents new data on fibroblasts in ACD and links to recent studies on other cell types and their signatures. The evidence presented is solid in that the data support claims of unique roles for subtypes of fibroblasts in ACD. Overall, this paper will be used as a resource by many in the skin inflammation field.

Reviewer #1 (Public Review):

Summary:

In this manuscript, Liu et al. used scRNA-seq to characterize cell type-specific responses during allergic contact dermatitis (ACD) in a mouse model, specifically the hapten-induced DNFB model. Using the scRNA-seq data, they deconvolved the cell types responsible for the expression of major inflammatory cytokines such as IFNG (from CD4 and CD8 T cells), IL4/13 (from basophils), IL17A (from gd T cells), and IL1B from neutrophils and macrophages. They found the highest upregulation of a type 1 inflammatory response, centering around IFNG produced by CD4 and CD8 T cells. They further identified a subpopulation of dermal fibroblasts that upregulate CXCL9/10 during ACD and provided functional genetic evidence in their mouse model that disrupting IFNG signaling to fibroblasts decreases CD8 T cell infiltration and overall inflammation. They identify an increase in IFNG-expressing CD8 T cells in human patient samples of ACD vs. healthy control skin and co-localization of CD8 T cells with PDGFRA+ fibroblasts, which suggests this mechanism is relevant to human ACD. This mechanism is reminiscent of recent work (Xu et al., Nature 2022) showing that IFNG signaling in dermal fibroblasts upregulates CXCL9/10 to recruit CD8 T cells in a mouse model of vitiligo. Overall, this is a very well-presented, clear, and comprehensive manuscript. The conclusions of the study are mostly well supported by data, but some aspects of the work could be improved by additional clarification of the identity of the cell types shown to be involved, including the exact subpopulation discovered by scRNA-seq and the subtype of CD8 T cell involved. The study was limited by its use of one ACD model (DNFB), which prevents an assessment of how broadly relevant this axis is. The human sample validation is slightly circumstantial and limited by the multiplexing capacity of immunofluorescence markers.

Strengths:

Through deep characterization of the in vivo ACD model, the authors were able to determine which cell types were expressing the major cytokines involved in ACD inflammation, such as IFNG, IL4/13, IL17A, and IL1B. These analyses are well-presented and thoughtful, showing first that the response is IFNG-dominant, then focusing on deeper characterization of lymphocytes, myeloid cells, and fibroblasts, which are also validated and complemented by FACS experiments using canonical markers of these cell types as well as IF staining. Crosstalk analyses from the scRNA-seq data led the authors to focus on IFNG signaling fibroblasts, and in vitro experiments demonstrate that CXCL9 and CXCL10 are expressed by fibroblasts stimulated by IFNG. In vivo functional genetic evidence demonstrates an important role for IFNG signaling in fibroblasts, as KO of Ifngr1 using Pdgfra-Cre Ifngr1 fl/fl mice, showed a reduction in inflammation and CD8 T cell recruitment.

Weaknesses:

The use of one model limits an understanding of how broad this fibroblast-T cell axis is during ACD. However, the authors chose the most commonly employed model and cited additional work in a vitiligo model (another type 1 immune response). The identity of the involved fibroblasts and T cells in the mouse model is difficult to assess as scRNA-seq identified subpopulations of these cell types, but most work in the Pdgfra-Cre Ifngr1 fl/fl mice used broad markers for these cell types as opposed to matched subpopulation markers from their scRNA-seq data. Human patient samples of ACD were co-stained with two markers at a time, demonstrating the presence of CD8+IFNG+ T cells, PDGFRA+CXCL10+ fibroblasts, and co-localization of PDGFRA+ fibroblasts and CD8+ T cells. However, no IF staining demonstrates co-expression of all 4 markers at once; thus, the human validation of co-localization of CD8+IFNG+ T cells and PDGFRA+CXCL10+ fibroblasts is ultimately indirect, although not a huge leap of faith. Although n=3 samples of healthy control and ACD samples are used, there is no quantification of any results to demonstrate the robustness of differences.

Reviewer #2 (Public Review):

Summary:

The investigators apply scRNA seq and bioinformatics to identify biomarkers associated with DNFB-induced contact dermatitis in mice. The bioinformatics component of the study appears reasonable and may provide new insights regarding TH1-driven immune reactions in ACD in mice. However, the IF data and images of tissue sections are not clear and should be improved to validate the model.

Strengths:

The bioinformatics analysis.

Weaknesses:

The IF data presented in 4H, 6H, 7E and 7F are not convincing and need to be correlated with routine staining on histology and different IF markers for PDGFR. Some of the IF staining data demonstrates a pattern inconsistent with its target.

eLife assessment

This valuable work provides a near-complete description of the mechanosensory bristles on the Drosophila melanogaster head and the anatomy and projection patterns of the bristle mechanosensory neurons that innervate them. The data presented are solid. The study has generated numerous resources for the community that will be of interest to neuroscientists in the field of circuits and behaviour, particularly those interested in mechanosensation and behavioural sequence generation.

Author Response

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

eLife assessment

This valuable work provides a near-complete description of the mechanosensory bristles on the Drosophila melanogaster head and the anatomy and projection patterns of the bristle mechanosensory neurons that innervate them. The data presented are solid. The study has generated numerous invaluable resources for the community that will be of interest to neuroscientists in the field of circuits and behaviour, particularly those interested in mechanosensation and behavioural sequence generation.

We express our gratitude to the Reviewers for their valuable suggestions, which significantly enhanced the manuscript. The revisions were undertaken, not with the expectation of acceptance, but rather driven by our sincere belief that these revisions would enhance the manuscript's impact for future readers.

Public Reviews:

Reviewer #1 (Public Review):

Sensory neurons of the mechanosensory bristles on the head of the fly project to the sub esophageal ganglion (SEZ). In this manuscript, the authors have built on a large body of previous work to comprehensively classify and quantify the head bristles. They broadly identify the nerves that various bristles use to project to the SEZ and describe their region-specific innervation in the SEZ. They use dye-fills, clonal labelling, and electron microscopic reconstructions to describe in detail the phenomenon of somatotopy - conserved peripheral representations within the central brain - within the innervation of these neurons. In the process they develop novel tools to access subsets of these neurons. They use these to demostrate that groups of bristles in different parts of the head control different aspects of the grooming sequence.

Reviewer #2 (Public Review):

The authors combine genetic tools, dye fills and connectome analysis techniques to generate a "first-of-its-kind", near complete, synaptic resolution map of the head bristle neurons of Drosophila. While some of the BMN anatomy was already known based on previous work by the authors and other researchers, this is the first time a near complete map has been created for the head BMNs at electron microscopy resolution.

Strengths:

(1) The authors cleverly use techniques that allow moving back and forth between periphery (head bristle location) and brain, as well as moving between light microscopy and electron microscopy data. This allows them to first characterize the pathways taken by different head BMNs to project to the brain and also characterize anatomical differences among individual neurons at the level of morphology and connectivity.

(2) The work is very comprehensive and results in a near complete map of all I’m head BMNs.

(3) Authors also complement this anatomical characterization with a first-level functional analysis using optogenetic activation of BMNs that results in expected directed grooming behavior.

Weaknesses:

(1) The clustering analysis is compelling but cluster numbers seem to be arbitrarily chosen instead of by using some informed metrics.

We made revisions to the manuscript that address this concern. Please see our response to “recommendations for authors” for a description of these revisions.

(2) It could help provide context if authors revealed some of the important downstream pathways that could explain optogenetics behavioral phenotypes and previously shown hierarchical organization of grooming sequences.

We made revisions to the manuscript that address this recommendation. Please see our response to “recommendations for authors” for a description of these revisions.

(3) In contrast to the rigorous quantitative analysis of the anatomical data, the behavioral data is analyzed using much more subjective methods. While I do not think it is necessary to perform a rigorous analysis of behaviors in this anatomy focused manuscript, the conclusions based on behavioral analysis should be treated as speculative in the current form e.g. calling "nodding + backward walking" as an avoidance response is not justified as it currently stands. Strong optogenetic activation could lead to sudden postural changes that due to purely biomechanical constraints could lead to a couple of backward steps as seen in the example videos. Moreover since the quantification is manual, it is not clear what the analyst interprets as backward walking or nodding. Interpretation is also concerning because controls show backward walking (although in fewer instances based on subjective quantification).

While unbiased machine vision-based methods would nicely complement the present work, this type of analysis is not yet working to distinguish between different head grooming movements. Therefore, we are currently limited to manual annotation for our behavioral analysis. That said, we do not believe that our manual annotation is subjective. The grooming movements that we examine in this work are distinguishable from each other through frame-by-frame manual annotation of video at 30 fps. Our annotation of the grooming and backward motions performed by flies are based on previous publications that established a controlled vocabulary defining each movement (Hampel et al., 2020a, 2017, 2015; Seeds et al., 2014). In this work, we added head nodding to this controlled vocabulary that is described in the Materials and methods. We have added additional text to the third paragraph of the Material and methods section entitled “Behavioral analysis procedures” that we hope better describes our behavioral analysis. This description now reads:

Head nodding was annotated when the fly tilted its head downward by any amount until it returned its head back in its original position. This movement often occurred in repeated cycles. Therefore, the “start” was scored at the onset of the first forward movement and the “stop” when the head returned to its original position on the last nod.

We do not make any firm conclusions about the head movements (nodding) and backwards motions. We refer to nodding as a descriptive term that would allow the reader to better understand what the behavior looks like. We make no firm conclusions about any behavioral functional role that either the nodding or the backward motions might have, with the exception of nodding in the context of grooming. We only suggest that the behaviors appear to be avoidance responses. Furthermore, backward walking was not mentioned. Instead we refer to backward motions. We are only reporting our annotations of these movements that do occur, and are significantly different from controls. We speculate that these could be avoidance responses based on support from the literature. Future studies will be required to understand whether these movements serve real behavioral roles.

Summary:

The authors end up generating a near-complete map of head BMNs that will serve as a long-standing resource to the Drosophila research community. This will directly shape future experiments aimed at modeling or functionally analyzing the head grooming circuit to understand how somatotopy guides behaviors.

Reviewer #3 (Public Review):

Eichler et al. set out to map the locations of the mechanosensory bristles on the fly head, examine the axonal morphology of the bristle mechanosensory neurons (BMNs) that innervate them, and match these to electron microscopy reconstructions of the same BMNs in a previously published EM volume of the female adult fly brain. They used BMN synaptic connectivity information to create clusters of BMNs that they show occupy different regions of the subesophageal zone brain region and use optogenetic activation of subsets of BMNs to support the claim that the morphological projections and connectivity of defined groups of BMNs are consistent with the parallel model for behavioral sequence generation.

The authors have beautifully cataloged the mechanosensory bristles and the projection paths and patterns of the corresponding BMN axons in the brain using detailed and painstaking methods. The result is a neuroanatomy resource that will be an important community resource. To match BMNs reconstructed in an electron microscopy volume of the adult fly brain, the authors matched clustered reconstructed BMNs with light-level BMN classes using a variety of methods, but evidence for matching is only summarized and not demonstrated in a way that allows the reader to evaluate the strength of the evidence. The authors then switch from morphology-based categorization to non-BMN connectivity as a clustering method, which they claim demonstrates that BMNs form a somatotopic map in the brain. This map is not easily appreciated, and although contralateral projections in some populations are clear, the distinct projection zones that are mentioned by the authors are not readily apparent. Because of the extensive morphological overlap between connectivity-based clusters, it is not clear that small projection differences at the projection level are what determines the post-synaptic connectivity of a given BMN cluster or their functional role during behavior. The claim the somatotopic organization of BMN projections is preserved among their postsynaptic partners to form parallel sensory pathways is not supported by the result that different connectivity clusters still have high cosine similarity in a number of cases (i.e. Clusters 1 and 3, or Clusters 1 and 2). Finally, the authors use tools that were generated during the light-level characterization of BMN projections to show that specifically activating BMNs that innervate different areas of the head triggers different grooming behaviors. In one case, activation of a single population of sensory bristles (lnOm) triggers two different behaviors, both eye and dorsal head grooming. This result does not seem consistent with the parallel model, which suggests that these behaviors should be mutually exclusive and rely on parallel downstream circuitry.

We made revisions to the manuscript that address this recommendation. Please see our response to “recommendations for authors” for a description of these revisions.

This work will have a positive impact on the field by contributing a complete accounting of the mechanosensory bristles of the fruit fly head, describing the brain projection patterns of the BMNs that innervate them, and linking them to BMN sensory projections in an electron microscopy volume of the adult fly brain. It will also have a positive impact on the field by providing genetic tools to help functionally subdivide the contributions of different BMN populations to circuit computations and behavior. This contribution will pave the way for further mechanistic study of central circuits that subserve grooming circuits.

Recommendations for the authors:

All three reviewers appreciated the work presented in this manuscript. There were also a few overlapping concerns that were raised that are summarised below, should the authors wish to address them:

Somatotopy: We recommend that the authors describe the extent of prior knowledge in more detail to highlight their contribution better.

We made revisions that better highlight the extent of prior knowledge about somatotopy. We describe how previous studies showed bristle mechanosensory neurons in insects are somatotopically organized, but these studies were not comprehensive descriptions of complete somatotopic maps for the head or body. To our knowledge, our study provides the first comprehensive and synaptic resolution somatotopic map of a head for any animal. This sets the stage for the complete definition of the interface between somatotopically-organized mechanosensory neurons and postsynaptic circuits, which has broad implications for future studies on aimed grooming, and mechanosensation in general. Below we itemize revisions to the Introduction, Discussion, and Figures to provide a clearer statement of the significance of our study as it relates to somatotopy.

(1) Newly added Figure 1 – figure supplement 1 more explicitly grounds the study in somatotopy, providing a working model of the organization of the circuit pathways that produce the grooming sequence. This model features somatotopy as shown in Figure 1 – figure supplement 1C.

(2) Figure 1 – figure supplement 1 is incorporated into the Introduction in the second, third, and fourth paragraphs, the first paragraph of the Results section titled “Somatotopically-organized parallel BMN pathways”, and the second and third paragraphs of the last Discussion section titled “Parallel circuit architecture underlying the grooming sequence”.

(3) We added text to the end of the fourth paragraph of the Introduction that now reads: “In this model, parallel-projecting mechanosensory neurons that respond to stimuli at specific locations on the head or body could connect with somatotopically-organized parallel circuits that elicit grooming of those locations (Figure 1 – figure supplement 1A-C). The previous discovery of a mechanosensory-connected circuit that elicits aimed grooming of the antennae provides evidence of this organization (Hampel 2015). However, the extent to which distinct circuits elicit grooming of other locations is unknown, in part, because the somatotopic projections of the mechanosensory neurons have not been comprehensively defined for the head or body.”

(4) There is a Discussion section that further explains the extent of prior knowledge and our contributions on somatotopy that is titled “A synaptic resolution somatotopic map of the head BMNs”. Additionally, the previous version of this section had a paragraph on the broader implications of our work as it relates to somatotopy across species. In light of the reviewer comments, we decided to make this paragraph into its own Discussion section to better highlight the broader significance of our work. This section is titled “First synaptic resolution somatotopic map of the head”.

The somatotopy isn't overtly obvious - perhaps they could try mapping presynaptic sites and provide landmarks to improve visualisation.

We made the following revisions to better highlight the head BMN somatotopy. One point of confusion from the previous manuscript version stemmed from us not explicitly defining the somatotopic organization that we observed. There seemed to be confusion that we were defining the head somatotopy based only on the small projection differences among BMNs from neighboring head locations. While we believe that these small differences indeed correspond to somatotopy, we failed to highlight that there are overt differences in the brain projections of BMNs from distant locations on the head. For example, Figure 5B (right panel) shows the distinct projections between the LabNv (brown) and AntNv (blue) BMNs that innervate bristles on the ventral and dorsal head, respectively. Thus, BMN types innervating neighboring bristles show overlapping projections with small projection differences, whereas those innervating distant bristles show non overlapping projections into distinct zones.

Our analysis of postsynaptic connectivity similarity also shows somatotopic organization among the BMN postsynaptic partners, as BMN types innervating the same or neighboring bristle populations show high connectivity similarity (Figure 8, old Figure 7). Below we highlight major revisions to the text and Figures that hopefully better reveal the head somatotopy.

(1) In the last paragraph of the Introduction we added text that explicitly frames the experiments in terms of somatotopic organization: “This reveals somatotopic organization, where BMNs innervating neighboring bristles project to the same zones in the CNS while those innervating distant bristles project to distinct zones. Analysis of the BMN postsynaptic connectome reveals that neighboring BMNs show higher connectivity similarity than distant BMNs, providing evidence of somatotopically organized postsynaptic circuit pathways.”

(2) We mention an example of overt somatotopy from Figure 5 in the Results section titled “EM-based reconstruction of the head BMN projections in a full adult brain”. The text reads “For example, BMNs from the Eye- and LabNv have distinct ventral and anterior projections, respectively. This shows how the BMNs are somatotopically organized, as their distinct projections correspond to different bristle locations on the head (Figure 5B,C).”

(3) In new Figure 8 (part of old Figure 7), we modified panels that correspond to the cosine similarity analysis of postsynaptic connectivity. The major revision was to plot the cosine similarity clusters onto the head bristles so that the bristles are now colored based on their clusters (C). This shows how neighboring BMNs cluster together, and therefore show similar postsynaptic connectivity. We believe that this provides a nice visualization of somatotopic organization in BMN postsynaptic connectivity. We also added the clustering dendrogram as recommended by Reviewer #2 (Figure 8A).

(4) In new Figure 8, we added new panels (D-F) that summarize our anatomical and connectomic analysis showing different somatotopic features of the head BMNs. Different BMN types innervate bristles at neighboring and distant proximities (D). BMNs that innervate neighboring bristles project into overlapping zones (E, example of reconstructed BM-Fr and -Ant neurons with non-overlapping BM-MaPa neurons) and show postsynaptic connectivity similarity (F, example connectivity map of three BM types on cosine similarity data).

(5) To accompany the new Figure 8D-F panels, we added a paragraph to summarize the different somatotopic features of the head BMNs that were identified based on our anatomical and connectomic analysis. This is the last paragraph in the Results section titled “Somatotopically-organized parallel BMN pathways”:

Our results reveal head bristle proximity-based organization among the BMN projections and their postsynaptic partners to form parallel mechanosensory pathways. BMNs innervating neighboring bristles project into overlapping zones in the SEZ, whereas those innervating distant bristles project to distinct zones (example of BM-Fr, -Ant, and -MaPa neurons shown in Figure 8D,E). Cosine similarity analysis of BMN postsynaptic connectivity revealed that BMNs innervating the same bristle populations (same types) have the highest connectivity similarity. Figure 8F shows example parallel connections for BM-Fr, -Ant, and -MaPa neurons (vertical arrows), where the edge width indicates the number of synapses from each BMN type to their major postsynaptic partners. Additionally, BMNs innervating neighboring bristle populations showed postsynaptic connectivity similarity, while BMNs innervating distant bristles show little or none. For example, BM-Fr and -Ant neurons have connections to common postsynaptic partners, whereas BM-MaPa neurons show only weak connections with the main postsynaptic partners of BM-Fr or -Ant neurons (Figure 8F, connections under 5% of total BMN output omitted). These results suggest that BMN somatotopy could have different possible levels of head spatial resolution, from specific bristle populations (e.g. Ant bristles), to general head areas (e.g. dorsal head bristles).

We also refer to Figure 8D-F to illustrate the different somatotopic features in the Discussion. These references can be found in the following Discussion sections titled “A synaptic resolution somatotopic map of the head BMNs (fourth paragraph)”, and “Parallel circuit architecture underlying the grooming sequence (second paragraph)”.

(6) In addition to improving the Figures, we provide additional tools that enable readers to explore the BMN somatotopy in a more interactive way. That is, we provide 5 different FlyWire.ai links in the manuscript Results section that enable 3D visualization of the different reconstructed BMNs (e.g. FlyWire.ai link 1).

Note: In working on old Figure 7 to address this Reviewer suggestion, we also reordered panels A-E. We believe that this was a more logical ordering than in the previous draft. These panels are now the only data shown in Figure 7, as the cosine similarity analysis is now in Figure 8. We hope that splitting these panels into two Figures will improve manuscript readability.

Light EM Mapping: A better description of methods by which this mapping was done would be helpful. Perhaps the authors could provide a few example parallel representations of the EM and light images in the main figure would help the reader better appreciate the strength of their approach.

We have done as the Reviewers suggested and added panels to Figure 6 that show examples of the LM and EM image matching (Figure 6A,B). We added two examples that used different methods for labeling the LM imaged BMNs, including MCFO labeling of an individual BM-InOc neuron and driver line labeling of a major portion of BM-InOm neurons using InOmBMN-LexA. These panels are referred to in the first paragraph of the Results section titled “Matching the reconstructed head BMNs with their bristles”. Note that examples for all LM/EM matched BMN types are shown in Figure 6 – figure supplement 2.

We had provided Figure 6 – figure supplement 2 in the reviewed manuscript that shows all the above requested “parallel representations of the EM and light images”. However, the Reviewer critiques made us realize that the purpose of this figure supplement was not clearly indicated. Therefore, we have revised Figure 6 – figure supplement 2 and its legend to make its purpose clearer. First, we changed the legend title to better highlight its purpose. The legend is now titled: “Matching EM reconstructed BMN projections with light microscopy (LM) imaged BMNs that innervate specific bristles”. Second, we added label designations to the figure panel rows that highlight the LM and EM comparisons. That is, the rows for light microscopy images of BMNs are indicated with LM and the rows for EM reconstructed BMN images are labeled with EM. Reviewer #3 had indicated that it was not clear what labeling methods were used to visualize the LM imaged BM-InOm neurons in Figure 6 – figure supplement 2N. Therefore, we added text to the figure and the legend to better highlight the different methods used. Panels A and B were also cropped to accommodate the above mentioned revisions.

The manuscript also provides an extensive Materials and methods section that describes the different lines of evidence that were used to assign the reconstructed BMNs as specific types. We changed the title to better highlight the purpose of this methods section to “Matching EM reconstructed BMN projections with light microscopy imaged BMNs that innervate specific bristles”. The evidence used to support the assignment of the different BMN types is also summarized in Figure 6 – figure supplement 3.

Parallel circuit model: The authors motivate their study with this. We're recommending that they define expectations of such circuitry, its alternatives (including implications for downstream pathways), and behavior before they present their results. We're also recommending that they interpret their behavioural results in the context of these circuits.

Our primary motivation for doing the experiments described in this manuscript was to help define the neural circuit architecture underlying the parallel model that drives the Drosophila grooming sequence. This manuscript provides a comprehensive assessment of the first layer of this circuit architecture. A byproduct of this work is a contribution that offers immediate utility and significance to the Drosophila connectomics community. Namely, the description of the majority of mechanosensory neurons on the head, with their annotation in the recently released whole brain connectome dataset (FlyWire.ai). In writing this manuscript, we tried to balance both of these things, which was difficult to write. We very much appreciate the Reviewers' comments that have highlighted points of confusion in our original draft. We hope that the revised draft is now clearer and more logically presented. We have made revisions to the text and provided a new figure supplement (Figure 1 - figure supplement 1) and new panels in Figure 8. Below we highlight the major revisions.

(1) The Introduction was revised to more explicitly ground the study in the parallel model, while also removing details that were not pertinent to the experiments presented in the manuscript.

The first paragraph introduces different features of the parallel model. To better focus the reader on the parts of the model that were being assessed in the manuscript, we removed the following sentences: “Performance order is established by an activity gradient among parallel circuits where earlier actions have the highest activity and later actions have the lowest. A winner-take-all network selects the action with the highest activity and suppresses the others. The selected action is performed and then terminated to allow a new round of competition and selection of the next action.” Note that these sentences are included in the third and fourth paragraphs of the last Discussion section titled “Parallel circuit architecture underlying the grooming sequence”.

The first paragraph of the Introduction now introduces a bigger picture view of the model that emphasizes the two main features: 1) a parallel circuit architecture that ensures all mutually exclusive actions to be performed in sequence are simultaneously readied and competing for output, and 2) hierarchical suppression among the parallel circuits, where earlier actions suppress later actions.

(2) Newly added Figure 1 – figure supplement 1 provides a working model of grooming (Reviewer # 1 suggestion). We now more strongly emphasize that the study aimed to define the parallel neural circuit architecture underlying the grooming sequence, focusing on the mechanosensory layer of this architecture. In particular, we refer to the new Figure 1 – figure supplement 1 that has been added to better convey the hypothesized grooming neural circuit architecture. Figure 1 – figure supplement 1 is incorporated into the Introduction (paragraphs two, three, and four), Results section titled “Somatotopically-organized parallel BMN pathways (first paragraph)”, and last Discussion section titled “Parallel circuit architecture underlying the grooming sequence (second and third paragraphs)”.

(3) New panels in Figure 8 update the model of parallel circuit organization as it relates to somatotopy (D-F). These panels show the parallel circuits hypothesized by the model, but also indicate convergence, with different possible levels of head resolution for these circuits. We describe above where these panels are referenced in the text.

(4) We added a new paragraph in the last Discussion section titled “Parallel circuit architecture underlying the grooming sequence” that better incorporates the results from this manuscript into the working model of grooming. This paragraph is shown below.

Here we define the parallel architecture of BMN types that elicit the head grooming sequence that starts with the eyes and proceeds to other locations, such as the antennae and ventral head. The different BMN types are hypothesized to connect with parallel circuits that elicit grooming of specific locations (described above and shown in Figure 1 – figure supplement 1A,C). Indeed, we identify distinct projections and connectivity among BMNs innervating distant bristles on the head, providing evidence supporting this parallel architecture (Figure 8D-F). However, we also find partially overlapping projections and connectivity among BMNs innervating neighboring bristles. Further, optogenetic activation of BMNs at specific head locations elicits grooming of both those locations and neighboring locations (Figure 9). These findings raise questions about the resolution of the parallel architecture underlying grooming. Are BMN types connected with distinct postsynaptic circuits that elicit aimed grooming of their corresponding bristle populations (e.g. Ant bristles)? Or are neighboring BMN types that innervate bristles in particular head areas connected with circuits that elicit grooming of those areas (e.g. dorsal or ventral head)? Future studies of the BMN postsynaptic circuits will be required to define the resolution of the parallel pathways that elicit aimed grooming.

Aside from this summary of major concerns, the detailed recommendations are attached below.

Reviewer #1 (Recommendations For The Authors):

I appreciate the quality and exhaustive body of work presented in this manuscript. I have a few comments that the authors may want to consider:

(1) The authors motivate this study by posing that it would allow them to uncover whether the complex grooming behaviour of flies followed a parallel model of circuit function. It would have been nice to have been introduced to what the alternative model might be and what each would mean for organisation of the circuit architecture. Some guiding schematics would go a long way in illustrating this point. Modifying the discussion along these lines would also be helpful.

We made several revisions to the manuscript that address this recommendation. Among these revisions, we added Figure 1 – figure supplement 1 that includes a working model for grooming. Please see above for a description of these revisions.

(2) The authors mention the body of work that has mapped head bristles and described somatotopy. It would be useful to discuss in more detail what these studies have shown and highlight where the gaps are that their study fills.

We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions.

(3) The dye-fills and reconstructions that are single colour could use a boundary to demarcate the SEZ. This would help in orienting the reader.

We agree with Reviewer #1 that Figure 4 and its supplements could use some indicator that would orient the reader with respect to the dye filled or stochastically labeled neurons. The images are of the entire SEZ in the ventral brain, and in the case of some panels, the background staining enables visualization of the brain (e.g. Figure 4H,M,N. To help orient the reader in this region, we added a dotted line to indicate the approximate SEZ midline. This also enables the reader to more clearly see which of the BMN types cross the midline.

Midline visual guides were added for Figure 4, Figure 4 – figure supplement 2, Figure 4 – figure supplement 3, Figure 4 – figure supplement 4, Figure 4 – figure supplement 5, Figure 4 – figure supplement 6, Figure 4 – figure supplement 7, Figure 4 – figure supplement 8, Figure 6 – figure supplement 2.

(4) The comparison between the EM and the fills/clones are not obvious. And particularly because they are not directly determined, it would be nice to have the EM reconstruction alongside the dye-fills. This would work very nicely in the supplementary figure with the multiple fills of the same bristles. I think this would really drive home the point.

We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions.

(5) Are there unnoticed black error-bars floating around in many of the gray-scale images?

The black bars were masking white scale bars in the images. We have removed the black bars and remade the images without scale bars. This was done for the following Figures: Figure 4, Figure 4 – figure supplement 2, Figure 4 – figure supplement 3, Figure 4 – figure supplement 4, Figure 4 – figure supplement 5, Figure 4 – figure supplement 6, Figure 4 – figure supplement 7, Figure 4 – figure supplement 8, Figure 6 – figure supplement 2.

Reviewer #2 (Recommendations For The Authors):

(1) The only point in the paper I found myself going back and forth between methods/supp and text was when authors discuss about the clustering. I think it would help the reader if a few sentences about cosine clustering used for connectivity based clustering were included in the main text. Also, for NBLAST hierarchical clustering, it would help if some informed metrics could be used for defining cluster numbers (e.g. Braun et al, 2010 PLOS ONE shows how Ward linkage cost could be used for hierarchical clustering).

Depending on where the cut height is placed on the dendrogram for cosine similarity of BMNs, different features of the BMN type postsynaptic connectivity are captured. As the number of clusters is increased (lower cut height), clustering is mainly among BMNs of the same type, showing that these BMNs have the highest connectivity similarity. As the number of clusters is reduced (higher cut height), BMNs innervating neighboring bristles on the head are clustered, revealing three general clusters corresponding to the dorsal, ventral, and posterior head. This reveals somatotopy based clustering among same and neighboring BMN types. The cut height shown in Figure 8 and Figure 8 – figure supplement 2 was chosen because it highlighted both of these features.

The NBLAST clustering shows similar results to the connectivity based clustering with respect to neighboring and distant BMN types. As the number of clusters increases BMNs of the same type are clustered, and these types can be further subdivided into morphologically distinct subtypes. As the number of clusters is reduced, the clustering captures neighboring BMNs. Thus, neighboring BMN types showed high morphology similarity (and proximity) with each other, and low similarity with distant BMN types.

Please see our responses to a Reviewer #3 critique below for further description of the clustering results.

On the same lines it would help if the clustering dendrograms were included in the main figure.

We thank Reviewer #2 for this comment. We have added the dendrogram to Figure 8A, a change that we feel makes this Figure much easier to understand.

(2) It could help provide intuition if the authors revealed some of the downstream targets and their implication in explaining the behavioral phenotypes.

While this will be the subject of at least two forthcoming manuscripts, we have added text to the present manuscript that provides insight into BMN postsynaptic targets. Our previous work (Hampel et al. 2015) described a mechanosensory connected neural circuit that elicits grooming of the antennae. While this previous study demonstrated that the Johnston’s organ mechanosensory neurons are synaptically and functionally connected with this circuit, our preliminary analysis indicates that it is also connected with BM-Ant neurons. We hypothesize that there are additional such circuits that are responsible for eliciting grooming of other head locations.

To better highlight potential downstream targets in the manuscript, we now mention the antennal circuit in the Introduction. This text reads: In this model, parallel-projecting mechanosensory neurons that respond to stimuli at specific locations on the head or body could connect with somatotopically-organized parallel circuits that elicit grooming of those locations (Figure 1 – figure supplement 1A-C). The previous discovery of a mechanosensory-connected circuit that elicits aimed grooming of the antennae provides evidence of this organization (Hampel 2015). However, the extent to which distinct circuits elicit grooming of other locations is unknown, in part, because the somatotopic projections of the mechanosensory neurons have not been comprehensively defined for the head or body.

There is also text in the Discussion that addresses this Reviewer comment. It describes the antennal circuit and mentions the possibility that other similar circuits may exist. This can be found in the third paragraph of the section titled “Circuits that elicit aimed grooming of specific head locations”.

(3) Authors find that opto activation of BMNs leads to grooming of targeted as well as neighboring areas. Is there any sequence observed here? i.e. first clean targeted area and then clean neighboring area? I wonder if the answer to this is something as simple as common post-synaptic targets which is essentially reducing the resolution of the BMN sensory map. Some more speculation on this interesting result could be helpful.

We appreciate and agree with this point from Reviewer #2, and have tried to better emphasize the possible implications for grooming that the overlapping projections and connectivity among BMNs innervating neighboring bristles may have. This is now better addressed in the Results and Discussion sections. Below we highlight where this is addressed:

(1) In the second paragraph of the Results section titled “Activation of subsets of head BMNs elicits aimed grooming of specific locations” we added text that suggests the possibility that grooming of the stimulated and neighboring locations could be due to the overlapping projections and connectivity. This text reads: This suggested that head BMNs elicit aimed grooming of their corresponding bristle locations, but also neighboring locations. This result is consistent with our anatomical and connectomic data indicating that BMNs innervating neighboring bristles show overlapping projections and postsynaptic connectivity similarity (see Discussion).

(2) In the fourth paragraph of the Discussion section titled “A synaptic resolution somatotopic map of the head BMNs”, we added a sentence to the end of the fourth paragraph that alludes to further discussion of this topic. This sentence reads: This overlap may have implications for aimed grooming behavior. For example, neighboring BMNs could connect with common neural circuits to elicit grooming of overlapping locations (discussed more below).

(3) In the fourth paragraph of the Discussion section titled “Circuits that elicit aimed grooming of specific head locations” there is a paragraph that mentions the possibility of mechanosensory convergence onto common postsynaptic circuits to promote grooming of the stimulated area, along with neighboring areas. This paragraph is below.

We find that activation of specific BMN types elicits both aimed grooming of their corresponding bristle locations and neighboring locations. This suggests overlap in the locations that are groomed with the activation of different BMN types. Such overlap provides a means of cleaning the area surrounding the stimulus location. Interestingly, our NBLAST and cosine similarity analysis indicates that neighboring BMNs project into overlapping zones in the SEZ and show common postsynaptic connectivity. Thus, we hypothesize that neighboring BMNs connect with common neural circuits (e.g. antennal grooming circuit) to elicit overlapping aimed grooming of common head locations.

(4) In the new second paragraph of the Discussion section titled “Parallel circuit architecture underlying the grooming sequence” we further discuss the issue of the BMN “sensory map. This paragraph is below.

Here we define the parallel architecture of BMN types that elicit the head grooming sequence that starts with the eyes and proceeds to other locations, such as the antennae and ventral head. The different BMN types are hypothesized to connect with parallel circuits that elicit grooming of specific locations (described above and shown in Figure 1 – figure supplement 1A,C). Indeed, we identify distinct projections and connectivity among BMNs innervating distant bristles on the head, providing evidence supporting this parallel architecture (Figure 8D-F). However, we also find partially overlapping projections and connectivity among BMNs innervating neighboring bristles. Further, optogenetic activation of BMNs at specific head locations elicits grooming of both those locations and neighboring locations (Figure 9). These findings raise questions about the resolution of the parallel architecture underlying grooming. Are BMN types connected with distinct postsynaptic circuits that elicit aimed grooming of their corresponding bristle populations (e.g. Ant bristles)? Or are neighboring BMN types that innervate bristles in particular head areas connected with circuits that elicit grooming of those areas (e.g. dorsal or ventral head)? Future studies of the BMN postsynaptic circuits will be required to define the resolution of the parallel pathways that elicit aimed grooming.

(4) If authors were to include a summary table that shows all known attributes about BMN type as columns that could be very useful as a resource to the community. Table columns could include attributes like "bristle name", "nerve tract", "FlyWire IDs of all segments corresponding to the bristle class". "split-Gal4 line or known enhancer" , etc.

We provided a table that includes much of this information after the manuscript had already gone out for review. We regret that this was not available. This is now provided as Supplementary file 3. This table provides the following information for each reconstructed BMN: BMN name, bristle type, nerve, flywire ID, flywire coordinates, NBLAST cluster (cut height 1), NBLAST cluster (cut height 5), and cosine cluster (cut height 4.5). Note that the driver line enhancers for targeting specific BMN types are shown in Figure 3I.

Specific Points:

Figure 4C-V:

• I find it a bit difficult to distinguish ipsi- from contra-lateral projections. Maybe indicate the midline as a thin, stippled line?

We thank the Reviewer #2 for this suggestion. We have now added lines in the panels in Figure 4C-V to indicate the approximate location of the midline. We also added lines to the Figure 4 – figure supplements as described above.

I think this Fig reference is wrong "the red-light stimulus also elicited backward motions with control flies (Figure 6B,C, control, black trace, Video 5)." should be Fig 8B,C

We have fixed this error.

Reviewer #3 (Recommendations For The Authors):

Introduction:

Motivating this study in terms of understanding the neural mechanisms that execute the parallel model seems to overstate what you will achieve with the current study. If you want to motivate it this way, I suggest focusing on the grooming sequence of the head along (eyes, antennae, proboscis).

We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions. Please note that many of the revisions focus on the head grooming sequence. We also made minor revisions to the Introduction that further emphasize the focus on head grooming.

Results:

Figure 1. Please indicate that this is a male fly in either the figure title or in the figure itself.

We added a male symbol to Figure 1A.

Figure 3. Panel J is referenced in the main body text and in the figure caption, but there is no Fig 3J.

Panel J is shown in the upper right corner of Figure 3. We realize that the placement of this panel is not ideal, but this was the only place that we could fit it. Additionally, the panel works nicely at that location to better enable comparison with panel C. We have revised the text in the Figure 3 legend to better highlight the location of this Figure panel: “Shown in the upper right corner of the figure are the aligned expression patterns of InOmBMN-LexA (red), dBMN-spGAL4 (green), and TasteBMN-spGAL4 (brown).”

We also added text to a sentence in the results section entitled “Head BMNs project into discrete zones in the ventral brain” that indicates the panel location. This text reads: To further visualize the spatial relationships between these projections, we computationally aligned the expression patterns of the different driver lines into the same brain space (Figure 3J, upper right corner).

Matching the BMNs to EM reconstructions: why cut the dendrogram at H=5? Would be better to determine cluster number using an unbiased method.

To match the morphologically distinct EM reconstructed BMNs to their specific bristles, we relied on different lines of evidence, including NBLAST results (discussed more below), dye fill/stochastic labeling/driver line labeling matches, published morphology, nerve projection, bristle number, proximity to other BMNs, and postsynaptic connectivity (summarized in Figure 6 – figure supplement 3). The following Materials and methods section provides a detailed description of the evidence used to assign each BMN type in “Matching EM reconstructed BMN projections with light microscopy imaged BMNs that innervate specific bristles”. In many cases, BMN type could be assigned with confidence solely based on morphological comparisons with our light level data (e.g. dye fills), in conjunction with bristle counts to indicate an expected number of BMNs showing similar morphology. Thus, the LM/EM matches and NBLAST clustering were largely complementary.

The EM reconstructed BMNs were matched as particular BMN types, in part based on examination of the NBLAST data at different cut heights. NBLAST clustering of the BMNs revealed general trends at higher and lower cut heights (Figure 6 – figure supplement 1A, Supplementary file 3). The lowest cut heights included mostly BMNs of the same type innervating the same bristle populations, and smaller clusters that subdivided into morphologically distinct subtypes (see Supplementary file 3 for clusters produced at cut height 1). This revealed that BMNs of the same type tended to show the highest morphological similarity with each other, but they also showed intratype morphological diversity. Higher cut heights produced clusters of BMNs innervating neighboring bristles populations (e.g. ventral head BMNs), showing high morphological similarity among neighboring BMN types.

We selected the cut height 5 shown in Figure 6 – figure supplement 1A,B because it captures examples of both same and neighboring type clustering. For example, it captures a cluster of mostly BM-Taste neurons (Cluster 16), and neighboring BMN types, including those from the dorsal head (Cluster 14) or ventral head (Cluster 15).

Based on reviewer comments, we realized that the way we wrote the BMN matching section in the Results indicated more reliance on the NBLAST clustering than what was actually necessary, distorting the way we actually matched the BMNs. Therefore, we softend the first couple of sentences to place less emphasis on the importance of the NBLAST. We also indicated that the readers can find the resulting clusters at different cut heights, referring to Figure 6 – figure supplement 1A and Supplementary file 3. The first two sentences of the first paragraph in the Results section titled “Matching the reconstructed head BMNs with their bristles” now read:

The reconstructed BMN projections were next matched with their specific bristle populations. The projections were clustered based on morphological similarity using the NBLAST algorithm (example clustering at cut height 5 shown in Figure 6 – figure supplement 1A,B, Supplementary file 3, FlyWire.ai link 2) (Costa et al., 2016). Clusters could be assigned as BMN types based on their similarity to light microscopy images of BMNs known to innervate specific bristles.

The number of reconstructed BMNs is remarkably similar to what is expected based on bristle counts for each group except for lnOm. Why do you think there is such a large discrepancy there?

We believe that there is a discrepancy between the number of reconstructed BM-InOm neurons and the number expected based on InOm bristle counts because these bristle counts were based on few flies and these numbers appear to be variable. We did not further investigate the numbers of InOm bristles in this manuscript because we only needed an estimate of their numbers, given that there is over an order of magnitude difference in the eye bristles versus any other head bristle population. Therefore, we could relatively easily conclude that the head BMNs were related to the InOm bristles, based on their sheer numbers and their morphology.

Figure 6 - figure supplement 2N, please describe these panels better. Main text says the upper image is from lnOmBMN-LexA, but the figure legend doesn't agree.

We have added text to the figure legend that now makes the contents of panel 2N clear to the reader. Further, we now indicate in the figure legend for each panel, the method used to obtain the labeled neurons (i.e. fill, MCFO, driver), to avoid similar confusion for the other panels.

Figure 6 - figure supplement 4D. How frequently is there a mismatch between the number of BMNs for a given type across hemispheres?

Although the full reconstruction of the BMNs on both sides of the brain was beyond the scope of this work, the BMNs on both sides have since been reconstructed and annotated (Schlegal et al. 2023). We plan to provide more analysis of BMNs on both sides of the brain in a forthcoming manuscript. However, the BMN numbers tend to show agreement on both sides of the brain. The table below shows a comparison between the two sides:

Author response table 1.

Figures 6 and 7. It would be helpful to include a reference brain in all panels that show cluster morphology. Without landmarks there is nothing to anchor the eye to allow the reader to see the described differences in BMN projection zones and patterns.

While we apologize for not making this specific change, we have made revisions to other parts of the manuscript to better highlight the somatotopic organization among the BMNs (revisions described above). Please note that we now provide FlyWire.ai publicly available links that enable readers to view the BMN projections in 3D. They can also toggle a brain mesh on and off to provide spatial reference.

"BMN somatotopic map": It would be helpful to show or describe in more detail what the unique branch morphology for each zone is. It is quite difficult to appreciate, as the groups also have a lot of overlap. Would the unique regions that the BMN groups innervate be easier to see if you plotted presynaptic sites by group? I am left unsure about whether there is a somatotopic map here.

We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions. Please note that we did not examine the fine branch morphological differences between BMN types having overlapping projections. Showing these differences would require more extensive anatomical analysis that is beyond the scope of this work. For showing definitive somatotopy, we focused on the overt differences between BMNs innervating bristles at distant locations on the head.

Overall the strict adherence to the parallel model impacts the interpretation of the data. It would be helpful for the authors to discuss which aspects of the current study are consistent with the parallel model and which results are not consistent.

We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions.

Discussion:

"Circuits that elicit aimed grooming of specific head locations": In the previous paragraph you mention "BMN types innervating neighboring bristle populations have overlapping projections into zones that correspond roughly to the dorsal, ventral, and posterior head. The overlap is likely functionally significant, as cosine similarity analysis revealed that neighboring head BMN types have common postsynaptic partners. However, overlap between neighboring BMN types is only partial, as they show differing projections and postsynaptic connectivity." Then in this paragraph, you say, "How do the parallel-projecting head BMNs interface with postsynaptic neural circuits to elicit aimed grooming of specific head locations? Different evidence supports the hypothesis that the BMNs connect with parallel circuits that each elicit a different aimed grooming movement (Seeds et al., 2014)." The overlapping postsynaptic BMN connectivity seems in conflict with the claim that the circuits are parallel.

We apologize for this confusion. We now better describe this apparent discrepancy between our results and the parallel model of grooming behavior. We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions.

We have made additional changes to the manuscript:

(1) We added Supplementary file 2 that includes links for downloading the image stacks used to generate panels in Figure 1, Figure 2, Figure 3, Figure 4, and figure supplements for these figures. These image stacks are stored in the Brain Image Library (BIL). Rows in the spreadsheet correspond to each image stack. Columns provide information about each stack including: figure panels that each image stack contributed to, image stack title, DOI for each stack (link provides metadata for each stack and file download link), image stack file name, genotype of imaged fly, and information about image stack. References to this file have been made at different locations throughout the text and Figure legends. We also added a section on the BIL data in the Materials and methods entitled “Light microscopy image stack storage and availability”. Old Supplementary file 2 has been renamed Supplementary file 3.

(2) We added a new reference for FlyWire.ai (Dorkenwald et al. 2023) that was posted as a preprint during the revision of this manuscript.

Author Response

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

Reviewer #1 (recommendations for the authors):

Additional suggestions for improvement are noted below:

(1) Additional 1. Lns 261-262, as well as abstract: The term 'aerobic fermentation' is not accurate in the context of this manuscript. This terminology should be reserved for conditions where lactate production is observed under optimal aerobic conditions. This is not the case in this study. More lactate was observed in the agr mutant only when cells were grown under microaerobic conditions, where some level of fermentation would be expected to be active (esp. if nitrate is not provided in media).

We modified the text by deleting reference to the “aerobic” fermentation as suggested by the reviewer:

Line 93 (abstract): “Deletion of agr increased both respiration and aerobic fermentation but decreased ATP levels and growth, suggesting that Δagr cells assume a hyperactive metabolic state in response to reduced metabolic efficiency.”

Line 184: “Collectively, these data suggest that Δagr increases respiration and aerobic fermentation to compensate for low metabolic efficiency.”

(2) Additionally, the authors' statement, 'The tendency of Δagr cells to forgo the additional ATP yield from acetate production in favor of NAD+-generating lactate (23, 24) underscores the importance of redox balance in Δagr cells,' appears contradictory to the data presented in Fig 5, where the Δagr mutant demonstrates an approximately threefold increase in acetate production during exponential growth compared to the wild-type strain. A clarification or adjustment in the manuscript may be necessary to ensure consistency and accurate interpretation.

In glucose-fermenting S. aureus, pyruvate can serve as an electron acceptor, generating lactate from lactate dehydrogenases. Acetyl-CoA production proceeds via the pyruvate formate-lyase reaction, which converts pyruvate to formate rather than CO2 and thus does not consume oxidized NAD+. Thus, at a general level, the tendency of fermenting cells to forgo the additional ATP yield from acetate production in favor of NAD+-generating ethanol synthesis underscores the importance of redox balance when respiration is suboptimal. This is especially true for fermenting Δagr strains, as evidenced by increased lactate production compared to their relatively ATP replete wild-type parental strains. However, in the interest of clarity, we removed the sentence in question, because it is not necessary and potentially confusing, and because the additional context it requires would detract from the manuscript by disrupting its sense of narrative and brevity.

(3) Ln 277-285: There still are errors in how this paragraph is worded. What the authors stated in the 'response to the reviewers' (question 13) and the changes they made in the text are different. Here again, the response to question 13 suggested the following, "Collectively, these observations suggest that a surge in NADH production and reductive stress in the Δagr strain induces a burst in respiration, but levels of NADH are saturating, thereby driving fermentation in the presence of oxygen." That bit of it where the authors suggest that fermentation was activated because NADH was saturating is only true under microaerobic conditions and not under oxygen rich conditions.

Reviewer #1 (comment under Review): Data presented in Figure 5 suggest the opposite - a surge in NADH accumulation leading to a decrease in the NAD/NADH ratio, rather than a surge in the 'consumption' of NADH. Clarifying this point in the manuscript would ensure accurate representation of the findings.

Responses to Comments 3 and a comment in the Review have been combined.

Line 280: We thank the Reviewer for their attention to detail in picking up our error in response to question 13 related to the difference in the revised text and “response to reviewers”. We modified the text accordingly.

“Microaerobic conditions and “consumption”: We have modified the wording and fixed the error with respect to “consumption” as pointed out by the reviewer (strikethrough/underlined):

Line 285: “Collectively, these observations suggest that a surge in NADH consumption accumulation and reductive stress in the Δagr strain induces a burst in respiration, but levels of NADH are saturating, thereby driving fermentation under microaerobic conditions in the presence of oxygen.”

Reviewer #2 (recommendations for the authors):

(1) The authors are requested to revise 'we expected a lower NAD+/NADH' in line 280 to 'we expected a higher NAD+/NADH.' Additionally, what was the glucose concentration in TSB media?

NAD+/NADH: We thank the Reviewer for their attention to detail in picking up our error. Our responses to Reviewer 1, Comment 3 above addresses this issue.

Glucose: We modified the Methods as suggested.

eLife assessment

This important study outlines how the agr quorum sensing system in Staphylococcus aureus confers long-lived protection against oxidative stress, thereby linking bacterial metabolism to virulence in this pathogen. While the findings, which are supported by solid data, seem at first glance to contradict earlier findings that show increased fitness of agr mutants under oxidative stress, the core conclusions of the study are well-substantiated. The topic of the paper holds broad relevance to microbiologists, especially those focusing on host-pathogen interactions and bacterial responses to ROS.

Reviewer #1 (Public Review):

As a pathogen, S. aureus has evolved strategies to evade the host's immune system. It effectively remains 'under the radar' in the host until it reaches high population densities, at which point it triggers virulence mechanisms, enabling it to spread within the host. The agr quorum sensing system is central to this process, as it coordinates the pathogen's virulence in response to its cell density.

In this study, Podkowik and colleagues suggest that cells activating agr signaling also benefit from protection against H2O2 stress, whereas inactivation of agr increases cell death. The underlying cause of this lack of protection is tied to an ATP deficit in the agr mutant, leading to increased glucose consumption and NADH production, ultimately resulting in a redox imbalance. In response to this imbalance, the agr mutant increases respiration, resulting in the endogenous production of ROS which synergizes with H2O2 to mediate killing of the agr mutant. Suppressing respiration in the agr mutant restored protection against H2O2 stress.

Additionally, the authors establish that agr-dependent protection against oxidative stress is also linked to RNAIII activation, and the subsequent block of Rot translation. However, the specific protective genes regulated by Rot remain unidentified. Thus, according to the evidence provided, agr triggers intrinsic mechanisms that not only decrease harmful ROS production within the cell but also alleviate its detrimental effects.

Interestingly, these protective mechanisms are long-lived, and guard the cells against external oxidative stressors such as H2O2, even after the agr system has been 'turned off' in the population.

Reviewer #2 (Public Review):

In their study, Podkowik et al. elucidate the protective role of the accessory gene regulator (agr) system in Staphylococcus aureus against hydrogen peroxide (H2O2) stress. Their findings demonstrate that agr safeguards the bacterium by controlling the accumulation of reactive oxygen species (ROS), independent of agr activation kinetics. This protection is facilitated through a regulatory interaction between RNAIII and Rot, impacting virulence factor production and metabolism, thereby influencing ROS levels. Notably, the study highlights the remarkable adaptive capabilities of S. aureus conferred by agr. The protective effects of agr extend beyond the peak of agr transcription at high cell density, persisting even during the early log-phase. This indicates the significance of agr-mediated protection throughout the infection process. The absence of agr has profound consequences, as observed by the upregulation of respiration and fermentation genes, leading to increased ROS generation and subsequent cellular demise. Interestingly, the study also reveals divergent effects of agr deficiency on susceptibility to hydrogen peroxide compared to ciprofloxacin. While agr deficiency heightens vulnerability to H2O2, it also upregulates the expression of bsaA, countering the endogenous ROS induced by ciprofloxacin. These findings underscore the complex and context-dependent nature of agr-mediated protection. Furthermore, in vivo investigations using murine models provide valuable insights into the importance of agr in promoting S. aureus fitness, particularly in the context of neutrophil-mediated clearance, with notable emphasis on the pulmonary milieu. Overall, this study significantly advances our understanding of agr-mediated protection in S. aureus and sheds light on the sophisticated adaptive mechanisms employed by the bacterium to fortify itself against oxidative stress encountered during infection.

The conclusions drawn in this paper are generally well-supported by the data.

eLife assessment

This fundamental study substantially advances our understanding of sibling chimerism in marmosets by demonstrating that chimerism is limited to hematopoietic cells. The evidence supporting these findings is compelling, demonstrated through comprehensive analyses, including single-cell RNA-seq data from multiple individuals and tissues. The work will be of broad interest to many fields of biology.

Reviewer #1 (Public Review):

Summary:

Del Rosario et al characterized the extent and cell types of sibling chimerism in marmosets. To do so, they took advantage of the thousands of SNPs that are transcribed in single-nucleus RNA-seq (snRNA-seq) data to identify the sibling genotype of origin for all sequenced cells across 4 tissues (blood, liver, kidney, and brain) from many marmosets. They found that chimerism is prevalent and widespread across tissues in marmosets, which has previously been shown. However, their snRNA-seq approach allowed them to identify precisely which cells were of sibling origin, and which were not. In doing so they definitively show that sibling chimerism across tissues is limited to cells of myeloid and lymphoid lineages. The authors then focus on a large sample of microglia sequenced across many brain regions to quantify: (1) variation in chimerism across brain regions in the same individual, and (2) the relative importance of genetic vs. environmental context on microglia function/identity.

(1) Much like across different tissues in the same individual, they found that the proportion of chimeric microglia varies across brain regions collected from the same individuals (as well as differing from the proportion of sibling cells found in the blood of the same animals), suggesting that cells from different genetic backgrounds may differ in their recruitment and/or proliferation across regions and local tissue contexts, or that this may be linked to stochastic bottleneck effects during brain development.

(2) Their (admittedly smaller sample size) analyses of host-sibling gene expression showed that the local environment dominates genotype.

All told, this thoughtful and thorough manuscript accomplishes two important goals. First, it all but closes a previously open question on the extent and cell origins of sibling chimerism. Second, it sets the stage for using this unique model system to examine, in a natural context, how genetic variation in microglia may impact brain development, function, and disease.

The conclusions of this paper are well supported by the data, and the authors exert appropriate care when extrapolating their results that come from smaller samples. However, there are a few concerns that should be addressed.

The "modest correlation" mentioned in lines 170-172 does not take into account the uncertainty in estimates of each chimeric cell proportion (although the plot shows those estimates nicely). This is particularly important for the macrophages, which are far less abundant. Perhaps a more appropriate way to model this would be in a binomial framework (with a random effect for individuals of origin). Here, you could model the sibling identity of each macrophage as a function of the proportion of sibling-origin microglia and then directly estimate the percent variance explained.

A similar (albeit more complicated because of the number of regions being compared) approach could be applied to more rigorously quantify the variation in chimerism across brain regions (L198-215; Figure 4). This would also help to answer the question of whether specific brain regions are more "amenable" to microglia chimerism than others.

While the sample size is small, it would be exciting to see if any microglia eQTL are driven by sibling chimerism across the marmosets.

L290-292: The authors should propose ways in which they could test the two different explanations proposed in this paragraph. For instance, a simulation-based modeling approach could potentially differentiate more stochastic bottleneck effects from recruitment-like effects.

While intriguing, the gene expression comparison (Figure 5) is extremely underpowered. It would be helpful to clarify this and note the statistical thresholds used for identifying DEGs (the black points in the figure).

Reviewer #2 (Public Review):

Summary:

This manuscript reports a novel and quite important study of chimerism among common marmosets. As the authors discuss, it has been known for years that marmosets display chimerism across a number of tissues. However, as the authors also recognize, the scope and details of this chimerism have been controversial. Some prior publications have suggested that the chimerism only involves cells derived from hematopoietic stem cells, while other publications have suggested more cell types can also be chimeric, including a wide range of cell types present in multiple organs. The present authors address this question and several other important issues by using snRNA-seq to track the expression of host and sibling-derived mRNAs across multiple tissues and cell types. The results are clear and provide strong evidence that all chimeric cells are derived from hematopoietic cell lineages.

This work will have an impact on studies using marmosets to investigate various biological questions but will have the biggest impact on neuroscience and studies of cellular function within the brain. The demonstration that microglia and macrophages from different siblings from a single pregnancy, with different genomes expressing different transcriptomes, are commonly present within specific brain structures of a single individual opens a number of new opportunities to study microglia and macrophage function as well as interactions between microglia, macrophages, and other cell types.

Strengths:

The paper has a number of important strengths. This analysis employs the first unambiguous approach providing a clear answer to the question of whether sibling-derived chimeric cells arise only from hematopoietic lineages or from a wider array of embryonic sources. That is a long-standing open question and these snRNA-seq data seem to provide a clear answer, at least for the brain, liver, and kidney. In addition, the present authors investigate quantitative variation in chimeric cell proportions across several dimensions, comparing the proportion of chimeric cells across individual marmosets, across organs within an individual, and across brain regions within an individual. All these are significant questions, and the answers have important implications for multiple research areas. Marmosets are increasingly being used for a range of neuroscience studies, and a better understanding of the process that leads to the chimerism of microglia and macrophages in the marmoset brain is a valuable and timely contribution. But this work also has implications for other lines of study. Third, the snRNA-seq data will be made available through the Brain Initiative NeMO portal and the software used to quantify host vs. sibling cell proportions in different biosamples will be available through GitHub.

Weaknesses:

I find no major weaknesses, but several minor ones. First, the main text of the manuscript provides no information about the specific animals used in this study, other than sex. Some basic information about the sources of animals and their ages at the time of study would be useful within the main paper, even though more information will be available in the supplementary material. Second, it is not clear why only 14 pairs of animals were used for estimating the correlation of chimerism levels in microglia and macrophages. Is this lower than the total number of pairwise comparisons possible in order to avoid using non-independent samples? Some explanation would be helpful. Finally, I think more analysis of the consistency and variability of gene expression in microglia across different regions of the brain would be valuable. Are there genetic pathways expressed similarly in host and sibling microglia, regardless of region of the brain? Are there pathways that are consistently expressed differently in host vs sibling microglia regardless of brain region?

eLife assessment

This important study uses citizen science-generated diversity records and quantitative methodologies to improve species distribution estimates. This combination of fields, technologies, and methodologies is solid and improves species distribution maps formerly based solely on limited data gathered by scientists in traditional ways/surveys. This paper will be of interest to researchers interested in citizen science and new sources of big data in biodiversity, and to biogeographers exploring the distributions of species on the planet.

Reviewer #1 (Public Review):

Summary:<br /> The study presented by Atsumi et al. is about using smartphone-driven, community-sourced data to enhance biodiversity monitoring. The idea is to leverage the widespread use of smartphones to gather data from the community quickly, contributing to a more comprehensive understanding of biodiversity. The authors discuss the importance of ecosystem services linked to biodiversity and the threats posed by human activities. It emphasizes the need for comprehensive biodiversity data to implement the Kunming-Montreal Global Biodiversity Framework. The 'Biome' mobile app, launched in Japan, uses species identification algorithms and gamification to gather over 6 million observations since 2019. While community-sourced data may have biases, incorporating it into Species Distribution Models (SDMs) improves accuracy, especially for endangered species. The app covers urban-natural gradients uniformly, enhancing traditional survey data biased towards natural areas. Combining these sources provides valuable insights into species distributions for conservation, protected area designation, and ecosystem service assessment.

Strengths:

The use of a smartphone app ('Biome') for community-driven species occurrence data collection represents an innovative and inclusive approach to biodiversity monitoring, leveraging the widespread use of smartphones. The app has successfully accumulated a large volume of species occurrence data since its launch in 2019, showcasing its effectiveness in rapidly gathering information from diverse locations. Despite challenges with certain taxa, the study highlights high species identification accuracy, especially for birds, reptiles, mammals, and amphibians, making the 'Biome' app a reliable tool for species observation. The integration of community-sourced data into Species Distribution Models (SDMs) improves the accuracy of predicting species distributions. This has implications for conservation planning, including the designation of protected areas and assessment of ecosystem services. The rapid accumulation of data and advancements in machine learning methods open up opportunities for conducting time-series analyses, contributing to the understanding of ecosystem stability and interaction strength over time. The study emphasizes the collaborative nature of the platform, fostering collaboration among diverse stakeholders, including local communities, private companies, and government agencies. This inclusive approach is essential for effective biodiversity assessment and decision-making. The platform's engagement with various stakeholders, including local communities, supports biodiversity assessment, management planning, and informed decision-making. Additionally, the app's role in fostering nature-positive awareness in society is highlighted as a significant contribution to creating a sustainable society.

Weaknesses:

While the studies make significant contributions to biodiversity monitoring, they also have some weaknesses. Firstly, relying on smartphone-driven, community-sourced data may introduce spatial and taxonomic biases. The 'Biome' app, for example, showed lower accuracy for certain taxa like seed plants, molluscs, and fishes, potentially impacting the reliability of the gathered data. Furthermore, the effectiveness of Species Distribution Models (SDMs) relies on the assumption that biases in community-sourced data can be adequately accounted for. The unique distribution patterns of the 'Biome' data, covering urban-natural gradients uniformly, might not fully represent the diversity of certain ecosystems, potentially leading to inaccuracies in the models. Moreover, the divergence in data distribution patterns along environmental gradients between 'Biome' data and traditional survey data raises concerns. The app data shows a more uniform distribution across natural-urban gradients, while traditional data is biased towards natural areas. This discrepancy may impact the representation of certain ecosystems and influence the accuracy of Species Distribution Models (SDMs). While the integration of 'Biome' data into SDMs improves accuracy, the study notes that controlling the sampling efforts is crucial. Spatially-biased sampling efforts in community-sourced data need careful consideration, and efforts to control biases are essential for reliable predictions.

Reviewer #3 (Public Review):

This study explores sensory prediction errors in the sensory cortex. It focuses on the question of how these signals are shaped by non-hierarchical interactions, specifically multimodal signals arising from same-level cortical areas. The authors used 2-photon imaging of mouse auditory cortex in head-fixed mice that were presented with sounds and/or visual stimuli while moving on a ball. First, responses to pure tones, visual stimuli, and movement onset were characterized. Then, the authors made the running speed of the mouse predictive of sound intensity and/or visual flow. Mismatches were created through the interruption of sound and/or visual flow for 1 second while the animal moved, disrupting the expected sensory signal given the speed of movement. As a control, the same sensory stimuli triggered by the animal's movement were presented to the animal decoupled from its movement. The authors suggest that auditory responses to the unpredicted silence reflect mismatch responses. That these mismatch responses were enhanced when the visual flow was congruently interrupted, indicates the cross-modal influence of prediction error signals.

This study's strengths are the relevance of the question and the design of the experiment. The authors are experts in the techniques used. The analysis explores neither the full power of the experimental design nor the population activity recorded with 2-photon, leaving open the question of to what extent what the authors call mismatch responses are not sensory responses to sound interruption. The auditory system is sensitive to transitions and indeed responses to the interruption of the sound are similar in quality, if not quantity, in the predictive and the control situation.

Reviewer #2 (Public Review):

In this study, Solyga and Keller use multimodal closed-loop paradigms in conjunction with multiphoton imaging of cortical responses to assess whether and how sensorimotor prediction errors in one modality influence the computation of prediction errors in another modality. Their work addresses an important open question pertaining to the relevance of non-hierarchical (lateral cortico-cortical) interactions in predictive processing within the neocortex.

Specifically, they monitor GCaMP6f responses of layer 2/3 neurons in the auditory cortex of head-fixed mice engaged in VR paradigms where running is coupled to auditory, visual, or audio-visual sensory feedback. The authors find strong auditory and motor responses in the auditory cortex, as well as weak responses to visual stimuli. Further, in agreement with previous work, they find that the auditory cortex responds to audiomotor mismatches in a manner similar to that observed in visual cortex for visuomotor mismatches. Most importantly, while visuomotor mismatches by themselves do not trigger significant responses in the auditory cortex, simultaneous coupling of audio-visual inputs to movement non-linearly enhances mismatch responses in the auditory cortex.

Their results thus suggest that prediction errors within a given sensory modality are non-trivially influenced by prediction errors from another modality. These findings are novel, interesting, and important, especially in the context of understanding the role of lateral cortico-cortical interactions and in outlining predictive processing as a general theory of cortical function.

In its current form, the manuscript lacks sufficient description of methodological details pertaining to the closed-loop training and the overall experimental design. In several scenarios, while the results per se are convincing and interesting, their exact interpretation is challenging given the uncertainty about the actual experimental protocols (more on this below). Second, the authors are laser-focused on sensorimotor errors (mismatch responses) and focus almost exclusively on what happens when stimuli deviate from the animal's expectations.

While the authors consistently report strong running-onset responses (during open-loop) in the auditory cortex in both auditory and visual versions of the task, they do not discuss their interpretation in the different task settings (see below), nor do they analyze how these responses change during closed-loop i.e. when predictions align with sensory evidence.

However, I believe all my concerns can be easily addressed by additional analyses and incorporation of methodological details in the text.

Major concerns:

(1) Insufficient analysis of audiomotor mismatches in the auditory cortex:

Lack of analysis of the dependence of audiomotor mismatches on the running speed: it would be helpful if the authors could clarify whether the observed audiomotor mismatch responses are just binary or scale with the degree of mismatch (i.e. running speed). Along the same lines, how should one interpret the lack of dependence of the playback halt responses on the running speed? Shouldn't we expect that during playback, the responses of mismatch neurons scale with the running speed?

Slow temporal dynamics of audiomotor mismatches: despite the transient nature of the mismatches (1s), auditory mismatch responses last for several seconds. They appear significantly slower than previous reports for analogous visuomotor mismatches in V1 (by the same group, using the same methods) and even in comparison to the multimodal mismatches within this study (Figure 4C). What might explain this sustained activity? Is it due to a sustained change in the animal's running in response to the auditory mismatch?

(2) Insufficient analysis and discussion of running onset responses during audiomotor sessions: The authors report strong running-onset responses during open-loop in identified mismatch neurons. They also highlight that these responses are in agreement with their model of subtractive prediction error, which relies on subtracting the bottom-up sensory evidence from top-down motor-related predictions. I agree, and, thus, assume that running-onset responses during the open loop in identified 'mismatch' neurons reflect the motor-related predictions of sensory input that the animal has learned to expect. If this is true, one would expect that such running-onset responses should dampen during closed-loop, when sensory evidence matches expectations and therefore cancels out this prediction. It would be nice if the authors test this explicitly by analyzing the running-related activity of the same neurons during closed-loop sessions.

(3) Ambiguity in the interpretation of responses in visuomotor sessions.

Unlike for auditory stimuli, the authors show that there are no obvious responses to visuomotor mismatches or playback halts in the auditory cortex. However, the interpretation of these results is somewhat complicated by the uncertainty related to the training history of these mice. Were these mice exclusively trained on the visuomotor version of the task or also on the auditory version? I could not find this info in the Methods. From the legend for Figure 4D, it appears that the same mice were trained on all versions of the task. Is this the case? If yes, what was the training sequence? Were the mice first trained on the auditory and then the visual version?

The training history of the animals is important to outline the nature of the predictions and mismatch responses that one should expect to observe in the auditory cortex during visuomotor sessions. Depending on whether the mice in Figure 3 were trained on visual only or both visual and auditory tasks, the open-loop running onset responses may have different interpretations.

a) If the mice were trained only on the visual task, how should one interpret the strong running onset responses in the auditory cortex? Are these sensorimotor predictions (presumably of visual stimuli) that are conveyed to the auditory cortex? If so, what may be their role?

b) If the mice were also trained on the auditory version, then a potential explanation of the running-onset responses is that they are audiomotor predictions lingering from the previously learned sensorimotor coupling. In this case, one should expect that in the visual version of the task, these audiomotor predictions (within the auditory cortex) would not get canceled out even during the closed-loop periods. In other words, mismatch neurons should constantly be in an error state (more active) in the closed-loop visuomotor task. Is this the case?

If so, how should one then interpret the lack of a 'visuomotor mismatch' aligned to the visual halts, over and above this background of continuous errors?<br /> As such, the manuscript would benefit from clearly stating in the main text the experimental conditions such as training history, and from discussing the relevant possible interpretations of the responses.

(4) Ambiguity in the interpretation of responses in multimodal versus unimodal sessions.

The authors show that multimodal (auditory + visual) mismatches trigger stronger responses than unimodal mismatches presented in isolation (auditory only or visual only). Further, they find that even though visual mismatches by themselves do not evoke a significant response, co-presentation of visual and auditory stimuli non-linearly augments the mismatch responses suggesting the presence of non-hierarchical interactions between various predictive processing streams.

In my opinion, this is an important result, but its interpretation is nuanced given insufficient details about the experimental design. It appears that responses to unimodal mismatches are obtained from sessions in which only one stimulus is presented (unimodal closed-loop sessions). Is this actually the case? An alternative and perhaps cleaner experimental design would be to create unimodal mismatches within a multimodal closed-loop session while keeping the other stimulus still coupled to the movement.

Given the current experiment design (if my assumption is correct), it is unclear if the multimodal potentiation of mismatch responses is a consequence of nonlinear interactions between prediction/error signals exchanged across visual and auditory modalities. Alternatively, could this result from providing visual stimuli (coupled or uncoupled to movement) on top of the auditory stimuli? If it is the latter, would the observed results still be evidence of non-hierarchical interactions between various predictive processing streams?

Along the same lines, it would be interesting to analyze how the coupling of visual as well as auditory stimuli to movement influences responses in the auditory cortex in close-loop in comparison to auditory-only sessions. Also, do running onset responses change in open-loop in multimodal vs. unimodal playback sessions?

Minor concerns and comments:

(1) Rapid learning of audiomotor mismatches: It is interesting that auditory mismatches are present even on day 1 and do not appear to get stronger with learning (same on day 2). The authors comment that this could be because the coupling is learned rapidly (line 110). How does this compare to the rate at which visuomotor coupling is learned? Is this rapid learning also observable in the animal's behavior i.e. is there a change in running speed in response to the mismatch?

(2) The authors should clarify whether the sound and running onset responses of the auditory mismatch neurons in Figure 2E were acquired during open-loop. This is most likely the case, but explicitly stating it would be helpful.

(3) In lines 87-88, the authors state 'Visual responses also appeared overall similar but with a small increase in strength during running ...'. This statement would benefit from clarification. From Figure S1 it appears that when the animal is sitting there are no visual responses in the auditory cortex. But when the animal is moving, small positive responses are present. Are these actually 'visual' responses - perhaps a visual prediction sent from the visual cortex to the auditory cortex that is gated by movement? If so, are they modulated by features of visual stimuli eg. contrast, intensity? Or, do these responses simply reflect motor-related activity (running)? Would they be present to the same extent in the same neurons even in the dark?

(4) The authors comment in the text (lines 106-107) about cessation of sound amplitude during audiomotor mismatches as being analogous to halting of visual flow in visuomotor mismatches. However, sound amplitude versus visual flow are quite different in nature. In the visuomotor paradigm, the amount of visual stimulation (photons per unit time) does not necessarily change systematically with running speed. Whereas, in the audiomotor paradigm, the SNR of the stimulus itself changes with running speed which may impact the accuracy of predictions. On a broader note, under natural settings, while the visual flow is coupled to movement, sound amplitude may vary more idiosyncratically with movement.

Perhaps such differences might explain why unlike in the case of visual cortex experiments, running speed does not affect the strength of playback responses in the auditory cortex.

Reviewer #1 (Public Review):

Summary:

The manuscript presents a short report investigating mismatch responses in the auditory cortex, following previous studies focused on the visual cortex. By correlating the mouse locomotion speed with acoustic feedback levels, the authors demonstrate excitatory responses in a subset of neurons to halts in expected acoustic feedback. They show a lack of responses to mismatch in the visual modality. A subset of neurons show enhanced mismatch responses when both auditory and visual modalities are coupled to the animal's locomotion.

While the study is well-designed and addresses a timely question, several concerns exist regarding the quantification of animal behavior, potential alternative explanations for recorded signals, correlation between excitatory responses and animal velocity, discrepancies in reported values, and clarity regarding the identity of certain neurons.

Strengths:

(1) Well-designed study addressing a timely question in the field.

(2) Successful transition from previous work focused on the visual cortex to the auditory cortex, demonstrating generic principles in mismatch responses.

(3) The correlation between mouse locomotion speed and acoustic feedback levels provides evidence for a prediction signal in the auditory cortex.

(4) Coupling of visual and auditory feedback shows putative multimodal integration in the auditory cortex.

Weaknesses:

(1) Lack of quantification of animal behavior upon mismatches, potentially leading to alternative interpretations of recorded signals.

(2) Unclear correlation between excitatory responses and animal velocity during halts, particularly in closed-loop versus playback conditions.

(3) Discrepancies in reported values in a few figure panels raise questions about data consistency and interpretation.

(4) Ambiguity regarding the identity of the [AM+VM] MM neurons.

eLife assessment

This study provides important findings on the modulation of cortical neuronal responses to sensory stimuli by motor-driven predictive signals. The study is methodologically sound and well-designed. The data, as analysed, provide incomplete support for the conclusion that audiomotor mismatch responses are observed in the auditory cortex and that these are strongly modulated by cross-modal signals.

Author Response

eLife assessment

This study demonstrates mRNA-specific regulation of translation by subunits of the eukaryotic initiation factor complex 3 (eIF3) using convincing methods, data, and analyses. The investigations have generated important information that will be of interest to biologists studying translation regulation. However, the physiological significance of the gene expression changes that were observed is not clear.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Herrmannova et al explore changes in translation upon individual depletion of three subunits of the eIF3 complex (d, e, and f) in mammalian cells. The authors provide a detailed analysis of regulated transcripts, followed by validation by RT-qPCR and/or Western blot of targets of interest, as well as GO and KKEG pathway analysis. The authors confirm prior observations that eIF3, despite being a general translation initiation factor, functions in mRNA-specific regulation, and that eIF3 is important for translation re-initiation. They show that the global effects of eIF3e and eIF3d depletion on translation and cell growth are concordant. Their results support and extend previous reports suggesting that both factors control the translation of 5'TOP mRNAs. Interestingly, they identify MAPK pathway components as a group of targets coordinately regulated by eIF3 d/e. The authors also discuss discrepancies with other reports analyzing eIF3e function.

We would like to note that the first sentence contains a typo; the correct expression is: “…of three subunits of the eIF3 complex (d, e, and h) in mammalian cells”.

Strengths:

Altogether, a solid analysis of eIF3 d/e/h-mediated translation regulation of specific transcripts. The data will be useful for scientists working in the Translation field.

Weaknesses:

The authors could have explored in more detail some of their novel observations, as well as their impact on cell behavior.

Many experiments are on-going in this direction. The original plan was to map all the effects in general and in as much detail as possible to select a few of them for future long-term projects.

Reviewer #2 (Public Review):

Summary:

mRNA translation regulation permits cells to rapidly adapt to diverse stimuli by fine-tuning gene expression. Specifically, the 13-subunit eukaryotic initiation factor 3 (eIF3) complex is critical for translation initiation as it aids in 48S PIC assembly to allow for ribosome scanning. In addition, eIF3 has been shown to drive transcript-specific translation by binding mRNA 5' cap structures through the eIF3d subunit. Dysregulation of eIF3 has been implicated in oncogenesis, however the precise eIF3 subunit contributions are unclear. Here, Herrmannová et al. aim to investigate how eIF3 subcomplexes, generated by knockdown (KD) of either eIF3e, eIF3d, or eIF3h, affect the global translatome. Using Ribo-seq and RNA-seq, the authors identified a large number of genes that exhibit altered translation efficiency upon eIF3d/e KD, while translation defects upon eIF3h KD were mild. eIF3d/e KD share multiple dysregulated transcripts, perhaps due to both subcomplexes lacking eIF3d. Both eIF3d/e KD increase the translation efficiency (TE) of transcripts encoding lysosomal, ER, and ribosomal proteins. This suggests a role of eIF3 in ribosome biogenesis and protein quality control. Many transcripts encoding ribosomal proteins harbor a TOP motif, and eIF3d KD and eIF3e KD cells exhibit a striking induction of these TOP-modified transcripts. On the other hand, eIF3d KD and eIF3e KD lead to a reduction of MAPK/ERK pathway proteins. Despite this downregulation, eIF3d KD and eIF3e KD activate MAPK/ERK signaling as ERK1/2 and c-Jun phosphorylation were induced. Finally, in all three knockdowns, MDM2 and ATF4 protein levels are reduced. This is notable because MDM2 and ATF4 both contain short uORFs upstream of the start codon, and further support a role of eIF3 in reinitiation. Altogether, Herrmannová et al. have gained key insights into precise eIF3-mediated translational control as it relates to key signaling pathways implicated in cancer.

Strengths:

The authors have provided a comprehensive set of data to analyze RNA and ribosome footprinting upon perturbation of eIF3d, eIF3e, and eIF3h. As described above in the summary, these data present many interesting starting points for understanding additional roles of the eIF3 complex and specific subunits in translational control.

Weaknesses:

• The differences between eIF3e and eIF3d knockdown are difficult to reconcile, especially since eIF3e knockdown leads to a reduction in eIF3d levels.

We agree and discuss this problem thoroughly in the corresponding section of our study.

• The paper would be strengthened by experiments directly testing what RNA determinants allow for transcript-specific translation regulation by the eIF3 complex. This would allow the paper to be less descriptive.

We carried out bioinformatic analysis dealing with specific RNA determinants that is presented as the last chapter of our study. A detailed, transcript-specific analysis of these determinants is underway, however, we consider them beyond the scope for this article.

• The paper would have more biological relevance if eIF3 subunits were perturbed to mimic naturally occurring situations where eIF3 is dysregulated. For example, eIF3e is aberrantly upregulated in certain cancers, and therefore an overexpression and profiling experiment would have been more relevant than a knockdown experiment.

This is indeed true and so far we have generated several stable cell lines individually overexpressing selected eIF3 subunits implicated in the observed cancer phenotypes. However, this is a completely different project of one of our PhD students, which will be published as a comprehensive study when completed.

Reviewer #3 (Public Review):

Summary:

In this article, Hermannova et al catalog the changes in ribosome association with mRNAs when the eukaryotic translation initiation factor 3 is disrupted by knocking down subunits of the multisubunit protein. They find that RNAs relying on TOP motifs for translation, such as ribosomal protein RNAs, and RNAs encoding proteins that modify other proteins in the ER or components of the lysosome are upregulated. In contrast, proteins encoding components of MAP kinase cascades are downregulated when subunits of eIF3 are knocked down.

Strengths:

The authors use ribosome profiling of well-characterized mutants lacking subunits of eIF3 and assess the changes in translation that take place. They supplement the ribosome association studies with western blotting to determine protein level changes of affected transcripts. They analyze what is being encoded by the transcripts undergoing translation changes, which is important for understanding more broadly how translation initiation factor levels affect cancer cell translatomes.

Weaknesses:

(1) The data are presented as a catalog of effects, and the paper would be strengthened if there were a clear model tying the various effects together or linking individual subunit knockdown to cancerous phenotypes. It is unclear what the hypothesis is for cells having more MAPK activity with less of the MAPK proteins being translated, so the main findings of the paper become observational without context.

As the signaling pathways are very complex and there is a frequent crosstalk among them (c-Jun can be activated by the ERK pathway as well as the JNK pathway, activated ERKs can phosphorylate many different transcription factors, etc.), we opted not to investigate the reported results any further in this study. As mentioned above, we have several ongoing, long-term projects aiming to elucidate the consequences of the observed changes in protein levels as well as in the phosphorylation status of the MAPK pathway constituents. The take home message of the present study is that eIF3 subunits (d and e) have control over the expression of many proteins involved in the MAPK/ERK pathway and that there is an independent effect (already present in the downregulation of eIF3h, which does not affect the MAPK protein expression) that leads to activation of the ERK pathway, which may be a direct consequence of compromised eIF3 function in general.

(2) The conclusions drawn are presented as very generalized other than in the last paragraph, but the experiments were only done in Hela cells. Since conclusions are being made about how translation changes affect MAP kinase signaling and there is mention in the abstract that dysregulation of these subunits is observed in cancer, at least one other cell line would need to be analyzed to provide evidence that the effects of subunit knockdown aren't cell-line specific.

There are several notes emphasizing that the data presented in this study were obtained only in HeLa cells. We agree that further research in other cell lines will be needed to confirm that what we observed is a general phenomenon. Nonetheless, as noted in the discussion, other reports have already been published strongly indicating that this phenomenon is not unique to HeLa cells (Li et al., 2021, PMID:34520790, HTR-8/SVneo cells). We will review our conclusions and further clarify that our results so far only apply to Hela cells.

(3) It is also unclear how replicates were performed and how many replicates were performed for several experiments. Biological replicates are mentioned, but what the authors did for biological replicates isn't defined and the description of the collection of cells for polysome/ribosome footprint/RNA seq samples makes it unclear whether the "biological replicates" are samples from separate transfections (true biological replicates) or different aliquots or wells from a single transfection (technical replicates) being run over a separate gradient. If using technical replicates, the data comparing the effects of knocking down D vs E vs H subunits are substantially weakened because subunit-specific differences could be the result of non-specific events that occurred in a transfection. It's also notable that while the pooled siRNAs will increase the potency of knockdown, it is possible that one or more of the siRNAs could have off-target effects, and analyzing individual siRNAs would be better for ensuring effects are specific.

We can reassure this reviewer that our Ribo-seq and RNA-Seq libraries were prepared from true biological replicates, grown, and transfected at different times. In fact, for each biological replicate, we used a new aliquot of cells from cryostock from the same batch and transfected the cells with the same passage number only. Multiple biological replicates were grown and all underwent a series of control experiments (polysomes, qPCR, western blot) as described in the article. Based on the results, 3 samples were selected for Ribo-Seq library preparation and 4 for RNA-Seq. We decided to add a fourth replicate for RNA-Seq to increase the data robustness, because RNA-Seq is used to normalize FPs to calculate TE, which was our main metric analyzed in this article.

As for the usage of the siRNA pool from Dharmacon/Horizon – our current article builds on our previous studies (Wagner et al. 2014 PMID: 24912683; Wagner et al. 2016 PMID: 27924037 and Herrmannová et al. 2020 PMID: 31863585), where we thoroughly characterized the effects of downregulation of individual eIF3 subunits on the growth, translation, composition and stability of eIF3 complex and on the 43S preinitiation complex assembly and subsequent mRNA recruitment. In all of these studies, we used the same siRNAs pools, the same cells and the same transfection protocol; therefore, we are convinced that our results are as coherent and reproducible as can possibly be. We have never noticed any off-target effects. Moreover, the ON-TARGETplus siRNA technology we employed uses a patented modification pattern that reduces the incidence of off-targets by up to 90% compared to unmodified siRNA (see the supplier's website for more information).

(4) Many of the changes in protein levels reported by Western are subtle. Data from all western blots making claims of quantitative differences should really be quantified relative to nontreated over-loading control or total protein quantified from the gel, and presented with a degree of error from biological replicates to make conclusions about differences in protein levels between samples.

Generally speaking, we agree with the reviewer’s opinion. In the original version of our study, we felt that it was not necessary to perform a quantification analysis to support our conclusions as it was not important whether a given protein was downregulated to, for example, 60% or 70%, as long as its amount was visibly reduced. The main message resided in the general trend, i.e. that the whole pathway is affected in a similar way. Nevertheless, in order to properly address this criticism, we will provide quantifications in the revised paper.

Reviewer #3 (Public Review):

Summary:

In this article, Hermannova et al catalog the changes in ribosome association with mRNAs when the eukaryotic translation initiation factor 3 is disrupted by knocking down subunits of the multisubunit protein. They find that RNAs relying on TOP motifs for translation, such as ribosomal protein RNAs, and RNAs encoding proteins that modify other proteins in the ER or components of the lysosome are upregulated. In contrast, proteins encoding components of MAP kinase cascades are downregulated when subunits of eIF3 are knocked down.

Strengths:

The authors use ribosome profiling of well-characterized mutants lacking subunits of eIF3 and assess the changes in translation that take place. They supplement the ribosome association studies with western blotting to determine protein level changes of affected transcripts. They analyze what is being encoded by the transcripts undergoing translation changes, which is important for understanding more broadly how translation initiation factor levels affect cancer cell translatomes.

Weaknesses:

(1) The data are presented as a catalog of effects, and the paper would be strengthened if there were a clear model tying the various effects together or linking individual subunit knockdown to cancerous phenotypes. It is unclear what the hypothesis is for cells having more MAPK activity with less of the MAPK proteins being translated, so the main findings of the paper become observational without context.

(2) The conclusions drawn are presented as very generalized other than in the last paragraph, but the experiments were only done in Hela cells. Since conclusions are being made about how translation changes affect MAP kinase signaling and there is mention in the abstract that dysregulation of these subunits is observed in cancer, at least one other cell line would need to be analyzed to provide evidence that the effects of subunit knockdown aren't cell-line specific.

(3) It is also unclear how replicates were performed and how many replicates were performed for several experiments. Biological replicates are mentioned, but what the authors did for biological replicates isn't defined and the description of the collection of cells for polysome/ribosome footprint/RNA seq samples makes it unclear whether the "biological replicates" are samples from separate transfections (true biological replicates) or different aliquots or wells from a single transfection (technical replicates) being run over a separate gradient. If using technical replicates, the data comparing the effects of knocking down D vs E vs H subunits are substantially weakened because subunit-specific differences could be the result of non-specific events that occurred in a transfection. It's also notable that while the pooled siRNAs will increase the potency of knockdown, it is possible that one or more of the siRNAs could have off-target effects, and analyzing individual siRNAs would be better for ensuring effects are specific.

(4) Many of the changes in protein levels reported by Western are subtle. Data from all western blots making claims of quantitative differences should really be quantified relative to nontreated over-loading control or total protein quantified from the gel, and presented with a degree of error from biological replicates to make conclusions about differences in protein levels between samples.

eLife assessment

This study demonstrates mRNA-specific regulation of translation by subunits of the eukaryotic initiation factor complex 3 (eIF3) using convincing methods, data, and analyses. The investigations have generated important information that will be of interest to biologists studying translation regulation. However, the physiological significance of the gene expression changes that were observed is not clear.

Reviewer #1 (Public Review):

Summary:

In this manuscript, Herrmannova et al explore changes in translation upon individual depletion of three subunits of the eIF3 complex (d, e, and f) in mammalian cells. The authors provide a detailed analysis of regulated transcripts, followed by validation by RT-qPCR and/or Western blot of targets of interest, as well as GO and KKEG pathway analysis. The authors confirm prior observations that eIF3, despite being a general translation initiation factor, functions in mRNA-specific regulation, and that eIF3 is important for translation re-initiation. They show that the global effects of eIF3e and eIF3d depletion on translation and cell growth are concordant. Their results support and extend previous reports suggesting that both factors control the translation of 5'TOP mRNAs. Interestingly, they identify MAPK pathway components as a group of targets coordinately regulated by eIF3 d/e. The authors also discuss discrepancies with other reports analyzing eIF3e function.

Strengths:

Altogether, a solid analysis of eIF3 d/e/h-mediated translation regulation of specific transcripts. The data will be useful for scientists working in the Translation field.

Weaknesses:

The authors could have explored in more detail some of their novel observations, as well as their impact on cell behavior.

Reviewer #2 (Public Review):

Summary:

mRNA translation regulation permits cells to rapidly adapt to diverse stimuli by fine-tuning gene expression. Specifically, the 13-subunit eukaryotic initiation factor 3 (eIF3) complex is critical for translation initiation as it aids in 48S PIC assembly to allow for ribosome scanning. In addition, eIF3 has been shown to drive transcript-specific translation by binding mRNA 5' cap structures through the eIF3d subunit. Dysregulation of eIF3 has been implicated in oncogenesis, however the precise eIF3 subunit contributions are unclear. Here, Herrmannová et al. aim to investigate how eIF3 subcomplexes, generated by knockdown (KD) of either eIF3e, eIF3d, or eIF3h, affect the global translatome. Using Ribo-seq and RNA-seq, the authors identified a large number of genes that exhibit altered translation efficiency upon eIF3d/e KD, while translation defects upon eIF3h KD were mild. eIF3d/e KD share multiple dysregulated transcripts, perhaps due to both subcomplexes lacking eIF3d. Both eIF3d/e KD increase the translation efficiency (TE) of transcripts encoding lysosomal, ER, and ribosomal proteins. This suggests a role of eIF3 in ribosome biogenesis and protein quality control. Many transcripts encoding ribosomal proteins harbor a TOP motif, and eIF3d KD and eIF3e KD cells exhibit a striking induction of these TOP-modified transcripts. On the other hand, eIF3d KD and eIF3e KD lead to a reduction of MAPK/ERK pathway proteins. Despite this downregulation, eIF3d KD and eIF3e KD activate MAPK/ERK signaling as ERK1/2 and c-Jun phosphorylation were induced. Finally, in all three knockdowns, MDM2 and ATF4 protein levels are reduced. This is notable because MDM2 and ATF4 both contain short uORFs upstream of the start codon, and further support a role of eIF3 in reinitiation. Altogether, Herrmannová et al. have gained key insights into precise eIF3-mediated translational control as it relates to key signaling pathways implicated in cancer.

Strengths:

The authors have provided a comprehensive set of data to analyze RNA and ribosome footprinting upon perturbation of eIF3d, eIF3e, and eIF3h. As described above in the summary, these data present many interesting starting points for understanding additional roles of the eIF3 complex and specific subunits in translational control.

Weaknesses:

- The differences between eIF3e and eIF3d knockdown are difficult to reconcile, especially since eIF3e knockdown leads to a reduction in eIF3d levels.

- The paper would be strengthened by experiments directly testing what RNA determinants allow for transcript-specific translation regulation by the eIF3 complex. This would allow the paper to be less descriptive.

- The paper would have more biological relevance if eIF3 subunits were perturbed to mimic naturally occurring situations where eIF3 is dysregulated. For example, eIF3e is aberrantly upregulated in certain cancers, and therefore an overexpression and profiling experiment would have been more relevant than a knockdown experiment.

Author Response

Public Reviews:

Reviewer #1 (Public Review):

The authors observed a decline in autophagy and proteasome activity in the context of Milton knockdown. Through proteomic analysis, they identified an increase in the protein levels of eIF2β, subsequently pinpointing a novel interaction within eIF subunits where eIF2β contributes to the reduction of eIF2α phosphorylation levels. Furthermore, they demonstrated that overexpression of eIF2β suppresses autophagy and leads to diminished motor function. It was also shown that in a heterozygous mutant background of eIF2β, Milton knockdown could be rescued. This work represents a novel and significant contribution to the field, revealing for the first time that the loss of mitochondria from axons can lead to impaired autophagy function via eIF2β, potentially influencing the acceleration of aging. To further support the authors' claims, several improvements are necessary, particularly in the methods of quantification and the points that should be demonstrated quantitatively. It is crucial to investigate the correlation between aging and the proteins eIF2β and eIF2α.

Thank you so much for your comments. We will further investigate the correlation between aging and the proteins eIF2β and eIF2α and include the results in the revised version.

Reviewer #2 (Public Review):

In the manuscript, the authors aimed to elucidate the molecular mechanism that explains neurodegeneration caused by the depletion of axonal mitochondria. In Drosophila, starting with siRNA depletion of Milton and Miro, the authors attempted to demonstrate that the depletion of axonal mitochondria induces the defect in autophagy. From proteome analyses, the authors hypothesized that autophagy is impacted by the abundance of eIF2β and the phosphorylation of eIF2α. The authors followed up the proteome analyses by testing the effects of eIF2β overexpression and depletion on autophagy. With the results from those experiments, the authors proposed a novel role of eIF2β in proteostasis that underlies neurodegeneration derived from the depletion of axonal mitochondria.

The manuscript has several weaknesses. The reader should take extra care while reading this manuscript and when acknowledging the findings and the model in this manuscript.

The defect in autophagy by the depletion of axonal mitochondria is one of the main claims in the paper. The authors should work more on describing their results of LC3-II/LC3-I ratio, as there are multiple ways to interpret the LC3 blotting for the autophagy assessment. Lysosomal defects result in the accumulation of LC3-II thus the LC3-II/LC3-I ratio gets higher. On the other hand, the defect in the early steps of autophagosome formation could result in a lower LC3-II/LC3-I ratio. From the results of the actual blotting, the LC3-I abundance is the source of the major difference for all conditions (Milton RNAi and eIF2β overexpression and depletion). In the text, the authors simply state the observation of their LC3 blotting. The manuscript lacks an explanation of how to evaluate the LC3-II/LC3-I ratio. Also, the manuscript lacks an elaboration on what the results of the LC3 blotting indicate about the state of autophagy by the depletion of axonal mitochondria.

We agree with the reviewer that multiple ways exist to interpret the LC3 blotting for the autophagy assessment. Thus, we analyzed the levels of p62, an autophagy substrate, and found that milton knockdown caused elevated levels of p62 (Figure 2B). Together, these results suggest that autophagic degradation is lowered.

Another main point of the paper is the up-regulation of eIF2β by depleting the axonal mitochondria leads to the proteostasis crisis. This claim is formed by the findings from the proteome analyses. The authors should have presented their proteomic data with much thorough presentation and explanation. As in the experiment scheme shown in Figure 4A, the author did two proteome analyses: one from the 7-day-old sample and the other from the 21-day-old sample. The manuscript only shows a plot of the result from the 7-day-old sample, but that of the result from the 21-day-old sample. For the 21-day-old sample, the authors only provided data in the supplemental table, in which the abundance ratio of eIF2β from the 21-day-old sample is 0.753, meaning eIF2β is depleted in the 21-day-old sample. The authors should have explained the impact of the eIF2β depletion in the 21-day-old sample, so the reader could fully understand the authors' interpretation of the role of eIF2β on proteostasis.

Thank you for your comments. We will include more analyses of the proteomic data in the next version of our manuscript. In this study, we aimed to elucidate the mechanisms by which depletion of axonal mitochondria induces proteostasis disruption prematurely. Thus, we did not investigate the roles of differentially expressed proteins in proteostasis at 21-day-old in milton knockdown. Aging disrupts proteostasis via multiple pathways: eIF2β levels may be lowered by feedback of earlier changes or via interaction with other age-related changes at 21-day-old. We will include more discussion in the next version of our manuscript.

The manuscript consists of several weaknesses in its data and explanation regarding translation.

(1) The authors are likely misunderstanding the effect of phosphorylation of eIF2α on translation. The P-eIF2α is inhibitory for translation initiation. However, the authors seem to be mistaken that the down-regulation of P-eIF2α inhibits translation. Thank you for your comment. We understand that the phosphorylation of eIF2α is inhibitory for translation initiation, as we described in page 9, Line 312-314. We propose a model in which autophagic defects caused by milton knockdown is mediate by upregulation of eIF2β, however, we are not arguing that the translational suppression in milton knockdown is caused by a reduction in p-eIF2α. We found that milton knockdown causes an increase in eIF2β, and overexpression of eIF2β copied phenotypes of milton knockdown such as autophagic defects (Figure 5 and 6). We also found that the increase in eIF2β reduces the level of p-eIF2α (Supplemental Figure 2), thus, eIF2α phosphorylation in milton knockdown may be caused by an increase in eIF2β. However, the effects of upregulation of eIF2β on the function of eIF2 complex is not fully understood. The translational suppression in milton knockdown may be caused by disruption of eIF2 complex, while it is also possible that it is mediated by a function of eIF2β that is yet-to-be-determined, or mediated by the pathways other than eIF2. We will include more details in the revised version.

(2) The result of polysome profiling in Figure 4H is implausible. By 10%-25% sucrose density gradient, polysomes are not expected to be observed. The authors should have used a gradient with much denser sucrose, such as 10-50%. Thank you for pointing it out. We are sorry, it was a mistake. The gradient was actually 10-50%, and we described it wrong. We will correct it in the revised version.

(3) Also on the polysome profiling, as in the method section, the authors seemed to fractionate ultra-centrifuged samples from top to bottom and then measured A260 by a plate reader. In that case, the authors should have provided a line plot with individual data points, not the smoothly connected ones in the manuscript. Thank you for pointing it out. We will replace the graph.

(4) For both the results from polysome profiling and puromycin incorporation (Figure 4H and I), the difference between control siRNA and Milton siRNA are subtle, if not nonexistent. This might arise from the lack of spatial resolution in their experiment as the authors used head lysate for these data but the ratio of Phospho-eIF2α/eIF2α only changes in the axons, based on their results in Figure 4E-G. The authors could have attempted to capture the spatial resolution for the axonal translation to see the difference between control siRNA and Milton siRNA.

Thank you for your comment. A new set of experiments with technical challenges will be required to capture the spatial resolution for the axonal translation. We will work on it and hope to achieve it in the future.

eLife assessment:

In flies defective for axonal transport of mitochondria, the authors report the upregulation of one subunit, the beta subunit, of the heterotrimeric eIF2 complex via mass spectroscopy proteome analysis. Neuronal overexpression of eIF2β phenocopied aspects of neuronal dysfunction observed when axonal transport of mitochondria was compromised. Conversely, lowering eIF2β expression suppressed aspects of neuronal dysfunction. While these are intriguing observations that are potentially useful, several technical weaknesses limit the interpretation and mean the evidence supporting the current claims is incomplete.

Reviewer #1 (Public Review):

The authors observed a decline in autophagy and proteasome activity in the context of Milton knockdown. Through proteomic analysis, they identified an increase in the protein levels of eIF2β, subsequently pinpointing a novel interaction within eIF subunits where eIF2β contributes to the reduction of eIF2α phosphorylation levels. Furthermore, they demonstrated that overexpression of eIF2β suppresses autophagy and leads to diminished motor function. It was also shown that in a heterozygous mutant background of eIF2β, Milton knockdown could be rescued. This work represents a novel and significant contribution to the field, revealing for the first time that the loss of mitochondria from axons can lead to impaired autophagy function via eIF2β, potentially influencing the acceleration of aging. To further support the authors' claims, several improvements are necessary, particularly in the methods of quantification and the points that should be demonstrated quantitatively. It is crucial to investigate the correlation between aging and the proteins eIF2β and eIF2α.

Reviewer #2 (Public Review):

In the manuscript, the authors aimed to elucidate the molecular mechanism that explains neurodegeneration caused by the depletion of axonal mitochondria. In Drosophila, starting with siRNA depletion of Milton and Miro, the authors attempted to demonstrate that the depletion of axonal mitochondria induces the defect in autophagy. From proteome analyses, the authors hypothesized that autophagy is impacted by the abundance of eIF2β and the phosphorylation of eIF2α. The authors followed up the proteome analyses by testing the effects of eIF2β overexpression and depletion on autophagy. With the results from those experiments, the authors proposed a novel role of eIF2β in proteostasis that underlies neurodegeneration derived from the depletion of axonal mitochondria.

The manuscript has several weaknesses. The reader should take extra care while reading this manuscript and when acknowledging the findings and the model in this manuscript.

The defect in autophagy by the depletion of axonal mitochondria is one of the main claims in the paper. The authors should work more on describing their results of LC3-II/LC3-I ratio, as there are multiple ways to interpret the LC3 blotting for the autophagy assessment. Lysosomal defects result in the accumulation of LC3-II thus the LC3-II/LC3-I ratio gets higher. On the other hand, the defect in the early steps of autophagosome formation could result in a lower LC3-II/LC3-I ratio. From the results of the actual blotting, the LC3-I abundance is the source of the major difference for all conditions (Milton RNAi and eIF2β overexpression and depletion). In the text, the authors simply state the observation of their LC3 blotting. The manuscript lacks an explanation of how to evaluate the LC3-II/LC3-I ratio. Also, the manuscript lacks an elaboration on what the results of the LC3 blotting indicate about the state of autophagy by the depletion of axonal mitochondria.

Another main point of the paper is the up-regulation of eIF2β by depleting the axonal mitochondria leads to the proteostasis crisis. This claim is formed by the findings from the proteome analyses. The authors should have presented their proteomic data with much thorough presentation and explanation. As in the experiment scheme shown in Figure 4A, the author did two proteome analyses: one from the 7-day-old sample and the other from the 21-day-old sample. The manuscript only shows a plot of the result from the 7-day-old sample, but that of the result from the 21-day-old sample. For the 21-day-old sample, the authors only provided data in the supplemental table, in which the abundance ratio of eIF2β from the 21-day-old sample is 0.753, meaning eIF2β is depleted in the 21-day-old sample. The authors should have explained the impact of the eIF2β depletion in the 21-day-old sample, so the reader could fully understand the authors' interpretation of the role of eIF2β on proteostasis.

The manuscript consists of several weaknesses in its data and explanation regarding translation.

(1) The authors are likely misunderstanding the effect of phosphorylation of eIF2α on translation. The P-eIF2α is inhibitory for translation initiation. However, the authors seem to be mistaken that the down-regulation of P-eIF2α inhibits translation.

(2) The result of polysome profiling in Figure 4H is implausible. By 10%-25% sucrose density gradient, polysomes are not expected to be observed. The authors should have used a gradient with much denser sucrose, such as 10-50%.

(3) Also on the polysome profiling, as in the method section, the authors seemed to fractionate ultra-centrifuged samples from top to bottom and then measured A260 by a plate reader. In that case, the authors should have provided a line plot with individual data points, not the smoothly connected ones in the manuscript.

(4) For both the results from polysome profiling and puromycin incorporation (Figure 4H and I), the difference between control siRNA and Milton siRNA are subtle, if not nonexistent. This might arise from the lack of spatial resolution in their experiment as the authors used head lysate for these data but the ratio of Phospho-eIF2α/eIF2α only changes in the axons, based on their results in Figure 4E-G. The authors could have attempted to capture the spatial resolution for the axonal translation to see the difference between control siRNA and Milton siRNA.

Author Response

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

Reviewer #2 (Recommendations For The Authors):

I would like to thank the authors for their comments. However, my request for additional experiments to consolidate this manuscript and text changes have not been addressed (point 1 and point 2), which I believe are essential for completion of this manuscript.

The reviewer raised the question about the relevant substrates of PARG in S-phase cells (point 1). As we explained in our previous response, the most important substrate of PARG is PARP1, since we observed increased chromatin-associated PARP1 and PARylated PARP1 in cells with PARG depletion. Moreover, PARP1 or PARP1/2 depletion rescued cell lethality caused by PARG depletion. These data strongly suggest that PARP1 is the major substrate of PARG in S phase cells. Of course, PARG may have additional substrates. In the future, we will perform proteomics experiments as suggested by this reviewer to identify additional PARG substrates, which may reveal new roles of PARG in S phase progression.

The reviewer also suggested us to re-organize our manuscript (point 2). However, we prefer to keep the manuscript as it is, since this is how the project evolved. The other reason we would like to share with the readers is the challenge to validate KO cells. This is an important lesson we learned from this study. We hope that this will raise the awareness of hypomorphic mutant cells we often use to draw conclusions about gene functions and/or genetic interactions. We understand that the current flow of our manuscript may bring some confusion. To avoid it, we included additional explanations at the beginning of this manuscript to draw attention to the readers that our initial KO cells may not be complete PARG KO cells, i.e. they may have residual PARG activity. We also included additional discussion of this important point in the Discussion section.

Moreover, WB analysis of PARG KO clones is inconclusive, as the additional prominent band at 50 kDa could be a degradation product. The authors should check PARG levels are localization by IF, which allows detection of intact proteins and their cellular localizations, since the shorter isoform should be localized in the cytosol. WB with PARG isoforms is missing important information regarding Mw of the PARG constructs and Mw labels of western blots, which makes is difficult to evaluate this data and compare to KO. Ideally, KO and PARG isoform samples should be all on one gel for proper comparison with different antibodies.

We appreciate the concerns raised by this reviewer. We agree that the additional prominent band at 50kDa could be a degradation product. As we explained in our previous response, despite using several PARG antibodies, we could not draw a clear conclusion which functional isoforms or truncated forms were expressed in our PARG KO cells.

Immunostaining experiments may not be more conclusive, since IF experiments rely on the same antibodies for recognizing endogenous PARG. Additionally, even a protein mainly localizes in the cytosol, we cannot exclude the possibility that a small fraction of this protein may localize in nuclei and have nuclear functions.

Instead, as we presented in our manuscript, we used a biochemical assay to measure PARG activity in cell lysate and showed that our initial PARG KO cells still have residual PARG activity. However, we could not detect any PARG activity in our complete/conditional PARG KO cells (cKO cells; these cells can only survive in the presence of PARP inhibitor). These data strongly suggest that PARG is essential for cell survival.

Reviewer #3 (Public Review):

These studies reveal an S-phase requirement for the PARG dePARylation enzyme in removing ADP-ribosylation from PAR-modified proteins whose PARylation is promoted by the presence of unligated Okazaki fragments. The excessive protein ADP-ribosylation observed in S-phase of PARG-depleted human cells leads to trapping of the PARP1 ADP-ribosylation enzyme on chromatin. The findings would be strengthened by identification of the relevant ADP-ribosylation substrates of PARG whose dePARylation is needed for progression through S-phase.

Comments on revised version:

In the revised version the authors have addressed some of the reviewers' concerns, but, despite the new explanatory paragraph on page 16, the paper remains confusing because as shown in Figure 7 at the end of the Results the PARG KO 293A cells that were analyzed at the beginning of the Results are not true PARG knockouts. The authors stated that they did not rewrite the Results because they wanted to describe the experiments in the order in which they were carried out, but there is no imperative for the experiments to be described in the order in which they were done, and it would be much easier for the uninitiated reader to appreciate the significance of these studies if the true PARG KO cell data were presented at the beginning, as all three of the original reviewers proposed.

While the authors have to some extent clarified the nature of the PARG KO alleles, they have not been able to identify the source of the residual PARG activity in the PARG KO cells, in part because different commercial PARG antibodies give different and conflicting immunoblotting results. Additional sequence characterization of PARG mRNAs expressed in the PARG cKO cells, and also in-depth proteomic analysis of the different PARG bands could provide further insight into the origins and molecular identities of the various PARG proteins expressed from the different KO PARG alleles, and determine which of them might retain catalytic activity.

The authors have made no progress in identifying which are the key PARG substrates required for S phase progression, although they suggest that PARP1 itself may be an important target.

eLife assessment

The demonstration that the PARG dePARylation enzyme is required in S phase to remove polyADP-ribose (PAR) protein adducts that are generated in response to the presence of unligated Okazaki fragments is potentially valuable, but the evidence is incomplete, and identification of relevant PARylated PARG substrates in S-phase is needed to understand the role of PARP1-mediated PARylation and PARG-catalyzed dePARylation in S-phase progression.

Reviewer #2 (Public Review):

Summary:

In this manuscript Nie et al investigate the effect of PARG KO and PARG inhibition (PARGi) on pADPR, DNA damage, cell viability and synthetic lethal interactions in HEK293A and Hela cells. Surprisingly, the authors report that PARG KO cells are sensitive to PARGi and show higher pADPR levels than PARG KO cells, which is abrogated upon deletion or inhibition of PARP1/PARP2. The authors explain the sensitivity of PARG KO to PARGi through incomplete PARG depletion and demonstrate complete loss of PARG activity when incomplete PARG KO cells are transfected with additional gRNAs in the presence of PARPi. Furthermore, the authors show that the sensitivity of PARG KO cells to PARGi is not caused by NAD depletion but by S-phase accumulation of pADPR on chromatin coming from unligated Okazaki fragments, which are recognized and bound by PARP1. Consistently, PARG KO or PARG inhibition show synthetic lethality with Pol beta, which is required for Okazaki fragment maturation. PARG expression levels in ovarian cancer cell lines correlate negatively with their sensitivity to PARGi.

Author Response

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The author should evaluate the possibility of naturally occurring arrhythmia due to the geometry of the tissues, by using voltage or calcium dye.

Answer: We thank the reviewer for this suggestion. We have performed new experiments using a voltage-sensitive fluorescent dye (i.e. FluoVolt) with data reported in the new Figure 4 + new results section “arrhythmia analysis”. Briefly, we found that our ring-shaped tissues are compatible with live fluorescence imaging. We were then able to show that our cardiac tissues beat regularly, without naturally occurring arrhythmias or extra beats. We could not detect any re-entrant waves in our tissues in the conditions offered by the speed of our camera. A specific paragraph has also been added to the discussion.

(2) There is only 50% survival after 20 days of culture in the optimized seeding group. Is there any way to improve it? The tissues had two compartments, cardiac and fibroblast-rich regions, where fibroblasts are responsible for maintaining the attachment to the glass slides. Do the cardiac rings detach from the glass slides and roll up? The SD of the force measurement is a quarter of the value, which is not ideal with such a high replicate number.

Answer: This paper report seminal data that will serve as a foundation for further use of the platform. We are currently expanding to other cell lines with improvement in survival (see https://insight.jci.org/articles/view/161356). We confirm that the rings do not detach. The pillar was specifically designed to avoid this (See figure 1B).

As the platform utilizes imaging analysis to derive contractile dynamics, calibration should be done based on the angle and the distance of the camera lens to the individual tissues to reduce the error. On the other hand, how reproducible of the pillars? It is highly recommended to mechanically evaluate the consistency of the hydrogel-based pillars across different wells and within the wells to understand the variance.

Answer: We propose a system and a measurement method that do not need calibration. Contraction amplitude is expressed as a ratio between the contracted / relaxed areas (See figure 3 A). There is thus no influence of the distance of the camera lens.

In order to evaluate the consistency of the mechanical properties of the hydrogel, we reproduced the experiment pictured in Figure1-Supplement 1, and measured the Young’s Modulus of three different gel solutions on different days. In the three experiments performed, we found values of 10.0-12.2 kPa, resulting in a final average value of 11.2 (+/- 0.6) kPa, coherent with the value reported in the article. We are therefore confident that the mechanical properties are consistent across and within wells. More extensive mechanical characterization of the molded gels would require the access to an Atomic Force Microscope (AFM), and is considered in the future.

The author should address the longevity and reproducibility issues, by working on the calibration of camera lens position/distance to tissues and further optimizing the seeding conditions with hydrogels such as collagen or fibrin, and/or making sure the PEG gels have high reproducibility and consistency.

Answer: This paper report seminal data that will serve as a foundation for further use of the platform. This platform (including the design, approach and choice of polymers) allows a fast and reproducible formation of an important number of cardiac tissues (up to 21 per well in a 96-well format, meaning a potential total of about 2,000 tissues) with a limited number of cells.

(3) The evaluation of the arrhythmia should be more extensively explained and demonstrated.

Answer : See answer to comment 1

(4) The results of isoproterenol should be checked as non-paced tissues should have increased beating frequency with increasing dosages. Dofetilide does not typically have a negative inotropic effect on the tissues. Please check on the cell viability before and after dosing

Answer : We agree with this reviewer on the principle. However, we have repeated the experiments and we confirm our results, i.e. increasing concentrations of isoproterenol induced a trend towards increase in the contraction force and significantly increased contraction and relaxation speeds without change in the beat rate (Figure 5C). We do not have a definitive explanation for this observation. Our hypothesis is that this increase in contraction and relaxation speeds induced by isoproterenol is translated, on average in our study, into an increase in contractile force rather than in an increase in contraction frequency. This may depend on the cell line used, and is very well illustrated in a recent paper from Mannhardt and colleagues (Stem cell reports. 2020; 15(4):983–998). Of the 10 different cell lines tested in engineered heart tissues, all show an increase in contraction and relaxation speeds after isoproterenol administration, but this is translated either into an increase in contractile force (4 cell lines) or into a shortening of the beat (3 cell lines), and only 2 cell lines show an increase in both parameters. Indeed, since iPSC-CMs are immature cardiac cells, it is rare to obtain a positive force-frequency relationship without any maturation medium or mechanical or electrical training. We agree that above a concentration of 10nM, dofetilide shows cardiotoxicity in our tissues as tissues completely stop beating.

Reviewer #2 (Recommendations For The Authors):

In addition to the general comments in the public review, I have the following specific suggestions to the authors, that would help improve the manuscript.

(1) Please describe the protocol for preparation of cardiac rings (shown in Figure 1C) in more detail. In particular, please describe how the tissues were transferred from the mold into the 96-well plate and how are they positioned and characterized during the study.

Answer: There is no transfer of the tissues as they directly form in the well, that is pre-equipped with the molded PEG gel (See Figure 1B and methods section). The in situ analysis is a strong asset of this platform.

(2) Please clarify the timepoints in this study. The overall schematic in Figure 1 C shows that the rings were formed on day 22 and then studied for 14 days, while Figure 2B shows data over 20 days following seeding, and Figure 3 shows data 14 days after seeding. It appears that these were separate studies (optimization of myocyte/fibroblast ratio followed by the main study.

Answer: Figure 1C is showing the timeline including the cardiomyocytes differentiation. hiPSC-CMs are indeed seeded in the wells 22 days after starting the differentiation, which represent the Day0 for tissue formation. We apologize for the confusion.

(3) Please explain if the number of rings per well (Figure 2) was used as the only criterion for selecting the myocyte/fibroblast ratio, and if so, why. Were these rings also characterized for their structural and contractile properties?

Answer: Figure 2 supplement 1 report the contractility data according to the different tested ratios, and show no differences. The number for generated ring-shaped tissues was indeed the only criterion retained.

(4) Please provide rationale for using the dermal rather than cardiac fibroblasts.

Answer: We had previous experience generating EHTs using dermal fibroblasts which are easier to obtain commercially. Our approach could in theory also work using cardiac fibroblasts, which we have not tested in the present study.

(5) Figure 2 panels C-E show an interesting segregation of cardiomyocytes into a thin cylindrical layer that does not appear to contain fibroblasts and a shorter and thicker cylinder containing fibroblasts mixed with occasional myocytes. Please specify at which time point this structure forms, and how does it change over time in culture? At which time point were the images taken? It would be helpful to include serial images taken over 1-14 days of study.

Answer: We thank the reviewer for this interesting comment. We have performed additional immunostainings (reported in Figure 2 supplement 3) on tissues at Day 1 and day 7 after seeding. The segregation appears in the 7 first days. It appears that 1 day after seeding the fibroblasts are not yet attached, although the cardiac fiber has already started to be formed. Seven days after seeding, fibroblasts are fully spread and attached, and the contractile ring is formed and well-aligned. Brightfield images are reported in Figure 1E.

(6) In the cardiomyocyte region (Figure 2D) the cells staining for troponin seem to be only at the surfaces. The thickness of the layer is only about 30-40 µµ, so one would assume that cell viability was not an issue. Please specify and discuss the composition of this region.

Answer: We agree but we think this is a technical issue as at the center of the tissue, tissue thickness will limit laser penetration, although at the surface (inner our outer), the laser infiltrates easily between the tissue and the PEG. Moreover, we see on the zoomed view of the tissue in Figure 2 Supplement 2 that we have a staining inside the cardiac fiber, which just appears less strong due to tissue thickness.

(7) Please also discuss segregation in terms of possible causes and the implications of apparently very limited contact between the two cell types, i.e., how representative is this two-region morphology of native heart tissue. Also, it would be interesting to know how the segregation has changed with the change in myocyte/fibroblast ratio.

Answer: We are not sure there is a very limited contact as the use of fibroblasts is critical to ensure the formation of tissues (i.e. no tissues can be formed if we avoid the use of fibroblasts). We agree that these ring-shaped cardiac tissues are not especially representative of a native heart tissue in terms of interactions between several cell types. They were developed as a surrogate for physiopathological and pharmacological experiments (see a recent application in https://insight.jci.org/articles/view/161356)

(8) There is interest and demonstrated ability to culture engineered cardiac tissues over longer periods of time. Please comment what was the rationale for selecting 14-day culture and if the system allows longer culture durations.

Answer: In line with this comment, we have studied the contractile parameters of our rings 28 days after seeding and compared to their contractile parameters at D14. We found a slight increase for all the parameters, which is significant for the maximum contraction speed. Nevertheless, the data is much more variable and the number of tissues is lower (29 for D14 against 17 for D28). Therefore, we demonstrated that long-term culture of our tissues is possible, however not yet optimized. Hence, the following physiological and pharmacological tests have been done at D14.

(9) Figure 3 documents the development of contractile parameters over 14 days of culture. Would it be possible to replace the arbitrary units with the actual values? Also, would it be possible to include the corresponding images of the rings taken at the same time points, to show the associated changes in ring morphologies.

Answer: Contraction amplitude is expressed as a ratio between the contracted / relaxed areas (See figure 3 A): it is a ratio, thus without unit. Corresponding images can be seen in Figure 1 E.

(10) The measured contraction stress, strain, and the speeds of contraction and relaxation improve from day 1 to day 7 and then plateau (Figure 3, Supplemental Figure 3. Please discuss this result.

Answer: The new immunostainings performed on tissues at Day 1 and Day 7 show the progressive alignment of the cardiomyocytes and the muscular fibers, with an almost complete organization at Day 7.

(11) The beating frequency does not appear to markedly change over time, while Figure 3B shows strong statistical significance (***) throughout the 14-day period. Please check/confirm.

Answer: We confirm this result.

(12) Please comment on the lack of effect of isoproterenol on beating frequency.

Answer: We agree with this reviewer on the principle. However, we have repeated the experiments and we confirm our results, i.e. increasing concentrations of isoproterenol induced a trend towards increase in the contraction force and significantly increased contraction and relaxation speeds without change in the beat rate (Figure 5C). We do not have a definitive explanation for this observation. Our hypothesis is that this increase in contraction and relaxation speeds induced by isoproterenol is translated, on average in our study, into an increase in contractile force rather than in an increase in contraction frequency. This may depend on the cell line used, and is very well illustrated in a recent paper from Mannhardt and colleagues (Stem cell reports. 2020; 15(4):983–998). Of the 10 different cell lines tested in engineered heart tissues, all show an increase in contraction and relaxation speeds after isoproterenol administration, but this is translated either into an increase in contractile force (4 cell lines) or into a shortening of the beat (3 cell lines), and only 2 cell lines show an increase in both parameters. Indeed, since iPSC-CMs are immature cardiac cells, it is rare to obtain a positive force-frequency relationship without any maturation medium or mechanical or electrical training.

(13) Please compare the contractile function of cardiac tissues measured in this study with data reported for other iPSC-derived tissue models.

Answer : A specific paragraph tackles this aspect in the discussion

eLife assessment

This paper reports a valuable platform for cardiac tissue cultivation. The throughput, consistency of the tissue, and the potential integration of high-throughput automation are an advantage over other approaches. The tissues and the platform are validated using appropriate methodology to provide convincing evidence of the tissue cultivation capability.

Reviewer #1 (Public Review):

The manuscript, "A versatile high-throughput assay based on 3D ring-shaped cardiac tissues generated from human induced pluripotent stem cell-derived cardiomyocytes," developed a unique culture platform with PEG hydrogel that facilitates the in-situ measurement of contractile dynamics of the engineered cardiac rings. The authors optimized the tissue seeding conditions, demonstrated tissue morphology with expressions of cardiac and fibroblast markers, mathematically modeled the equation to derive contractile forces and other parameters based on imaging analysis, and concluded by testing several compounds with known cardiac responses.

The authors answered my questions with appropriate experiments and explanation.

(1) This paper presents an intriguing platform that creates miniature cardiac rings with merely thousands of cardiomyocytes per tissue in a 96-well plate format. The shape of the ring and the squeezing motion can recapitulate the contraction of the cardiac chamber to a certain degree. However, Thavandiran et al. (PNAS 2013) created a larger version of the cardiac ring and found that electrical propagation revealed spontaneous infinite loop-like cycles of activation propagation traversing the ring. This model was used to mimic a reentrant wave during arrhythmia. Therefore, there are concerns about whether a large number of cardiac tissues experience arrhythmia due to geometry-induced re-entry current and cannot be used as a healthy tissue model.

In the new experiment, the authors demonstrated with voltage-sensitive dye that these miniaturized tissues do not experience any arrhythmia, potentially due to their small size.

(2) The platform can produce 21 cardiac rings per well in 96-well plates, with the throughput being the highest among competing platforms. The resulting tissues exhibit good sarcomere striation due to the strain from the pillars. However, emerging questions pertain to culture longevity and reproducibility among tissues. According to Figure 1E, uneven ring formation around the pillar leads to tissue thinning and breakage. Only 50% survival is observed after 20 days of culture in the optimized seeding group. Are there any strategies to improve this survival rate? Additionally, do the cardiac rings detach from the glass slides and roll up, given the two compartments with cardiac and fibroblast-rich regions where fibroblasts maintain attachment to the glass slides? Moreover, the standard deviation of force measurement is a quarter of the value, which is suboptimal given the high replicate number. As the platform utilizes imaging analysis to derive contractile dynamics, calibration based on the angle and distance of the camera lens to individual tissues should be conducted to reduce error. On the other hand, how reproducible are the pillars? It is highly recommended to mechanically evaluate the consistency of the hydrogel-based pillars across different wells and within wells to understand the variance.

The authors stated that the platform has been tested and improved with multiple cell lines to enhance tissue survival rates. The methodology of image capture and calculation of contractile dynamics were explained in detail to address concerns. Moreover, the reproducibility of the pillars was demonstrated by consistent results of Young's Modulus (AFM) from each pillar, showing low standard deviations.

(3) Does the platform allow the observation of non-synchronized beating when testing with compounds? This can be extremely important as the intended applications of this platform are drug testing and cardiac disease modeling. The author should elaborate on the method in the manuscript and explain the obtained results in detail.

Referring to Question #1, the platform does not present arrythmia potentially due to the small size of the tissue.

(4) The results of drug testing are interesting. Isoperenoral is typically causing positive chronotropic and positive inotropic responses, where inotropic responses are difficult to obtain due to low tissue maturity. It is inconsistent with other reported results that cardiac rings do not exhibit increased beating frequency, but slightly increased forces only.

The authors repeated the experiment with the same results and hypothesized that the results would be line-dependent, since the maturation of iPSC-CM is not consistent. The additional dose curves provided more information on the tissue behaviors against well-known compounds.

Overall, the manuscript is well-written, and the designed platform presents unique advantages for high-throughput cardiac tissue culture. The paper has adequate data to demonstrate the proof-of-concept study of the platform. The throughput, consistency of the tissue, and the potential integration of high-throughput automation would be the highlights of this platform.

Author Response

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

Public Reviews

We thank the reviewers for their insightful comments and helpful suggestions that allowed us to improve the manuscript.

Reviewer #1:

Thermogenic adipocyte activity associate with cardiometabolic health in humans but decline with age. Identifying the underlying mechanisms of this decline is therefore highly important.

To address this task, Holman and co-authors investigated the effects of two major determinants of thermogenic activity: cold, which induce thermogenic de novo differentiation as well as conversion of dormant thermogenic inguinal adipocytes: and aging, which strongly reduce thermogenic activity. The authors study young and middle-aged mice at thermoneutrality and following cold exposure.

Using linage tracing, the authors conclude that the older group produce less thermogenic adipocytes from progenitor differentiation. However, they found no differences between thermogenic differentiation capacity between the age groups when progenitors are isolated and differentiated in vitro. This finding is consistent with previous findings in humans, demonstrating that progenitor cells derived from dormant perirenal brown fat of humans differentiate into thermogenic adipocytes in vitro. Taken together, this underscores that age-related changes in the microenvironment rather than autonomous alterations in the ASPCs explain the age-related decline in thermogenic capacity. This is an important finding in terms of identifying new approaches to switch dormant adipocytes into an active thermogenic phenotype.

To gain insight into the age-related changes, the authors use single cell and single nuclei RNA sequencing mapping of their two age groups, comparing thermoneutral and cold conditions between the two groups. Interestingly, where the literature previously demonstrated that de novo lipogenesis (DNL) occurs in relation to thermogenic activation, the authors show that DNL in fact is activated in a white adipocyte cell type, whereas the beige thermogenic adipocytes form a separate cluster.

Considering recent findings, that adipose tissue contains several subtypes of ASPCs and adipocytes, mapping the changes at single cell resolution following cold intervention provides an important contribution to the field, in particular as an older group with limited thermogenic adaptation is analyzed in parallel with a younger, more responsive group. This model also allowed for detection of microenvironment as a determining factor of thermogenic response.

The use of only two time points (young and middle-aged) along the aging continuum limits the conclusions that can be made on aging as the only driver of the observed differences between the groups. It should for example be noted that the older mice had higher weights and larger fat depots, thus the phenotype is complex and this should be taken into consideration when interpreting the data.

In conclusion, this study provides an important resource for further studies on how to reactivate dormant thermogenic fat and potentially improve metabolic health.

(1) The authors claim "Aging impairs cold-induced beige adipogenesis and adipocyte metabolic reprogramming". It is previously established in humans that aging strongly associate with a decline in thermogenic capacity. With this in mind, it is easy to accept that the reduced browning observed in the older group is due to age. However, the older group also have larger adipose depots, which also can be a confounding factor. I, therefore, recommend bringing this into the discussion and putting more focus on the complexity of the phenotype. For example, it could be discussed whether the de novo lipogenesis less due to that the adipocytes of older mice is already filled with more lipids. Additional time points along the aging continuum would be needed to make a strong conclusion about age as the determinant, but even so, aging is complex and further definitions and discussion would be needed.

We agree with the reviewer regarding the confounding effect of body weight changes. We have added a paragraph to the discussion (pasted below) to comment on the complexity of the phenotype and the contributing role of linked changes in body weight/composition.

“Aging is a complex process, and unsurprisingly, many pathways have been linked to the aging-related decline in beiging capacity. For example, increased adipose cell senescence, impaired mitochondrial function, elevated PDGF signaling and dysregulated immune cell activity during aging diminish beige fat formation (Benvie et al., 2023; Berry et al., 2017; Goldberg et al., 2021; Nguyen et al., 2021). Of note, older mice exhibit higher body and fat mass, which is associated with metabolic dysfunction and reduced beige fat development. While the effects of aging and altered body composition are difficult to separate, previous studies suggest that the beiging deficit in aged mice is not solely attributable to changes in body weight (Rogers et al., 2012). Further studies, including additional time points across the aging continuum may help clarify the role of aging and ascertain when beiging capacity decreases.”

(2) The study would gain from more comparisons to existing human studies and discussion on the translation potential of the findings. For example, how does the adipocyte subtypes identified in the current study translate to subtypes identified in human adipose tissue (e.g. Emont et al).

We analyzed the human adipose tissue atlas from Emont et al. 2022 (PMID: 35296864). We did not find any obvious homologous human adipocyte subtypes. However, this and other available human single cell studies have not investigated the effects of cold exposure on white adipose tissue depots, which may be necessary to reveal DNL-high and especially beige adipocytes.

(3) The group has contributed multiple studies demonstrating that Prdm16 is a major inducer of a thermogenic phenotype, and the literature shows that Prdm16 promote a thermogenic phenotype in favour of a fibrogenic aging phenotype. It would therefore be interesting to see how Prdm16 is regulated in the current data set, across adipocytes subtypes, age groups and temperature conditions.

We thank the reviewer for this comment. Previous studies showed that PRDM16 protein and not mRNA levels are downregulated during aging (Wang et al., 2019, Cell Metab, PMID: 31155495; Wang et al., 2022, Nature, PMID: 35978186). Consistent with this, we did not observe an agingassociated reduction in Prdm16 mRNA levels in adipocytes in our dataset. We did observe enrichment of Prdm16 mRNA levels in beige adipocytes relative to other adipocyte clusters. We included these data in Fig. 5F.

(4) In Figure 1, it is difficult to understand why the 6 weeks cold exposure is not shown in relation to the thermoneutrality, 3 days and 2-week cold exposure? It would be useful to have this in the same graph relating the levels and showing all four marker genes for all time points.

These experiments were done at different times using separate groups of mice. We have now clarified this in the figure legend.

(5) The older mice had larger inguinal fat depots, suggesting more lipids stored. The morphology of adipose tissue has previously been shown to be modulated by cold acclimation and is also the main similarity between brown adipose tissue in adult humans and young mice beige adipose tissue. Fig S2b suggests smaller adipocytes in the young group. It would also be useful, for comparison to published data, if authors show tissue sections with H&E of their model.

Good point. We added panels showing H&E staining of serial iWAT sections, showing changes in tissue morphology across age and temperature conditions (Figure S1F).

(6) The authors use t-tests to compare the differences induced by e.g. cold or min vs max cell culture media etc, within each age group. However, in my opinion, a two-way Anova with post-tests would be more informative as this would allow for testing the effects of the two age categories on any quantitative variable and allow for addressing whether there is an interaction between the categories.

Following the reviewer’s recommendation, we applied two-way ANOVA with a Tukey correction for multiple comparisons for categorical comparisons with different age groups and conditions. P values from all significant multiple comparison tests are now included within the methods section.

(7) In Figure 5F, please include Adipoq expression between clusters and please add a reference to why Nnat is considered a canonical white adipocyte marker.

We added Adipoq to the violin plot in Figure 5F, showing differential expression across adipocyte clusters. We included a line in the results section to highlight this observation:

“Interestingly, Adiponectin (Adipoq) was differentially expressed across adipocyte clusters, with higher levels in Npr3-high and DNL-high cells.”

We removed “canonical” and added references for Nnat and Lep as white marker genes.

(8) After 14 days of cold exposure, it looks like the DNL high population divides into two populations, did the authors explore if there was any differences between these clusters?

We also noticed this apparent division and explored this question. However, upon increasing the resolution for clustering and splitting the DNL high population, there were no obvious differentially expressed genes that defined the two subclusters. Thus, we opted to keep them together.

(9) As cold treatment transform a subset of cells, can authors perform a data-driven analysis to visualize the directions in their single nuclei data sets by using monocle pseudotime and/or velocity analyses?

This is a good question. We spent a long time trying to address this question using several trajectory and pseudotime analysis methods, including Velocity (scVelo), Slingshot and Dynoverse. Unfortunately, we were unable to obtain concordant results using at least two different methods and felt that the analyses were unreliable.

Reviewer #2:

This manuscript focused on why aging leads to decreased beiging of white adipose tissue. The authors used an inducible lineage tracing system and provided in vivo evidence that de novo beige adipogenesis from Pdgfra+ adipocyte progenitor cells is blocked during early aging in subcutaneous fat. Single-cell RNA sequencing of adipocyte progenitor cells and in vitro assays showed that these cells have similar beige adipogenic capacities in vitro. Single-cell nucleus RNA sequencing of mature adipocytes indicated that aged mice have more Npr3 high-expressing adipocytes in the subcutaneous fat from aged mice.

Meanwhile, adipocytes from aged mice have significantly lower expression of genes involved in de novo lipogenesis, which may contribute to the declined beige adipogenesis.

The mechanism that leads to age-related impairment of white adipose tissue beiging is not very clear. The finding that Pdgfra+ adipocyte progenitor cells contribute to beige adipogenesis is novel and interesting. It is more intriguing that the aging process represses Pdgfra+ adipocyte progenitor cells from differentiating into beige adipocytes during cold stimulation. Mature adipocytes that have high de novo lipogenesis activity may support beige adipogenesis is also novel and worth further pursuing. The study was carried out with a nice experimental design, and the authors provided sufficient data to support the major conclusions. I only have a few comments that could potentially improve the manuscript.

(1) It is interesting that after three days of cold exposure, aged mice also have much fewer beige adipocytes. Is de novo adipogenesis involved at this early stage? Or does the previous beige adipocyte that acquired white morphology have a better "reactivation" in young mice? It would be nice if the author could discuss the possibilities.

This is a good question. We did not evaluate beige adipogenesis at the 3d timepoint. However, a previous study demonstrates that 3d of cold exposure is sufficient to promote de novo beige adipogenesis (Wang et al., Nat Med. 2013, PMID: 23995282). We observed that beige adipogenesis from Pdgfra+ cells are a relatively minor contributor to beige adipocyte development, even after long term cold exposure in young mice. Based on these data, we presume that beige adipocyte activation (or re-activation) is the dominant mechanism for beige adipocyte development.

To clarify this point, we have included the following lines in the manuscript:

“Previous studies in mice using an adipocyte fate tracking system show that a high proportion of beige adipocytes arise via the de novo differentiation of ASPCs as early as 3 days of cold (Wang et al., 2013).”

“Based on these findings, we presume that mature (dormant beige) adipocytes serve as the major source of beige adipocytes in our cold-exposure paradigm. However, long-term cold exposure also recruits smooth muscle cells to differentiate into beige adipocytes; a process that we did not investigate here (Berry et al., 2016; Long et al., 2014; McDonald et al., 2015; Shamsi et al., 2021).”

(2) Is the absolute number of Pdgfra+ cells decreased in aged mice? It would be nice to include quantifications of the percentage of tomato+ beige adipocytes in total tomato+ cells to reflect the adipogenic rate.

We presented FACS quantification of tdTomato+/Pdgfra+ cells in Fig. 2B. We added a graph showing the percentage of Pdgfra+ cells of total live, lin- cells in adipose tissue; this showed no difference between young and aged mice. We did not perform FACS quantification of tdTomato+ beige adipocytes due to the technical challenges with sorting adipocytes. Quantification of total tdTomato+ cells was also unreliable and inconsistent due to the widespread labeling of fibroblasts, blood vessels, along with traced adipocytes. Thus, we did not include this analysis.

(3) Line 112, the sentence seems to be not finished.

This has been corrected.

eLife assessment

This fundamental study provides evidence that de novo beige adipogenesis from Pdgfra+ adipocyte progenitor cells is blocked during early aging in subcutaneous fat. The depth of the data at early ages is compelling, with rigorous cell tracing methodology employed. The study will aid in identifying new approaches to switch dormant adipocytes into an active thermogenic phenotype, and should be of interest to cell biologists at large.

Reviewer #1 (Public Review):

Thermogenic adipocyte activity associate with cardiometabolic health in humans, but decline with age. Identifying the underlying mechanisms of this decline is therefore highly important.

To address this task, Holman and co-authors present compelling data from their investigations of the effects of two major determinants of thermogenic activity: cold, which induce thermogenic de novo differentiation as well as conversion of dormant thermogenic inguinal adipocytes: and aging, which strongly reduce thermogenic activity. The authors study young and middle-aged mice at thermoneutrality and following cold exposure.

Using linage tracing, the authors conclude that the older group produce less thermogenic adipocytes from progenitor differentiation. However, they found no differences between thermogenic differentiation capacity between the age groups when progenitors are isolated and differentiated in vitro. This finding is consistent with previous findings in humans, demonstrating that progenitor cells derived from dormant perirenal brown fat of humans differentiate into thermogenic adipocytes in vitro. Taken together, this underscores that age-related changes in the microenvironment rather than autonomous alterations in the ASPCs explain the age related decline in thermogenic capacity, This is an important finding in terms of identifying new approaches to switch dormant adipocytes into an active thermogenic phenotype.

To gain insight into the age-related changes, the authors use single cell and single nuclei RNA sequencing mapping of their two age groups, comparing thermoneutral and cold conditions between the two groups. Interestingly, where the literature previously demonstrated that de novo lipogenesis (DNL) occurs in relation to thermogenic activation, the authors show that DNL in fact is activated in a white adipocyte cell type, whereas the beige thermogenic adipocytes form a separate cluster.

Considering recent findings, that adipose tissue contains several subtypes of ASPCs and adipocytes, mapping the changes at single cell resolution following cold intervention provides an important contribution to the field, in particular as an older group with limited thermogenic adaptation is analyzed in parallel with a younger, more responsive group. This model also allowed for detection of microenvironment as a determining factor of thermogenic response.

The use of only two time points (young and middle-aged) along the aging continuum limits the conclusions that can be made on aging as the only driver of the observed differences between the groups. Furthermore, as the authors also discuss, aging is a complex phenotype, and in this case the older mice were heavier and had larger fat depots, which should be taken into consideration when interpreting the data.

In conclusion, this study provides an important resource for further studies, which should investigate how the findings can be translated into humans for reactivation of dormant thermogenic fat and a potential improvement of metabolic health.

Reviewer #2 (Public Review):

This manuscript focused on why aging leads to decreased beiging of white adipose tissue. The authors used an inducible lineage tracing system and provided in vivo evidence that de novo beige adipogenesis from Pdgfra+ adipocyte progenitor cells is blocked during early aging in subcutaneous fat. Single-cell RNA sequencing of adipocyte progenitor cells and in vitro assays showed that these cells have similar beige adipogenic capacities in vitro. Single-cell nucleus RNA sequencing of mature adipocytes indicated that aged mice have more Npr3 high-expressing adipocytes in the subcutaneous fat from aged mice. Meanwhile, adipocytes from aged mice have significantly lower expression of genes involved in de novo lipogenesis, which may contribute to the declined beige adipogenesis.

The mechanism that leads to age-related impairment of white adipose tissue beiging is not very clear. The finding that Pdgfra+ adipocyte progenitor cells contribute to beige adipogenesis is novel and interesting. It is more intriguing that the aging process represses Pdgfra+ adipocyte progenitor cells from differentiating into beige adipocytes during cold stimulation. Mature adipocytes that have high de novo lipogenesis activity may support beige adipogenesis is also novel and worth further pursuing. The study was carried out with a nice experimental design, and the authors provided sufficient data to support the major conclusions. I only have a few comments that could potentially improve the manuscript.

(1) It is interesting that after three days of cold exposure, aged mice also have much fewer beige adipocytes. Is de novo adipogenesis involved at this early stage? Or does the previous beige adipocyte that acquired white morphology have a better "reactivation" in young mice? It would be nice if the author could discuss the possibilities.

(2) Is the absolute number of Pdgfra+ cells decreased in aged mice? It would be nice to include quantifications of the percentage of tomato+ beige adipocytes in total tomato+ cells to reflect the adipogenic rate.

eLife assessment

This important study combines fMRI and electrophysiology in sedated and awake rats to show that LFPs strongly explain spatial correlations in resting-state fMRI but only weakly explain temporal variability. They propose that other, electrophysiology-invisible mechanisms contribute to the fMRI signal. The evidence supporting the separation of spatial and temporal correlations is convincing, however, the support of electrophysiological-invisible mechanisms is incomplete, considering alternative potential factors that could account for the differences in spatial and temporal correlation that were observed. This work will be of interest to researchers who study the mechanisms behind resting-state fMRI.

Reviewer #1 (Public Review):

Tu et al investigated how LFPs recorded simultaneously with rsfMRI explain the spatiotemporal patterns of functional connectivity in sedated and awake rats. They find that connectivity maps generated from gamma band LFPs (from either area) explain very well the spatial correlations observed in rsfMRI signals, but that the temporal variance in rsfMRI data is more poorly explained by the same LFP signals. The authors excluded the effects of sedation in this effect by investigating rats in the awake state (a remarkable feat in the MRI scanner), where the findings generally replicate. The authors also performed a series of tests to assess multiple factors (including noise, outliers, and nonlinearity of the data) in their analysis.

This apparent paradox is then explained by a hypothetical model in which LFPs and neurovascular coupling are generated in some sense "in parallel" by different neuron types, some of which drive LFPs and are measured by ePhys, while others (nNOS, etc.) have an important role in neurovascular coupling but are less visible in Ephys data. Hence the discrepancy is explained by the spatial similarity of neural activity but the more "selective" LFPs picked up by Ephys account for the different temporal aspects observed.

This is a deep, outstanding study that harnesses multidisciplinary approaches (fMRI and ephys) for observing brain activity. The results are strongly supported by the comprehensive analyses done by the authors, which ruled out many potential sources for the observed findings. The study's impact is expected to be very large.

There are very few weaknesses in the work, but I'd point out that the 1-second temporal resolution may have masked significant temporal correlations between LFPs and spontaneous activity, for instance, as shown by Cabral et al Nature Communications 2023, and even in earlier QPP work from the Keilholz Lab. The synchronization of the LFPs may correlate more with one of these modes than the total signal. Perhaps a kind of "dynamic connectivity" analysis on the authors' data could test whether LFPs correlate better with the activity at specific intervals. However, this could purely be discussed and left for future work, in my opinion.

Author Response

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

Response to Reviewers’ Public Comments

We are grateful for the reviewers’ comments. We have modified the manuscript accordingly and detail our responses to their major comments below.

(1) Reviewer 2 was concerned that transformation of continuous functional data into categorical form could reduce precision in estimating the genetic architecture.

We agree that transforming continuous data into categories may reduce resolution, but it also improves accuracy when the continuous data are affected by measurement noise. In our dataset, many genotypes are at the lower bound of measurement, and the variation in measured fluorescence among these genotypes is largely or entirely caused by measurement noise. By transforming to categorical data, we dramatically reduced the effect of this noise on the estimation of genetic effects. We modified the results and discussion sections to address this point.

(2) Reviewer 2 asked about generalizability of our findings.

Because our paper is the first use of reference-free analysis of a 20-state combinatorial dataset, generalizability is at this point unknown. However, a recent manuscript from our group confirms the generality of the simplicity of genetic architecture: using reference-free methods to analyze 20 published combinatorial deep mutational scans, several of which involve 20-state libraries, we found that main and pairwise effects account for virtually all of the genetic variance across a wide variety of protein families and types of biochemical functions (Park Y, Metzger BPH, Thornton JW. 2023. The simplicity of protein sequence-function relationships. BioRxiv, 2023.09.02.556057). Concerning the facilitating effect of epistasis on the evolution of new functions, we speculate that this result is likely to be general: we have no reason to think that the underlying cause of this observation – epistasis brings genotypes with different functions closer in sequence space to each other and expands the total number of functional sequences – arises from some peculiarity of the mechanisms of steroid receptor DBD folding or DNA binding. However, we acknowledge that our data involve sequence variation at those sites in the protein that directly mediate specific protein-DNA contact; it is plausible that sites far from the “active site” may have weaker epistatic interactions and therefore have weaker effects on navigability of the landscape. We have addressed these issues in the discussion.

(3) Reviewer 3 asked “in which situation would the authors expect that pairwise epistasis does not play a crucial role for mutational steps, trajectories, or space connectedness, if it is dominant in the genotype-phenotype landscape?”

The question addressed in our paper is not whether epistasis shapes steps, trajectories or connectedness in sequence space but how it does so and what its particular effects are on the evolution of new functions. The dominant view in the field has been that the primary role of epistasis is to block evolutionary paths. We show, however, that in multi-state sequence space, epistasis facilitates rather than impedes the evolution of new functions. It does this by increasing the number of functional genotypes and bringing genotypes with different functions closer together in sequence space. This finding was possible because of the difference in approach between our paper and prior work: most prior work considered only direct paths in a binary sequence space between two particular starting points – and typically only considering optimization of a single function – whereas we studied the evolution of new functions in a multi-state amino acid space, under empirically relevant epistasis informed by complete combinatorial experiments. The result is a clear demonstration that the net effect of real-world levels of epistasis on navigability of the multidimensional sequence landscape is to make the evolution of new functions easier, not harder.

(4) Reviewer 3 asked for “an explanation of how much new biological results this paper delivers as compared with the paper in which the data were originally published.”

Starr 2017 did not use their data to characterize the underlying genetic architecture of function by estimating main and epistatic effects of amino acid states and combinations; it also did not evaluate the importance of epistasis in generating functional variants, determining the transcription factor’s specificity, or shaping evolutionary navigability on the landscape.

(5) Reviewer 3 requested an explanation of how the results would have been (potentially) different if a reference-based approach were used, and how reference-based analysis compares with other reference-free approaches to estimating epistasis.

This topic has been covered in detail in a recent manuscript from our group (Park et al. Biorxiv 2023.09.02.556057). Briefly, reference-free approaches provide the most efficient explanation of an entire genotype-phenotype map, explaining the maximum amount of genetic variance and reducing sensitivity to experimental noise and missing genotypes compared to reference-based approaches. Reference-based approaches tend to infer much more epistasis, especially higher-order epistasis, because measurement error and local idiosyncrasy near the wild-type sequence propagate into spurious high-order terms. Reference-based analyses are appropriate for characterizing only the immediate sequence neighborhood of a particular “wild-type” protein of interest. Reference-free approaches are therefore best suited to understanding genotype-phenotype landscapes as a whole. We have clarified these issues in the revised discussion.

(6) Reviewer 3 suggested that the comparison between the full and main-effects-only model should involve a re-estimation of main effects in the latter case.

This is indeed what we did in our analysis. We have clarified the description in the results and methods sections to make this clear.

(7) Reviewer 3 asked about the applicability of the approach to data beyond those analyzed in the present study and requirements to use it.

Our approach could be used for any combinatorial DMS dataset in which the phenotypic data are categorical (or can be converted to categorical form). Complete sampling is not required: a virtue of reference-free analysis is that by averaging the estimated effects of states and combinations over all variants that contain them, reference-free analysis is highly robust to missing data (except at the highest possible order of epistasis, where only a single variant represents a high-order effect) as long as variant sampling is unbiased with respect to phenotype. All the required code are publicly available at the github link provided in this manuscript. We have also described a general form of reference-free analysis for continuous data and applied it to 20 protein datasets in a recent publication (Park et al. Biorxiv 2023.09.02.556057).

(8)Reviewer 3 suggested that the text could be shortened and made less dense.

We agree and have done a careful edit to streamline the narrative.

Response to Reviewers’ Non-Public Recommendations

(1) Reviewer 1 noted that specific epistatic effects might in some cases produce global nonlinearities in the genotype-phenotype relationship. They then asked how our results might change if we did not impose a nonlinear transformation as part of the genotype-phenotype model. The reviewer’s underlying concern was that the non-specific transformation might capture high-order specific epistatic effects and thus reducing their importance.

Because our data are categorical, we required a model that characterizes the effect of particular amino acid states and combinations on the probability that a variant is in a null, weak, or strong activation class. A logistic model is the classic approach to this kind of analysis. The model structure assumes that amino acid states and combinations have additive effects on the log-odds of being in one functional class versus the lower functional class(es); the only nonlinear transformation is that which arises mathematically when log-odds are transformed into probability through the logistic link function. Thinking through the reviewer’s comment, we have concluded that our model does not make any explicit transformation to account for nonlinearity in the relationship between the effects of specific sequence states/combinations and the measured phenotype (activation class). If additional global nonlinearities are present in the genotype-phenotype relationship – such as could be imposed by limited dynamic range in the production of the fluorescence phenotype or the assay used to measure it – it is possible that the sigmoid shape of the logistic link function may also accommodate these nonlinearities. We have noted this part in the revised manuscript.

(2) Reviewer 1 observed that our model seems to prefer sets of several pairwise interactions among states across sites rather than fewer high-order interactions among those same states.

This finding arises because the pattern of phenotypic variation across genotypes in our dataset is consistent with that which would be produced by pairwise interactions rather than by high-order interactions. In a reference-free framework, these patterns are distinct from each other: a group of second-order terms cannot fit the patterns produced by high-order epistasis, and high-order terms cannot fit the pattern produced by pairwise interactions. Similarly, main-effect terms cannot fit the pattern of phenotypes produced by a pairwise interaction, and a pairwise epistatic term cannot fit the pattern produced by main effects of states at two sites. For example, third-order terms are required when the genotypes possessing a particular triplet of states deviate from that expected given all the main and second-order effects of those states; this deviation cannot be explained by any combination of first- and second-order effects.

We explain this point in detail in our recent manuscript (Park Y, Metzger BPH, Thornton JW. 2023. The simplicity of protein sequence-function relationships. BioRxiv, 2023.09.02.556057) and we summarize it here. Consider the simple example of two sites with two possible states (genotypes 00, 01, 10, and 11). If there are no main effects and no pairwise effects, this architecture will generate the same phenotype for all four variants – the global average (or zero-order effect). If there are pairwise effects but no main effects, this architecture will generate a set of phenotypes on which the average phenotype of genotypes with a 0 at the first site (00 and 01) equals the global average – as does the average of those with 0 at the second site (00 and 10). The epistatic effect causes the individual genotypes to deviate from the global average. This pattern can be fit only by a pairwise epistatic term, not by first-order terms. Conversely, if there are main effects but no pairwise effects, then the average phenotype of genotypes 00 and 01 will deviate from the global average (by an amount equal to the first-order effect), as will the average of (00 and 10): the phenotype of each genotype will be equal to the sum of the relevant first-order effects for the state it contains. This pattern cannot be fit by second-order model terms. The same logic extends to higher orders: a cluster of second-order terms cannot explain variation generated by third-order epistasis, because third-order variation is by definition is the deviation from the best second-order model.

(3) Reviewer 1 suggested several places in the text where citations to prior work would be appropriate.

We appreciate these suggestions and have modified the manuscript to refer to most of these works.

(4) Reviewer 1 pointed to the paper of Gong et al eLife 2013 and asked whether it is known how robust the proteins in our study are to changes in conformation/stability compared to other proteins, and whether this might impact the likelihood of observing higher-order epistasis in this system.

The DBDs that we study here are very stable, and previous work shows that mutations affect DNA specificity primarily by modifying the DBD’s affinity rather than its stability (McKeown et al., Cell 2014). Additionally, Gong et al.’s findings pertain to a globally nonlinear relationship between stability and function, which arises from the Boltzmann relationship between the energy of folding and occupancy of the folded state. Because our data are categorical – based on rank-order of measured phenotype rather than fluorescence as a continuous phenotype – the kind of global nonlinearity observed in Gong’s study are not expected to produce spurious estimates of epistasis in our work. We have modified the discussion to discuss the point.

(5) Reviewer 1 asked a) why the epistatic models produce landscapes on which variants have fewer neighbors on average than main-effects only models and b) why the average distance from all ERE-specific nodes to all SRE-specific nodes is greater with epistasis (but the average distance from ERE to nearest SRE is lower with epistasis).

In the main effects-only landscape, the functional genotypes are relatively similar to each other, because each must contain several of the states that contribute the most to a positive genetic score. Moreover, ERE-specific nodes are similar to each other, and SRE-specific nodes are similar to each other, because each must contain one or more of a relatively small number of specificity-determining states. When epistasis is added to the genetic architecture, two things happen: 1) more genotypes become functional because there are more combinations that can exceed the threshold score to produce a functional activator and 2) these additional functional variants are more different from each other – in general, and within the classes of ERE- or SRE-specific variants – because there are now more diverse combinations of states that can yield either phenotype. As a result, a broader span of sequence space is occupied, but ERE- and SRE-specific variants are more interspersed with each other. This means that the average distance between all pairs of nodes is greater, and this applies to all ERE-SRE pairs, as well. However, the interspersing means that the closest single SRE to any particular ERE is closer than it was without epistasis. We have added this explanation to the main text.

(6) Reviewer 2 asked us to explain why average path length increases with pairwise epistasis as the strength of selection for specificity increases.

This behavior occurs because of the existence of a local peak in the pairwise model. Genotypes on this peak contained few connections to other genotypes, all of which were less SRE specific. Thus, with strong selection, i.e. high population size, the simulations became stuck on the local peak, cycling among the genotypes many times before leaving, resulting in a large increase in the mean step number. As shown in the rest of the figure, when the longest set of paths are removed, there are still differences in the average number of steps with and without epistasis. This issue is described in the methods section.

(7) Reviewers made several suggestions for clarity in the text and figures.

We have modified the paper to address all of these comments.

(8) Reviewer 3 stated that the code should be available.

The code is available at https://github.com/JoeThorntonLab/DBD.GeneticArchitecture.

Reviewer #2 (Public Review):

The authors aimed to understand how epistasis influences the genetic architecture of the DNA-binding domain (DBD) of steroid hormone receptor. An ordinal regression model was developed in this study to analyze a published deep mutational scanning dataset that consists of all combinatorial amino acid variants across four positions (i.e. 160,000 variants). This published dataset measured the binding of each variant to the estrogen receptor response element (ERE, sequence: AGGTCA) as well as the steroid receptor response element (SRE, sequence: AGAACA). This model has major strengths of being reference free and able to account for global nonlinearity in the genotype-phenotype relationship. Thorough analyses of the modelling results have performed, which provided convincing results to support the importance of epistasis in promoting evolution of protein functions. This conclusion is impactful because many previous studies have shown that epistasis constrains evolution. The novelty this study will likely stimulate new ideas in the field. The model will also likely be utilized by other groups in the community.

eLife assessment

This study includes fundamental findings on protein evolution, namely that changes in function are largely attributable to pairwise rather than higher-order interactions, and that epistasis potentiates rather than constrains evolutionary paths. Compelling evidence supporting the conclusions is provided by applying a new model to a previously generated experimental dataset on deep mutational scanning of the DNA-binding domain (DBD) of steroid hormone receptor. The implications of this work are of considerable interest to protein biochemistry, evolutionary biology, and numerous other fields.

Reviewer #1 (Public Review):

Metzger et al develop a rigorous method filling an important unmet need in protein evolution - analysis of protein genetic architecture and evolution using data from combinatorially complete 20^N variant libraries. Addressing this need has become increasingly valuable, as experimental methods for generating these datasets expand in scope and scale. Their method integrates two key features - (1) it reports the effects of mutations relative to the average across all variants, rather than a particular genotype, making it useful for examining global genetic architecture, and (2) it does this for all possible 20 states at each site, in contrast to the binary analyses in prior work. These features are not individually novel but integrating them into a single analysis framework is novel and will be valuable to the protein evolution community. Using a previously published dataset generated by two of the authors, they conclude that (1) changes in function are largely attributable to pairwise but not higher-order interactions, and (2) epistasis potentiates, rather than constrains, evolutionary paths. These findings are well-supported by the data. Overall, this work has important implications for predicting the relationship between genotype and phenotype, which is of considerable interest to protein biochemistry, evolutionary biology, and numerous other fields.

Author Response

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

Reviewer #1 (Public Review):

Summary:

The authors were trying to understand the relationship between the development of large trunks and longirrostrine mandibles in bunodont proboscideans of Miocene, and how it reflects the variation in diet patterns.

Strengths:

The study is very well supported, written, and illustrated, with plenty of supplementary material. The findings are highly significant for the understanding of the diversification of bunodont proboscideans in Asia during Miocene, as well as explaining the cranial/jaw disparity of fossil lineages. This work elucidates the diversification of paleobiological aspects of fossil proboscideans and their evolutionary response to open environments in the Neogene using several methods. The authors included all Asian bunodont proboscideans with long mandibles and I suggest that they should use the expression "bunodont proboscideans" instead of gomphotheres.

Weaknesses:

I believe that the only weakness is the lack of discussion comparing their results with the development of gigantism and long limbs in proboscideans from the same epoch.

Thank you for your comprehensive review and positive feedback on our study regarding the co-evolution of feeding organs in bunodont proboscideans during the Miocene. We appreciate your suggestion, and have decided to use the term "bunodont elephantiforms" (for more explicit clarification, we use elephantiforms to exclude some early proboscideans, like Moeritherium, ect.) instead of "gomphotheres," and we will make this change in our revised manuscript. We also appreciate the potential weakness you mentioned regarding the lack of discussion comparing our results with the development of gigantism and long limbs in proboscideans from the same epoch. We agree with the reviewer’s suggestion, and we are aware that gigantism and long limbs are potential factors for trunk development. Gigantism resulted in the loss of flexibility in elephantiforms, and long limbs made it more challenging for them to reach the ground. A long trunk serves as compensation for these limitations. limb bones were rare to find in our material, especially those preserved in association with the skull.

Reviewer #2 (Public Review):

This study focuses on the eco-morphology, the feeding behaviors, and the co-evolution of feeding organs of longirostrine gomphotheres (Amebelodontidae, Choerolophodontidae, and Gomphotheriidae) which are characterised by their distinctive mandible and mandible tusk morphologies. They also have different evolutionary stages of food acquisition organs which may have co-evolve with extremely elongated mandibular symphysis and tusks. Although these three longirostrine gomphothere families were widely distributed in Northern China in the Early-Middle Miocene, the relative abundances and the distribution of these groups were different through time as a result of the climatic changes and ecosysytems.

These three groups have different feeding behaviors indicated by different mandibular symphysis and tusk morphologies. Additionally, they have different evolutionary stages of trunks which are reflected by the narial region morphology. To be able to construct the feeding behavior and the relation between the mandible and the trunk of early elephantiformes, the authors examined the crania and mandibles of these three groups from the Early and Middle Miocene of northern China from three different museums and also made different analyses.

The analyses made in the study are:

(1) Finite Element (FE) analysis: They conducted two kinds of tests: the distal forces test, and the twig-cutting test. With the distal forces test, advantageous and disadvantageous mechanical performances under distal vertical and horizontal external forces of each group are established. With the twig-cutting test, a cylindrical twig model of orthotropic elastoplasity was posed in three directions to the distal end of the mandibular task to calculate the sum of the equivalent plastic strain (SEPS). It is indicated that all three groups have different mandible specializations for cutting plants.

(2) Phylogenetic reconstruction: These groups have different narial region morphology, and in connection with this, have different stages of trunk evolution. The phylogenetic tree shows the degree of specialization of the narial morphology. And narial region evolutionary level is correlated with that of character-combine in relation to horizontal cutting. In the trilophodont longirostrine gomphotheres, co-evolution between the narial region and horizontal cutting behaviour is strongly suggested.

(3) Enamel isotopes analysis: The results of stable isotope analysis indicate an open environment with a diverse range of habitats and that the niches of these groups overlapped without obvious differentiation.

The analysis shows that different eco-adaptations have led to the diverse mandibular morphology and open-land grazing has driven the development of trunk-specific functions and loss of the long mandible. This conclusion has been achieved with evidence on palaecological reconstruction, the reconstruction of feeding behaviors, and the examination of mandibular and narial region morphology from the detailed analysis during the study.

All of the analyses are explained in detail in the supplementary files. The 3D models and movies in the supplementary files are detailed and understandable and explain the conclusion. The conclusions of the study are well supported by data.

We appreciate your detailed and insightful review of our study. Your summary accurately captures the essence of our research, and we are pleased to note that multiple research methods were used to demonstrate our conclusions. Your recognition of the evidence-based conclusions from paleoecological, feeding behavior reconstruction, and morphological analyses reinforces the validity of our findings. Once again, we appreciate your time and thoughtful reviews.

Reviewer #1 (Recommendations For The Authors):

Thank you very much for the invitation to review this amazing manuscript. It is very well written and supported, and I have only minor suggestions to improve the text:

(1) Some references are not in chronological sequence in the text, and this should be reviewed.

We greatly appreciate the positive comments of the reviewer. We revised the reference of the manuscript as the reviewer’s suggestion.

(2) I suggest the use of the expression "bunodont proboscideans" instead of Gomphotheres because there is no agreement if Amebelodontidae and Choerolophodontidae are within Gomphotheriidae, as well as some brevirrostrine bunodont proboscideans from South America. So I think it is ok to use "Gomphotheriidae", but not gomphotheres to refer to all bunodont proboscideans included in the study.

The reviewer is correct. Using “gomphotheres” to refer to these three groups is inappropriate. We have replaced “gomphotheres” with "bunodont elephantiforms" throughout the entire manuscript. Here, we use “elephantiforms”, not “proboscideans”, to avoid confusion with some early proboscidean members like Moeritherium, ect.

(3) I was expecting some discussion on the development of large trunks related to the gigantism in these bunodont proboscideans, regarding the huge skulls and the columnar limbs.

We appreciate this suggestion, and we are aware that gigantism is a potential factor for trunk development. It is difficult to compare the three groups (Amebelodontidae, Choerolophodontidae, and Gomphotheriidae) in terms of their weight and limb bone length, because in our material, limb bones were rarely found, especially those associated with cranial material. Nevertheless, at this stage, all elephantiforms had significantly enlarged cranial sizes and limb bone lengths compared to early members like Phiomia. Gigantism caused the loss of flexibility in elephantiforms, and even the long limbs made it more difficult for an elephantiform to reach the ground. A long trunk compensates for this evolutionary change. Exploring these aspects further is a part of our future work.

(4) The reference to Alejandro et al should be replaced by Kramarz et al (and the correct surname of the authors). The name and surname of this reference need to be corrected. The correct names are Kramarz, A., Garrido, A., Bond, M. 2019. Please correct this in the text too.

We thank the reviewer for catching this error. This reference has been corrected.

Reviewer #2 (Recommendations For The Authors):

I believe your paper will lead to other studies on other Proboscidean groups on the evolution of the mandible and trunk. There are some corrections in the text:

• In line 199 in the text in pdf, "Tassy, 1994" should be "Tassy, 1996".

• In line 241, "studied" should be "studies"

• In line 313, "," after the word "tool" should be "."

We appreciate the reviewer for pointing these errors out and have revised these based on the suggestions.

• In the References, you write "et al." in some references. You should write the names of all of the authors.

• In the References: "Lister AM. 2013" and "Shoshani&Tassy" are not referenced in the text.

• In the References: "Tassy P. Gaps, parsimony, and early Miocene elephantoids (Mammalia), with a re-evaluation of Gomphotherium annectens (Matsumoto, 1925). Zool. J. Linn." should be "Tassy P. 1994. Gaps, parsimony, and early Miocene elephantoids (Mammalia), with a re-evaluation of Gomphotherium annectens (Matsumoto, 1925). Zool. J. Linn. 112, 1-2, 101-117" and replaced before "Tassy P. 1996".

We appreciate the reviewer’s suggestions and have revised these references.

Reviewer #1 (Public Review):

Summary:

The authors were trying to understand the relation between the development of large trunks and longirrostrine mandibles in bunodont proboscideans of Miocene, and how it reflects the variation in diet patterns.

Strengths:

The study is very well supported, written, and illustrated, with plenty Supplementary materials. The authors included all Asian bunodont proboscideans with long mandibles and I suggest that they should use the expression "bunodont proboscideans" instead of gomphotheres.

Weaknesses:

I believe that the only weakness is the lack of discussion comparing their results with the development of gigantism and long limbs in proboscideans from the same epoch.

The authors reviewed the manuscript according to my suggestions and responded well to all my comments.

eLife assessment

This study presents fundamental findings on the evolution of extremely elongated mandibular symphysis and tusks in longirostrine gomphotheres from the Early and Middle Miocene of northern China. The integration of multiple methods provides compelling results in the eco-morphology, behavioral ecology, and co-evolutionary biology of these taxa. In doing so, the authors elucidate the diversification of fossil proboscideans and their likely evolutionary responses to late Cenozoic global climatic changes.

Author Response

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

Reviewer #1

The authors provided experimental data in response to my comments/suggestions in the revision. Overall, most points were appropriate and satisfactory, but some issues remain.

(1) It is not fully addressed how atypical survivors are generated independently of Rad52-mediated homologous recombination.

The newly provided data indicate that the formation of atypical telomeres is independent of the Rad52 homologous recombination pathway.

"The atypical telomeres clones exhibit non-uniform telomere pattern", but the TG-hybridized signals after XhoI digestion are clear and uniform.

"Atypical telomere" clones may carry circular chromosomes embedded with short TG repeats, rather than linear chromosomes. In other words, atypical telomeres may differ from telomeres, the ends of chromosomes. Is atypical telomere formation dependent on NHEJ? Given that "two chromosomes underwent intra-chromosomal fusions" (Line 248), are atypical telomere clones detected frequently in SY13 cells containing two chromosomes?

We thank the reviewer’s questions. Frankly, we have not been able to determine the chromosome structures in these so-called "atypical survivors". As we mentioned in the manuscript, there could be mixed telomere structures, e.g. TG tract amplification, intro-chromosome telomere fusion and inter-chromosome telomere fusion. Worse still, these 'atypical survivors' may not have maintained a stable genome, and their karyotype may have undergone stochastic changes during passages. To avoid misunderstanding, we change the term "atypical" to "uncharacterized" in the revised manuscript.

We have previously shown that deletion of YKU70 does not affect MMEJ-mediated intra-chromosome fusion in single-chromosome SY14 cdc13Δ cells (Wu et al., 2020). In SY12 cells, double knockout of TLC1 and YKU resulted in synthetic lethality, and we were unable to continue our investigation. The result of synthetic lethality of TLC1 and YKU70 double deletion was shown in the Figure 7B in the reviewed preprint version 1, and the result was not included in the reviewed preprint version 2 in accordance with the reviewer's instructions.

"Atypical” survivors could be detected in SY13 cells (Figure 1D), but the frequency of their formation in the SY13 strain appeared to be lower than in SY12. As one can imagine, SY13 contains two chromosomes and its survivors should have a higher frequency of intra-chromosome fusions.

(2) From their data, it is possible that X and Y elements influence homologous recombination, type 1 and type 2 (type X), at telomeres. In particular, the presence of X and Y elements appears to be important for promoting type 1 recombination. In other words, although not essential, subtelomeres have some function in maintaining telomeres. I suggest that the authors include author response image 4 in the text. They could revise their conclusion and the paper title accordingly.

According to this suggestion, we have included author response image 4 in the revised manuscript as Figure 2E, Figure 5D, Figure 6C and Figure 6E. Accordingly, we have changed the title as “Elimination of subtelomeric repeat sequences exerts little effect on telomere essential functions in Saccharomyces cerevisiae”.

(3) Minor points: The newly added data indicate that X survivors are generated in a type 2-dependent manner. The authors could discuss how Y elements were eroded while retaining X elements (line 225, Figure 2A).

Thank this reviewer’s suggestion. We have discussed it in the revised manuscript (p.13 line 244-245). When telomere was deprotected, chromosome end resection took place. Since SY12 only has one Y’-element, it is hard to search homology sequences to repair the Y’-element in XVI-L. When the X-element in XVI-L was exposed by further resection, it is easier to find homology sequences to repair. So, in Type X survivor the Y’-element was eroded while retaining X-element.

Reviewer #2

I would like to congratulate the authors for their work and the efforts they put in improving the manuscript. The major criticism I had previously, ie testing the genetic requirements for the survivor subtypes, has been met. Below are a few minor comments that don't necessarily require a response.

(1) I think the Author response image 6 could have been included in the manuscript. I understand that the authors don't want to overinterpret survivor subtype frequencies, but this figure would have suggested some implication of Rad51 in the emergence of survivors even in the absence of Y' elements. At this stage, however, it is up to the authors, and leaving this figure out is also fine in my opinion.

According to the suggestion, the author response image 6 has been presented as Figure 6—figure supplement 7.

(2) Chromosome circularization seems to rely on microhomologies. Previously, the authors proposed that SY14 circularization depended on SSA (Wu et al. 2020), but here, since circularization appears to be Rad52-independent, it is likely to be based on MMEJ rather than SSA (although there are contradictory results on Rad52's role in MMEJ in the literature).

Yes, we mentioned it in the revised manuscript.

(3) p. 28 lines 511-513: "The erosion sites and fusion sequences differed from those observed in SY12 tlc1Δ-C1 cells (Figure 2D), suggesting the stochastic nature of chromosomal circularization": I don't think they are necessarily stochastic, because the sequences beyond the telomeres are now modified, the available microhomologies have changed as well.

We agreed with your opinion. In different chromosomes, there tend to be some hotspots for chromosome fusion. For example, in Figure 6C and 6F the resection site in Chr1 and Chr2 was the same in SY12XYΔ+Y tlc1Δ-C1 and SY12XYΔ tlc1Δ-C1. So, we speculate that there are some hotspots for chromosome fusion, but which site the cell will choose in one round chromosome fusion event is stochastic.

(4) Typos and other errors:

• p. 3 line 52: "subtelomerice" and "varies" are mispelled.

• p. 5 line 78: "processes" should be "process".

• Supp files are mislabelled (the numbers do not correspond to file name).

• Supp file 2: how come SY12 has only one Y' element and SY13 has two?

• p. 10 line 175: "emerging" should be "emergence".

• p.15 line 276: "counter-selected" should be "being counter-selected" or "counterselection".

• p. 29 line 523: "the formation of them" should be "their formation".

• p. 37 line 653: "could have been an ideal tool": the sentence is grammatically incorrect. Writing "AND could have been an ideal tool" is enough to make it structurally correct.

Thanks for pointing these errors out. We have corrected them in the revised manuscript. For the question “how come SY12 has only one Y' element and SY13 has two?” we were not sure at this moment. We speculated that one of the Y’ might be lost during genetic engineering of the chromosomes by CRISPR–Cas9 system.

Reviewer #3

The authors included statistical analyses of the qPCR data (Fig 4B) as requested, but did not comment on the striking difference in expression of MPH3 and HSP32 in the SY12 strain compared to BY4742. An improvement of the manuscript is the inclusion of rad52 tlc1 strains in their analyses, demonstrating that the "atypical and circular survivors" arose independently of homologous recombination. In addition, by analyzing rad51 and rad50 mutant strain they could demonstrate that the "type X" survivors had similar molecular requirements to type II survivors. Overall, the revised submission improves the article.

We thank the reviewer’s comments and suggestions. The SY12 strain (with three chromosomes) exhibited lower expression levels of both MPH3 and HSP32 compared to the parental strain BY4742 (with 16 chromosomes). We speculated that with the reduced chromosome numbers, the silencing proteins appeared to no longer be titrated by other telomeres that have been deleted. We have added these comments in the revised manuscript.

Wu, Z.J., Liu, J.C., Man, X., Gu, X., Li, T.Y., Cai, C., He, M.H., Shao, Y., Lu, N., Xue, X., et al. (2020). Cdc13 is predominant over Stn1 and Ten1 in preventing chromosome end fusions. Elife 9.

Reviewer #3 (Public Review):

This study investigates subtelomeric repetitive sequences in the budding yeast Saccharomyces cerevisiae, known as Y' and X-elements. Taking advantage of yeast strain SY12 that contains only 3 chromosomes and six telomeres (normal yeast strains contain 32 telomeres) the authors are able to generate a strain completely devoid of Y'- and X-elements.

Strengths:

They demonstrate that the SY12 delta XY strain displays normal growth, with stable telomeres of normal length that were transcriptionally silenced, a key finding with wide implications for telomere biology. Inactivation of telomerase in the SY12 and SY12 delta XY strains frequently resulted in survivors that had circularized all three chromosomes, hence bypassing the need for telomeres altogether. They show that survivors with fused chromosomes and so-called atypical survivors arise independently of the central recombination protein Rad52. The SY12 and SY12 delta XY yeast strains can become a useful tool for future studies of telomere biology. The conclusions of this manuscript are well supported by the data and are valuable for researchers studying telomeres.

Weaknesses:

A weakness of the manuscript is the analysis of telomere transcriptional silencing. They state: "The results demonstrated a significant increase in the expression of the MPH3 and HSP32 upon Sir2 deletion, indicating that telomere silencing remains effective in the absence of X and Y'-elements". However, for the SY12 strain, their analyses indicate that the difference between the WT and sir2 strains is nonsignificant. In addition, a striking observation is that the SY12 strain (with only three chromosomes) express much less of both MPH3 and HSP32 than the parental strain BY4742 (16 chromosomes), both in the presence and absence of Sir2.

eLife assessment

This important study advances our understanding of the biological significance of the DNA sequence adjacent to telomeres. The data presented convincingly demonstrate that subtelomeric repeats are non-essential and have a minimal, if any, role in maintaining telomere integrity of budding yeast. The work will be of interest to the telomere community specifically and the genome integrity community more broadly.

Reviewer #1 (Public Review):

The authors have generated a set of yeast S. cerevisiae strains containing different numbers of chromosomes.<br /> Elimination of telomerase activates homologous recombination (HR) to maintain telomeres in cells containing the original 16 chromosomes. However, elimination of telomerase leads to circularization of cells containing a single or two chromosomes. The authors examined whether the subtelomeric sequences X and Y' promote HR-mediated telomere maintenance using the strain SY12 carrying three chromosomes. They found that the subtelomeric sequences X and Y' are dispensable for cell proliferation and HR-mediated telomere maintenance in telomerase-minus SY12 cells. They conclude that subtelomeric X and Y' sequences do not play essential roles in both telomerase-proficient and telomerase-null cells and propose that these sequences represent remnants of genome evolution.

Interestingly, telomerase-minus SY12 generates survivors that are different from well-established Type I or Type II survivors. The authors uncover atypical telomere formation which does not depend on the Rad52 homologous recombination pathway.

Strengths:

The authors examined whether the subtelomeric sequences X and Y' promote HR-mediated telomere maintenance using the strain SY12 carrying three chromosomes. They show that subtelomeres do not have essential roles in telomere maintenance and cell proliferation.

Weaknesses:

It is not fully addressed how atypical survivors are generated independently of Rad52-mediated homologous recombination.<br /> It remains possible that X and Y elements influence homologous recombination, type 1 and type 2 (type X), at telomeres. In particular, the presence of X and Y elements appears to be important for promoting type 1 recombination, although the authors conclude "Elimination of subtelomeric repeat sequences exerts little effect on telomere functions".

Reviewer #2 (Public Review):

Summary:

In this work, Hu and colleagues investigate telomerase-independent survival in Saccharomyces cerevisiae strains engineered to have different chromosome numbers. The authors describe the molecular patterns of survival that change with fewer chromosomes and that differ from the well-described canonical Type I and Type II, including chromosome circularization and other atypical outcomes. They then take advantage of the strain with 3 chromosomes to examine the effect of deleting all the subtelomeric elements, called X and Y'. For most of the tested phenotypes, they find no significant effect of the absence of X- and Y'-element, and show that they are not essential for survivor formation. They speculate that X- and Y'-elements are remnants of ancient telomere maintenance mechanisms.

Strengths:

This work advances our understanding of the telomerase-independent strategies available to the cell by altering the structure of the genome and of the subtelomeres, a feat that was enabled by the set of strains they engineered previously. By using strains with non-standard genome structures, several alternative survival mechanisms are uncovered, revealing the diversity and plasticity of telomere maintenance mechanisms. Overall, the conclusions are well supported by the data, with adequate sample sizes for investigating survivors. The assessment of the genetic requirements for survivors in strains with different chromosome numbers greatly improved the quality of this work. The molecular analyses based on Southern blots are also very well-conducted.

Weaknesses:

The authors discovered alternative telomerase-independent survival strategies beyond the well-described type I and II (including circularization, type X and atypical, as they called them) at play in the context of reduced number of chromosomes. Their work provides a molecular and a partial genetic characterization of these survival pathways. A more thorough analysis of the frequency of each type of survivors and their genetic requirements would have advanced our understanding or the diversity of survival strategies in the absence of telomerase. However, as noted by the authors, the quantification of the rate of emergence of survivors (and their subtypes) is very difficult to achieve. This comment is therefore not meant as a criticism but rather as a perspective on exciting future research avenues.

Author Response

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

eLife assessment:

This valuable study describes a new role of epithelial intercellular adhesion molecule 1 (ICAM-1) protein in controlling bile duct size. The effect is mediated via EBP-50 and subapical actomyosin to regulate size of bile canaliculi. These solid findings have theoretical and practical implications in hepatology and human disorders of bile ducts.

Public Reviews:

In this study, Cacho-Navas et al. describe the role of ICAM-1 expressed on the apical membrane of bile canaliculi and its function to control the bile canaliculi (BCs) homeostasis. This is a previously unrecognized function of this protein in hepatocytes. The same authors have previously shown that basolateral ICAM-1 plays a role in controlling lymphocyte adhesion to hepatocytes during inflammation and that this interaction is responsible for the loss of polarity of hepatocytes during disease states.

This new study shows that ICAM-1 is mainly localized in the apical domain of the BC and in association with EBP-50, communicates with the subapical acto-myosin ring to regulate the size and morphology of the BC. They used the well-known immortal cell line of liver cells (HepG2) in which they deleted ICAM-1 gene by CRISPR-Cas9 editing and hepatic organoids derived from WT and ICAM-1-KO mice. alternating KO as well as rescue experiments. They show that in the absence of apical ICAM-1, the BC become dilated.

The data sufficiently support the conclusions of the study.

Recommendations for the authors:

We would like to thank the editor and reviewer for recognizing the manuscript's value and the solid nature of the data. We are also thankful to them for acknowledging that the manuscript supports the conclusions. Below, we have addressed their commentaries and questions in a point-by-point rebuttal document:

We have a few suggestions to improve the manuscript:

(1) HepG2 cells form canaliculi-like structures but are not the ideal system to study the apical basal polarity. On the other hand, hepatic organoids can assume a hepatocyte-like phenotype, when cultured under specific conditions but are not functionally comparable to hepatocytes organized in a 3D structure with a hollow lumen that does not recapitulate the BC physiological structure. Therefore, primary hepatocyte in collagen sandwich would be the best model to study the polarization of BCs and could be isolated from WT and ICAM-1-KO mice, that are available. Some of the major findings should be confirmed in this system.

We adopted the culture of hepatic organoids as an experimental strategy motivated by the difficulties to culture primary hepatocytes experienced in previous analyses (RegleroReal, Cell Rep, 2014). The generation of organoids or mature hepatocytes from various sources of stem cells is a commonly employed strategy in hepatocyte cell biology (Meyer et al. EMBO Rep, 2023), due to the difficulties in maintaining mature hepatic epithelial cell cultures for longer than a few hours.

The hepatic organoids we have used in the manuscript are being accepted as advanced cellular strategies for a broad range of fields (Belenguer, Nat Commun, 2022; de Crignis, eLife, 2021; Huch, Cell, 2015). Despite they have some morphological differences with real hepatocytes, we conducted a thorough characterization of their organization identifying canalicular-like structures with functional (CFDA) and molecular (HA-4) markers, which we believe adds value to the manuscript. In addition, the organoid technology has allowed us to import the bipotent precursors to get an permanent source of hepatic cells without the need to import and use the ICAM-1_KO mice, in line with the current guides to reduce animal experimentation.

Taking this into account and to further validate data obtained with our cellular systems, we carried out a quantification of the canalicular diameter in livers from WT and ICAM1_KO cells (New Figure 8B), which validates our data on human cell lines and organoids. We acknowledge that the data obtained from hepatic tissues cannot rule out the contribution of immune cell adhesion to changes in the hepatocyte architecture. However, these experiments, together with the aforementioned organoids and human cell lines, strongly suggest a role for hepatic ICAM-1 in regulating canalicular size.

(2) Overexpression of proteins was used in the study. While this approach is an easier means to visualize, without the use of specific antibodies, it is known to alter the distribution of the protein compared to the endogenous one.

Most of our characterization has been done with antibodies or other fluorescent tools against endogenous proteins localized at BCs: CD59, F-actin, EBP50, MHC, MLC…. In addition, we have included MDR1-GFP and GFP-Rab11, the latter to analyze the subapical compartment (SAC) surrounding BCs. As requested by the reviewer, we now include in a new Supplementary Figure 1C the confocal analyses of endogenous canalicular markers, radixin and MRP2, as well as a new Supplementary Figure 1D containing the staining of an endogenous marker of the SAC, plasmolipin/PLLP (Fraticelli et al, Nat Cell Biol, 2015; Cacho-Navas, Cell Mol Life Sci, 2022), which is consistent with the previous analyses performed with GFP-Rab11.

(3) In the absence of ICAM-1, BCs change shape and dimension but still show the presence of microvilli. What happens to the distribution of polarized transporters like Mrp2, or the transport of bile acids (CFDA clearance) in vivo in the KO animal?

Thank you for this comment. We have analyzed this transporter in murine livers and human hepatic cells. MRP2 distribution does not significantly change and is concentrated in BCs also in ICAM-1_KO livers (New Figure 8C). Likewise, ICAM-1 gene edition does not affect MRP2 localization in the polarized human hepatic epithelial cell line in vitro (Supplementary Figure 1C). We cannot rule out changes for this transporter in other murine liver cell types in vivo, such as sinusoidal endothelial cells, which we believe should be further addressed in a different piece of work.

(4) Does the lack of ICAM-1 affect the cell viability, proliferation or cell size?

ICAM-1_KO cells proliferate slightly more slowly than their WT counterparts, with no detected changes in cell size and death. We present these data in Supplementary Figure 1, A and B.

(5) Are the findings recapitulated in the livers of ICAM-1 KO animals?

ICAM-1 KO animals present enlarged BCs, which is consistent with the main findings of the manuscript (Figure 8B).

The text needs to be more concise. Some of the concepts, in particular those already published, should be condensed. There is a large amount of experiments that are difficult to connect logically. Possibly, cartoons summarizing the approach of the figure could help the reader.

The text of Results and Discussion sections has been shortened by almost 100 words, despite the additional panels and experiments are now described and discussed. New cartoons have been added in Figure 5G and Figure 8F, in addition to those previously included in Figure 1 and Supplementary Figure 6, the latter containing a graphical descriptions of the main conclusions.

Also, more detailed information about statistical analysis (what post-test was used?), concentration of cytokines, and description of the mouse model should be included in the methods.

Cytokine concentrations have been included in the legend of Figure 3 and in the Cell and Culture section of Methods. A brief description of the ICAM-1_KO mouse and the corresponding reference for further information is also provided in the Organoid Culture section of Methods. A statistical analysis section describing the post-test used is also included at the end of Methods. The references of anti-plasmolipin, anti-radixin and antiMRP2 antibodies, as well as the new fixation methods used for immunofluorescence are also included in the corresponding Antibody List and in the Confocal Microscopy section of Methods, respectively . .

Figure 3D. Sample names should be added as in the rest of the figures.

The arrangement of sample names in Figure 3D has been revised and is now similar to that of Figure 3A.

eLife assessment

The authors report useful findings on novel function of apical ICAM1 in regulating bile duct homeostasis in the liver. The strength of evidence is solid using appropriate methodolgy with only minor weakness. The findings will be of interest to researchers in hepatology and membrane traffic biology.

Reviewer #1 (Public Review):

In this study Cacho-Navas et al. describes the role of ICAM-1 expressed on the apical membrane of bile canaliculi and its function to control the homeostasis of the bile canaliculi (BCs). This is a previously unrecognized function of this protein in hepatocytes. The same authors have previously shown that basolateral ICAM-1 plays a role in controlling lymphocyte adhesion to hepatocytes during inflammation and that this interaction is responsible on the loss of polarity of hepatocytes during the disease.<br /> In this new study they show that ICAM-1, is mainly localized in the apical domain of the BC and in association with EBP-50, comunicates with the subapical acto-myosin ring to regulate the size and morphology of the BC.<br /> In this study they used the well-known immortal cell line of liver cells (HepG2) in which they knocked-out ICAM-1 using CRISPR-Cas9 editing and hepatic organoid derived from WT and ICAM-1-KO mice. alternating knocking-out as well as rescue experiments they show that in the absence of apical ICAM-1, the BC dimension and shape are altered.<br /> The conclusions of the study are sufficiently supported by the data.

Comments on revision:

The authors have addressed most of the reviewer's comments in the re-submission, however the use of the organoids as a model to study bile canaliculi is still not convincing.<br /> The HA-4 staining and the space wehere CFDA is secreted does not overlap considering the nuclei position in the middle z-stack section. Also, the interdigitations between cells identified by EM do not form an enclosed space as we should expect for a bile canaliculi.<br /> I understand that other studies have used these organoids to show some hepatocytic functions but at the same time none has characterized before the formation of bile canaliculi as suggested in this study. Therefore a characterization showing the expression of specific markers (i.e mrp2, bsep) should be provided to support this claim.<br /> I would suggest the authors to carefully read the helpful review by Marsee et al., Cell Stem Cell 2021 that clearly and carefully address the classification and validation of liver organoids from experts in the field.

eLife assessment

The paper reports rare compound heterozygous deletion variants that affect the kinase domains of non-receptor tyrosine kinases TNK and ACK1 in families with human systemic lupus erythematosus (SLE). Using a mouse experimental model and human induced pluripotent stem cell (hiPSC)-derived macrophages, the study provides solid evidence that clarifies cause-effect relationships and that suggests a potential cellular mechanism underlying the resultant nephritis. With the identification of novel SLE-related genes, this manuscript provides an important basis for understanding the molecular and cellular basis of SLE pathogenesis.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors revealed that genetic deficiencies of ACK1 and BRK are associated with human SLE. First, the authors found that compound heterozygous deleterious variants in the kinase domains of the non-receptor tyrosine kinases (NRTK) TNK2/ACK1 in one multiplex family and PTK6/BRK in another family. Then, by an experimental blockade of ACK1 or BRK in a mouse SLE model, they found an increase in glomerular IgG deposits and circulating autoantibodies. Furthermore, they reported that ACK and BRK variants from the SLE patients impaired the MERTK-mediated anti-inflammatory response to apoptotic cells in human induced pluripotent stem cells (hiPSC)-derived macrophages. This work identified new SLE-associated ACK and BRK variants and a role for the NRTK TNK2/ACK1 and PTK6/BRK in efferocytosis, providing a new molecular and cellular mechanism of SLE pathogenesis.

Strengths:

This work identified new SLE-associated ACK and BRK variants and a role for the NRTK TNK2/ACK1 and PTK6/BRK in efferocytosis, providing a new molecular and cellular mechanism of SLE pathogenesis.

Weaknesses:

Although the manuscript is well-organized and clearly stated, there are some points below that should be considered:

* In this study, the authors used forward genetic analyses to identify novel gene mutations that may cause SLE, combined with GWAS studies of SLE. To further explore the importance of these variants, haplotype analysis of two candidate genes could be performed, to observe the evolution and selection relationship of candidate genes in the population (UK 1000 biobank, for example).

* Although the authors focused on SLE and macrophage efferocytosis in their studies, direct evidence of how macrophage efferocytosis significantly affects SLE is lacking. This point should at least be explicitly introduced and discussed by citing appropriate literature.

* It is still not clear how the target molecules identified in this paper may influence macrophage efferocytosis. More direct evidence should be established.

* For some transcriptional repressors mentioned in their studies, the authors should check whether there is clear experimental evidence. If not, it is recommended to supplement the experimental verifications for clarity.

* In Figures 4C and 4D, it is seen that the usage of inhibitors causes cytoskeletal changes, however this reviewer would not have expected such large change. Did the authors check whether the cells die after heavy treatment by the inhibitors?

Reviewer #1 (Public Review):

Summary:

The authors report compound heterozygous deleterious variants in the kinase domains of the non-receptor tyrosine kinases (NRTK) TNK2/ACK1 in familial SLE. They suggest that ACK1 and BRK deficiencies are associated with human SLE and impair efferocytosis.

Strengths:

The identification of similar mutations in non-receptor tyrosine kinases (NRTKs) in two different families with familial SLE is a significant finding in human disease. Furthermore, the paper provides a detailed analysis of the molecular mechanisms behind the impairment of efferocytosis caused by mutations in ACK1 and BRK.

Weaknesses:

A critical point in this paper is whether the loss of function of ACK1 or BRK contributes to the onset of familial SLE. The authors emphasize that inhibitors of ACK1/BRK worsened IgG deposition in the kidneys in a pristane-induced SLE model, which contributes not to the onset but to the exacerbation of SLE, thus only partially supporting their claim.

Reviewer #1 (Public Review):

Summary:

This manuscript investigates the regulation of chlorophyll biosynthesis in rice embryos, focusing on the role of OsNF-YB7. The rigorous experimental approach, combining genetic, biochemical, and molecular analyses, provides a robust foundation for these findings. The research achieves its objectives, offering new insights into chlorophyll biosynthesis regulation, with the results convincingly supporting the authors' conclusions.

Strengths:

The major strengths include the detailed experimental design and the findings regarding OsNF-YB7's inhibitory role.

Weaknesses:

However, the manuscript's discussion on the practical implications for agriculture and the evolutionary analysis of regulatory mechanisms could be expanded.

eLife assessment

This is an important study on the regulation of chlorophyll biosynthesis in rice embryos. It provides insights into the genetic and molecular interactions that underlie chlorophyll accumulation, highlighting the inhibition of OsGLK1 by OsNF-YB7 and the broader implications for understanding chloroplast development and seed maturation in angiosperms. The results presented, including mutation analysis, gene expression profiles, and protein interaction studies, provide convincing evidence for the function of OsNF-YB7 as a repressor in the chlorophyll biosynthesis pathway.

Reviewer #2 (Public Review):

Summary:

The authors set out to establish the role of the rice LEC1 homolog OsNF-YB7 in embryo development, especially as it pertains to the development of photosynthetic capacity, with chlorophyll production as a primary focus.

Strengths:

The results are well-supported and each approach used complements each other. There are no major questions left unanswered and the central hypothesis is addressed in every figure.

Weaknesses:

There are a handful of sections that could use clarifying for readers, but overall this is a solidly composed manuscript.

The authors clearly achieved their aims; the results compellingly establish a disparity between how this system operates in rice and Arabidopsis. Conclusions are thoroughly supported by the provided data and interpretations. This work will force a reconsideration of the value of Arabidopsis as a model organism for embryo chlorophyll biosynthesis and possibly photosynthesis during embryo maturation more broadly, as rice is a major crop organism and it very clearly does not follow the Arabidopsis model. It will thus be useful to carry out similar tests in other organisms rather than relying on Arabidopsis and attempting to more fully establish the regulatory mechanism in rice.

Reviewer #3 (Public Review):

Summary:

In this study, the authors set out to understand the mechanisms behind chlorophyll biosynthesis in rice, focusing in particular on the role of OsNF-YB7, an ortholog of Arabidopsis LEC1, which is a positive regulator of chlorophyll (Chl) biosynthesis in Arabidopsis. They showed that OsNF-YB7 loss-of-function mutants in rice have chlorophyll-rich embryos, in contrast to Arabidopsis LEC1 loss-of-function mutants. This contrasting phenotype led the authors to carry out extensive molecular studies on OsNF-YB7, including in vitro and in vivo protein interaction studies, gene expression profiling, and protein-DNA interaction assays. The evidence provided well supported the core arguments of the authors, emphasising that OsNF-YB7 is a negative regulator of Chl biosynthesis in rice embryos by mediating the expression of OsGLK1, a transcription factor that regulates downstream Chl biosynthesis genes. In addition, they showed that OsNF-YB7 interacts with OsGLK1 to negatively regulate the expression of OsGLK1, demonstrating the broad involvement of OsNF-YB7 in rice Chl biosynthetic pathways.

Strengths:

This study clearly demonstrated how OsNF-YB7 regulates its downstream pathways using several in vitro and in vivo approaches. For example, gene expression analysis of OsNF-YB7 loss-of-function and gain-of-function mutants revealed the expression of selected downstream chl biosynthetic genes. This was further validated by EMSA on the gel. The authors also confirmed this using luciferase assays in rice protoplasts. These approaches were used again to show how the interaction of OsNF-YB7 and OsGLK1 regulates downstream genes. The main idea of this study is very well supported by the results and data.

Weaknesses:

From an evolutionary perspective, it is interesting to see how two similar genes have come to play opposite roles in Arabidopsis and rice. It would have been more interesting if the authors had carried out a cross-species analysis of AtLEC1 and OsNF-YB7. For example, overexpressing AtLEC1 in an osnf-yb7 mutant to see if the phenotype is restored or enhanced. Such an approach would help us understand how two similar proteins can play opposite roles in the same mechanism within their respective plant species.

eLife assessment

This important study combines a range of biophysical techniques to carry out a series of compelling experiments to explore whether glutamine binding protein binds glutamine via an induced fit or a conformational selection process. The evidence supporting the major conclusion of the work is convincing, although it may not be generalized to other protein-ligand or protein-protein systems. The work will be of broad interest to biochemists and biophysicists.

Reviewer #1 (Public Review):

Here the authors discuss mechanisms of ligand binding and conformational changes in GlnBP (a small E Coli periplasmic binding protein, which binds and carries L-glutamine to the inner membrane ATP-binding cassette (ABC) transporter). The authors have distinguished records in this area and have published seminal works. They include experimentalists and computational scientists. Accordingly, they provide comprehensive, high-quality, experimental and computational work.

They observe that apo- and holo- GlnBP does not generate detectable exchange between open and (semi-) closed conformations on timescales between 100 ns and 10 ms. Especially, the ligand binding and conformational changes in GlnBP that they observe are highly correlated. Their analysis of the results indicates a dominant induced-fit mechanism, where the ligand binds GlnBP prior to conformational rearrangements. They then suggest that an approach resembling the one they undertook can be applied to other protein systems where the coupling mechanism of conformational changes and ligand binding.

They argue that the intuitive model where ligand binding triggers a functionally relevant conformational change was challenged by structural experiments and MD simulations revealing the existence of unliganded closed or semi-closed states and their dynamic exchange with open unbound conformations, discuss alternative mechanisms that were proposed, their merits and difficulties, concluding that the findings were controversial, which, they suggest is due to insufficient availability of experimental evidence to distinguish them. As to further specific conclusions they draw from their results, they determine that a conformational selection mechanism is incompatible with their results, but induced fit is. They thus propose induced fit as the dominant pathway for GlnBP, further supported by the notion that the open conformation is much more likely to bind substrate than the closed one based on steric arguments.

Considering the landscape of substrate-free states, in my view, the closed state is likely to be the most stable and, thus most highly populated. As the authors note and I agree that state can be sterically infeasible for a deep-pocketed substrate. As indeed they also underscore, there is likely to be a range of open states. If the populations of certain states are extremely low, they may not be detected by the experimental (or computational) methods. The free energy landscape of the protein can populate all possible states, with the populations determined by their relative energies. In principle, the protein can visit all states. Whether a particular state is observed depends on the time the protein spends in that state. The frequencies, or propensities, of the visits can determine the protein function. As to a specific order of events, in my view, there isn't any. It is a matter of probabilities which depend on the populations (energies) of the states. The open conformation that is likely to bind is the most favorable, permitting substrate access, followed by minor, induced fit conformational changes. However, a key factor is the ligand concentration. Ligand binding requires overcoming barriers to sustain the equilibrium of the unliganded ensemble, thus time. If the population of the state is low, and ligand concentration is high (often the case in in vitro experiments, and high drug dosage scenarios) binding is likely to take place across a range of available states.

This is however a personal interpretation of the data. The paper here, which clearly embodies massive careful, and high-quality work, is extensive, making use of a range of experimental approaches, including isothermal titration calorimetry, single-molecule Förster resonance energy transfer, and surface-plasmon resonance spectroscopy. The problem the authors undertake is of fundamental importance.

Reviewer #2 (Public Review):

Summary:

The manuscript by Han et al and Cordes is a tour-de-force effort to distinguish between induced fit and conformational selection in glutamine binding protein (GlnBP). It is important to say that I don't agree that a decision needs to be made between these two limiting possibilities in the sense that whether a minor population can be observed depends on the experiment and the energy difference between the states. That said, the authors make an important distinction which is that it is not sufficient to observe both states in the ligand-free solution because it is likely that the ligand will not bind to the already closed state. The ligand binds to the open state and the question then is whether the ligand sufficiently changes the energy of the open state to effectively cause it to close. The authors point out that this question requires both a kinetic and a thermodynamic answer. Their "method" combines isothermal titration calorimetry, single-molecule FRET including key results from multi-parameter photon-by-photon hidden Markov modelling (mpH2MM), and SPR. The authors present this "method" of combination of experiments as an approach to definitively differentiate between induced fit and conformational selection. I applaud the rigor with which they perform all of the experiments and agree that others who want to understand the exact mechanism of protein conformational changes connected to ligand binding need to do such a multitude of different experiments to fully characterize the process. However, the situation of GlnBP is somewhat unique in the high affinity of the Gln (slow off-rate) as compared to many small molecule binding situations such as enzyme-substrate complexes. It is therefore not surprising that the kinetics result in an induced fit situation. In the case of the E-S complexes I am familiar with, the dissociation is much more rapid because the substrate binding affinity is in the micromolar range and therefore the re-equilibration of the apo state is much faster. In this case, the rate of closing and opening doesn't change much whether ligand is present or not. Here, of course, once the ligand is bound the re-equilibration is slow. Therefore, I am not sure if the conclusions based on this single protein are transferrable to most other protein-small molecule systems. I am also not sure if they are transferrable to protein-protein systems where both molecules the ligand and the receptor are expected to have multiscale dynamics that change upon binding.

Strengths:

The authors provide beautiful ITC data and smFRET data to explore the conformational changes that occur upon Gln binding. Figure 3D and Figure 4 (mpH2MM data) provide the really critical data. The multi-parameter photon-by-photon hidden Markov modelling (mpH2MM) data. In the presence of glutamine concentrations near the Kd, two FRET-active sub-populations are identified that appear to interconvert on timescales slower than 10 ms. They then do a whole bunch of control experiments to look for faster dynamics (Figure 5). They also do TIRF smFRET to try to compare their results to those of previous publications. Here, they find several artifacts are occurring including inactivation of ~50% of the proteins. They also perform SPR experiments to measure the association rate of Gln and obtain expectedly rapid association rates on the order of 10^8 M-1s-1.

Weaknesses:

Looking at the traces presented in the supplementary figures, one can see that several of the traces have more than one molecule present. The authors should make sure that they use only traces with a single photobleaching event for each fluorophore. One can see steps in some of the green traces that indicate two green fluorophors (likely from 2 different molecules) in the traces. This is one of the frequent problems with TIRF smFRET with proteins, that only some of the spots represent single molecules and the rest need to be filtered out of the analysis.

The NMR experiments that the authors cite are not in disagreement with the work presented here. NMR is capable of detecting "invisible states" that occur in 1-5% of the population. SmFRET is not capable of detecting these very minor states. I am quite sure that if NMR spectroscopists could add very high concentrations of Gln they would also see a conversion to the closed population.

eLife assessment

This paper provides a useful analysis of the variation of the burden of strokes across geographic regions, finding differences in the relationship between strokes and their comorbidities. This dataset and the correlations found within will be a resource for directing the focus of future investigations. The statistical analyses are incomplete.

Reviewer #1 (Public Review):

Summary:

The paper measures the prevalence and mortality of stroke and its comorbidities across geographic regions in order to find differences in risks that may lead to more effective guidance for these subpopulations. It also does a genetic analysis to look for variants that may drive these phenotypic variations.

Strengths:

The data provided here will provide a foundation for a lot of future research into the causes of the observed correlations as well as whether the observed differences in comorbidities across regions have clinically relevant effects on risk management.

Weaknesses:

As with any cross-national analysis of rates, the data is vulnerable to differences in classification and reporting across jurisdictions. Furthermore, given the increased death rate from COVID-19 associated with many of these comorbid conditions and the long-term effects of COVID-19 infection on vascular health, it is expected that many of the correlations observed in this dataset will shift along with the shifting health of the underlying populations.

Reviewer #2 (Public Review):

Summary:

The authors have analyzed ethnogeographic differences in the comorbidity factors, such as diabetes and heart disease, for the incidences of stroke and whether it leads to mortality.

Strengths:<br /> The idea is interesting and the data are compelling. The results are technically solid.

The authors identify specific genetic loci that increase the risk of a stroke and how they differ by region.

Weaknesses:

The presentation is not focused. It would be better to include p-values and focus presentation on the main effects of the dataset analysis.

eLife assessment

This study provides valuable information on how Arg-II participates in cardiac aging. Although the phenotypic data appear robust, the study is incomplete in elucidating the mechanisms, particularly in explaining how Arg II influences IL-1b and affects cardiac aging. It would be beneficial to investigate the possibility of NO involvement in this mice model. A co-culture system may be required to understand the non-cell-autonomous functions of macrophages. Lastly, the MI mouse model may not be directly linked to cardiac aging.

Reviewer #1 (Public Review):

Summary:

The manuscript by Duilio M. Potenza et al. explores the role of Arginase II in cardiac aging, majorly using whole-body arg-ii knock-out mice. In this work, the authors have found that Arg-II exerts non-cell-autonomous effects on aging cardiomyocytes, fibroblasts, and endothelial cells mediated by IL-1b from aging macrophages. The authors have used arg II KO mice and an in vitro culture system to study the role of Arg II. The authors have also reported the cell-autonomous effect of Arg-II through mitochondrial ROS in fibroblasts that contribute to cardiac aging. These findings are sufficiently novel in cardiac aging and provide interesting insights. While the phenotypic data seems strong, the mechanistic details are unclear. How Arg II regulates the IL-1b and modulates cardiac aging is still being determined. The authors still need to determine whether Arg II in fibroblasts and endothelial contributes to cardiac fibrosis and cell death. This study also lacks a comprehensive understanding of the pathways modulated by Arg II to regulate cardiac aging.

Strengths:

This study provides interesting information on the role of Arg II in cardiac aging.

The phenotypic data in the arg II KO mice is convincing, and the authors have assessed most of the aging-related changes.

The data is supported by an in vitro cell culture system.

Weaknesses:

The manuscript needs more mechanistic details on how Arg II regulates IL-1b and modulates cardiac aging.

The authors used whole-body KO mice, and the role of macrophages in cardiac aging is not studied in this model. A macrophage-specific arg II Ko would be a better model.

Experiments need to validate the deficiency of Arg II in cardiomyocytes.

The authors have never investigated the possibility of NO involvement in this mice model.

A co-culture system would be appropriate to understand the non-cell-autonomous functions of macrophages.

The Myocardial infarction data shown in the mice model may not be directly linked to cardiac aging.

Reviewer #2 (Public Review):

Summary:

The results from this study demonstrated a cell-specific role of mitochondrial enzyme arginase-II (Arg-II) in heart aging and revealed a non-cell-autonomous effect of Arg-II on cardiomyocytes, fibroblasts, and endothelial cells through the crosstalk with macrophages via inflammatory factors, such as by IL-1, as well as a cell-autonomous effect of Arg-II through mtROS in fibroblasts contributing to cardiac aging phenotype. These findings highlight the significance of non-cardiomyocytes in the heart and bring new insights into the understanding of pathologies of cardiac aging. It also provides new evidence for the development of therapeutic strategies, such as targeting the ArgII activation in macrophages.

Strengths:

This study targets an important clinical challenge, and the results are interesting and innovative. The experimental design is rigorous, the results are solid, and the representation is clear. The conclusion is logical and justified.

Weaknesses:

The discussion could be extended a little bit to improve the realm of the knowledge related to this study.

eLife assessment

This useful paper looks for correlations between immunophenotypic markers and several measures of HIV reservoir volume in cross-sectional cohorts of people living with HIV on ART using several bioinformatic and machine-learning tools. The level of evidence linking these variables is incomplete given possible confounding variables, lack of directionality & effect size, and mechanistic basis.

Reviewer #1 (Public Review):

Summary:

Semenova et al. have studied a large cross-sectional cohort of people living with HIV on suppressive ART, N=115, and performed high dimensional flow cytometry to then search for associations between immunological and clinical parameters and intact/total HIV DNA levels.

A number of interesting data science/ML approaches were explored on the data and the project seems a serious undertaking. However, like many other studies that have looked for these kinds of associations, there was not a very strong signal. Of course, the goal of unsupervised learning is to find new hypotheses that aren't obvious to human eyes, but I felt in that context, there were (1) results slightly oversold, (2) some questions about methodology in terms mostly of reservoir levels, and (3) results were not sufficiently translated back into meaning in terms of clinical outcomes.

Strengths:

The study is evidently a large and impressive undertaking and combines many cutting-edge statistical techniques with a comprehensive experimental cohort of people living with HIV, notably inclusive of populations underrepresented in HIV science. A number of intriguing hypotheses are put forward that could be explored further. Sharing the data could create a useful repository for more specific analyses.

Weaknesses:

Despite the detailed experiments and methods, there was not a very strong signal for the variable(s) predicting HIV reservoir size. The Spearman coefficients are ~0.3, (somewhat weak, and acknowledged as such) and predictive models reach 70-80% prediction levels, though sometimes categorical variables are challenging to interpret.

There are some questions about methodology, as well as some conclusions that are not completely supported by results, or at minimum not sufficiently contextualized in terms of clinical significance.

On associations: the false discovery rate correction was set at 5%, but data appear underdetermined with fewer observations than variables (144vars > 115ppts), and it isn't always clear if/when variables are related (e.g inverses of one another, for instance, %CD4 and %CD8).

The modeling of reservoir size was unusual, typically intact and defective HIV DNA are analyzed on a log10 scale (both for decays and predicting rebound). Also sometimes in this analysis levels are normalized (presumably to max/min?, e.g. S5), and given the large within-host variation of level we see in other works, it is not trivial to predict any downstream impact of normalization across population vs within-person.

Also, the qualitative characterization of low/high reservoir is not standard and naturally will split by early/later ART if done as above/below median. Given the continuous nature of these data, it seems throughout that predicting above/below median is a little hard to translate into clinical meaning.

Lastly, the work is comprehensive and appears solid, but the code was not shared to see how calculations were performed.

Reviewer #2 (Public Review):

Summary:

Semenova et. al., performed a cross-sectional analysis of host immunophenotypes (using flow cytometry) and the peripheral CD4+ T cell HIV reservoir size (using the Intact Proviral DNA Assay, IPDA) from 115 people with HIV (PWH) on ART. The study mostly highlights the machine learning methods applied to these host and viral reservoir datasets but fails to interpret these complex analyses into (clinically, biologically) interpretable findings. For these reasons, the direct translational take-home message from this work is lost amidst a large list of findings (shown as clusters of associated markers) and sentences such as "this study highlights the utility of machine learning approaches to identify otherwise imperceptible global patterns" - lead to overinterpretation of their data.

Strengths:

Measurement of host immunophenotyping measures (multiparameter flow cytometry) and peripheral HIV reservoir size (IPDA) from 115 PWH on ART.

Major Weaknesses:

(1) Overall, there is little to no interpretability of their machine learning analyses; findings appear as a "laundry list" of parameters with no interpretation of the estimated effect size and directionality of the observed associations. For example, Figure 2 might actually give an interpretation of each X increase in immunophenotyping parameter, we saw a Y increase/decrease in HIV reservoir measure.

(2) The correlations all appear to be relatively weak, with most Spearman R in the 0.30 range or so.

(3) The Discussion needs further work to help guide the reader. The sentence: "The correlative results from this present study corroborate many of these studies, and provide additional insights" is broad. The authors should spend some time here to clearly describe the prior literature (e.g., describe the strength and direction of the association observed in prior work linking PD-1 and HIV reservoir size, as well as specify which type of HIV reservoir measures were analyzed in these earlier studies, etc.) and how the current findings add to or are in contrast to those prior findings.

(4) The most interesting finding is buried on page 12 in the Discussion: "Uniquely, however, CD127 expression on CD4 T cells was significantly inversely associated with intact reservoir frequency." The authors should highlight this in the abstract, and title, and move this up in the Discussion. The paper describes a very high dimensional analysis and the key takeaways are not clear; the more the author can point the reader to the take-home points, the better their findings can have translatability to future follow-up mechanistic and/or validation studies.

(5) The authors should avoid overinterpretation of these results. For example in the Discussion on page 13 "The existence of two distinct clusters of PWH with different immune features and reservoir characteristics could have important implications for HIV cure strategies - these two groups may respond differently to a given approach, and cluster membership may need to be considered to optimize a given strategy." It is highly unlikely that future studies will be performing the breadth of parameters resulting here and then use these directly for optimizing therapy.

(6) There are only TWO limitations listed here: cross-sectional study design and the use of peripheral blood samples. (The subsequent paragraph notes an additional weakness which is misclassification of intact sequences by IPDA). This is a very limited discussion and highlights the need to more critically evaluate their study for potential weaknesses.

(7) A major clinical predictor of HIV reservoir size and decay is the timing of ART initiation. The authors should include these (as well as other clinical covariate data - see #12 below) in their analyses and/or describe as limitations of their study.

Reviewer #3 (Public Review):

Summary:

This valuable study by Semenova and colleagues describes a large cross-sectional cohort of 115 individuals on ART. Participants contributed a single blood sample which underwent IPDA, and 25-color flow with various markers (pre and post-stimulation). The authors then used clustering, decision tree analyses, and machine learning to look for correlations between these immunophenotypic markers and several measures of HIV reservoir volume. They identified two distinct clusters that can be somewhat differentiated based on total HIV DNA level, intact HIV DNA level, and multiple T cell cellular markers of activation and exhaustion.

The conclusions of the paper are supported by the data but the relationships between independent and dependent variables in the models are correlative with no mechanistic work to determine causality. It is unclear in most cases whether confounding variables could explain these correlations. If there is causality, then the data is not sufficient to infer directionality (ie does the immune environment impact the HIV reservoir or vice versa or both?). In addition, even with sophisticated and appropriate machine learning approaches, the models are not terribly predictive or highly correlated. For these reasons, the study is very much hypothesis-generating and will not impact cure strategies or HIV reservoir measurement strategies in the short term.

Strengths:

The study cohort is large and diverse in terms of key input variables such as age, gender, and duration of ART. Selection of immune assays is appropriate. The authors used a wide array of bioinformatic approaches to examine correlations in the data. The paper was generally well-written and appropriately referenced.

Weaknesses:

(1) The major limitation of this work is that it is highly exploratory and not hypothesis-driven. While some interesting correlations are identified, these are clearly hypothesis-generating based on the observational study design.

(2) The study's cross-sectional nature limits the ability to make mechanistic inferences about reservoir persistence. For instance, it would be very interesting to know whether the reservoir cluster is a feature of an individual throughout ART, or whether this outcome is dynamic over time.

(3) A fundamental issue is that I am concerned that binarizing the 3 reservoir metrics in a 50/50 fashion is for statistical convenience. First, by converting a continuous outcome into a simple binary outcome, the authors lose significant amounts of quantitative information. Second, the low and high reservoir outcomes are not actually demonstrated to be clinically meaningful: I presume that both contain many (?all) data points above levels where rebound would be expected soon after interruption of ART. Reservoir levels would also have no apparent outcome on the selection of cure approaches. Overall, dividing at the median seems biologically arbitrary to me.

(4) The two reservoir clusters are of potential interest as high total and intact with low % intact are discriminated somewhat by immune activation and exhaustion. This was the most interesting finding to me, but it is difficult to know whether this clustering is due to age, time on ART, other co-morbidity, ART adherence, or other possible unmeasured confounding variables.

(5) At the individual level, there is substantial overlap between clusters according to total, intact, and % intact between the clusters. Therefore, the claim in the discussion that these 2 cluster phenotypes may require different therapeutic approaches seems rather speculative. That said, the discussion is very thoughtful about how these 2 clusters may develop with consideration of the initial insult of untreated infection and / or differences in immune recovery.

(6) The authors state that the machine learning algorithms allow for reasonable prediction of reservoir volume. It is subjective, but to me, 70% accuracy is very low. This is not a disappointing finding per se. The authors did their best with the available data. It is informative that the machine learning algorithms cannot reliably discriminate reservoir volume despite substantial amounts of input data. This implies that either key explanatory variables were not included in the models (such as viral genotype, host immune phenotype, and comorbidities) or that the outcome for testing the models is not meaningful (which may be possible with an arbitrary 50/50 split in the data relative to median HIV DNA volumes: see above).

(7) The decision tree is innovative and a useful addition, but does not provide enough discriminatory information to imply causality, mechanism, or directionality in terms of whether the immune phenotype is impacting the reservoir or vice versa or both. Tree accuracy of 80% is marginal for a decision tool.

(8) Figure 2: this is not a weakness of the analysis but I have a question about interpretation. If total HIV DNA is more predictive of immune phenotype than intact HIV DNA, does this potentially implicate a prior high burden of viral replication (high viral load &/or more prolonged time off ART) rather than ongoing reservoir stimulation as a contributor to immune phenotype? A similar thought could be applied to the fact that clustering could only be detected when applied to total HIV DNA-associated features. Many investigators do not consider defective HIV DNA to be "part of the reservoir" so it is interesting to speculate why these defective viruses appear to have more correlation with immunophenotype than intact viruses.

(9) Overall, the authors need to do an even more careful job of emphasizing that these are all just correlations. For instance, HIV DNA cannot be proven to have a causal effect on the immunophenotype of the host with this study design. Similarly, immunophenotype may be affecting HIV DNA or the correlations between the two variables could be entirely due to a separate confounding variable.

(10) In general, in the intro, when the authors refer to the immune system, they do not consistently differentiate whether they are referring to the anti-HIV immune response, the reservoir itself, or both. More specifically, the sentence in the introduction listing various causes of immune activation should have citations. (To my knowledge, there is no study to date that definitively links proviral expression from reservoir cells in vivo to immune activation as it is next to impossible to remove the confounding possible imprint of previous HIV replication.) Similarly, it is worth mentioning that the depletion of intact proviruses is quite slow such that provial expression can only be stimulating the immune system at a low level. Similarly, the statement "Viral protein expression during therapy likely maintains antigen-specific cells of the adaptive immune system" seems hard to dissociate from the persistence of immune cells that were reactive to viremia.

(11) Given the many limitations of the study design and the inability of the models to discriminate reservoir volume and phenotype, the limitations section of the discussion seems rather brief.

Reviewer #1 (Public Review):

Summary:

The current study aims to quantify associations between the regular use of proton-pump inhibitors (PPI) - defined as using PPI most days of the week during the last 4 weeks at one cross-section in time - with several respiratory outcomes up to several years later in time. There are 6 respiratory outcomes included: risk of influenza, pneumonia, COVID-19, other respiratory tract infections, as well as COVID-19 severity and mortality).

Strengths:

Several sensitivity analyses were performed, including i) estimation of the e-value to assess how strong unmeasured confounders should be to explain observed effects, ii) comparison with another drug with a similar indication to potentially reduce (but not eliminate) confounding by indication.

Weaknesses:

(1) The main exposure of interest seems to be only measured at one time-point in time (at study enrollment) while patients are considered many years at risk afterwards without knowing their exposure status at the time of experiencing the outcome. As indicated by the authors, PPI are sometimes used for only short amounts of time. It seems biologically implausible that an infection was caused by using PPI for a few weeks many years ago.

(2) Previous studies have shown that by focusing on prevalent users of drugs, one often induces several biases such as collider stratification bias, selection bias through depletion of susceptible, etc.

(3) It seems Kaplan Meier curves are not adjusted for confounding through e.g. inverse probability weighting. As such the KM curves are currently not informative (or the authors need to make clearer that curves are actually adjusted for measured confounding).

(4) Throughout the manuscript the authors seem to misuse the term multivariate (using one model with e.g. correlated error terms to assess multiple outcomes at once) when they seem to mean multivariable.

(5) Given multiple outcomes are assessed there is a clear argument for accounting for multiple testing, which following the logic of the authors used in terms of claiming there is no association when results are not significant may change their conclusions. More high-level, the authors should avoid the pitfall of stating there is evidence of absence if there is only an absence of evidence in a better way (no statistically significant association doesn't mean no relationship exists).

(6) While the authors claim that the quantitative bias analysis does show results are robust to unmeasured confounding, I would disagree with this. The e-values are around 2 and it is clearly not implausible that there are one or more unmeasured risk factors that together or alone would have such an effect size. Furthermore, if one would use the same (significance) criteria as used by the authors for determining whether an association exists, the required effect size for an unmeasured confounder to render effects 'statistically non-significant' would be even smaller.

(7) Some patients are excluded due to the absence of follow-up, but it is unclear how that is determined. Is there potentially some selection bias underlying this where those who are less healthy stop participating in the UK biobank?

(8) Given that the exposure is based on self-report how certain can we be that patients e.g. do know that their branded over-the-counter drugs are PPI (e.g. guardium tablets)? Some discussion around this potential issue is lacking.

(9) Details about the deprivation index are needed in the main text as this is a UK-specific variable that will be unfamiliar to most readers.

(10) It is unclear how variables were coded/incorporated from the main text. More details are required, e.g. was age included as a continuous variable and if so was non-linearity considered and how?

(11) The authors state that Schoenfeld residuals were tested, but don't report the test statistics. Could they please provide these, e.g. it would already be informative if they report that all p-values are above a certain value.

(12) The authors would ideally extend their discussion around unmeasured confounding, e.g. using the DAGs provided in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832226/, in particular (but not limited to) around severity and not just presence/absence of comorbidities.

(13) The UK biobank is known to be highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. The potential problems this might create in terms of collider stratification bias - as highlighted here for example: https://www.nature.com/articles/s41467-020-19478-2 - should be discussed in greater detail and also appreciated more when providing conclusions.

Reviewer #2 (Public Review):

Summary:

Zeng et al investigate in an observational population-based cohort study whether the use of proton pump inhibitors (PPIs) is associated with an increased risk of several respiratory infections among which are influenza, pneumonia, and COVID-19. They conclude that compared to non-users, people regularly taking PPIs have increased susceptibility to influenza, pneumonia, as well as COVID-19 severity and mortality. By performing several different statistical analyses, they try to reduce bias as much as possible, to end up with robust estimates of the association.

Strengths:

The study comprehensively adjusts for a variety of critical covariates and by using different statistical analyses, including propensity-score-matched analyses and quantitative bias analysis, the estimates of the associations can be considered robust.

Weaknesses:

As it is an observational cohort study there still might be bias. Information on the dose or duration of acid suppressant use was not available, but might be of influence on the results. The outcome of interest was obtained from primary care data, suggesting that only infections as diagnosed by a physician are taken into account. Due to the self-limiting nature of the outcome, differences in health-seeking behavior might affect the results.

eLife assessment

In this study, the authors offer a theoretical explanation for the emergence of nematic bundles in the actin cortex, carrying implications for the assembly of actomyosin stress fibers. As such, the study is a valuable contribution to the field actomyosin organization in the actin cortex. While the theoretical work is solid, experimental evidence in support of the model assumptions remains incomplete. The presentation could be improved to enhance accessibility for readers without a strong background in hydrodynamic and nematic theories.

Reviewer #1 (Public Review):

Summary: In this article, Mirza et al developed a continuum active gel model of actomyosin cytoskeleton that account for nematic order and density variations in actomyosin. Using this model, they identify the requirements for the formation of dense nematic structures. In particular, they show that self-organization into nematic bundles requires both flow-induced alignment and active tension anisotropy in the system. By varying model parameters that control active tension and nematic alignment, the authors show that their model reproduces a rich variety of actomyosin structures, including tactoids, fibres, asters as well as crystalline networks. Additionally, discrete simulations are employed to calculate the activity parameters in the continuum model, providing a microscopic perspective on the conditions driving the formation of fibrillar patterns.

Strengths: The strength of the work lies in its delineation of the parameter ranges that generate distinct types of nematic organization within actomyosin networks. The authors pinpoint the physical mechanisms behind the formation of fibrillar patterns, which may offer valuable insights into stress fiber assembly. Another strength of the work is connecting activity parameters in the continuum theory with microscopic simulations.

Weaknesses: This paper is a very difficult read for nonspecialists, especially if you are not well-versed in continuum hydrodynamic theories. Efforts should be made to connect various elements of theory with biological mechanisms, which is mostly lacking in this paper. The comparison with experiments is predominantly qualitative. It is unclear if the theory is suited for in vitro or in vivo actomyosin systems. The justification for various model assumptions, especially concerning their applicability to actomyosin networks, requires a more thorough examination. The classification of different structures demands further justification. For example, the rationale behind categorizing structures as sarcomeric remains unclear when nematic order is perpendicular to the axis of the bands. Sarcomeres traditionally exhibit a specific ordering of actin filaments with alternating polarity patterns. Similarly, the criteria for distinguishing between contractile and extensile structures need clarification, as one would expect extensile structures to be under tension contrary to the authors' claim. Additionally, its unclear if the model's predictions for fiber dynamics align with observations in cells, as stress fibers exhibit a high degree of dynamism and tend to coalesce with neighboring fibers during their assembly phase. Finally, it seems that the microscopic model is unable to recapitulate the density patterns predicted by the continuum theory, raising questions about the suitability of the simulation model.

Reviewer #2 (Public Review):

Summary:

The article by Waleed et al discusses the self organization of actin cytoskeleton using the theory of active nematics. Linear stability analysis of the governing equations and computer simulations show that the system is unstable to density fluctuations and self organized structures can emerge. While the context is interesting, I am not sure whether the physics is new. Hence I have reservations about recommending this article.

Strengths:

(i) Analytical calculations complemented with simulations (ii) Theory for cytoskeletal network

Weaknesses:

Not placed in the context or literature on active nematics.

Reviewer #3 (Public Review):

The manuscript "Theory of active self-organization of dense nematic structures in the actin cytoskeleton" analysis self-organized pattern formation within a two-dimensional nematic liquid crystal theory and uses microscopic simulations to test the plausibility of some of the conclusions drawn from that analysis. After performing an analytic linear stability analysis that indicates the possibility of patterning instabilities, the authors perform fully non-linear numerical simulations and identify the emergence of stripe-like patterning when anisotropic active stresses are present. Following a range of qualitative numerical observations on how parameter changes affect these patterns, the authors identify, besides isotropic and nematic stress, also active self-alignment as an important ingredient to form the observed patterns. Finally, microscopic simulations are used to test the plausibility of some of the conclusions drawn from continuum simulations.

The paper is well written, figures are mostly clear and the theoretical analysis presented in both, main text and supplement, is rigorous. Mechano-chemical coupling has emerged in recent years as a crucial element of cell cortex and tissue organization and it is plausible to think that both, isotropic and anisotropic active stresses, are present within such effectively compressible structures. Even though not yet stated this way by the authors, I would argue that combining these two is of the key ingredients that distinguishes this theoretical paper from similar ones. The diversity of patterning processes experimentally observed is nicely elaborated on in the introduction of the paper, though other closely related previous work could also have been included in these references (see below for examples).

To introduce the continuum model, the authors exclusively cite their own, unpublished pre-print, even though the final equations take the same form as previously derived and used by other groups working in the field of active hydrodynamics (a certainly incomplete list: Marenduzzo et al (PRL, 2007), Salbreux et al (PRL, 2009, cited elsewhere in the paper), Jülicher et al (Rep Prog Phys, 2018), Giomi (PRX, 2015),...). To make better contact with the broad active liquid crystal community and to delineate the present work more compellingly from existing results, it would be helpful to include a more comprehensive discussion of the background of the existing theoretical understanding on active nematics. In fact, I found it often agrees nicely with the observations made in the present work, an opportunity to consolidate the results that is sometimes currently missed out on. For example, it is known that self-organised active isotropic fluids form in 2D hexagonal and pulsatory patterns (Kumar et al, PRL, 2014), as well as contractile patches (Mietke et al, PRL 2019), just as shown and discussed in Fig. 2. It is also known that extensile nematics, \kappa<0 here, draw in material laterally of the nematic axis and expel it along the nematic axis (the other way around for \kappa>0, see e.g. Doostmohammadi et al, Nat Comm, 2018 "Active Nematics" for a review that makes this point), consistent with all relative nematic director/flow orientations shown in Figs. 2 and 3 of the present work.

The results of numerical simulations are well-presented. Large parts of the discussion of numerical observations - specifically around Fig. 3 - are qualitative and it is not clear why the analysis is restricted to \kappa<0. Some of the observations resonate with recent discussions in the field, for example the observation of effectively extensile dynamics in a contractile system is interesting and reminiscent of ambiguities about extensile/contractile properties discussed in recent preprints (https://arxiv.org/abs/2309.04224). It is convincingly concluded that, besides nematic stress on top of isotropic one, active self-alignment is a key ingredient to produce the observed patterns.

I compliment the authors for trying to gain further mechanistic insights into this conclusion with microscopic filament simulations that are diligently performed. It is rightfully stated that these simulations only provide plausibility tests and, within this scope, I would say the authors are successful. At the same time, it leaves open questions that could have been discussed more carefully. For example, I wonder what can be said about the regime \kappa>0 (which is dropped ad-hoc from Fig. 3 onward) microscopically, in which the continuum theory does also predict the formation of stripe patterns - besides the short comment at the very end? How does the spatial inhomogeneous organization the continuum theory predicts fit in the presented, microscopic picture and vice versa?

Overall, the paper represents a valuable contribution to the field of active matter and, if strengthened further, might provide a fruitful basis to develop new hypothesis about the dynamic self-organisation of dense filamentous bundles in biological systems.

eLife assessment

This valuable study describes mRNA shortening during cellular stress and interestingly observes that this shortening is dependent on localization in stress granules. Surprisingly, this mRNA shortening does not appear to require the shortening of polyA tails. These are in principle novel findings, but the evidence for them is currently incomplete. Additional experiments would help bolster confidence in how the authors interpret their data.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors employed direct RNA sequencing with nanopores, enhanced by 5' end adaptor ligation, to comprehensively interrogate the human transcriptome at single-molecule and nucleotide resolution. They conclude that cellular stress induces prevalent 5' end RNA decay that is coupled to translation and ribosome occupancy. Contrary to the literature, they found that, unlike typical RNA decay models in normal conditions, stress-induced RNA decay is dependent on XRN1 but does not depend on the removal of the poly(A) tail. The findings presented are interesting but a substantial amount of work is needed to fully establish these paradigm-shifting findings.

Strengths:

These are paradigm-shifting observations using cutting-edge technologies.

Weaknesses:

The conclusions do not appear to be fully supported by the data presented.

Reviewer #2 (Public Review):

In the manuscript "Full-length direct RNA sequencing uncovers stress-granule dependent RNA decay upon cellular stress", Dar, Malla, and colleagues use direct RNA sequencing on nanopores to characterize the transcriptome after arsenite and oxidative stress. They observe a population of transcripts that are shortened during stress. The authors hypothesize that this shortening is mediated by the 5'-3' exonuclease XRN1, as XRN1 knockdown results in longer transcripts. Interestingly, the authors do not observe a polyA-tail shortening, which is typically thought to precede decapping and XRN1-mediated transcript decay. Finally, the authors use G3BP1 knockout cells to demonstrate that stress granule formation is required for the observed transcript shortening.

The manuscript contains intriguing findings of interest to the mRNA decay community. That said, it appears that the authors at times overinterpret the data they get from a handful of direct RNA sequencing experiments. To bolster some of the statements additional experiments might be desirable.

A selection of comments:

(1) Considering that the authors compare the effects of stress, stress granule formation, and XRN1 loss on transcriptome profiles, it would be desirable to use a single-cell system (and validated in a few more). Most of the direct RNAseq is performed in HeLa cells, but the experiments showing that stress granule formation is required come from U2OS cells, while short RNAseq data showing loss of coverage on mRNA 5'ends is reanalyzed from HEK293 cells. It may be plausible that the same pathways operate in all those cells, but it is not rigorously demonstrated.

(2) An interesting finding of the manuscript is that polyA tail shortening is not observed prior to transcript shortening. The authors would need to demonstrate that their approach is capable of detecting shortened polyA tails. Using polyA purified RNA to look at the status of polyA tail length may not be ideal (as avidity to oligodT beads may increase with polyA tail length and therefore the authors bias themselves to longer tails anyway). At the very least, the use of positive controls would be desirable; e.g. knockdown of CCR4/NOT.

(3) The authors use a strategy of ligating an adapter to 5' phosphorylated RNA (presumably the breakdown fragments) to be able to distinguish true mRNA fragments from artifacts of abortive nanopore sequencing. This is a fantastic approach to curating a clean dataset. Unfortunately, the authors don't appear to go through with discarding fragments that are not adapter-ligated (presumably to increase the depth of analysis; they do offer Figure 1e that shows similar changes in transcript length for fragments with adapter, compared to Figure 1d). It would be good to know how many reads in total had the adapter. Furthermore, it would be good to know what percentage of reads without adapters are products of abortive sequencing. What percentage of reads had 5'OH ends (could be answered by ligating a different adapter to kinase-treated transcripts). More read curation would also be desirable when building the metagene analysis - why do the authors include every 3'end of sequenced reads (their RNA purification scheme requires a polyA tail, so non-polyadenylated fragments are recovered in a non-quantitative manner and should be discarded).

(4) The authors should come to a clear conclusion about what "transcript shortening" means. Is it exonucleolytic shortening from the 5'end? They cannot say much about the 3'ends anyway (see above). Or are we talking about endonucleolytic cuts leaving 5'P that then can be attached by XRN1 (again, what is the ratio of 5'P and 5'OH fragments; also, what is the ratio of shortened to full-length RNA)?

(5) The authors should clearly explain how they think the transcript shortening comes about. They claim it does not need polyA shortening, but then do not explain where the XRN1 substrate comes from. Does their effect require decapping? Or endonucleolytic attacks?

(6) XRN1 KD results in lengthened transcripts. That is not surprising as XRN1 is an exonuclease - and XRN1 does not merely rescue arsenite stress-mediated transcript shortening, but results in a dramatic transcript lengthening.

Reviewer #3 (Public Review):

The work by Dar et al. examines RNA metabolism under cellular stress, focusing on stress-granule-dependent RNA decay. It employs direct RNA sequencing with a Nanopore-based method, revealing that cellular stress induces prevalent 5' end RNA decay that is coupled to translation and ribosome occupancy but is independent of the shortening of the poly(A) tail. This decay, however, is dependent on XRN1 and enriched in the stress granule transcriptome. Notably, inhibiting stress granule formation in G3BP1/2-null cells restores the RNA length to the same level as wild-type. It suppresses stress-induced decay, identifying RNA decay as a critical determinant of RNA metabolism during cellular stress and highlighting its dependence on stress-granule formation.

This is an exciting and novel discovery. I am not an expert in sequencing technologies or sequencing data analysis, so I will limit my comments purely to biology and not technical points. The PI is a leader in applying innovative sequencing methods to studying mRNA decay.

One aspect that appeared overlooked is that poly(A) tail shortening per se does lead to decapping. It is shortening below a certain threshold of 8-10 As that triggers decapping. Therefore, I found the conclusion that poly(A) tail shortening is not required for stress-induced decay to be somewhat premature. For a robust test of this hypothesis, the authors should consider performing their analysis in conditions where CNOT7/8 is knocked down with siRNA.

Similarly, as XRN1 requires decapping to take place, it necessitates the experiment where a dominant-negative DCP2 mutant is over-expressed.

Are G3BP1/2 stress granules required for stress-induced decay or simply sites for storage? This part seems unclear. A very worthwhile test here would be to assess in XRN1-null background.

Finally, the authors speculate that the mechanism of stress-induced decay may have evolved to relieve translational load during stress. But why degrade the 5' end when removing the cap may be sufficient? This returns to the question of assessing the role of decapping in this mechanism.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this paper, Song, Shi, and Lin use an existing deep learning-based sequence model to derive a score for each haplotype within a genomic region, and then perform association tests between these scores and phenotypes of interest. The authors then perform some downstream analyses (fine-mapping, various enrichment analyses, and building polygenic scores) to ensure that these associations are meaningful. The authors find that their approach allows them to find additional associations, the associations have biologically interpretable enrichments in terms of tissues and pathways, and can slightly improve polygenic scores when combined with standard SNP-based PRS.

Strengths:

• I found the central idea of the paper to be conceptually straightforward and an appealing way to use the power of sequence models in an association testing framework.

• The findings are largely biologically interpretable, and it seems like this could be a promising approach to boost power for some downstream applications.

Weaknesses:

• The methods used to generate polygenic scores were difficult to follow. In particular, a fully connected neural network with linear activations predicting a single output should be equivalent to linear regression (all intermediate layers of the network can be collapsed using matrix-multiplication, so the output is just the inner product of the input with some vector). Using the last hidden layer of such a network for downstream tasks should also be equivalent to projecting the input down to a lower dimensional space with some essentially randomly chosen projection. As such, I am surprised that the neural network approach performs so well, and it would be nice if the authors could compare it to other linear approaches (e.g., LASSO or ridge regression for prediction; PCA or an auto-encoder for converting the input to a lower dimensional representation).

Response: We thank the reviewer for the recognition and valuable suggestion on our work. Just as the reviewer suggested, our polygenic prediction procedure is equivalent to linear transformation and in this revision, we indeed found that it was unnecessary to use neural network framework to replace linear model. Indeed, both our result and previous work indicated that linear model fitted polygenic traits better than non-linear one, which was also the reason we chose linear activation for neural network in the original manuscript.

In this revision, we followed the reviewer’s suggestion to apply a more straightforward linear framework for polygenic prediction. We first calculated weighted sum of HFS for each block (1,361 independent blocks in total), then, in each target ancestry, we used LASSO regression to integrate them with SNP PRS into one final score. We also conducted comparative analysis in British European test set and found that LASSO, ridge and elastic net gave similar result, and LASSO performed slightly better. By applying this straightforward framework and sliding window strategy, we moderately improved the prediction performance.

Line 349: “Using height as a representative trait, we first estimated the proportion of variance captured by top loci, and found that HFS of loci with PIP>0.4 (n=5,101) captured roughly 80% of variance explained by all genome-wide loci (n=1,200,024 corresponded to sling-window strategy; Figure 5A). We then calculated HFS+LDAK in non-British European (NBE), South Asian (SAS), East Asian (EAS) and African (AFR) population in UK Biobank, and observed 17.5%, 16.1%, 17.2% and 39.8% improvement over LDAK alone (p=3.21×10-16, 0.0001, 0.002 and 0.001, respectively. Figure 5C).”

Author response image 1.

• A very interesting point of the paper was the low R^2 between the HFS scores in adjacent windows, but the explanation of this was unclear to me. Since the HFS scores are just deterministic functions of the SNPs, it feels like if the SNPs are in LD then the HFS scores should be and vice versa. It would be nice to compare the LD between adjacent windows to the average LD of pairs of SNPs from the two windows to see if this is driven by the fact that SNPs are being separated into windows, or if sei is somehow upweighting the importance of SNPs that are less linked to other SNPs (e.g., rare variants).

Response: We thank the reviewer for the suggestion on understanding LD mechanism. In this revision, we used chromosome 1 as an example and calculate the pairwise LD among all SNPs within two adjacent loci. As shown in Figure S1 (below), although HFS-based LD is still significantly lower than median SNP-based LD (paired Wilcoxon test p=1.76e-5), we found that median SNP LD between loci was still lower than what typically observed between adjacent SNPs in GWAS (histogram of x axis; median =0.06). We reasoned that dividing SNPs into block is one of the reasons that HFS suffer less LD than standard GWAS, but not the whole story.

Author response image 2.

We agree with the reviewer that the effect of rare variants could also play an important role. In fact, sei author has also found that rare variants tended to have larger sei-predicted effects. We conducted an approximate analysis that remove all rare variants and repeated HFS calculation. Indeed, here HFS LD has profoundly raised to median=0.14, indicating that involving rare variants was vital for low LD.

Author response image 3.

Line 123: “Further evaluation indicated that this low LD was led by two factors: integration of rare variant impacts and segmentation. Firstly, excluding rare variants from HFS caused the LD raised to median=0.14 (Method; Figure S2C). Secondly, median LD of SNPs from adjacent loci was 0.06, which was significantly higher than HFS LD (paired Wilcoxon p=1.76×10-5) but significantly lower than HFS LD without rare variants (paired Wilcoxon p<2.2×10-16).”

• There were also a number of robustness checks that would have been good to include in the paper. For instance, do the findings change if the windows are shifted? Do the findings change if the sequence is reverse-complemented?

Response: Following the reviewer’s suggestion, we conducted a sliding window analysis where all loci were shifted 2048 bp, thereby doubling the total number of loci. In fine-mapping analysis, more than 90% of the causal loci were reproduced in sliding window analysis, either by themselves or by a overlapping locus:

Line 207: “29.4% of causal loci (PIP>0.95) in the original analysis were still causal in sliding window analysis. 31.1% and 29.3% of causal loci whose 5’ and 3’ overlapping locus had PIP>0.95 in sliding window analysis, respectively, while themselves were no longer causal.”

In polygenic prediction analysis, sliding window strategy significantly improved prediction accuracy, as we discussed in question 1.

As for the issue of reverse complement, the nature of sei input layer is to encode both strand in a symmetric manner, such that the output for both strands would be the same. We have also run sei on the reverse complement (generated by seqkit seq -r -p) to verify that original sequence and reverse complement give the same output.

• It was also difficult to contextualize the present work in terms of recent results showing that sequence models tend to not do very well at predicting cross-individual expression changes (and such results presumably hold for predicting cross-individual chromatin changes). In particular, it would be good for the authors to contrast their findings with the work of Alex Sasse and colleagues (https://www.biorxiv.org/content/10.1101/2023.03.16.532969.abstract) and Connie Huang and colleagues (https://www.biorxiv.org/content/10.1101/2023.06.30.547100.abstract).

Response: Following the reviewer’s suggestion, we added a new discussion paragraph on the issue of sequence model performance on interindividual variations. In brief, we suggest that although the drawback of lack of cross-individual training sets exists and future improvement is necessary, chromatin changes could be better predicted than gene expression. This is because the latter task requires information on long range interaction, which varies among genes and are difficult to be captured by using reference genome as training set. We made a schematic to clarify this:

Author response image 4.

We also noticed a few recent studies that directly validated sei predictions by experiments and showed significant accuracy, such as https://doi.org/10.1016/j.neuron.2022.12.026. Taken together, while we agreed that it is necessary to improve sequence model by adding more cross-individual training samples, the current SOTA model sei could still provide unique value to our study.

Line 423: “The challenge of using sequence-based deep learning (DL) models in HFS applications is further compounded by their difficulty in predicting variations between individuals. Recent studies(Huang et al., 2023; Sasse et al., 2023) indicate that DL models, trained on the reference human genome, demonstrate limited accuracy in predicting gene expression levels across different individuals. This limitation is likely due to the models' inability to account for long-range regulatory patterns, which are crucial for understanding the impact of variants on gene expression and vary across genes. In contrast, our study leveraged sequence-determined functional genomic profiles in association studies, which mitigates this issue to an extent. For instance, although sei cannot identify the specific gene regulated by a given input sequence, it can predict changes in the sequence's functional activity. Future improvements in DL models' ability to predict interindividual differences could be achieved by incorporating cross-individual data in the training process. An example of such data is the EN-TEX(Rozowsky et al., 2023) dataset, which aligns functional genomic peaks with the specific individuals and haplotypes they correspond to.”

Reviewer #2 (Public Review):

Summary:

In this work, Song et al. propose a locus-based framework for performing GWAS and related downstream analyses including finemapping and polygenic risk score (PRS) estimation. GWAS are not sufficiently powered to detect phenotype associations with low-frequency variants. To overcome this limitation, the manuscript proposes a method to aggregate variant impacts on chromatin and transcription across a 4096 base pair (bp) loci in the form of a haplotype function score (HFS). At each locus, an association is computed between the HFS and trait. Computing associations at the level of imputed functional genomic scores should enable the integration of information across variants spanning the allele frequency spectrum and bolster the power of GWAS.

The HFS for each locus is derived from a sequence-based predictive model. Sei. Sei predicts 21,907 chromatin and TF binding tracks, which can be projected onto 40 pre-defined sequence classes ( representing promoters, enhancers, etc.). For each 4096 bp haplotype in their UKB cohort, the proposed method uses the Sei sequence class scores to derive the haplotype function score (HFS). The authors apply their method to 14 polygenic traits, identifying ~16,500 HFS-trait associations. They finemap these trait-associated loci with SuSie, as well as perform target gene/pathway discovery and PRS estimation.

Strengths:

Sequence-based deep learning predictors of chromatin status and TF binding have become increasingly accurate over the past few years. Imputing aggregated variant impact using Sei, and then performing an HFS-trait association is, therefore, an interesting approach to bolster power in GWAS discovery. The manuscript demonstrates that associations can be identified at the level of an aggregated functional score. The finemapping and pathway identification analyses suggest that HFS-based associations identify relevant causal pathways and genes from an association study. Identifying associations at the level of functional genomics increases the portability of PRSs across populations. Imputing functional genomic predictions using a sequence-based deep learning model does not suffer from the limitation of TWAS where gene expression is imputed from a limited-size reference panel such as GTEx.

However, there are several major limitations that need to be addressed.

Major concerns/weaknesses:

(1) There is limited characterization of the locus-level associations to SNP-level associations. How does the set of HFS-based associations differ from SNP-level associations?

Response: We thank the reviewer for the recognition and the valuable suggestion on our manuscript. Following the reviewer’s suggestion, in this revision we added a paragraph to compare the basic characteristics between HFS-based and SNP-based association study. These comparisons suggested that HFS had no advantage in testing marginal association, but performed better in detecting causal associations.

Line 144: “When comparing HFS association with the standard SNP-based GWAS on the same data, we found that 98% of significant HFS loci also harbored a significant SNP. There were a few cases (n=0~5) where significant HFS loci did not harbored even marginal SNP association (GWAS p>0.01), which were due to the lack of common SNP in these loci. HFS association p value was higher than GWAS p value in 95 % of significant loci, suggested that HFS did not improve power to detect marginal effect. The genomic control inflation factor (λGC) for the HFS association test varied between 0.99 for asthma and 1.50 for height, closely resembling the SNP GWAS (Pearson Correlation Coefficient [PCC]=0.91, paired t-test p=0.16; Method and Figure S3). We concluded that HFS-based association tests had adequate power and do not introduce additional p-value inflation.”

(2) A clear advantage of performing HFS-trait associations is that the HFS score is imputed by considering variants across the allele frequency spectrum. However, no evidence is provided demonstrating that rare variants contribute to associations derived by the model. Similarly, do the authors find evidence that allelic heterogeneity is leveraged by the HFS-based association model? It would be useful to do simulations here to characterize the model behavior in the presence of trait-associated rare variants.

Response: Following the reviewer’s suggestion, we conducted a sensitivity analysis that removed all rare (MAF<0.01) variants and repeated the HFS analysis (HFScommon) on chromosome 1. In linear association analysis, we found that 10.6% of HFS signals (p<5×10-8) were missed by HFScommon. In fine-mapping, 55.3% of HFS causal signals (PIP>0.95) were missed by HFScommon. We concluded that rare variants played an important role in the performance of HFS, especially its advantages in fine-mapping.

Line 175: “We also found that rare variants played an important role in the good find-mapping performance of HFS: when variants with MAF<0.01 were removed, 55.3% of the causal signals would be missed in HFS+SUSIE analysis.”

We then attempted to conduct a simulation analysis where rare variants were causal to the phenotype, and the association statistics were the same as real GWAS of height. However, such simulation seemed not to properly reflect real scenario: no matter how we changed the association between rare variants and the phenotype, HFS association p-value could hardly reached the significance level of SNP association. We proposed that this is because simulation could not properly reflect how variants impact functional genomics: in fact, when randomly selected a rare variant as causal variant, there is high possibility that it had no impact on functional genomics, therefore its HFS would be close to zero. When such a variant was set as causal (which is unlikely in real scenario), HFS would not properly capture the association. We reasoned that it might be difficult to evaluate HFS by simulation, since the nonlinear relation between SNP and HFS as well as among SNPs were difficult to be properly simulated.

Author response image 5.

(3) Sei predicts chromatin status / ChIP-seq peaks in the center of a 4kb region. It would therefore be more relevant to predict HFS using overlapping sequence windows that tile the genome as opposed to using non-overlapping windows for computing HFS scores. Specifically, in line 482, the authors state that "the HFS score represents overall activity of the entire sequence, not only the few bp at the center", but this would not hold given that Sei is predicting activity at the center for any sequence.

Response: We thank the reviewer for the suggestion on sliding window design. In this revision, we shifted all loci 2,048 bp to double the number of loci and repeated the fine-mapping and polygenic prediction analysis. For fine-mapping, we found that the result was generally robust with regard to sliding window procedure, and the majority of the causal associations were retained:

Line 207: “29.4% of causal loci (PIP>0.95) in the original analysis were still causal in sliding window analysis. 31.1% and 29.3% of causal loci whose 5’ and 3’ overlapping locus had PIP>0.95 in sliding window analysis, respectively, while themselves were no longer causal.”

In polygenic prediction, sliding window analysis provided a significantly improved performance compared with previous analysis on non-overlapping loci:

However, since in this revision we have several updates on the polygenic prediction procedure, it was difficult to quantify how much improvement was led by sliding window design. Thus, we directly showed the new result in figure 5 but did not compare it with the original result.

We also modified the previously imprecise statement to:

Line 490: “…it integrated information of the entire sequence, not only the few bp at the center.”

(4) Is the HFS-based association going to miss coding variation and several regulatory variants such as splicing variants? There are also going to be cases where there's an association driven by a variant that is correlated with a Sei prediction in a neighboring window. These would represent false positives for the method, it would be useful to identify or characterize these cases.

Response: As the reviewer suggested, sei captured only functional genomic features and is by nature prone not to perform well when the causal variants impact protein sequences. In this revision, we characterized this by focusing on causal exonic variants (SNP PIP>0.95):

Line 322: “On the other hand, HFS perform worse than SNP-based fine-mapping on exonic regions. Taking height as an example, PolyFun detected 125 causal SNPs (PIP>0.95) in the exonic regions, but only 16% (20) of loci that harbored them also reached PIP>0. 5 (11 reached PIP>0.95) in HFS+SUSIE analysis. Among the 105 loci that missed such signals (HFS PIP<0.5), 12 had a nearby locus (within 10kb) showing HFS PIP>0.95, which likely reflected false positive led by LD. Thus, SNP-based analysis should be prioritized over HFS in coding regions.”

Additional minor concerns:

(1) It's not clear whether SuSie-based finemapping is appropriate at the locus level, when there is limited LD between neighboring HFS bins. How does the choice of the number of causal loci and the size of the segment being finemapped affect the results and is SuSie a good fit in this scenario?

Response: Following the reviewer’s suggestion, we reran SUSIE under different predefined causal loci number (from 2 to 10), and found that the identified causal loci were consistent.

Author response image 6.

Line 211: “Besides, HFS+SUSIE was also robust when the predefined number of causal loci (L=2 to 10) was changed, and the number of detected loci were not changed.”

As for the size of segmentation, we divided the predefined segmentations (independent blocks detected by LDetect) into two half and reran SUSIE, and found that three additional causal loci emerged in one half. This suggested that using too small segmentation might increase the false positive rate. However, since there is no LD between independent blocks (which was guaranteed by LDetect), it is not necessary to use even longer blocks.

Author response image 7.

Line 133: “Simulation analysis revealed that when a non-reference sequence class score was associated the trait, reference class score could still capture median 70% of HFS-trait association R2.”

(2) It is not clear how a single score is chosen from the 117 values predicted by Sei for each locus. SuSie is run assuming a single causal signal per locus, an assumption which may not hold at ~4kb resolution (several classes could be associated with the trait of interest). It's not clear whether SuSie, run in this parameter setting, is a good choice for variable selection here.

Response: As we discussed below (question 3), in this revision we no longer applied SUSIE to find one sequence class score for each locus due to the impact of overfitting, and use the reference sequence class uniformly for all loci. As reviewer suggested, we applied simulation to evaluate how this procedure influence HFS performance, especially when multiple sequence class of the same locus is causal to the phenotype. We found that reference sequence class score could capture median 69.1% of phenotypic R2 when the causal sequence class is not the reference, and captured median 59.2% of R2 when there was 2~5 non-reference causal class. We concluded that the loss led by skipping sequence class selection is mild, and it is necessary to do so in consideration of the risk of overfitting.

Author response image 8.

(3) A single HFS score is being chosen from amongst multiple tracks at each locus independently. Does this require additional multiple-hypothesis correction?

Response: We agree with the reviewer that choosing the sequence class for each locus represented multiple testing, and with additional experiments we indeed observed some evidences of overfitting of this procedure. Thus, in this revision, we no longer applied the per-locus feature selection procedure, but instead used the sequence class corresponded to the reference (hg38) sequence. Consequently, additional multiple-testing correction is avoided with this procedure. We admitted that such simplification missed certain information, but as mentioned above, such lost is moderate, and is necessary to ensure statistical robustness and reduce false positive. In fact, with such simplification we better controlled the inflation factor of HFS GWAS and got better portability in polygenic prediction.

(4) The results show that a larger number of loci are identified with HFS-based finemapping & that causal loci are enriched for causal SNPs. However, it is not clear how the number of causal loci should relate to the number of SNPs. It would be really nice to see examples of cases where a previously unresolved association is resolved when using HFS-based GWAS + finemapping.

Response: In this revision, we did not observe a clear relation between causal loci number and causal gene number. The only trend is that SNP-based fine-mapping seemed to perform better at coding regions, in accordance with the fact that HFS capture functional genomic signals. We also added new interpretations to highlight some examples where HFS resolve previously unresolved association signals. For example,

Line 287: “Specifically, in 1q32.1 region, HFS+SUSIE identified two loci with PIP>0.9 (Figure 4B). SNP-based association also found significant association in this region, but SNP fine-mapping(Weissbrod et al., 2020) could not resolve this signal and only found seven signals between PIP=0.1 to 0.5.”

(5) Sequence-based deep learning model predictions can be miscalibrated for insertions and deletions (INDELs) as compared to SNPs. Scaling INDEL predictions would likely improve the downstream modeling.

Response: Following the reviewer’s suggestion, we conducted a sensitivity analysis that removed all indel on chromosome 1 and repeated HFS analysis. Removing indel has indeed increased the number of significant (p<5e-8) association by 9%, but also slightly increased inflation factor (paired wilcox test p=0.0001). In fine mapping analysis, removing indel caused a 4.7% decrement in the number of detected causal association (PIP>0.95). We reasoned that the potential miscalibration on indel has indeed impacted the statistical power of HFS, but the proper approach to control this impact might not be direct and is still await optimizing. In this revision, we still kept all indels in the analysis, since we proposed that the power of fine-mapping is more important than the power of marginal association.

Line 213: “Lastly, removing insertion and deletion would reveal 9% more significant association (p<5×10-8) but 4.7% less causal association (PIP>0.95), and slightly increased inflation factor (Wilcoxon p=0.0001, Figure S4).”

Author response image 9.

Reviewer #1 (Recommendations For The Authors):

It was unclear to me why the sei output was rounded to two decimal places to "avoid influence of sei prediction noise". Wouldn't rounding introduce additional noise?

Response: We thank the reviewer for pointing out our inadequate description. The rounding procedure is used to mask the low value that likely did not reflect any real change. The idea is that, even if a variant actually does not bring about any functional changes, sei would still output a very low HFS value that is not equal to, but close to, zero. By rounding procedure, such low values would be set to zero, which could avoid noise. We have added this rationale to the method section:

Line 529: “This is due to the fact that even if a variant actually makes no impact on functional genomics, sei would still output a value that are close to but not equal to reference sequence class score. Rounding procedure would set such HFS to zero and remove the random value from sei.”

Minor comments / typos:

• There are many typos in the abstract.

Response: We have revised the typo and grammar issues in the abstract in this revision.

• I believe "Arachnoid acid-intelligence" should be "Arachidonic acid-intelligence".

• Consistently there is no space between text and parenthetical citations. For example, "sei(Chen et al., 2022)" should be "sei (Chen et al., 2022)".

• Line 110: "at least one non-reference haplotypes" --> "at least one non-reference haplotype".

• Line 155: "data-based method" --> "data-based methods".

• Lines 165-166: "functionally importance" --> "functional importance".

Response: We have made these revisions accordingly.

• Line 210: the sentence containing "this annotation on conditioned of a set of baseline annotations" is unclear.

Response: We have revised this sentence as “…regressed the PIP against this annotation, with a set of baseline annotations included as covariates, similar to the LDSC framework.”

• Line 213: "association" --> "associations".

• Line 219: "association" --> "associations".

• Line 251: "result" --> "results".

• Line 269: "result" --> "results".

• Line 289: "known to involved" --> "known to be involved".

• Line 356: "LDAK along" --> "LDAK alone".

• Line 362: "BOLT-LMM along" --> "BOLT-LMM alone".

• Supplement: "Hihglighted" --> "Highlighted".

Response: We have made these revisions accordingly.

• Line 444: Were "British ancestry Caucasians" defined as individuals that self-identified as "white British"? If so, then they should be described as "self-identified "white British"".

Response: As the reviewer pointed out, we have changed the description as self-identified British ancestry Caucasians.

Reviewer #2 (Recommendations For The Authors):

(1) A 2022 cistrome-wide association study (CWAS) computed associations between genetically-predicted chromatin activity and phenotypes. Adding a reference to this paper would be helpful. https://pubmed.ncbi.nlm.nih.gov/36071171/

Response: Following the reviewer’s suggestion, we discussed the similarity between CWAS and our study:

Line 89: “In line with this notion, a recent similar strategy called cistrome-wide association study (CWAS) integrated variant-chromatin activity and variant-phenotype association to boost power of genetic study of cancer. (Baca et al., 2022).”

(2) Line 487 states: "We applied sei to predict 21,906 functional genomic tracks for each sequence, without normalizing for histone mark." It's not clear what normalization is being referred to here.

Response: We have revised the sentence to:

Line 495: “We applied sei to predict 21,906 functional genomic tracks for each sequence, without normalizing for histone mark (divided each track score by the sum of histone mark score) as suggested by the sei author.”

(3) The figures are extremely low resolution, they need to be updated.

Response: In this revision, we uploaded separate pdf file for each figure to provide high resolution graphs.

(4). The results section was difficult to follow and would benefit from being written more clearly.

Response: In this revision, we re-arranged some of the result section to better clarify the main idea. We moved all statistical results to the bracket and focused our main text on the interpretation. For example,

Line 123: “Further evaluation indicated that this low LD was led by two factors: integration of rare variant impacts and segmentation. Firstly, excluding rare variants from HFS caused the LD raised to median=0.14 (Method; Figure S2C). Secondly, median LD of SNPs from adjacent loci was 0.06, which was significantly higher than HFS LD (paired Wilcoxon p=1.76×10-5) but significantly lower than HFS LD without rare variants (paired Wilcoxon p<2.2×10-16).”

(5) "Along" is used several times in the final results section (PRS estimation), this should be "alone".

Response: We have modified all misused “along” by “alone” in this revision.

(6) Instead of using notation identifying genomic location, it might be clearer to provide gene names when illustrating examples of trait-associated promoters.

Response: In this revision, we added gene name of the corresponding promoters to the main text to better clarify the findings.