Reviewer #3 (Public Review):

The work by Ghasemahmad et al. has the potential to significantly advance our understanding of how neuromodulators provide internal-state signals to the basolateral amygdala (BLA) while an animal listens to social vocalizations.

Ghasemahmad et al. made changes to the manuscript that have significantly improved the work. In particular, the transparency in showing the underlying levels of Ach, DA, and 5HIAA is excellent. My previous concerns have been adequately addressed.

eLife assessment

This study presents important new insights into how best to address common problems encountered in the statistical analysis of neural data, including those related to temporal autocorrelations and unknown variables. The authors show that certain approaches, including those using cross-validation and permutation tests, are better than others at controlling error rates, particularly false negatives. At present, the evidence presented is incomplete, including a lack of more rigorous theoretical justifications for the differences observed between the different approaches tested and a focus on p-values without considering effect sizes, but could be improved with substantial revisions that address these issues.

Reviewer #1 (Public Review):

Summary:<br /> This paper describes a comparison of different statistical methods for model comparison and covariate selection in neural encoding models. It shows in particular that issues arising from temporal autocorrelation and missing variables can lead to statistical tests with substantially higher false positive rates than expected from theory. The paper proposes methods for overcoming these problems, in particular cross-validation with cyclical shift permutation tests. The results are timely, important, and likely to have a broad impact. In particular, the paper shows that cell tuning classification can vary dramatically with the testing procedure, which is an important lesson for the field as a whole.

Strengths:<br /> - Novel and important comparison of different methods for variable selection in nested models.

Weaknesses:<br /> - Does not (yet) examine effect sizes<br /> - Does not motivate/explain key methods clearly enough in the main text.

General Comments:<br /> 1. My first general comment is that the paper in its current form focuses on the "null hypothesis significance testing" (NHST) paradigm. That is, it is focused on binary tests about what variables to include (or not include) in a regression model, and the false-positive rates of such tests. However, the broader statistics community has recently seen a shift away from NHST and towards a statistical reporting paradigm focused on effect sizes. See for example:<br /> - "Scientists rise up against statistical significance". Nature, March 2019.<br /> - Moving to a World Beyond "p < 0.05". RL Wasserstein, AL Schirm, NA Lazar. The American Statistician, 2019.

In light of this shift, I think the paper would be substantially strengthened if the authors could add a description of effect sizes for the statistical procedures they consider. Thus, for example, in cases where a procedure selects the wrong model (e.g., by selecting a variable that should not be included), how large is the inferred regression weight, and/or how large is the improvement in prediction performance (e.g. test log-likelihood) from including the erroneous regressor? How strong is the position tuning ascribed to a MEC cell that is inappropriately classified as having position tuning under one of the sub-optimal procedures? (Figure 7 shows some example place maps, but it would be nice to see a more thorough and rigorous analysis).

My suspicion would be that even when the hypothesis test gives a false positive, the effect sizes tend to remain small... but it is certainly possible that I'm mistaken, or that inferred effect sizes are more accurate for some procedures than others.

2. My only other major criticism relates to clarity and readability: in particular, the various procedures discussed in the paper ("forward selection", "maxT correction", "permutation test with cyclic shifts") are not clearly explained in the main paper, but are relegated to the Methods. Although I think it is useful to keep many of the mathematical details in the methods section, it would benefit the reader to have a general and intuitive explanation of the key methods within the flow of the main paper. The first paragraph of the Results section is particularly underdeveloped and hard to read and could benefit from a substantial revision to introduce and motivate the terms and procedures more clearly. I would recommend moving much of the text from the Methods into the Results section, or at the very least adding a paragraph describing the general idea/motivation for each method in Results.

Reviewer #2 (Public Review):

This paper considers methods for statistical analysis of autocorrelated neural recording time series: an important question for neuroscience, that is underappreciated in the community. The paper makes a valuable contribution to this topic by comparing methods based on cross-validation and cyclic shift on simulated grid-cell data. My main suggestions regard clarity, which would greatly benefit from a more didactic approach: explaining the methods compared to the main text and providing more explanatory figures. But there are also some additional analyses that would strengthen the paper.

There are two ways to build support for the validity of a statistical method: by mathematically proving that it is valid, or by empirically verifying it with simulated data where the correct answer is known. A mathematical proof removes all doubt to validity but empirical validation can still be useful even without proof, as it demonstrates that the method works in at least some circumstances. For empirical validation to be most convincing, it helps to also show some situations where the method doesn't work, ideally by varying a continuous parameter that reliably moves the simulation from a situation where it works to one where it doesn't. If the method works in all but extremely unrealistic cases, this builds confidence that it will work on real data.

The main conclusion of this paper's simulations is that the cyclic shift method most often detects valid correlations, while still not exceeding the false positive rate expected for a valid test. Readers may take this paper as indicating that the circular shift method is safe in all circumstances, but this is not correct. The authors acknowledge that circular shift can sometimes be invalid, and have made modifications to mitigate the problem. But there is neither a mathematical proof that these mitigations work, nor an analysis of the circumstances under which they succeed and fail. I doubt a formal proof is possible since there are likely situations in which even the new methods give false positive results. So the authors should include an empirical test of their modified circular shift method as compared to plain circular shift in various simulations. To gain confidence in the new method it is important to characterize the situations where both methods succeed; where the new method succeeds but traditional cyclic shift gives false positive errors; and situations in which both fail. If situations where the new method fails are so unrealistic that they would never occur in real data, we can have better confidence in the method.

The main contributions of the paper are the modifications to circular shifting and cross-validation that avoid problems of temporal contiguity, but these are only described in the Methods section. But this is a methods paper, so the description of the new methods should be in the main text, including explanatory figures currently in the Methods.

The introduction presents two problems that can occur in neural data: autocorrelation, and omitted variables. However, it is not clear that the current methods help with the problem of omitted variables. In fact, I don't see how any analysis method could solve the problem of omitted variables. If an experimenter observes a correlation between X and Y, there is no way to know this isn't because a third variable Z correlates with X and influences Y, without any effect of X on Y. It is generally impossible to prove causation without making randomized manipulations of one variable; although some methods claim to infer causality by observing all variables that could possibly have a causal effect, this is unlikely to occur in neuroscience. In any case, the problem of omitted variables seems irrelevant to the current study and could be removed.

The list of analysis methods mentioned in the first paragraph of the introduction (eg TDA, LVM) seems irrelevant: it is not clear how the methods evaluated here would be used to assess the significance of those methods. Better to stick to a description of how correlations are difficult to detect in autocorrelated signals, which is what the current methods address.

Reviewer #3 (Public Review):

Summary:<br /> The authors consider various statistical testing frameworks for model selection in the context of neuronal tuning. They consider cross-validation as a baseline scheme, and show various corrections and modifications to existing cross-validation schemes together with the underlying data/sign shuffling procedures for finding null distributions. Through careful simulations, they show that some of these tests are expectedly too conservative or too optimistic, and show that a log-likelihood-based test statistic with a cyclic shift permutation test for obtaining null distribution and Bonferroni correction strikes the right balance between hits and false detection. They further apply these tests to calcium imaging data from the mouse entorhinal cortex to identify grid cells (i.e., cells for which position is selected as a relevant variable).

Strengths:<br /> The paper is very well written, easy to follow, and enjoyable to read. It addresses an important issue in modern neuroscience, which is drawing conclusions based on data with missing or (unaccounted for) auto-correlated covariates.

Weaknesses:<br /> The paper would benefit from including more rigorous theoretical justification on why some of the procedures examined here outperform the others. This could be done in a stylized example with a Gaussian linear model, for which some of the used statistics have well-known distributions.

Comparisons with false discovery rate (FDR) control, as a more appropriate measure of performance when dealing with many comparisons, would benefit the existing comparisons merely based on Bonferroni correction.

Including spiking history in the generalized linear models (GLMs) used in analyzing the mouse data could be beneficial, as existing literature points to the importance of spiking history as a relevant covariate.

eLife assessment

The aim of this important study is to functionally characterize neuronal circuits underlying the escape behavior in Drosophila larvae. Upon detection of a noxious stimulus, larvae follow a series of stereotyped movements that include bending their body, rolling, and crawling away. This paper combines quantitative behavioral analyses, cell-type specific manipulations, optogenetics, calcium imaging, immunostaining, and connectomic analysis to provide convincing evidence of an inhibitory descending pathway that controls the switch from rolling to fast crawling behaviors of the larval escape response.

Reviewer #1 (Public Review):

Summary:<br /> Zhu et al. set out to better understand the neural mechanisms underlying Drosophila larval escape behavior. The escape behavior is comprised of several sequenced movements, including a lateral roll motion followed by fast crawling. The authors specifically were looking to identify neurons important for the roll-to-crawl transition.

Strengths:<br /> This paper is clearly written. The experiments are logical and complementary. They support the author's main claim that SeIN128 is a type of descending neuron that is both necessary and sufficient to modulate the termination of rolling.

Weaknesses:<br /> -This manuscript is narrowly focused on Drosophila larval escape behavior. It would be more accessible to a broader audience if this work was put into a larger context of descending control.<br /> -In general, the rigor is high. However, a few control experiments are missing.

Reviewer #2 (Public Review):

Summary:<br /> This study discovered a neural mechanism that serves as a switch from rolling to fast crawling behaviors in Drosophila larvae. It addressed important open questions of how neural circuits determine the sequence of locomotor behaviors and how animals switch from one behavior to another. Overall, its results support the conclusions. The experimental approaches should be described more clearly.

The escape behavior of Drosophila larvae includes rolling followed by fast crawling, where the neural mechanism of this sequence is unclear. The authors identified SeIN128, a group of descending neurons that facilitates rolling termination and shortens crawling latency. By investigating the EM connectome of larval CNS, they found that SeIN128 receives inputs from Basin-2 and A00c neurons, which are reported to facilitate rolling. SeIN128 makes reciprocal inhibitory synapses onto Basin-2 and A00c. Gad staining indicates that SeIN128 neurons are GABAergic, and inhibition of SeIN128 caused increased rolling probability and prolonged rolling. RNAi knockdown of GABA receptors in Basins further validated that SeIN128 inhibits Basins via GABAergic inputs. Lastly, the authors found that SeIN128 inhibits rolling induced by two types of Basin neurons, Basin-2 and Basin-4. Overall, SeIN128 forms a feedback inhibition ensemble that terminates rolling and shifts the animal to crawling.

Strengths:<br /> - The question (i.e., the neural circuitry of action selection) addressed by this study is important.<br /> - Larval and adult Drosophila is a powerful model system in neuroscience study, with rich genetic tools, diverse behaviors, and well-studied nervous systems. This study makes good use of them.<br /> - The experiments, analyses, and results are mostly rigorous and support the major claims. This study combined multiple innovative approaches, such as automated, machine-learning-based behavioral assays, EM reconstruction of larval CNS neurons, and genetic manipulation of specific neurons.

Weaknesses:<br /> - The description of methods and quantification for certain analyses are not clear or detailed enough for a comprehensive judgment of rigorousness, or for other scientists to repeat the experiments. This especially applies to the algorithm.<br /> - "Corkscrew-like rolling" is not an accurate term for larval rolling. The neuromuscular basis of rolling was recently studied by Cooney et. al., showing that rolling is the circumferential propagation of muscle activity where all segments contract similarly and synchronously.<br /> - The readability of the manuscript (text and figures) needs improvement, especially in making it understandable for a general audience. The addition of visual representations, simplifying the complex names of neurons, avoiding overall long sentences, and providing sufficient background introduction may help.

Reviewer #3 (Public Review):

Summary: Drosophila larvae exhibit characteristic escape behavior in response to a noxious stimulus. The underlying nociceptive circuit that regulates the temporal dynamics of escape behavior - bending, rolling, and crawling remains unclear. Using behavioral prototypes with optical stimulation and imaging, the authors show the function of descending neurons (SeIN128) in the termination of the rolling and subsequent initiation of the crawling behavior. The study further establishes the functional connectome of SeIN128, Basin-2, and A00c neurons, forming an inhibitory feedback circuit that regulates the rolling-escape sequences.

Strength: The study provides anatomical and functional evidence for temporal dynamics of escape behaviors in Drosophila larvae. Authors convincingly show the function of bilaterally descending neurons (previously identified SeIN128 neurons) in the transition of escape sequences. Based on the previous studies and functional connectome analysis, the study shows that SeIN128 neurons form a GABAergic feedback circuit with Basin-2, a second-order interneuron, and A00c, an ascending neuron downstream of Basin-2. Activation of SeIN128 neurons terminates the rolling by suppressing Basin-2 activity, facilitating subsequent rapid escape crawling. Thus, it establishes the function of feedback inhibition in temporal dynamics of escape behavior and contributes to a mechanistic understanding of the nociceptive circuits.

Weakness: The manuscript is written clearly; however, the presentation of the data needs to be improved for readability. The data and discussion establish the function of SeIN128 and Basin-2 in escape behavior, but the role of A00c neurons needs to be clarified.

eLife assessment

This study presents a valuable finding on the influence of visual uncertainty and Bayesian cue combination on implicit motor adaptation in young healthy participants. The evidence supporting the claims of the authors is solid, although a better discussion of the link between the model variables and the outcomes of related behavioral experiments would strengthen the conclusions. The work will be of interest to researchers in sensory cue integration and motor learning.

Reviewer #1 (Public Review):

This valuable study demonstrates a novel mechanism by which implicit motor adaptation saturates for large visual errors in a principled normative Bayesian manner. Additionally, the study revealed two notable empirical findings: visual uncertainty increases for larger visual errors in the periphery, and proprioceptive shifts/implicit motor adaptation are non-monotonic, rather than ramp-like. This study is highly relevant for researchers in sensory cue integration and motor learning. However, I find some areas where statistical quantification is incomplete, and the contextualization of previous studies to be puzzling.

Issue #1: Contextualization of past studies.

While I agree that previous studies have focused on how sensory errors drive motor adaptation (e.g., Burge et al., 2008; Wei and Kording, 2009), I don't think the PReMo model was contextualized properly. Indeed, while PReMo should have adopted clearer language - given that proprioception (sensory) and kinaesthesia (perception) have been used interchangeably, something we now make clear in our new study (Tsay, Chandy, et al. 2023) - PReMo's central contribution is that a perceptual error drives implicit adaptation (see Abstract): the mismatch between the felt (perceived) and desired hand position. The current paper overlooks this contribution. I encourage the authors to contextualize PReMo's contribution more clearly throughout. Not mentioned in the current study, for example, PReMo accounts for the continuous changes in perceived hand position in Figure 4 (Figure 7 in the PReMo study).

There is no doubt that the current study provides important additional constraints on what determines perceived hand position: Firstly, it offers a normative Bayesian perspective in determining perceived hand position. PReMo suggests that perceived hand position is determined by integrating motor predictions with proprioception, then adding a proprioceptive shift; PEA formulates this as the optimal integration of these three inputs. Secondly, PReMo assumed visual uncertainty to remain constant for different visual errors; PEA suggests that visual uncertainty ought to increase (but see Issue #2).

Issue #2: Failed replication of previous results on the effect of visual uncertainty.

2a. A key finding of this paper is that visual uncertainty linearly increases in the periphery; a constraint crucial for explaining the non-monotonicity in implicit adaptation. One notable methodological deviation from previous studies is the requirement to fixate on the target: Notably, in the current experiments, participants were asked to fixate on the target, a constraint not imposed in previous studies. In a free-viewing environment, visual uncertainty may not attenuate as fast, and hence, implicit adaptation does not attenuate as quickly as that revealed in the current design with larger visual errors. Seems like this current fixation design, while important, needs to be properly contextualized considering how it may not represent most implicit adaptation experiments.

2b. Moreover, the current results - visual uncertainty attenuates implicit adaptation in response to large, but not small, visual errors - deviates from several past studies that have shown that visual uncertainty attenuates implicit adaptation to small, but not large, visual errors (Tsay, Avraham, et al. 2021; Makino, Hayashi, and Nozaki, n.d.; Shyr and Joshi 2023). What do the authors attribute this empirical difference to? Would this free-viewing environment also result in the opposite pattern in the effect of visual uncertainty on implicit adaptation for small and large visual errors?

2c. In the current study, the measure of visual uncertainty might be inflated by brief presentation times of comparison and referent visual stimuli (only 150 ms; our previous study allowed for a 500 ms viewing time to make sure participants see the comparison stimuli). Relatedly, there are some individuals whose visual uncertainty is greater than 20 degrees standard deviation. This seems very large, and less likely in a free-viewing environment.

2d. One important confound between clear and uncertain (blurred) visual conditions is the number of cursors on the screen. The number of cursors may have an attenuating effect on implicit adaptation simply due to task-irrelevant attentional demands (Parvin et al. 2022), rather than that of visual uncertainty. Could the authors provide a figure showing these blurred stimuli (gaussian clouds) in the context of the experimental paradigm? Note that we addressed this confound in the past by comparing participants with and without low vision, where only one visual cursor is provided for both groups (Tsay, Tan, et al. 2023).

Issue #3: More methodological details are needed.

3a. It's unclear why, in Figure 4, PEA predicts an overshoot in terms of perceived hand position from the target. In PReMo, we specified a visual shift in the perceived target position, shifted towards the adapted hand position, which may result in overshooting of the perceived hand position with this target position. This visual shift phenomenon has been discovered in previous studies (e.g., (Simani, McGuire, and Sabes 2007)).

3b. The extent of implicit adaptation in Experiment 2, especially with smaller errors, is unclear. The implicit adaptation function seems to be still increasing, at least by visual inspection. Can the authors comment on this trend, and relatedly, show individual data points that help the reader appreciate the variability inherent to these data?

3c. The same participants were asked to return for multiple days/experiments. Given that the authors acknowledge potential session effects, with attenuation upon re-exposure to the same rotation (Avraham et al. 2021), how does re-exposure affect the current results? Could the authors provide clarity, perhaps a table, to show shared participants between experiments and provide evidence showing how session order may not be impacting results?

3d. The number of trials per experiment should be detailed more clearly in the Methods section (e.g., Exp 4). Moreover, could the authors please provide relevant code on how they implemented their computational models? This would aid in future implementation of these models in future work. I, for one, am enthusiastic to build on PEA.

3f. In addition to predicting a correlation between proprioceptive shift and implicit adaptation on a group level, both PReMo and PEA (but not causal inference) predict a correlation between individual differences in proprioceptive shift and proprioceptive uncertainty with the extent of implicit adaptation (Tsay, Kim, et al. 2021). Interestingly, shift and uncertainty are independent (see Figures 4F and 6C in Tsay et al, 2021). Does PEA also predict independence between shift and uncertainty? It seems like PEA does predict a correlation.

References:

Avraham, Guy, Ryan Morehead, Hyosub E. Kim, and Richard B. Ivry. 2021. "Reexposure to a Sensorimotor Perturbation Produces Opposite Effects on Explicit and Implicit Learning Processes." PLoS Biology 19 (3): e3001147.<br /> Makino, Yuto, Takuji Hayashi, and Daichi Nozaki. n.d. "Divisively Normalized Neuronal Processing of Uncertain Visual Feedback for Visuomotor Learning."<br /> Parvin, Darius E., Kristy V. Dang, Alissa R. Stover, Richard B. Ivry, and J. Ryan Morehead. 2022. "Implicit Adaptation Is Modulated by the Relevance of Feedback." BioRxiv. https://doi.org/10.1101/2022.01.19.476924.<br /> Shyr, Megan C., and Sanjay S. Joshi. 2023. "A Case Study of the Validity of Web-Based Visuomotor Rotation Experiments." Journal of Cognitive Neuroscience, October, 1-24.<br /> Simani, M. C., L. M. M. McGuire, and P. N. Sabes. 2007. "Visual-Shift Adaptation Is Composed of Separable Sensory and Task-Dependent Effects." Journal of Neurophysiology 98 (5): 2827-41.<br /> Tsay, Jonathan S., Guy Avraham, Hyosub E. Kim, Darius E. Parvin, Zixuan Wang, and Richard B. Ivry. 2021. "The Effect of Visual Uncertainty on Implicit Motor Adaptation." Journal of Neurophysiology 125 (1): 12-22.<br /> Tsay, Jonathan S., Anisha M. Chandy, Romeo Chua, R. Chris Miall, Jonathan Cole, Alessandro Farnè, Richard B. Ivry, and Fabrice R. Sarlegna. 2023. "Implicit Motor Adaptation and Perceived Hand Position without Proprioception: A Kinesthetic Error May Be Derived from Efferent Signals." BioRxiv. https://doi.org/10.1101/2023.01.19.524726.<br /> Tsay, Jonathan S., Hyosub E. Kim, Darius E. Parvin, Alissa R. Stover, and Richard B. Ivry. 2021. "Individual Differences in Proprioception Predict the Extent of Implicit Sensorimotor Adaptation." Journal of Neurophysiology, March. https://doi.org/10.1152/jn.00585.2020.<br /> Tsay, Jonathan S., Steven Tan, Marlena Chu, Richard B. Ivry, and Emily A. Cooper. 2023. "Low Vision Impairs Implicit Sensorimotor Adaptation in Response to Small Errors, but Not Large Errors." Journal of Cognitive Neuroscience, January, 1-13.

Reviewer #2 (Public Review):

Summary:<br /> The authors present the Perceptual Error Adaptation (PEA) model, a computational approach offering a unified explanation for behavioral results that are inconsistent with standard state-space models. Beginning with the conventional state-space framework, the paper introduces two innovative concepts. Firstly, errors are calculated based on the perceived hand position, determined through Bayesian integration of visual, proprioceptive, and predictive cues. Secondly, the model accounts for the eccentricity of vision, proposing that the uncertainty of cursor position increases with distance from the fixation point. This elegantly simple model, with minimal free parameters, effectively explains the observed plateau in motor adaptation under the implicit motor adaptation paradigm using the error-clamp method. Furthermore, the authors experimentally manipulate visual cursor uncertainty, a method established in visuomotor studies, to provide causal evidence. Their results show that the adaptation rate correlates with perturbation sizes and visual noise, uniquely explained by the PEA model and not by previous models. Therefore, the study convincingly demonstrates that implicit motor adaptation is a process of Bayesian cue integration

Strengths:<br /> In the past decade, numerous perplexing results in visuomotor rotation tasks have questioned their underlying mechanisms. Prior models have individually addressed aspects like aiming strategies, motor adaptation plateaus, and sensory recalibration effects. However, a unified model encapsulating these phenomena with a simple computational principle was lacking. This paper addresses this gap with a robust Bayesian integration-based model. Its strength lies in two fundamental assumptions: motor adaptation's influence by visual eccentricity, a well-established vision science concept, and sensory estimation through Bayesian integration. By merging these well-founded principles, the authors elucidate previously incongruent and diverse results with an error-based update model. The incorporation of cursor feedback noise manipulation provides causal evidence for their model. The use of eye-tracking in their experimental design, and the analysis of adaptation studies based on estimated eccentricity, are particularly elegant. This paper makes a significant contribution to visuomotor learning research.

Weaknesses:<br /> The paper provides a comprehensive account of visuomotor rotation paradigms, addressing incongruent behavioral results with a solid Bayesian integration model. However, its focus is narrowly confined to visuomotor rotation, leaving its applicability to broader motor learning paradigms, such as force field adaptation, saccadic adaptation, and de novo learning paradigms, uncertain. The paper's impact on the broader fields of neuroscience and cognitive science may be limited due to this specificity. While the paper excellently demonstrates that specific behavioral results in visuomotor rotation can be explained by Bayesian integration, a general computational principle, its contributions to other motor learning paradigms remain to be explored. The paper would benefit from a discussion on the model's generality and its limitations, particularly in relation to the undercompensating effects in other motor learning paradigms.

Reviewer #3 (Public Review):

Summary<br /> In this paper, the authors model motor adaptation as a Bayesian process that combines visual uncertainty about the error feedback, uncertainty about proprioceptive sense of hand position, and uncertainty of predicted (=planned) hand movement with a learning and retention rate as used in state space models. The model is built with results from several experiments presented in the paper and is compared with the PReMo model (Tsay, Kim, et al., 2022) as well as a cue combination model (Wei & Körding, 2009). The model and experiments demonstrate the role of visual uncertainty about error feedback in implicit adaptation.

In the introduction, the authors notice that implicit adaptation (as measured in error-clamp-based paradigms) does not saturate at larger perturbations, but decreases again (e.g. Moorehead et al., 2017 shows no adaptation at 135{degree sign} and 175{degree sign} perturbations). They hypothesized that visual uncertainty about cursor position increases with larger perturbations since the cursor is further from the fixated target. This could decrease the importance assigned to visual feedback which could explain lower asymptotes.

The authors characterize visual uncertainty for 3 rotation sizes in the first experiment, and while this experiment could be improved, it is probably sufficient for the current purposes. Then the authors present a second experiment where adaptation to 7 clamped errors is tested in different groups of participants. The models' visual uncertainty is set using a linear fit to the results from experiment 1, and the remaining 4 parameters are then fit to this second data set. The 4 parameters are 1) proprioceptive uncertainty, 2) uncertainty about the predicted hand position, 3) a learning rate, and 4) a retention rate. The authors' Perceptual Error Adaptation model ("PEA") predicts asymptotic levels of implicit adaptation much better than both the PReMo model (Tsay, Kim et al., 2022), which predicts saturated asymptotes, or a causal inference model (Wei & Körding, 2007) which predicts no adaptation for larger rotations. In a third experiment, the authors test their model's predictions about proprioceptive recalibration, but unfortunately, compare their data with an unsuitable other data set. Finally, the authors conduct a fourth experiment where they put their model to the test. They measure implicit adaptation with increased visual uncertainty, by adding blur to the cursor, and the results are again better in line with their model (predicting overall lower adaptation) than with the PReMo model (predicting equal saturation but at larger perturbations) or a causal inference model (predicting equal peak adaptation, but shifted to larger rotations). In particular, the model fits experiment 2 and the results from experiment 4 show that the core idea of the model has merit: increased visual uncertainty about errors dampens implicit adaptation.

Strengths<br /> In this study, the authors propose a Perceptual Error Adaptation model ("PEA") and the work combines various ideas from the field of cue combination, Bayesian methods, and new data sets, collected in four experiments using various techniques that test very different components of the model. The central component of visual uncertainty is assessed in the first experiment. The model uses 4 other parameters to explain implicit adaptation. These parameters are 1) learning and 2) retention rate, as used in popular state space models, and the uncertainty (variance) of 3) predicted and 4) proprioceptive hand position. In particular, the authors observe that asymptotes for implicit learning do not saturate, as claimed before, but decrease again when rotations are very large and that this may have to do with visual uncertainty (e.g. Tsay et al., 2021, J Neurophysiol 125, 12-22). The final experiment confirms predictions of the fitted model about what happens when visual uncertainty is increased (overall decrease of adaptation). By incorporating visual uncertainty depending on retinal eccentricity, the predictions of the PEA model for very large perturbations are notably different from and better than, the predictions of the two other models it is compared to. That is, the paper provides strong support for the idea that visual uncertainty of errors matters for implicit adaptation.

Weaknesses<br /> Although the authors don't say this, the "concave" function that shows that adaptation does not saturate for larger rotations has been shown before, including in papers cited in this manuscript.

The first experiment, measuring visual uncertainty for several rotation sizes in error-clamped paradigms has several shortcomings, but these might not be so large as to invalidate the model or the findings in the rest of the manuscript. There are two main issues we highlight here. First, the data is not presented in units that allow comparison with vision science literature. Second, the 1 second delay between the movement endpoint and the disappearance of the cursor, and the presentation of the reference marker, may have led to substantial degradation of the visual memory of the cursor endpoint. That is, the experiment could be overestimating the visual uncertainty during implicit adaptation.

The paper's third experiment relies to a large degree on reproducing patterns found in one particular paper, where the reported hand positions - as a measure of proprioceptive sense of hand position - are given and plotted relative to an ever-present visual target, rather than relative to the actual hand position. That is, 1) since participants actively move to a visual target, the reported hand positions do not reflect proprioception, but mostly the remembered position of the target participants were trying to move to, and 2) if the reports are converted to a difference between the real and reported hand position (rather than the difference between the target and the report), those would be on the order of ~20{degree sign} which is roughly two times larger than any previously reported proprioceptive recalibration, and an order of magnitude larger than what the authors themselves find (1-2{degree sign}) and what their model predicts. Experiment 3 is perhaps not crucial to the paper, but it nicely provides support for the idea that proprioceptive recalibration can occur with error-clamped feedback.

Perhaps the largest caveat to the study is that it assumes that people do not look at the only error feedback available to them (and can explicitly suppress learning from it). This was probably true in the experiments used in the manuscript, but unlikely to be the case in most of the cited literature. Ignoring errors and suppressing adaptation would also be a disastrous strategy to use in the real world, such that our brains may not be very good at this. So the question remains to what degree - if any - the ideas behind the model generalize to experiments without fixation control, and more importantly, to real-life situations.

Specific comments:<br /> A small part of the manuscript relies on replicating or modeling the proprioceptive recalibration in a study we think does NOT measure proprioceptive recalibration (Tsay, Parvin & Ivry, JNP, 2020). In this study, participants reached for a visual target with a clamped cursor, and at the end of the reach were asked to indicate where they thought their hand was. The responses fell very close to the visual target both before and after the perturbation was introduced. This means that the difference between the actual hand position, and the reported/felt hand position gets very large as soon as the perturbation is introduced. That is, proprioceptive recalibration would necessarily have roughly the same magnitude as the adaptation displayed by participants. That would be several times larger than those found in studies where proprioceptive recalibration is measured without a visual anchor. The data is plotted in a way that makes it seem like the proprioceptive recalibration is very small, as they plot the responses relative to the visual target, and not the discrepancy between the actual and reported hand position. It seems to us that this study mostly measures short-term visual memory (of the target location). What is astounding about this study is that the responses change over time to begin with, even if only by a tiny amount. Perhaps this indicates some malleability of the visual system, but it is hard to say for sure.

Regardless, the results of that study do not form a solid basis for the current work and they should be removed. We would recommend making use of the dataset from the same authors, who improved their methods for measuring proprioception shifts just a year later (Tsay, Kim, Parvin, Stover, and Ivry, JNP, 2021). Although here the proprioceptive shifts during error-clamp adaptation (Exp 2) were tiny, and not quite significant (p<0.08), the reports are relative to the actual location of the passively placed unseen hand, measured in trials separate from those with reach adaptation and therefore there is no visual target to anchor their estimates to.

Experiment 1 measures visual uncertainty with increased rotation size. The authors cite relevant work on this topic (Levi & Klein etc) which has found a linear increase in uncertainty of the position of more and more eccentrically displayed stimuli.

First, this is a question where the reported stimuli and effects could greatly benefit from comparisons with the literature in vision science, and the results might even inform it. In order for that to happen, the units for the reported stimuli and effects should (also) be degrees of visual angle (dva).

As far as we know, all previous work has investigated static stimuli, where with moving stimuli, position information from several parts of the visual field are likely integrated over time in a final estimate of position at the end of the trajectory (a Kalman filter type process perhaps). As far as we know, there are no studies in vision science on the uncertainty of the endpoint of moving stimuli. So we think that the experiment is necessary for this study, but there are some areas where it could be improved.

Then, the linear fit is done in the space of the rotation size, but not in the space of eccentricity relative to fixation, and these do not necessarily map onto each other linearly. If we assume that the eye-tracker and the screen were at the closest distance the manufacturer reports it to work accurately at (45 cm), we would get the largest distances the endpoints are away from fixation in dva. Based on that assumed distance between the participant and monitor, we converted the rotation angles to distances between fixation and the cursor endpoint in degrees visual angle: 0.88, 3.5, and 13.25 dva (ignoring screen curvature, or the absence of it). The ratio between the perturbation angle and retinal distance to the endpoint is roughly 0.221, 0.221, and 0.207 if the minimum distance is indeed used - which is probably fine in this case. But still, it would be better to do fit in the relevant perceptual coordinate system.

The first distance (4 deg rotation; 0.88 dva offset between fixation and stimulus) is so close to fixation (even at the assumed shortest distance between eye and screen) that it can be considered foveal and falls within the range of noise of eye-trackers + that of the eye for fixating. There should be no uncertainty on or that close to the fovea. The variability in the data is likely just measurement noise. This also means that a linear fit will almost always go through this point, somewhat skewing the results toward linearity. The advantage is that the estimate of the intercept (measurement noise) is going to be very good. Unfortunately, there are only 2 other points measured, which (if used without the closest point) will always support a linear fit. Therefore, the experiment does not seem suitable to test linearity, only to characterize it, which might be sufficient for the current purposes. We'd understand if the effort to do a test of linearity using many more rotations requires too much effort. But then it should be made much clearer that the experiment assumes linearity and only serves to characterize the assumed linearity.

Final comment after the consultation session:<br /> There were a lot of discussions about the actual interpretation of the behavioral data from this paper with regards to past papers (Tsay et al. 2020 or 2021), and how it matches the different variables of the model. The data from Tsay 2020 combined both proprioceptive information (Xp) and prediction about hand position (Xu) because it involves active movements. On the other hand, Tsay et al. 2021 is based on passive movements and could provide a better measure of Xp alone. We would encourage you to clarify how each of the variables used in the model is mapped onto the outcomes of the cited behavioral experiments.

The reviewers discussed this point extensively during the consultation process. The results reported in the Tsay 2020 study reflect both proprioception and prediction. However, having a visual target contributes more than just prediction, it is likely an anchor in the workspace that draws the response to it. Such that the report is dominated by short-term visual memory of the target (which is not part of the model). However, in the current Exp 3, as in most other work investigating proprioception, this is calculated relative to the actual direction.

The solution is fairly simple. In Experiment 3 in the current study, Xp is measured relative to the hand without any visual anchors drawing responses, and this is also consistent with the reference used in the Tsay et al 2021 study and from many studies in the lab of D. Henriques (none of which also have any visual reach target when measuring proprioceptive estimates). So we suggest using a different data set that also measures Xp without any other influences, such as the data from Tsay et al 2021 instead.

These issues with the data are not superficial and can not be solved within the model. Data with correctly measured biases (relative to the hand) that are not dominated by irrelevant visual attractors would actually be informative about the validity of the PEA model. Dr. Tsay has so much other that we recommend using a more to-the-point data set that could actually validate the PEA model.

eLife assessment

This study presents useful results on glutamine-rich motifs in relation to protein expression and alternative genetic codes. The solid data are based on bioinformatic approaches that are employed to systematically uncover sequence features associated with proteome-wide amino acid distribution and biological processes.

Reviewer #2 (Public Review):

Summary:<br /> This study seeks to understand the connection between protein sequence and function in disordered regions enriched in polar amino acids (specifically Q, N, S and T). While the authors suggest that specific motifs facilitate protein-enhancing activities, their findings are correlative, and the evidence is incomplete. Similarly, the authors propose that the re-assignment of stop codons to glutamine-encoding codons underlies the greater user of glutamine in a subset of ciliates, but again, the conclusions here are, at best, correlative. The authors perform extensive bioinformatic analysis, with detailed (albeit somewhat ad hoc) discussion on a number of proteins. Overall, the results presented here are interesting but are unable to exclude competing hypotheses.

Strengths:<br /> Following up on previous work, the authors wish to uncover a mechanism associated with poly-Q and SCD motifs explaining proposed protein expression-enhancing activities. They note that these motifs often occur IDRs and hypothesize that structural plasticity could be capitalized upon as a mechanism of diversification in evolution. To investigate this further, they employ bioinformatics to investigate the sequence features of proteomes of 27 eukaryotes. They deepen their sequence space exploration uncovering sub-phylum-specific features associated with species in which a stop-codon substitution has occurred. The authors propose this stop-codon substitution underlies an expansion of ploy-Q repeats and increased glutamine distribution.

Weaknesses:<br /> The authors were provided with a series of suggested changes to improve clarity, and a series of concerns raised. Some of these have been addressed but many have not. At this point, I do not see my role as telling the authors how to re-write their manuscript, but many of the concerns raised in my original review remain, and the authors have done little to allay those concerns in their revisions.

eLife assessment

The authors have greatly expanded their important hippocampome.org resource about rodent hippocampal cell types, their physiological properties, and their interactions. With version 2.0, they make a significant advance in providing a user-friendly means to make computer models of hippocampal circuits. The work is convincing, and there are only minor reservations that the figures may be too complex.

Reviewer #2 (Public Review):

The authors have greatly expanded their helpful hippocampome.org resource for the community regarding hippocampal cell types and their interactions from many perspectives. The many updates from v1.0 to v1.12 are nicely summarized in Table 1.

With v2.0, they now achieve the original vision of their project - to enable data-driven spiking neural network simulations of rodent hippocampal circuits. This work thus moves hippocampome.org from not only being a useful resource but also being able to launch simulations in which the models have direct links to the experimental literature. This will not only be of interest to the vast hippocampal community, but also to the diverse computational neuroscience community as theoretical models can potentially be "experimentally tested" with v2.0 to allow theoretical insights to be more biologically applicable.

Reviewer #3 (Public Review):

Summary:

The authors aim to provide a multidisciplinary resource on the structural and physiological organization of the hippocampal system and make the available experimental data available for further theoretical work, providing tools to do so in a very flexible and user-friendly way. Since this is a new version of an already existing data-resource, the authors certainly reach their aim and fulfil expectations that the reader might have. The content of the database is as good as the original data, collected from the published knowledge-database, sometimes with help of the original authors, and the overall quality depends further on how the data are curated by the team of authors and many others who helped them. That process is briefly described and more details are available in descriptions of previous versions and on the website. The data extraction, examples of how data can be used and the part on attempts to model the hippocampus are exiting and open doors to new and exciting research opportunities.

Strengths:

Excellent description with many outlined opportunities. Nicely illustrated and inviting to explore the online database. The database itself is easy to navigate and to access relevant information, allowing to do further research on the available data.

Weaknesses:

The figures are complex, containing a heavy information load. One needs some general knowlegde of the system in order to grasp the enormous potential of what is provided.

Author Response

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

Reviewer #1 (Recommendations for The Authors):

1) While the specificity of the observed muscle phenotypes seems clear, the subsequent molecular analysis of Numb protein interactors does not seem to consider the potential involvement of Numb-like. The authors should demonstrate the relative expression levels of Numb and Numb-like in the models used, and establish the specificity of the antibodies used in IP, western and staining experiments.

Response: Perhaps the most convincing evidence that the anti-Numb antibody did not pull down Numb-like is that this protein was not detected among immunoprecipitated protein complexes pulled down by the anti-Numb antibody used. The antibody used in the immunoprecipitation was validated by the supplier and was previously reported to immunoprecipitate Numb [1, 2]. We previously demonstrated that a morpholino against Numb mRNA almost completely eliminated the band detected by this antibody and that this band was at the expected molecular weight [ref]. In our hands, mRNA levels for Numb-like in skeletal muscle are 5-10-fold lower than those for Numb [3]. We have been unable to detect Numb-like protein in healthy adult skeletal muscle by immunoblotting or immunofluorescence staining. Taking all of these findings together, it seems unlikely that the antibodies used for immunoprecipitating Numb-protein complexes pulls down Numb-like.

2) The authors use PCR to investigate Numb isoform expression and conclude that p65 is likely the dominant protein isoform expressed. While this agrees with the single band observed in Supp Figure 4A, a positive control for exon 9 excluded and included isoforms in the PCR reactions would strengthen this conclusion.

Response: The amplicons shown in Supplemental 4 were sequenced. The clones corresponded to the isoforms with the exon 3 present or removed. No amplicons containing exon 9 were detected. The following sentence was added to the Analysis of Splice Variants section of Methods to address this point: “PCR products were cloned using the TOPO TA cloning system (ThermoFisher) and multiple resulting clones were sequenced to confirm that the expected products were generated.”

3) PCR analysis of total Numb and Numb-like expression levels are not shown. This is important given the specificity of the Numb antibodies used for AP-MS experiments are not described and some Numb antibodies are well known to also recognize Numb-like. Two different Numb antibodies were used for Western and immunoprecipitation but the specificity for Numb and Numb-like is not described. In particular, does the antibody used in the AP-MS experiment recognize both Numb and Numb-like? Supplementary Table 1 does not list Numb or Numb-like, but presumably peptides were identified?

Response: As noted above, the specificity of anti-Numb antibodies was confirmed in previous studies [3]. Importantly, Numb-like mRNA levels are 5-10-fold lower than Numb mRNA, and NumbL protein is undetectable in healthy adult skeletal muscle by Western. The physiology data reported in this manuscript supports the conclusion that a single KO of Numb is sufficient to recapitulate the physiological phenotype of Numb/Numb-like KO . We therefore reason that the majority, if not all, of the physiological contribution of these proteins to muscle contractility due to Numb (Fig. 1).

4) The validation experiment used the same Numb antibody for immunoprecipitation, immunoblotted with Septin 7. A reciprocal IP of Septin 7 and blotted with Numb should be performed. In addition, a Numb-like IP or immunoblot would also be useful to demonstrate the specificity of the interaction. Efforts to map the interaction between Numb and Septin 7 would be useful to demonstrate specificity of the interaction and strategies to establish the biological relevance of the interaction.

Response: We agree with the reviewer and attempted several IPs with anti-Septin7 antibodies. These were unsuccessful. In a new collaboration, Dr. Italo Cavini (University of Sao Paulo) has used machine-learning-based approaches to model binding between Numb and several septins, including Septin 7. The analysis suggests that binding of Numb with septins involves a domain of Numb that has not yet been ascribed a function in protein-protein interactions. These computational predictions require experimental validation but provide rational starting point for experiments to define the domains responsible for these interactions. Such experiments were included in our recent NIH R01 renewal application. We hope to be able to report on results of confirmatory experiments of these computational models in the future.

5) Other septins were identified in the AP-MS experiment and might have been anticipated to also be disrupted by Numb/Numb-like deletion. Are these septins known to interact in a complex?

Response: This is an excellent question. Septins have conserved motifs providing a clear reason to imagine that many different mammalian septins could directly interact with Numb. Septins form heterooligomers consisting of complexes formed by 3, 6 or 8 septins [4]. It is likely that when Numb binds to one septin, antibodies against Numb pull down other septins present in the septin oligomer to which Numb is bound. The following paragraph was added to the discussion: “Our findings suggest that Numb may also interact with other septins such as septins 2, 9 and 10, which were also identified with a high level of confidence as Numb interacting proteins by our LC/MS/MS analysis. Our data to not allow us to determine if Numb binds directly to these septins. Septins contain highly conserved regions, and, consequently, if one such region of septin 7 interacts with Numb, then many septins would be expected to directly bind Numb through the same domain. However, because septins self-oligomerize, is possible that when Numb binds to one septin, antibodies against Numb could also pull down other septins present in the septin oligomer to which Numb is bound regardless of whether or not they are also bound by Numb. “

6) The text for Figure 5 describes analysis of Septin localization in inducible Numb/Numb-like cKO muscle, but the figure indicates only Numb is knocked out. Please clarify.

Response: We apologize for this oversight on our part. The Legend to Figure 5 has been corrected.

7) Supplementary Figure 2 seems to show that TAM treatment increases Numb expression. Please clarify. Also, please correct reference 9.

Response: The figure was incorrectly labeled. We apologize for this oversight and have corrected the figure in the revised manuscript.

Reviewer #2 (Recommendations for The Authors):

Overall, the manuscript is well written. I do have a few minor issues/concerns, which are detailed below.

Abstract: Please be a little more specific regarding which where the tissue came from (i.e. humans, mice, cell) when referring to your previous studies.

Response: The abstract has been revised as requested.

Introduction: Please be more specific regarding the technique used for detecting ultrastructural changes. I assume it was done with TEM, but the reference is listed as an "invalid citation" in your reference list.

Response: The introduction was revised as requested and the citation was updated to reference a valid citation.

Methods / Numb Co-Immunoprecipitation: Please indicated the level of confluency of the C2C12 cells as this will alter gene expression.

Response: As indicated in the updated Methods section, confluent C2C12 cells were switched to differentiation media (low serum) for seven days. When harvested, the cells had differentiated and fused into myotubes.

Methods / Immunohistochemical Staining: The first sentence needs to be edited regarding plurality and grammar.

Response: Thank you for this comment. The text was revised accordingly.

Results / GWAS and WGS Identify...: Please spell out phosphodiesterase (I assume) for PDE4D

Response: This change was incorporated in the text.

eLife assessment

This convincing study demonstrates a potentially important role for the factor Numb in skeletal muscle excitation-contraction coupling, since a Numb knockout reduced contractile force. The authors thus demonstrate a novel role for Numb in calcium release in skeletal muscle.

Reviewer #1 (Public Review):

The authors investigate the function of the PTB domain containing adaptor protein Numb in skeletal muscle structure and function. In particular, the effects of reduced Numb expression in aging muscle is proposed as a mechanism for reduced contractile function associated with sarcopenia. Using ex-vivo analysis of conditional Numb and Numblike knockout muscle the authors demonstrate that loss of Numb but not the related Numblike gene expression perturbs muscle force generation. In order to explore the molecular mechanisms involved, Numb interacting proteins were identified in C2C12 cell cultured myotubes by immunoprecipitation and LC-MS/MS. The authors identify Septin 7 as well as Septin 2, 9 and 10 as a Numb binding proteins and demonstrate that loss of Numb/Numblike in myofibers causes changes in Septin 7 subcellular localization. Of note, whether additional septins form a complex or are also disrupted by Numb/Numblike loss remains an interesting area for further investigation. Additional investigation of the specificity and mapping of the Numb-Septin 7 (or another Septin) interaction would be of interest and provide an approach for future studies to demonstrate the biological relevance and specificity of the Numb-Septin 7 interaction in skeletal muscle

Reviewer #2 (Public Review):

Summary:

The main purpose of this investigation was to 1) compare the effects of a single knockout (sKO) of Numb or a double knockout (dKO) of Numb and NumbL on ex-vivo physiological properties of the extensor digitorium longus (EDL) muscle in C57BL/6NCrl mice; and 2) analyze protein complexes isolated from C2C12 myotubes via immunoprecipitation and LC/MS/MS for potential Numb binding partners. The main findings are 1) the muscles from sKO and dKO were significantly weaker with little difference between the sKO and dKO lines, indicating the reduced force is mainly due to the inactivation of the Numb gene; and 2) there were 11 potential Numb binding proteins that were identified and cytoskeletal specific proteins including Septin 7.

Strengths:

Straight-forward yet elegant design to help determine the important role the Numb has in skeletal muscle.

Weaknesses:

There were a limited number of samples (3-6) that were used for the physiological experiments; however, there was a very large effect size in terms of differences in muscle tension development between the induced KO models and the controls.

Author Response

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

eLife assessment

This important study reports jAspSnFR3, a biosensor that enables high spatiotemporal resolution of aspartate levels in living cells. To develop this sensor, the authors used a structurally guided amino acid substitution in a glutamate/aspartate periplasmic binding protein to switch its specificity towards aspartate. The in vitro and in cellulo functional characterization of the biosensor is convincing, but evidence of the sensor's effectiveness in detecting small perturbations of aspartate levels and information on its behavior in response to acute aspartate elevations in the cytosol are still lacking.

We thank the reviewers and editors for the detailed assessment of our work and for their constructive feedback. Most comments have now been experimentally addressed in the revised manuscript, which we feel is substantially improved from the initial draft.

Public Reviews:

Reviewer #1 (Public Review):

In this manuscript, Davidsen and coworkers describe the development of a novel aspartate biosensor jAspSNFR3. This collaborative work supports and complements what was reported in a recent preprint by Hellweg et al., (bioRxiv; doi: 10.1101/2023.05.04.537313). In both studies, the newly engineered aspartate sensor was developed from the same glutamate biosensor previously developed by the authors of this manuscript. This coincidence is not casual but is the result of the need to find tools capable of measuring aspartate levels in vivo. Therefore, it is undoubtedly a relevant and timely work carried out by groups experienced in aspartate metabolism and in the generation of metabolite biosensors.

Reviewer #2 (Public Review):

In this work the IGluSnFR3 sensor, recently developed by Marvin et al (2023) is mutated position S72, which was previously reported to switch the specificity from Glu to Asp. They made 3 mutations at this position, selected a S72P mutant, then made a second mutation at S27 to generate an Asp-specific version of the sensor. This was then characterized thoroughly and used on some test experiments, where it was shown to detect and allow visualization of aspartate concentration changes over time. It is an incremental advance on the iGluSnFR3 study, where 2 predictable mutations are used to generate a sensor that works on a close analog of Glu, Asp. It is shown to have utility and will be useful in the field of Asp-mediated biological effects.

Reviewer #3 (Public Review):

In this manuscript, Davidsen and collaborators introduce jAspSnFR3, a new version of aspartate biosensor derived from iGluSnFR3, that allows monitoring in real-time aspartate levels in cultured cells. A selective amino acids substitution was applied in a key region of the template to switch its specificity from glutamate to aspartate. The jAspSnFR3 does not respond to other tested metabolites and performs well, is not toxic for cultured cells, and is not affected by temperature ensuring the possibility of using this tool in tissues physiologically more relevant. The high affinity for aspartate (KD=50 uM) allowed the authors to measure fluctuations of this amino acid in the physiological range. Different strategies were used to bring aspartate to the minimal level. Finally, the authors used jAspSnFR3 to estimate the intracellular aspartate concentration. One of the highlights of the manuscript was a treatment with asparagine during glutamine starvation. Although didn't corroborate the essentiality of asparagine in glutamine depletion, the measurement of aspartate during this supplementation is a glimpse of how useful this sensor can be.

Reviewer #1 (Recommendations For The Authors):

The authors should evaluate the effectiveness of the sensor in detecting small perturbations of aspartate levels and its behavior in response to acute aspartate elevations in the cytosol. In vivo aspartate determinations were performed exclusively in conditions that cause aspartate depletion. By means the use of mitochondrial respiratory inhibitors or aspartate withdrawal, it was determined the reliability of the sensor performing readings during relatively long periods, until reaching a steady-state of aspartate-depletion 12-60 hours later. Although in Hellweg and coworkers, it has been demonstrated that a related aspartate sensor could detect increases in aspartate in cell overexpressing the aspartate-glutamate GLAST transporter, the differences reported here between both sensors advise testing whether this aspect is also improved, or not, using jAspSNFR3.

Similarly, Davidsen et al. did not test if the sensor can be able to detect transient variations in cytosolic aspartate levels. In proliferative cells aspartate synthesis is linked to NAD+ regeneration by ETC (Sullivan et al., 2015, Cell), indeed the authors deplete aspartate using CI or CIII inhibitors but do not analyze if those are recovered, and increased, after its removal. Furthermore, the sequential addition of oligomycin and uncouplers could generate measurable fluctuations of aspartate in the cytosol.

We agree with the reviewer that only including situations of aspartate depletion in our cell culture experiments provided an incomplete evaluation of the utility of this biosensor. In the revised manuscript we provide three additional experiments using secondary treatments that restore aspartate synthesis to conditions that initially caused aspartate depletion. First, we conducted experiments where cells expressing jAspSnFR3/NucRFP were changed into media without glutamine, inducing aspartate depletion, with glutamine being replenished at various time points to observe if GFP/RFP measurements recover. As expected, glutamine withdrawal caused a decay in the GFP/RFP signal and we found that restoring glutamine caused a subsequent restoration of the GFP/RFP signal at all time points, with each fully recovering the GFP/RFP signal over time (Revised Manuscript Figure 2E). Next, we conducted the experiment suggested by the reviewer, testing whether the published finding, that oligomycin induced aspartate limitation can be remedied by co-treatment with electron transport chain uncouplers, could be visualized using jAspSnFR3 measurements of GFP/RFP. Indeed, after 24 hours of oligomycin induced aspartate depletion, treatment with the ETC uncoupler BAM15 dose dependently restored GFP/RFP signal (Revised Manuscript Figure 2G). Finally, we also measured whether the ability of pyruvate to mitigate the decrease in aspartate upon co-treated with rotenone (Figure 2B) could also be detected in a sequential treatment protocol after aspartate depletion. Indeed, after 24 hours of aspartate depletion by rotenone treatment, the GFP/RFP signal was rapidly restored by additional treatment with pyruvate (Revised Manuscript Figure 2, figure supplement 1C). Collectively, these results provide support for the utility of jAspSnFR3 to measure transient changes in aspartate levels in diverse metabolic situations, including conditions that restore aspartate to cells that had been experiencing aspartate depletion.

Reviewer #2 (Recommendations For The Authors):

Weaknesses: Sensor basically identical to iGluSnFR3, but nevertheless useful and specific. The results support the conclusions, and the paper is very straightforward. I think the work will be useful to people working on the effects of free aspartate in biology and given it is basically iGluSnFR3, which is widely used, should be very reproducible and reliable.

We appreciate the reviewer’s comment that sensor is useful for specific detection of aspartate. We agree that the advance of the paper is primarily in demonstrating its utility to measure aspartate, rather than any fundamental innovation on the biosensor approach. We hope the fact that jAspSnFR3 derives from a well validated biosensor (iGluSnFR3) will support its adoption.

Reviewer #3 (Recommendations For The Authors):

Although this is a well-performed study, I have some comments for the authors to address:

1) A red tag version of the sensor (jAspSnFR3-mRuby3) was generated for normalization purposes, with this the authors plan to correct GFP signal from expression and movement artifacts. I naturally interpret "movement artifacts" as those generated by variations in cell volume and focal plane during time-lapse experiments. However, it was mentioned that jAspSnFR3-mRuby3 included a histidine tag that may induce a non-specific effect (responses to the treatment with some amino acids). This suggests that a version without the tag needs to be generated and that an alternative design needs to be set for normalization purposes. A nuclear-localized RFP was expressed in a second attempt to incorporate RFP as a normalization signal. Here the cell lines that express both signals (sensor and RFP) were generated by independent lentiviral transductions (insertions). Unless the number of insertions for each construct is known, this approach will not ensure an equimolar expression of both proteins (sensor and RFP). In this scenario is not clear how the nuclear expression of RFP will help the correction by expression or monitor changes in cell volume. The authors may be interested in attempting a bicistronic system to express both the sensor and RFP.

The reviewer noted several potential issues concerning the use of RFP for normalization, which will be separated into sections below:

Movement artifacts:

We are glad the reviewer raised this issue since we see how it was confusingly worded. We have deleted the text “and movement artefacts” from the sentence.

His-tag and non-specific responses to some amino acids:

We also found it concerning that non-specific responses to amino acids could potentially contribute to our RFP normalization signal, and so we conducted additional experiments to address whether this was likely to be an issue in intracellular measurements. We first tested whether the non-specific signal was related to the histidine tag, or was intrinsic to the mRuby3 protein itself, by comparing the fluorescence response to a titration of histidine (which showed the largest effect of red fluorescence), aspartate, and GABA (structurally related to glutamate and aspartate, but lacking a carboxylate group) across a group of mRuby containing variants, with or without histidine tags. We replicated the non-specific signal originally observed in jAspSnFR3-mRuby3-His and found that another biosensor with a histidine tagged on the C terminus of mRuby3 had a similar response (iGlucoSnFR2.mRuby3-His), as did mRuby3-His alone, indicating that the aspect of being fused with jAspSnFR3 or another binding protein was not required for this effect. Additionally, we also compared the fluorescence response of lysates expressing mRuby2 and mRuby3 without histidine tags and found that the non-specific signal was essentially absent (Revised Manuscript Figure 1, figure supplement 4B-D). Collectively. These data support our original hypothesis that the histidine tag was responsible for the non-specific signal, alleviating concerns about more substantial protein design issues or with using nuc-RFP for normalization. Since we also found that measuring aspartate signal using GFP/RFP ratios from cells with linked the jAspSnFR3-Ruby3-His agreed with measurements from cells separately expressing jAspSnFR3 and nucRFP (without a His tag), and the amino acid concentrations needed to significantly alter His tagged Ruby3 signal are above those typically found in cells, we conclude that this is unlikely to be a significant factor in cells. Nonetheless, we have added all the relevant data to the manuscript to allow readers to make their own decision about which construct would be best for their purposes.

Original text:

"Surprisingly, the mRuby3 component responds to some amino acids at high millimolar concentrations, indicating a non-specific effect, potentially interactions with the C-terminal histidine tag (Figure 1—figure Supplement 2, panel B). Notably, this increase in fluorescence is still an order of magnitude lower than the green fluorescence response and it occurs at amino acid concentrations that are unlikely to be achieved in most cell types."

Revised text:

"Surprisingly, the mRuby3 fluorescence of affinity-purified jAspSnFR3.mRuby3 responds to some amino acids at high millimolar concentrations, indicating a non-specific effect (Figure 1—figure Supplement 4, panel A). This was determined to be due to an unexpected interaction with the C-terminal histidine tag and could be reproduced with other proteins containing mRuby3 and purified via the same C-terminal histidine tag (Figure 1—figure Supplement 4, panel B and C). Interestingly, a structurally related, non-amino acid compound, GABA, does not elicit a change in red fluorescence; indicating, that only amino acids are interacting with the histidine tag (Figure 1—figure Supplement 4, panel D). Nevertheless, most of our cell culture experiments were performed with nuclear localized mRuby2, which lacks a C-terminal histidine tag, and these measurements correlated with those using the histidine tagged jAspSnFR3-mRuby3 construct (Figure 1—figure Supplement 1 panel D)."

Lentiviral transductions

We agree that splitting the two fluorescent proteins across two expression constructs and infections effectively guarantees that there will not be equimolar expression of jAspSnFR3 and RFP, however we do not think equimolar expression is necessary in this context. The primary goal of RFP measurements in these experiments (and in experiments using the jAspSnFR3-mRuby3 fused construct) is to control for global alterations in protein expression that might confound the interpretation that a change in GFP fluorescence corresponds to a change in aspartate levels. While a bicistronic system is arguably a better approach to improve the similarity of expression of jAspSnFR3 and nuc-RFP in a cell, we only require that the cells have consistent expression of both proteins across all cells in the population, not that the expression of one necessarily be a similar molarity to the other. We accomplish consistent expression of proteins by single cell cloning after expression of jAspSnFR3 and nucRFP (or jAspSnFR3-mRuby3), and screening for clones that have high enough expression of both proteins such that they are well detected by standard Incucyte conditions. Given that our data do not identify an obvious downside to separate expression of jASPSnFR3 and nuc-RFP compared to the fused jAspSnFR3-mRuby3 construct (where the fluorescent proteins are truly equimolar) (Figure 2, Figure Supplement 1C), we elected to prioritize the separate jAspSnFR3 and nuc-RFP combination, which provides additional opportunities to measure cell number in the same experiment (see below).

2) The authors were interested in establishing the temporal dynamics of aspartate depletion by genetics and pharmaceutical means. For the inhibition of mitochondrial complex I rotenone and metformin were used. Although the assays are clearly showing aspartate depletion the report of cell viability is missing. Considering that glutamine deprivation induces arrest in cell proliferation, I think will be important to know the conditions of the cell cultures after 60 hours of treatment with such inhibitors.

We agree that ensuring that cells are still viable in conditions where aspartate is depleted, as determined by GFP/RFP in jAspSnFR3 expressing cells, is an important goal. To this end, we added a new experiment investigating the restoration of glutamine on the GFP/RFP signal at different time points after glutamine depletion (Revised Manuscript Figure 2E, see response to reviewer 1). One advantage of using the nuclear RFP as a normalization marker is that it also enables measurements of nuclei counts, a surrogate measurement for cell number. In the same glutamine depletion experiment we therefore measured cell counts using nuclear RFP incidences and confluency as measurements of cell proliferation/growth. In both cases, the arrest in cell proliferation upon glutamine withdrawal was obvious, as was the restoration of cell proliferation following glutamine replenishment, with the amount of growth delay corresponding to the length of glutamine withdrawal (Revised Manuscript Figure 2, Figure Supplement 2A-B). Nonetheless, there was no obvious lasting defects in restarting cell proliferation even after 12 hours of glutamine withdrawal, indicating that cell viability is preserved. In the case of mitochondrial inhibitors, we also observe even that after 24 hours of treatment with oligomycin or rotenone, restoration of aspartate synthesis from BAM15 or pyruvate, respectively, can also restore GFP/RFP signal, supporting the conclusion that cellular metabolism is still active in these conditions (Revised Manuscript Figure 2G; Revised Manuscript Figure 2, figure supplement 1C).

3) The pH sensitivity was checked in vitro with jAspSnFR3-mRuby3 and the sensor reported suitable for measurements at physiological pH. It would be an opportunity to revisit the analysis for pH sensitivity in cultured cells using an untagged version of jAspSnFR3 coupled, for example, to a sensor for pH.

We thank the reviewer for the suggestion and agree that pH effects on sensor signal could be a confounding factor in some conditions. Unfortunately, measuring intracellular pH is not trivial and using multiple fluorescent sensors that change simultaneously would be complex to interpret, particularly in the absence of controls to unambiguously control intracellular pH and aspartate concentrations. Thus, we believe that proper investigation of the variable of pH is beyond the scope of this study. Nonetheless, we agree that measuring the contribution of pH to sensor signal is an important goal for future work, particularly if deploying it in conditions likely to cause substantial pH differences, such as comparing compartmentalized signal of jAspSnFR3 in the cytosol and mitochondria. We have added the following italicized text to the conclusions section to underscore this point:

“Another potential use for this sensor would be to dissect compartmentalized metabolism, with mitochondria being a critical target, although incorporating the influence of pH on sensor fluorescence will be an important consideration in this context.”

4) While the authors take an interesting approach to measuring intracellular aspartate concentration, it will be highly desirable if a calibration protocol can be designed for this sensor. Clearly, glutamine depletion grants a minimal ("zero") aspartate concentration. However, having a more dynamic way for calibration will facilitate the introduction of this tool for metabolism studies. This may be achieved by incorporating a cultured cell that already expresses the transporter or by ectopic expression in the cells that have already been used.

We appreciate the suggestion and would similarly desire a calibration protocol to serve as a quantitative readout of aspartate levels from fluorescence signal, if possible. While we do calibrate jAspSnFR3 fluorescence in purified settings, conducting an analogous experiment intracellularly is currently difficult, if not impossible. While we have several methods to constrain the production rate of aspartate (glutamine withdrawal, mitochondrial inhibitors, and genetic knockouts of GOT1 and GOT2), we cannot prevent cells from decreasing aspartate consumption and so cannot get a true intracellular zero to aid in calibration. Additionally, the impermeability of aspartate to cell membranes makes it challenging to specifically control intracellular concentrations using environmental aspartate, and the best-known aspartate transporter (SLC1A3) is concentrative and so has the reciprocal problem. Considering these issues, we are wary of implying to readers that any specific fluorescence measurement can be used to directly interpret aspartate concentration given the many variables that can impact its signal, both related to the biosensor system itself (expression of jAspSnFR3, expression of Nuc-RFP, sensitivity and settings of the fluorescence detector) and based on cell intrinsic variability (differences in basal ASP levels, different sensitivity to treatments, influence of pH, etc.). We maintain that jAspSnFR3 has utility to measure relative changes in aspartate within a cell line across treatment conditions and over time, but absolute quantitation of aspartate still will require complementary approaches, like mass spectrometry, enzymatic assays, or NMR.

5) jAspSnFR3 seems to have the potential to be incorporated easily for several research groups as a main tool. In general, a minor correction to replace F/F with ΔF/F in the text.

Thank you for catching this error, the text has been edited accordingly.

eLife assessment

This important study reports jAspSnFR3, a biosensor that enables high spatiotemporal resolution of aspartate levels in living cells. To develop this sensor, the authors used a structurally guided amino acid substitution in a glutamate/aspartate periplasmic binding protein to switch its specificity towards aspartate. The in vitro and in cellulo functional characterization of the biosensor is convincing, but evidence of the sensor's effectiveness in detecting small perturbations of aspartate levels and information on its behavior in response to acute aspartate elevations in the cytosol are still lacking.

Reviewer #1 (Public Review):

In this manuscript, Davidsen and coworkers describe the development of a novel aspartate biosensor jAspSNFR3. This collaborative work supports and complements what was reported in a recent preprint by Hellweg et al., (bioRxiv.; doi: 10.1101/2023.05.04.537313). In both studies, the newly engineered aspartate sensor was developed from the same glutamate biosensor previously developed by the authors of this manuscript. This coincidence is not casual but is the result of the need to find tools capable of measuring aspartate levels in vivo. Therefore, it is undoubtedly a relevant and timely work carried out by groups experienced in aspartate metabolism and in the generation of metabolite biosensors.

Reviewer #2 (Public Review):

Summary: To create a robust and specific fluorescent sensor for aspartate.

Strengths: Good quality characterisation in a range of environments and experimental conditions.

Weaknesses: Sensor basically identical to iGluSnFR3, but nevertheless useful and specific. The results support the conclusions, and the paper is very straightforward. I think the work will be useful to people working on the effects of free aspartate in biology and given it is basically iGluSnFR3, which is widely used, should be very reproducible and reliable.

Other context - it is a good quality study, although seems to be somewhat incremental.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, Davidsen and collaborators introduce jAspSnFR3, a new version of aspartate biosensor derived from iGluSnFR3, that allows to monitor in real-time aspartate levels in cultured cells. A selective amino acids substitution was applied in a key region of the template to switch its specificity from glutamate to aspartate. The jAspSnFR3 does not respond to other tested metabolites and performs well, is not toxic for cultured cells, and is not affected by temperature ensuring the possibility of using this tool in tissues physiologically more relevant. The high affinity for aspartate (KD=50 uM) allowed the authors to measure fluctuations of this amino acid in the physiological range. Different strategies were used to bring aspartate to the minimal level. Finally, the authors used jAspSnFR3 to estimate the intracellular aspartate concentration.

Strengths:<br /> One of the highlights of the manuscript was a treatment with asparagine during glutamine starvation. Although didn`t corroborate the essentiality of asparagine in glutamine depletion, the measurement of aspartate during this supplementation is a glimpse of how useful this sensor can be.

Weaknesses:<br /> Although this is a well-performed study, I have some comments for the authors to address:<br /> 1-A red tag version of the sensor (jAspSnFR3-mRuby3) was generated for normalization purposes, with this the authors plan to correct GFP signal from expression and movement artifacts. I naturally interpret "movement artifacts" as those generated by variations in cell volume and focal plane during time-lapse experiments. However, it was mentioned that jAspSnFR3-mRuby3 included a histidine tag that may induce a non-specific effect (responses to the treatment with some amino acids). This suggests that a version without the tag needs to be generated and that an alternative design needs to be set for normalization purposes. A nuclear-localized RFP was expressed in a second attempt to incorporate RFP as a normalization signal. Here the cell lines that express both signals (sensor and RFP) were generated by independent lentiviral transductions (insertions). Unless the number of insertions for each construct is known, this approach will not ensure an equimolar expression of both proteins (sensor and RFP). In this scenario is not clear how the nuclear expression of RFP will help the correction by expression or monitor changes in cell volume. The authors may be interested in attempting a bicistronic system to express both the sensor and RFP.<br /> 2-The authors were interested in establishing the temporal dynamics of aspartate depletion by genetics and pharmaceutical means. For the inhibition of mitochondrial complex I rotenone and metformin were used. Although the assays are clearly showing aspartate depletion the report of cell viability is missing. Considering that glutamine deprivation induces arrest in cell proliferation, I think will be important to know the conditions of the cell cultures after 60 hours of treatment with such inhibitors.<br /> 3-The pH sensitivity was checked in vitro with jAspSnFR3-mRuby3 and the sensor reported suitable for measurements at physiological pH. It would be an opportunity to revisit the analysis for pH sensitivity in cultured cells using an untagged version of jAspSnFR3 coupled, for example, to a sensor for pH.<br /> 4-While the authors take an interesting approach to measuring intracellular aspartate concentration, it will be highly desirable if a calibration protocol can be designed for this sensor. Clearly, glutamine depletion grants a minimal ("zero") aspartate concentration. However, having a more dynamic way for calibration will facilitate the introduction of this tool for metabolism studies. This may be achieved by incorporating a cultured cell that already expresses the transporter or by ectopic expression in the cells that have already been used.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work, the authors provide evidence to show that an increase in Kv7 channels in hilar mossy cells of Fmr1 knock out mice results in a marked decrease in their excitability. The reduction in excitatory drive onto local hilar interneurons produces an increased excitation/inhibition ratio in granule cells. Inhibiting Kv7 channels can help normalize the excitatory drive in this circuit, suggesting that they may represent a viable target for targeted therapeutics for fragile-x syndrome.

Strengths:

The work is supported by a compelling and thorough set of electrophysiological studies. The authors do an excellent job of analysing their data and present a very complete data set.

We thank the Reviewer for the positive comments.

Weaknesses:

There are no significant weaknesses in the experimental work, however the complexity of the data presentation and the lack of a schematic showing the organizational framework of this circuit make the data less accessible to non-experts in the field. I highly encourage a graphical abstract and network diagram to help individuals understand the implications of this work.

We thank the Reviewer for the suggestion, and added a schematic of the dentate network organization (Figure 1A).

The work is important as it identifies a unique regional and cell-specific abnormality in Fmr1 KO mice, showing how the loss of one gene can result in region-specific changes in brain circuits.

Reviewer #2 (Public Review):

Summary:

Deng et al. investigate, for the first time to my knowledge, the role that hippocampal dentate gyrus mossy cells play in Fragile X Syndrome. They provide strong evidence that, in slice preparations from Fmr1 knockout mice, mossy cells are hypoactive due to increased Kv7 function whereas granule cells are hyperactive compared to slices from wild-type mice. They provide indirect evidence that the weakness of mossy cell-interneuron connections contributes to granule cell hyperexcitability, despite converse adaptations to mossy cell inputs. The authors show that application of the Kv7 inhibitor XE991 is able to rescue granule cell hyperexcitability back to wild-type baseline, supporting the overall conclusion that inhibition of Kv7 in the dentate may be a potential therapeutic approach for Fragile X Syndrome. However, any claims regarding specific circuit-based intervention or analysis are limited by the exclusively pharmacological approach of the manipulations.

Strengths:

Thorough electrophysiological characterization of mossy cells in Fmr1 knockout mice, a novel finding.

Their electrophysiological approach is quite rigorous: patched different neuron types (GC, MC, INs) one at a time within the dentate gyrus in FMR1 KO and WT, with and without 'circuit blockade' by pharmacologically inhibiting neurotransmission. This allows the most detailed characterization possible of passive membrane/intrinsic cell differences in the dentate gyrus of Fmr1 knockout mice.

Provide several examples showing the use of Kv7 inhibitor XE991 is able to rescue excitability of granule cell circuit in Fmr1 knockout mice (AP firing in the intact circuit, postsynaptic current recordings, theta-gamma coupling stimulation).

We thank the Reviewer for the positive comments.

Weaknesses:

The implications for these findings and the applicability of the potential treatment for the disorder in a whole animal are limited due to the fact that all experiments were done in slices.

We appreciate the Reviewer’s point and agree. To address this concern, we have revised the Discussion to state that “the applicability of a circuit-wide approach as a potential treatment in vivo will require extensive future behavioral analyses, which are beyond the scope of the current study”. We also now emphasize in Discussion that “these findings provide a proof-of-principle demonstration that a circuit-based intervention can normalize dynamic E/I balance and restore dentate circuit output in vitro”.

The authors' interpretation of the word 'circuit-based' is problematic - there are no truly circuit-specific manipulations in this study due to the reliance on pharmacology for their manipulations. While the application of the Kv7 inhibitor may have a predominant effect on the circuit through changes to mossy cell excitability, this manipulation would affect many other cells within the dentate and adjacent brain regions that connect to the dentate that express Kv7 as well.

We appreciate the reviewer’s point but would like to clarify that by using a term “circuit-based” we did not intend to imply that it is a “’circuit-specific” intervention. Our intended interpretation of the term ‘circuit-based’ stems from the following reasoning: the dentate circuit has two types of excitatory neurons which show opposite excitability defects in FXS mice, thus presenting an irreconcilable conflict to correct pharmacologically for each cell type individually. Instead, we sought an approach to correct the overall dentate circuit output, rather than to restore excitability defects of individual cell types. Notably, when we pharmacologically isolated granule cells from the circuit, inhibition of Kv7 failed to restore their excitability, suggesting that normalization of the dentate output depends on the circuit activity. Since we focused on correcting dentate output using such a circuit-dependent approach, we used the term ‘circuit-based intervention’ to emphasize this notion.

Reviewer #3 (Public Review):

The paper by Deng, Kumar, Cavalli, Klyachko describes that, unlike in other cell types, loss of Fmr1 decreases the excitability of hippocampal mossy cells due to up-regulation of Kv7 currents. They also show evidence that while muting mossy cells appears to be a compensatory mechanism, it contributes to the higher activity of the dentate gyrus, because the removal of mossy cell output alleviates the inhibition of dentate principal cells. This may be important for the patho-mechanism in Fragile X syndrome caused by the loss of Fmr1.

These experiments were carefully designed, and the results are presented ‎in a very logical, insightful, and self-explanatory way. Therefore, this paper represents strong evidence for the claims of the authors. In the current state of the manuscript, there are only a few points that need additional explanation.

We thank the Reviewer for the positive comments.

One of the results, which is shown in the supplementary dataset, does not fit the main conclusions. Changes in the mEPSC frequency suggest that in addition to the proposed network effects, there are additional changes in the synaptic machinery or synapse number that are independent of the actual activity of the neurons. Since the differences of the mEPSC and sEPSC frequencies are similar and because only the latter can signal network effects, while the former is typically interpreted as a presynaptic change, it cannot be claimed that sEPSC frequency changes are due to the hypo-excitability of mossy cells.

We thank the Reviewer for this important point and agree. To address this concern, we now state in Results that “We note that changes in the excitatory drive onto interneurons include both mEPSC and sEPSC frequencies, which reflect not only potential deficits in excitability of their input cells, such as MCs, but also changes in synaptic connectivity/function, that may arise from homeostatic circuit reorganization/compensation (see Discussion)”.

We also now emphasize this point in Discussion by stating that “alterations in excitatory drives, including both mEPSC and sEPSC frequencies onto interneurons, suggest changes in the excitatory synapse number and/or function. Together with alterations in inhibitory drives these changes may reflect compensatory circuit reorganization of both excitatory and inhibitory connections, including mossy cell synapses”.

We also note in Discussion that “Such circuit reorganization can explain the balanced E/I drive onto granule cells in Fmr1 KO mice we observed in the basal state, which can result from reorganization of excitatory and inhibitory axonal terminals”.

Notably, our findings that Kv7 blocker acting by increasing MC excitability is sufficient to correct dentate output, supports the notion that hypo-excitability of mossy cells is a major factor contributing to dentate circuit E/I imbalance. This does not exclude the presence of additional mechanisms contributing to E/I imbalance, such as changes of synaptic connectivity or release machinery. To reflect this point, we revised the Results to temper the initial claim that “this analysis supports the notion that the hypo-excitability of MCs in Fmr1 KO mice caused (now replaced with “is a major factor contributing to”) the reduction of excitatory drive onto hilar interneurons, which ultimately results in reduced local inhibition”.

An apparent technical issue may imply a second weak point in the interpretation of the results. Because the IPSCs in the PP stimulation experiments (Fig 8) start within a few milliseconds, it is unlikely that its first ‎components originate from the PP-GC-MC-IN feedforward inhibitory circuit. The involvement of this circuit and MCs in the Kv7-dependent excitability changes is the main implication of the results of this paper. But this feedforward inhibition requires three consecutive synaptic steps and EPSP-AP couplings, each of them lasting for at least 1ms + 2-5ms. Therefore, the inhibition via the PP-GC-MC-IN circuit can be only seen from 10-20ms after PP stimulation. The earlier components of the cPSCs should originate from other circuit elements that are not related to the rest of the paper. Therefore, more isolated measurements on the cPSC recordings are needed ‎which consider only the later phase of the IPSCs. This can be either a measurement of the decay phase or a pharmacological manipulation that selectively enhances/inhibits a specific component of the proposed circuit.

We appreciate the Reviewer’s point. As we mentioned in Results: “The EPSP measured in granule cells in response to the PP stimulation integrates both excitatory and inhibitory synaptic inputs onto granule cells, including the direct synaptic input from the PP and all the PP stimulation-associated feedforward and feedback synaptic inputs. In other words, the EPSP in granule cells integrates all dentate circuit ‘operations’.” As the Reviewer pointed out, this is also the case in the measurements of cPSCs, which comprise all of PP stimulation-associated feedforward and feedback inhibition. We thank the Reviewer for the suggestion to isolate specific components of IPSC. However, we did not attempt to do it in this study for three reasons. First, activity of all of these circuit components likely overlaps extensively in time and it is difficult to identify the specific time point that can separate contributions from earlier canonical feed-forward and feed-back components from the contribution of the later MC-dependent PP-GC-MC-IN feed-forward component. Notably the tri-synapse PP-GC-MC-IN component differs temporarily from the canonical di-synaptic (PP-GC-IN) feed-back inhibition only by a single synaptic activation step, resulting in only a few milliseconds difference. Moreover, the temporal differences in the contributions of these components vary widely among different recordings making a uniform analysis very difficult. Second, we used three different metrics to assess E/I changes in cPSC measurements, which capture a wide range of temporal processes and their integration, including peak-to-peak measurements, the charge transfer, and the excitation window metrics. Third, the principal readout in our study was the overall dentate output (i.e., granule cell firing), which reflects the integration of all dentate circuit ‘operations’ thus making the overall cPSC measurements appropriate, in our view, for this readout.

I suggest refraining from the conclusions saying "‎MCs provide at least ~51% of the excitatory drive onto interneurons in WT and ~41% in KO mice", because too many factors (eg. IN cell types, slice condition, synaptic reliability) are not accounted for in these actual numbers, and these values are not necessary for the general observation of the paper.

We thank the reviewer for this suggestion, and have revised the manuscript accordingly.

There are additional minor issues about the presentation of the results.

We have carefully checked and corrected the minor errors that reviewer pointed out.

Recommendations for the authors:

Revisions that are considered essential for improved assessment regarding the strengths of support of the claims:

• Temper claims regarding circuit-based effects

• Temper claims regarding very specific quantitative assessments of synaptic drives

• Differentiate between monosynaptic inputs and inputs arriving through multiple synaptic contacts with proper analytical techniques.

We appreciate these suggestions and have revised the manuscript to address the concerns raised by the reviewers.

Reviewer #1 (Recommendations For The Authors):

The authors do an outstanding job of reviewing and presenting all of their data. This is a paper I will recommend all of my trainees read, as it is an excellent example of a complete research project. While I am impressed with the effort involved, I also wondered if the complexity and thoroughness of their presentations could make the story less accessible to non-expert readers. My comments are simply intended to help them present a more coherent and succinct story to a wider audience, though I am not sure I really provide any meaningful changes. This is simply a very thorough and complete body of work that the authors should be commended for. After reading it I felt they had gone above and beyond what most authors would provide in terms of data to support their story, and thus I had no doubt that a change in Kv7 plays a role in changing the excitability of the network.

We thank the Reviewer for the positive comments and great suggestions. We have made numerous changes to present our work in a more coherent and succinct way, in part by re-plotting some of the figures, as well as by adding a schematic of the dentate circuit in Figure 1.

Figure 1. A visual of mossy cells and the local circuit they are studying would be a useful addition to Figure. 1. I also feel this is important for conveying the story of how hypo-excitability can impact the E/I of the network. I think it has to be more of a cell structure/circuit-based figure than is presented in Supplementary Figure 8.

We thank the reviewer for this suggestion. We have added a schematic of the dentate circuit with all major cell types involved in Figure 1A.

Figure 1. A, B, and C tell a coherent story and are easy to understand. The interpretation of the phase plot in D is harder to access. Perhaps having this as a separate figure and providing a clearer presentation of the way the phaseplot was created (see Figure 3 Bove et al., 2019, Neuroscience 418; DOI: 10.1016/j.neuroscience.2019.08.048)

We appreciate the Reviewer’s point and agree. In order to keep Figure 1 more concise and readable, we removed the phase plot in the revised version. This change did not negatively impact the result presentation because the primary aim of this plot was to visualize changes in voltage threshold in an alternative way, but it was already clearly shown by the ramp-evoked AP traces (revised Figure 1D, insert), and thus was not essential to show.

Figure 1 E-N might be better situated in a supplementary graph as the characteristics of the AP aren't changing.

We understand the Reviewer’s point, but we feel it would be better to keep all action potential metrics together in one figure, to show that only a specific subset of parameters was affected in Fmr1 KO mice.

Figure 2: (A-D) I am not sure having so many figures is required given the focus is on having a small change in Ir at one membrane potential. I do worry that the significance appears to be due to 2 cells with an IR of over 100 in the WT group and 2 with an IR of around 62 in the KO group. All other cells are between 75-100 in both groups. I also worry a bit bc in the literature IRs between 55 and 125 seem to be commonly reported by groups that do this work normally (Buzsacki, Westbrook, etc.). I would be cautious about making too much out of this result.

We thank the Reviewer for these comments. We have performed additional analyses of these data, as also suggested by Reviewer 3 (Point #1), and improved presentation of the data in Figure 2D-F by showing the effect of XE991 on increasing input resistance in WT vs KO. We also plotted other panels in a similar way to show the comparisons between WT and KO, as well as comparisons within genotype +/- XE991, which makes the results easy to follow. For more details, please also see the response to Reviewer 3, Point 1.

Figure 2D-E: As in the text, this result is really pointing towards there being a Kv7 issue. Worries about the data in D aside, I think these two figures alone tell a clearer story. Figure 3 on the other hand tells a story of the effects of blocking Kv7 on membrane potential. Is this central to the story the others are trying to tell?

We thank the reviewer for this point. We believe that Figure 2, Figure 3 and Figure 4—figure supplement 1 together provide strong and multifaceted evidence to support changes in Kv7 function in Fmr1 KO mossy cells.

Figure 3. This is an interesting finding that shows how detailed their analysis was. Showing that the change in holding current in KO animals is greater than in WT is the first solid piece of evidence that there is a change in Kv7 in these cells that affects their excitability.

We appreciate the reviewer’s comment. As mentioned above, we believe that Figure 2, Figure 3 and Figure 4—figure supplement 1 together provide strong and multifaceted evidence to support changes in Kv7 function in Fmr1 KO mossy cells.

Figures 4 and 5 provide additional detail to support the idea that Kv& changes by showing how the E/I ratio and spontaneous minis are shifted in KO animals.

We thank the Reviewer for the comments.

Figures 6-8 build a compelling story for the reduction in excitatory drive in mossy cells affecting the network dynamics in excitatory/inhibitory interactions in DG cells.

We appreciate the Reviewer’s comment.

Reviewer #2 (Recommendations For The Authors):

1) Other than location and characteristic morphology, the other parameters that were used to identify mossy cells and granule cells were also parameters used to find differences in cellular properties between wild-type and Fmr1 KO mice (RMP, sEPSC frequency, etc.), which would confound the results shown. The use of available transgenic mouse lines would provide for a more unbiased screen of these cells. Afterhyperpolarization was also used as a parameter while screening cells, yet none of the data on this measurement is shown.

We thank the reviewer for this point and agree that transgenic mouse lines provide a more unbiased way to identify various types of neurons. However, since the present study involves analyses of at least three different types of neurons, establishing multiple transgenic lines labeling different types of dentate neurons in the Fmr1 KO mouse model would be very time consuming and beyond the current resources of the lab. We would also like to clarify that the three types of dentate neurons are easily distinguished according to the large differences in location, morphology and basal electrophysiological properties, none of which were essential in defining differences between genotypes. Specifically, granule cells are located in the granule cell layer, have a small cell body (<10 m), RMP around -80mV, capacitance ~20 pF, and infrequent sEPSCs (<20 events/min); mossy cells are located in the hilus, have a large cell body (>15 m), RMP around -65 mV, capacitance >100 pF, and fast afterhyperpolarization less than -10 mV (WT –5.1 ± 0.7 mV, KO -5.8 ± 0.5 mV); interneurons are located in the hilus or border of granule cell layer, have a relative smaller cell body (10-15 m), RMP around -55 mV, capacitance <60 pF, and afterhyperpolarization larger than -15 mV (WT -20.4 ± 1.3 mV, KO -19.8 ±1.4 mV). We note that the cells that could not be definitively classified into the three categories were not included in analyses, and we have now clarified this further in the Methods. To address the reviewer’s second concern regarding AHP, we now provided the corresponding values in the Methods.

2) A definitive way to test the cell-autonomous nature of the Kv7 changes would be to use female mice, who will have a mosaic of cells affected by the fragile X chromosome, and the Fmr1 KO cells could be engineered to express GFP to help identify them from wild-type cells.

We agree and appreciate this suggestion. This could be an interesting follow up study to further verify the cell-autonomous nature of Kv7 changes.

3) The authors heavily rely on XE991 as a selective Kv7 blocker. Is it blocking all Kv7 channels at the concentration used? If so, given the significant expression of Kv7 in the dentate as shown by Western blot, is it surprising that there is no effect of this inhibitor on wild-type slices in most cases?

We thank the reviewer for this important point. We used 10x of IC50 concentration in the present study, suggesting that more than 80% of Kv7 should be blocked. Notably, we observed several effects of XE991 in WT mice: it significantly increased input resistance (new Figure 2D-F), and strongly enhanced AP firing evoked by step depolarization (Figure 7E-H), although we did not observe effect of XE991 in WT in the analyses of spiking evoked by theta-gamma stimulation in Figure 8. However, this is not surprising. If a parameter we measured is predominately cell-autonomous (for example, input resistance), the effects of XE991 are easy to observe. However, if a parameter reflects integration of all dentate circuit operations (for example, AP probability in response to theta-gamma stimulation), it is difficult to detect the effect of XE991 in WT mice because the dentate circuit of WT mice has larger capability to maintain E/I balance in response to XE991.

4) E/I ratio is a helpful concept, and it is heavily relied upon in the results text, but statistically shaky, especially for sEPSC:sIPSCs since you are combining uncertainty in the sEPSC and sIPSC to make one very uncertain ratio that doesn't undergo any subsequent statistical confirmation (such as in Fig 4I).

We appreciate the reviewer’s point and apologize for the confusion in presentation of Fig 4I (and 5I), due to lack of detailed explanation. The E/I ratio shown in Figs. 4I (and 5I) is a single data-point estimate calculated from the mean values of independent sEPSC and sIPSC measurements (Figs. 4G-H and 5G-H, respectively). This ratio was used only as an estimate/illustration of the changes, rather than a precise determination of the shift in E/I balance. Because there is only one data-point for this ratio, statistical analysis is not possible. For this reason we performed extensive additional analyses in Figures 7 and 8, in which the EPSC and IPSC were measured from the same cells and at the same time to define the actual E/I ratio with the corresponding statistical analyses (i.e., a real matched and dynamic E/I ratio).

5) Is this mGlur2/CB1 specificity to PP/granule and MC axons, respectively, true in the Fmr1 KO mice? It is possible that mGluR2 and CB1 expression patterns are altered in FMR1 KO, thus the assumption used to isolate these distinct inputs may not hold true.

This is a very good point. We do assume that the specificity of Group II mGluR and CB1 is similar between Fmr1 KO and WT mice, but this is an assumption that we have not directly verified. However, our results in Figures 7 and 8 strongly support this assumption, because if it were not true, then our intervention would be unlikely to correct the excessive dentate output.

6) XE991 only normalized GC firing when other cells were not pharmacologically blocked. The authors suggest this means blockage of MC Kv7 reduces GC excitability back to normal...presumably by increasing MC --> IN --> GC firing. This is a conclusion from many indirect comparisons (comparing XE991 effect on GC with/without GABA and glutamate blockers; comparing MC firing rates with/without XE991, and using CB1 agonist versus mGluR2 agonist to say it is mossy cells that are mostly controlling INs) - a clincher experiment would be to acutely knockdown Kv7 in mossy cells specifically and measure GC and IN firing.

Thank you, this is a great suggestion. Indeed, as an expansion of this project, in the future studies we are planning to manipulate excitability of mossy cells through manipulating Kv7, or using chemogenetic or optogenetic approaches.

7) The reasoning behind the FMRP-Kv7 connection is quite weak, citing the paper Darnell 2011 as "translational target", but FMRP has myriad translational targets.

We agree, and attempted to define the mechanism of increased Kv7 function using co-immunoprecipitation approach, as well as immunostaining to look at cell-type specific expression changes. However, both of these approaches were difficult to interpret due to technical limitations of the available antibodies. We also note that “We did not further investigate the precise mechanisms underlying enhancement of Kv7 function in the absence of FMRP, since the present study primarily focuses on the functional consequences of abnormal cellular and circuit excitability”. To address this concern, we extensively discussed the potential mechanisms of FMRP-Kv7 connection, acknowledged in Discussion that “further studies will be needed to elucidate the precise mechanism responsible for the increased Kv7 function in Fmr1 KO mice”, and will continue to investigate it in the future studies.

8) The authors attempt to look for changes in Kv7 expression with Western blot, but since they hypothesize that Kv7 changes are mainly in the mossy cells, it is perhaps not surprising that they would not be able to see any changes when they look at dentate as a whole. Staining for Kv7 subunits to look at expression on a cellular level would be beneficial.

We appreciate the reviewer’s suggestion. We attempted to perform the suggested experiments using immunostaining for KCNQ2, KCNQ3 and KCNQ5 in different subtypes of dentate neurons. However, these experiments failed to produce interpretable results due to technical limitations of the available antibodies.

9) Is Kv7 localization or splice/composition different in FMR1 KO mice?

This is a very good point. As we mentioned in Point 8 above, we were not able to perform these experiments and do not have the answer at this point.

10) Regarding the 3 subtypes of interneurons in the dentate, the authors are pooling data based on similar intrinsic properties, but this conclusion may be affected by the low number of recorded neurons for the regular-spiking type. In addition, it is unclear whether these different interneuron types have differential circuit connectivity (most likely) which would make it imperative to keep circuit analysis for interneurons segregated into these cell types.

We appreciate the reviewer’s point. Indeed, these different interneuron types may have distinct circuit connectivity and contributions to circuit activity. However, identification of these 3 types of interneurons and determination of their respective functions is in itself a very extensive set of experiments which is beyond the scope of the current manuscript. We also note that the functional readout of circuit activity in our measurements was the AP firing and EPSPs evoked in granule cells by PP stimulation, which integrate all dentate circuit operations, including all of the feedforward and feedback loops which are mediated by all of these different types of interneurons. For simplicity, we thus pooled all interneuron data for the purposes of this study. But we fully agree that extensive future work is required to elucidate interneuron-type specific changes in Fmr1 KO mice and their contributions to the dentate circuit dysfunction.

11) To do statistics treating each cell individually, and therefore assuming each cell is independent of one another, is not correct. Two cells from the same mouse will be more similar than two cells from different mice, therefore they are not independent data points. Nested statistical methods (n cells from o slices from p mice) will be important in future work, as discussed by (Aarts et al., Nat. Neurosci. 2014).

We agree with the Reviewer’s point and appreciate this suggestion. In the present study, the cells tested in electrophysiological experiments were from at least 3 different mice for each condition, which help minimize this kind of errors.

Reviewer #3 (Recommendations For The Authors):

Is there a difference in the Rin at -45mV of the control cell after the application of XE991? This is important to appreciate whether the XE991-sensitive conductances contribute to the basal excitability of MCs. Furthermore, the statistical comparison of the Rin at -45mV of the FXS animals in the control solution and in the presence of XE991 would be also important‎. Actually, the most accurate measurement would be to show a difference in the acute Kv7-blockade between control and FXS animals, if that is possible with this blocker. Additionally, it would be also informative if the bar graphs in Fig.2 D & E were merged for this purpose, similarly as in the later figures.

We thank the Reviewer for this suggestion and agree. Following this suggestion, we have re-plotted the data in Figure 2 accordingly. Specifically, we now show that XE991 significantly increased input resistance in both WT and KO mossy cells, and the effect of XE991 on increasing input resistance was markedly larger in KO than WT mossy cells. For other figures, we have plotted data in a similar way to show the comparisons between WT and KO, as well as comparisons within genotype +/- XE991.

Because of the cell-to-cell variability of the voltage responses, it would be more informative and representative if the average of traces from all cells were shown in Fig.2 D & E.

We agree with the Reviewer’s point. For clarity of presentation, we presented the cell-to-cell variability of the data as scatter points of input resistance values in the bar graph (Figure 2E), together with the representative traces (Figure 2D). Plotting the average traces from all cells would result in a total of 30 traces for all the WT and KO mice, which is difficult to visually assess clearly.

On page 7, please clarify the recorded cell type in this sentence: "In ‎contrast, WIN markedly reduced the number of sEPSCs in both WT and KO mice...".

We thank the Reviewer for pointing out this omission and have clarified it in the revised version.

In Figures 6 C, F, and I, the title of the Y-axis should be normalized frequency. Please also correct the figure legend accordingly because the current sentence can be also interpreted as the absolute or total number of events that were compared, irrespective of the duration of the recordings.

We thank the Reviewer for this point and have corrected the revised version accordingly.

Reviewer #1 (Public Review):

Summary:

In this work the authors provide evidence to show that an increase in Kv7 channels in hilar mossy cells of Fmr1 knock out mice results in a marked decrease in their excitability. The reduction in excitatory drive onto local hilar interneurons produces an increased excitation/inhibition ratio in granule cells. Inhibiting Kv7 channels can help normalize the excitatory drive in this circuit, suggesting that they may represent a viable target for targeted therapeutics for fragile-x syndrome.

Strengths:

The work is supported by a compelling and thorough set of electrophysiological studies. The authors do an excellent job of analysing their data and present a very complete data set.

Weaknesses:

There are no significant weaknesses in the experimental work, however the complexity of the data presentation and the lack of a schematic showing the organizational framework of this circuit make the data less accessible to non-experts in the field. I highly encourage a graphical abstract and network diagram to help individuals understand the implications of this work.

The work is important as it identifies a unique regional and cell specific abnormality in Fmr1 KO mice, showing how the loss of one gene can result in region specific changes in brain circuits.

Reviewer #2 (Public Review):

Summary:

Deng et al. investigate, for the first time to my knowledge, the role that hippocampal dentate gyrus mossy cells play in Fragile X Syndrome. They provide compelling evidence that, in slice preparations from Fmr1 knockout mice, mossy cells are hypoactive due to increased Kv7 function whereas granule cells are hyperactive compared to slices from wild-type mice. They provide strong evidence that weakness of mossy cell-interneuron connections contribute to granule cell hyperexcitability, despite converse adaptations to mossy cell inputs. The authors show that application of the Kv7 inhibitor XE991 is able to rescue granule cell hyperexcitability back to wild-type baseline, supporting the overall conclusion that inhibition of Kv7 in the dentate may be a potential therapeutic approach for Fragile X Syndrome.

Strengths:

Thorough electrophysiological characterization of mossy cells in Fmr1 knockout mice, a novel finding.

Their electrophysiological approach is quite rigorous: patched different neuron types (GC, MC, INs) one at a time within the dentate gyrus in FMR1 KO and WT, with and without 'circuit blockade' by pharmacologically inhibiting neurotransmission. This allows the most detailed characterization possible of passive membrane/intrinsic cell differences in dentate gyrus of Fmr1 knockout mice.

Provide several examples showing the use of Kv7 inhibitor XE991 is able to rescue excitability of granule cell circuit in Fmr1 knockout mice (AP firing in intact circuit, postsynaptic current recordings, theta-gamma coupling stimulation)

Weaknesses:

Previously identified weaknesses have been addressed.

Reviewer #3 (Public Review):

The first part of the review was prepared after the first submission of the paper. After this, the authors made several changes in the manuscript. These changes are assessed at the end of the review.

First part:

The paper by Deng, Kumar, Cavalli, Klyachko describe that, unlike in other cell types, loss of Fmr1 decreases the excitability of hippocampal mossy cells due to up-regulation of Kv7 currents. They also show evidence that while muting mossy cells appears to be a compensatory mechanism, it contribute to the higher activity of the dentate gyrus, because the removal of mossy cell output alleviate the inhibition of dentate principal cells. This may be important for the patho-mechanism in Fragile X syndrome caused by the loss of Fmr1.

These experiments were carefully designed, and the results are presented ‎in a very logical, insightful and self-explanatory way. Therefore, this paper represents strong evidences for the claims of the authors. In the current state of the manuscript there are only a few points that need additional explanation.

One of the results, that is shown in the supplementary dataset, does not fit to the main conclusions. Changes in the mEPSC frequency suggest that in addition to the proposed network effects, there are additional changes in the synaptic machinery or synapse number that are independent of the actual activity of the neurons. Since the differences of the mEPSC and sEPSC frequencies are similar and because only the latter can signal network effects, while the former is typically interpreted as a presynaptic change, it cannot be claimed that sEPSC frequency changes are due to the hypo-excitability of mossy cells.

An apparent technical issue may imply a second weak point in the interpretation of the results. Because the IPSCs in the PP stimulation experiments (Fig8) start within a few milliseconds, it is unlikely that its first ‎components originate from the PP-GC-MC-IN feedforward inhibitory circuit. The involvement of this circuit and MCs in the Kv7-dependent excitability changes is the main implication of the results of this paper. But this feedforward inhibition requires three consecutive synaptic steps and EPSP-AP couplings, each of them lasting for at least 1ms + 2-5ms. Therefore, the inhibition via the PP-GC-MC-IN circuit can be only seen from 10-20ms after PP stimulation. The earlier components of the cPSCs should originate from other circuit elements that are not related to the rest of the paper. Therefore, more isolated measurements on the cPSC recordings are needed ‎which consider only the later phase of the IPSCs. This can be either a measurement on the decay phase or a pharmacological manipulation that selectively enhance/inhibit a specific component of the proposed circuit.

I suggest refraining from the conclusions saying "‎MCs provide at least ~51% of the excitatory drive onto interneurons in WT and ~41% in KO mice", because too many factors (eg. IN celll types, slice condition, synaptic reliability) are not accounted for these actual numbers, and these values are not necessary for the general observation of the paper.

There are additional minor issues about the presentation of the results that are explained in the private recommendation for the Authors.

Review after the revision:

The authors accepted my suggestions and made changes in the manuscript to address my point about the interpretation of the mEPSC changes.<br /> The second point was related to the interpretation of the stimulation evoked multisynaptic compound responses. Specifically, the IPSC components in the PP stimulation experiments start within a few milliseconds, and I pointed out that it is unlikely that its first ‎components originate from the PP-GC-MC-IN feedforward inhibitory circuit. The authors provided strong arguments for the interpretation of these compound responses in their reply and the conclusions are consistent with these complex results.

Additional minor issues were fully addressed.

I still think that this is a strong paper that provides new insights into the mechanisms of Fragile X syndrome at the level of single neurons and local network. The extensive series of experiments convincingly support the main findings that in addition to contributing to the underlying mechanisms of this disease also highlight how delicately neuronal activity is balanced even in constrained conditions.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

I highly appreciate this study and found the paper to be very well-written and easy to follow. However, a more extensive discussion of what I summarized under "weakness" would strengthen the paper. This may include a broader discussion of the canopy effect itself and the most relevant literature on its extent in rainforest settings in general and primate foods in particular, as well as more details on the dietary behavior of modern orangutans (stratigraphy of orangutan foods) and how seasonal their diet is. The extreme seasonality in orangutan plant food availability should be discussed. Now there are only 2 sentences in the discussion (lines 304-312) and I find the word "plant' only twice overall, though variation in plant food d18O is what drives variation in orangutan dental d18O values.

We very much appreciate the support of this reviewer, and their feedback about the clarity of the paper. As noted in the provisional reply to reviewers, we are happy to add additional context about the issue of isotopic enrichment within forest canopies, and have expanded the original paragraph in the discussion devoted to this subject. We made reference to the fact that orangutan diets vary by season and site in the original submission, and have now acknowledged that seasonal diet variation may also contribute to variation in enamel isotope values.

Also, I'd like to note that there has been only one recent study so far that made some level of an attempt to find a breastfeeding effect in orangutans using fecal isotope data. Tsutaya et al. 2022 (AJBA) report some seasonality in adult orangutan fecal isotope values, which could be relevant here as well. But also they reported some data from 2 to 7-year-old orangutan offspring and did not see any breastfeeding pattern in isotope values here either. Probably not too surprising at this older age, but still worth noting in the context of this study.

There is a 2019 study that sampled fecal isotopes in 43 mother-infant orangutan pairs and found a different pattern than Tsutaya et al. (2022), although these data have not been published in full (Knott et al. (2019) AJBA 168, S68, 128-129). Given these contradictions, the fact that neither study serially sampled the first two years of life, and caveats to fecal isotope sampling of wild primates reviewed in Bădescu et al. (2023: American Journal of Primatology 2023;e235), introducing these nitrogen isotope studies does not aide in the interpretation of oxygen isotope data during intensive nursing, and thus is beyond the scope of this paper. The seasonality Tsutaya et al. (2022) reported in adult fecal samples was for carbon isotopes rather than nitrogen isotopes, and its relevance to the current study is unclear given that the orangutan plant foods measured did not show seasonal variation in carbon isotopes. As requested above, we have noted orangutans’ dietary seasonality might influence the variation of oxygen isotope values.

Reviewer #2 (Recommendations For The Authors):

First, the manuscript offers upfront flashy numbers with respect to the number of samples, but what the reader really needs to know upfront is the number of individuals and the number of teeth per individual. These facts are buried and make the reader work too hard to keep track. While the specimen ID numbers are valuable in the table, perhaps a different ID could be used in the text, such as individuals modern Borneo A and B, fossil Sumatra A and B, etc.? Similarly, it would be helpful to remind readers of each locality - Borneo or Sumatra, modern or fossil.

Tables 1 and 2 and the first sentence of the results and the materials and methods stated that we measured 18 teeth in this study. It is likely that the placement of the tables at the very end of the manuscript in the submitted version made the sample sizes and specimen information less evident to the reviewer. In response to this critique we have now added the number of teeth to the abstract, and trust that when the tables are placed within the text as indicated it will be easier to follow textual references to particular individuals. Museum identification codes have been provided in two previous publications of these teeth, and we retain them here for consistency.

Second, the manuscript mentions some climate change in Sumatra, but what about Borneo?

The results on the Bornean fossil teeth stated: “The range of values from these two fossil molars (14.2–24.8 ‰) markedly exceeds the range of modern Bornean orangutans (12.7–20.0 ‰) (Figure 4), with the mean δ18O value at least 2‰ heavier, suggesting possibly drier conditions with greater seasonality during their formation.” In the final section of the discussion, we devoted two paragraphs to discussing evidence for climate change at Niah Cave in Borneo - more than we devote to discussing such data from Sumatra.

The most valuable figure in the manuscript is Figure 3 showing the serial sampling of modern teeth. It would be incredibly useful to see a similar graph for the fossils and a graph of the modern and fossils together for each island. The violin plots demonstrate a range of values but fail to provide the important seasonality signals. The manuscript is promising but as written is difficult to follow, and the results and conclusions with regard to climate change need more demonstration. On a minor note, I found myself wanting to know about the dates of fossils before knowing the isotopic values. You might wish to move the dating section to precede the isotopes.

As requested, we have added an additional Supplemental figure making the comparisons of seasonality between fossil and modern individual more evident.

eLife assessment

This important study presents convincing evidence for the use of orangutan teeth as terrestrial proxies to reconstruct rainfall regimes, while exploring the potentially conflicting impact of breastfeeding signals. The findings will be of broad interest for those using and developing methods and tools to reconstruct environmental conditions in the historical and archaeological past.

Reviewer #1 (Public Review):

Summary:<br /> The authors measured the oxygen stable isotope ratios in six orangutan teeth using a state of the art micro-sampling technique (SHRIMP SI) to gather substantial multi-year isotopic data for six modern and five fossil orangutan individuals from Borneo and Sumatra. This fine-scale sampling technique allowed to address the fundamental question if breastfeeding affects the oxygen isotope ratios in teeth forming in the first one to two years of life, during which orangutans can be assumed to largely depend on breastmilk. The authors provide compelling evidence that the consumption of milk does not appear to affect the overall isotopic profile in early forming teeth. They conclude that this allows us to use these teeth as terrestrial/arboreal isotopic proxies in paleoenvironmental research, which would provide an invaluable addition to otherwise largely marine climate records in this regions.

Strengths:<br /> The overall large sample size of orangutan dental isotope records as well as the rigorous dating of the fossil specimens provide a strong dataset for addressing the outlined questions. The direct comparison of modern and fossil orangutan specimens provides a valuable evaluation of the use of these modern and past environmental proxies, with some discussion of the implications for the environmental conditions during the expansion of early modern humans into this region of the world.

Weakness:<br /> The authors illustrate that all orangutan individuals sampled, modern and fossil, show a considerable amount of isotopic variation between and within their teeth. Some of this variation is clearly associated with isotopic shifts in precipitation, but some will also be linked to the variation in oxygen isotopes within the forest itself and the many plant foods it produces for the orangutan. In the future, the systematic measurement of oxygen isotopes across orangutan food items, forest canopies and precipitation could help differentiate how much of the observed isotopic variation in teeth is indeed related to climatic shifts alone.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript provides microprobe serial oxygen isotope data from thin-sectioned modern and fossil orangutan teeth in an effort to reconstruct seasonality of rainfall in Borneo and Sumatra. The authors also explore the hypothesis that nursing could affect early tooth (first molar) isotope values. They find that all molars yield similar oxygen isotope values and therefore conclude that future research need not exclude use of first molars. With regard to seasonality, the modern orangutans yield similar results from both islands. The authors suggest differences between modern and fossil orangutan teeth.

Strengths:<br /> The study employs a sampling method that captures serial isotope values within thin sections of teeth using a microprobe that provides much higher resolution than traditional hand-held drilling.

Weaknesses:<br /> The study only examines six modern and six fossil orangutan individuals. Of those, only four modern individuals were samples across multiple molars.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study addressed an alternative hypothesis to temporal binding phenomena. In temporal binding, two events that are separated in time are "pulled" towards one another, such that they appear more coincidental. Previous research has shown evidence of temporal binding events in the context of actions and multisensory events. In this context, the author revisits the well-known Libet clock paradigm, in which subjects view a moving clock face, press a button at a time of their choosing to stop the clock, a tone is played (after some delay), and then subjects move the clock dial to the point where the one occurred (or when the action occurred). Classically, the reported clock time is a combination of the action and sound times. The author here suggests that attention can explain this by a mechanism in which the clock dial leads to a roving window of spatiotemporal attention (that is, it extends in both space and time around the dial). To test this, the author conducted a number of experiments where subjects performed the Libet clock experiment, but with a variety of different stimulus combinations. Crucially, a visual detection task was introduced by flashing a disc at different positions along the clock face. The results showed that detection performance was also "pulled" towards the action event or sensory event, depending on the condition. A model of roving spatiotemporal attention replicated these effects, providing further evidence of the attentional window.

Strengths:

The study provides a novel explanation for temporal binding phenomena, with clear and cleverly designed experiments. The results provide a nice fit to the proposed model, and the model itself is able to recapitulate the observed effects.

Weaknesses:

Despite the above, the paper could be clearer on why these effects are occurring. In particular, the control experiment introduced in Experiment 3 is not well justified. Why should a tactile stimulus not lead to a similar effect? There are possibilities here, but the author could do well to lay them out. Further, from a perspective related to the attentional explanation, other alternatives are not explored. The author cites and considers work suggesting that temporal binding relies on a Bayesian cue combination mechanism, in which the estimate is pulled towards the stimulus with the lowest variance, but this is not discussed. None of this necessarily detracts from the findings, but otherwise makes the case for attention less clear.

I would like to thank the reviewer for the helpful comments and recommendations. Regarding Experiment 3, the rationale is this. We showed in Experiments 1 and 2 that, for outcome binding, there were two types of difference between Action Sound condition and Sound Only condition: the reported time of sound onset (i.e. the reported clock hand location at the sound onset) and the attention distribution. To experimentally test the relevance of the attention difference to the difference of reported time, we created a situation where the attention difference could be minimised and then checked the difference of reported time. We found that when the attention difference was controlled for between the two conditions, the difference of reported time was also gone, thus providing further evidence for a close link between attention and time report in the current testing paradigm. Therefore, Experiment 3 was primarily targeting the experimental evidence for the claim of the current study. What we needed in Experiment 3 was a condition that could have a smaller attention difference with the Action Sound condition than the attention difference between Sound Only and Action Sound conditions in Experiments 1 and 2. We expected that a tactile stimulus before the sound onset could work, without a clear prediction of the strength of the tactile stimulus in shifting attention, which was also not necessary. This experimental manipulation was a nice fit for the purpose of experiment 3, as we could empirically measur the effectiveness of the tactile stimulus on attention shift and then relate it to the changes in outcome binding.

As the reviewer correctly suggested, the Bayesian framework has been applied in several studies to explain the time judgement distortion in sensorimotor situations (e.g. the temporal binding effect studied here). However, the current study asked what temporal binding is really about when it is measured with the Libet clock method. Is it really about a distortion in time perception (which the Bayesian account tries to explain)? Or is it also about attention? The results showed that the spatiotemporal attention distribution is at least a confound in measuring the perceived time of an event using the Libet clock method. Therefore, the Bayesian account raised in previous studies is relevant when explaining the distortion in time perception, given that it really exists. We here asked if the distortion really exists, and to what extent.

Reviewer #2 (Public Review):

Summary:

Temporal binding, generally considered a timing illusion, results from actions triggering outcomes after a brief delay, distorting perceived timing. The present study investigates the relationship between attention and the perception of timing by employing a series of tasks involving auditory and visual stimuli. The results highlight the role of attention in event timing and the functional relevance of attention in outcome binding.

Strengths:

• Experimental Design: The manuscript details a well-structured sequence of experiments investigating the attention effect in outcome binding. Thoughtful variations in manipulation conditions and stimuli contribute to a thorough and meaningful investigation of the phenomenon.

• Statistical Analysis: The manuscript employs a diverse set of statistical tests, demonstrating careful selection and execution. This statistical approach enhances the reliability of the reported findings.

• Narrative Clarity: Both in-text descriptions and figures provide clear insights into the experiments and their results, facilitating readers in following the logic of the study.

Weaknesses:

• Conceptual Clarity: The manuscript aims to integrate key concepts in human cognitive functions, including attention, timing perception, and sensorimotor processes. However, before introducing experiments, there's a need for clearer definitions and explanations of these concepts and their known and unknown interrelationships. Given the complexity of attention, a more detailed discussion, including specific types and properties, would enhance reader comprehension.

• Computational Modeling: The manuscript lacks clarity in explaining the model architecture and setup, and it's unclear if control comparisons were conducted. These details are critical for readers to properly interpret attention-related findings in the modeling section. Providing a clearer overview of these aspects will improve the overall understanding of the computational models used.

I would like to thank the reviewer for the helpful comments and recommendations. The attention in the current study, which has been made clearer in the revised manuscript, refers specifically to visuospatial attention. It is presented as a key factor shaping the results of timing report obtained with the clock method, thereby contributing to the explanation of temporal binding. Indeed, attention has been mentioned previously in a similar context, but was treated vaguely as a kind of general cognitive resources. The current study specifically tested and verified that the visuospatial attention paid to the clock face influenced the timing reports. This point has been discussed in a dedicated paragraph in the discussion section of the revised manuscript.

The modelling of the timing report using the attention data was based on a very simple idea: The clock hand location receiving more attention should be given more weight when participants made the timing report (i.e. reporting the clock hand position). The weight for each location was calculated using the detection rate at each location. The relevant methods section has been extensively revised to provide a step-by-step implementation of the modelling, with rationales and pitfalls in the interpretation of the modelling results given (also in the discussion section).

eLife assessment

This important paper examined how attention affects temporal binding. Through a combination of careful experimental designs and computational modelling, this study provides solid evidence highlighting the role of attention in shaping temporal binding. Overall, the present findings will be of interest to cognitive scientists studying phenomena related to time perception, temporal binding, and spatial attention.

Reviewer #1 (Public Review):

Summary: This study addressed an alternative hypothesis to temporal binding phenomena. In temporal binding, two events that are separated in time are "pulled" towards one another, such that they appear more coincidental. Previous research has shown evidence of temporal binding events in the context of actions and multisensory events. In this context, the author revisits the well-known Libet clock paradigm, in which subjects view a moving clock face, press a button at a time of their choosing to stop the clock, a tone is played (after some delay), and then subjects move the clock dial to the point where the one occurred (or when the action occurred). Classically, the reported clock time is a combination of the action and sound times. The author here suggests that attention can explain this by a mechanism in which the clock dial leads to a roving window of spatiotemporal attention (that is, it extends in both space and time around the dial). To test this, the author conducted a number of experiments where subjects performed the Libet clock experiment, but with a variety of different stimulus combinations. Crucially, a visual detection task was introduced by flashing a disc at different positions along the clock face. The results showed that detection performance was also "pulled" towards the action event or sensory event, depending on the condition. A model of roving spatiotemporal attention replicated these effects, providing further evidence of the attentional window.

The study provides a novel explanation for temporal binding phenomena, with clear and cleverly designed experiments. The results provide a nice fit to the proposed model, and the model itself is able to recapitulate the observed effects.

Reviewer #2 (Public Review):

Summary:<br /> Temporal binding, generally considered a timing illusion, results from actions triggering outcomes after a brief delay, distorting perceived timing. The present study investigates the relationship between attention and the perception of timing by employing a series of tasks involving auditory and visual stimuli. The results highlight the role of attention in event timing and the functional relevance of attention in outcome binding.

Strengths:<br /> - Experimental Design: The manuscript details a well-structured sequence of experiments investigating the attention effect in outcome binding. Thoughtful variations in manipulation conditions and stimuli contribute to a thorough and meaningful investigation of the phenomenon.<br /> - Statistical Analysis: The manuscript employs a diverse set of statistical tests, demonstrating careful selection and execution. This statistical approach enhances the reliability of the reported findings.<br /> - Narrative Clarity: Both in-text descriptions and figures provide clear insights into the experiments and their results, facilitating readers in following the logic of the study.

Weaknesses:<br /> - Conceptual Clarity: The manuscript aims to integrate key concepts in human cognitive functions, including attention, timing perception, and sensorimotor processes. However, before introducing experiments, there's a need for clearer definitions and explanations of these concepts and their known and unknown interrelationships. Given the complexity of attention, a more detailed discussion, including specific types and properties, would enhance reader comprehension.<br /> - Computational Modeling: The manuscript lacks clarity in explaining the model architecture and setup, and it's unclear if control comparisons were conducted. These details are critical for readers to properly interpret attention-related findings in the modeling section. Providing a clearer overview of these aspects will improve the overall understanding of the computational models used.

eLife assessment

This study presents useful work comparing different techniques for monitoring insect species in agricultural settings, including a brand new one using optical sensors. That said, the data were analysed using an inadequately-described -- or potentially inadequate -- framework, and more careful thought must be given to the interpretation of the results before the new methodology can be used as a starting point for insect studies in agricultural fields and beyond.

Reviewer #1 (Public Review):

The article offers a comparative study between various methodologies to evaluate the abundance, richness, and diversity of insects from data obtained in a large-scale field experiment. The experiment is impressive in view of the number of locations, its spatial coverage, the number of instruments or methods used, and the data collected appears rich and worthy of multiple publications. The paper focuses on the validation of a novel approach based on optical sensors. These sensors collect the backscattered light from flying insects in their field of view and can retrieve the wingbeat frequency and the body-to-wing backscattering ratios.<br /> Unfortunately, the paper is poorly written and hard to read, with a lack of clear sections, and an overall confusing structure. The methods, metrics, and data analysis are not properly and thoroughly described, making it sometimes difficult to evaluate the validity of the approach.<br /> Most importantly, the methodology to retrieve the richness and diversity from optical sensors seems flawed. While the scope and scale of the experiment is valuable, I do not believe that this article supports the authors' claim. The main criticisms are described in more detail below.

1) The Material and Method section is poorly structured. The article focuses on a series of metrics to evaluate biodiversity from three independent methods: optical sensors, malaise traps, and net sweeping. The authors need to provide a clear and thorough description of what the metrics to be studied are, and how those metrics are evaluated for each method. While it is the main focus of the paper, the term "biodiversity metrics" is never properly defined, it is used in the singular form in both the title and abstract, then in its plural form in the rest of the paper, making the reader further doubt what exactly it means. It is then discussed using the correlation value retrieved when studying richness, so is the biodiversity metric the same as richness? Studying biodiversity remains a complex and sometimes contentious subject and this term, especially when measured by three different methods, is far from obvious. The term "community metrics" is defined as abundance, richness, and diversity; is that the same as biodiversity metrics? In any case, the method section should thoroughly describe how each of those metrics is calculated from the raw data collected by each method. This information is somewhat there, but in a very unorganized way, making it difficult to read. I would recommend organizing this section with multiple and clear sections: 1) describing the metrics that are meant to be studied, 2) the location, dates and time, type of crops, and other general information about the experiment, 3) description and methods around optical sensors, 4) description and methods around malaise traps, 5) description and methods around the sweeping. The last 3 sections should describe how it retrieves the previously defined metrics, potentially using equations.

2) Regarding the calculation of the body-to-wing ratio, sigma is described as a "signal" line 195, then is described as intensity counts in Figure 2; isn't it really the backscattering optical cross-section? It changes significantly over time during the signal, so how is one value of sigma calculated? Is it the average of the whole insect event? The maximum?

3) The "ecosystem services" paragraph is really confusing and needs to be rewritten.

4) Like for the method section, the result section should be structured around the comparison of each metric, abundance, richness, and diversity, or any other properly defined metrics described in the method, so that the result section is consistent with the method section.

5) The abundance is not correlated; interestingly, malaise traps and sweeping are even less correlated which further supports the claims by the authors that new and improved methods are needed. This part of the results could be further developed. A linear fit could be added to Figure 4.

6) Richness and diversity are the most problematic. Again, the method is poorly described, with pieces of explanation spread out throughout the paper, but my understanding is the following: the optical sensor retrieves two features from each insect signal, wbf, and BWR. Clustering is made using DBSCAN which has 2 parameters: minimum number of signals, and merge distance. It is important to note that these two parameters will greatly influence the number of clusters found by DBSCAN. The richness obtained by optical sensors is defined as the number of clusters and the diversity is evaluated from it as well. Hence, both diversity and richness are greatly dependent on the chosen parameters. The DBSCAN parameters are chosen by maximizing the Spearman correlation between richness obtained by the optical sensors and richness by the capture methods. I see a major problem here: if you optimize the parameters, that directly impact the retrieved diversity and richness by optical sensors, to have the best correlation with either the richness or diversity of the other methods, you will automatically create a correlation between the richness and diversity retrieved by the optical sensors and alternative methods. The p-value in Figure 6 does not represent the probability of the correlation hypothesis being false anymore, since the whole process is based on artificially forcing the correlation from the start.

7) In addition, the clustering method provides values higher than 80, which is quite unrealistic with just 2 features, wbf and BWR. It is clear from many studies using optical sensors that the features from optical sensors are subject to variability. Wbf has naturally some variances within the same species, not to mention temperature dependency. Backscattering cross sections will also heavily function on the insect's orientation (facing or sideways) while crossing the cone of light, and, even though it is a ratio, the collection efficiency of the instrument telescope and scattering efficiency of the target will be impacted by the position of the insects within the cone of light, which will also impact the variability on the BWR. While those features can still be used, obtaining 80 clusters from two variables with such statistical fluctuations is simply not credible. Additional features could help, such as the two wavelengths mentioned in the description of the optical sensor but are never mentioned again.

The conclusion then states that the study serves as the first field validation. I disagree; the abundance doesn't correlate, and the richness and diversity evaluations are flawed. While I do think there is great value in the work done by the authors through this impressive field experiment, and in general in their work toward the development of entomological optical sensors, I believe the data analysis and communication of the results do not support the conclusions drawn.

Reviewer #2 (Public Review):

Summary:

The manuscript by Rydhmer et al. proposes a new technology to survey insects. They deployed optical sensors in agricultural landscapes and contrast their results to those in classical malaise and sweep nets survey methodologies. They found the results of optical sensors to be comparable with classical survey methodologies. The authors discuss the pros and cons of their near-infrared sensor.

Strengths:<br /> Contrasting the results of optical sensors with those obtained with classical malaise and sweep nets was a clever idea.

Weaknesses:<br /> Maybe the first most important shortcoming is the lack of a larger question the new technology can help to answer. If the authors could frame their aims not only as a new tool to sample insects but maybe along the lines of a hypothesis to test in their (agricultural) field of research, this could be a more meaningful article.

The second more important shortcoming is the lack of more complex analyses. The authors seem to be so fixed on counts of abundance and species that they miss the opportunity to look for more complex patterns in their data. The addition of a simple analysis like an NMDS (to test composition changes) could improve the manuscript significantly.

The ecosystem process (granivory) assay is currently poorly contextualized and explained across the text; I was surprised to find this part in M&M without previous warning. It seems to me that adding this part could be a nice addition to the manuscript (see my comment above). But this needs to be explained better in all sections of the manuscript.

As I think that addressing my previous points will reshape the manuscript in important ways, I refrain from giving more specific details at this point. But there are some! Maybe only to mention that Figures 4 and 6 would benefit from individual regressions by crop and Figure 5 from adding results from optical sensors.

eLife assessment

This valuable manuscript describes a genetic system in yeast used to find mutations in two distinct amino acid transporters that enable the cells to utilize additional amino acids as a nitrogen source. The study provides solid evidence in membrane proteins of a phenomenon that has been previously described in enzymes: that substrate specificity can be altered through the introduction of point mutations to either the ligand binding site or gating helices. This work establishes that amino acid transporters likely evolved specific functionality/specificity from an ancestral transporter that could transport most amino acids.

Reviewer #1 (Public Review):

Summary:<br /> The evolution of transporter specificity is currently unclear. Did solute carrier systems evolve independently in response to a cellular need to transport a specific metabolite in combination with a specific ion or counter metabolite, or did they evolve specificity from an ancestral protein that could transport and counter-transport most metabolites? The present study addresses this question by applying selective pressure to Saccharomyces cerevisiae and studying the mutational landscape of two well-characterised amino acid transporters. The data suggest that AA transporters likely evolved from an ancestral transporter and then specific sub-families evolved specificity depending on specific evolutionary pressure.

Strengths:<br /> The work is based on sound logic and the experimental methodology is well thought through. The data appear accurate, and where ambiguity is observed (as in the case of citruline uptake by AGP1), in vitro transport assays are carried out to verify transport function.

Weaknesses:<br /> Although the data and findings are well described, the study lacked additional contextual information that would support a clear take-home message.

Reviewer #2 (Public Review):

Summary:<br /> This paper describes evolution experiments performed on yeast amino acid transporters aiming at the enlargement of the substrate range of these proteins. Yeast cells lacking 10 endogenous amino acid transporters and thus being strongly impaired to feed on amino acids were again complemented with amino acid transporters from yeast and grown on media with amino acids as the sole nitrogen source.

In the first set of experiments, complementation was done with seven different yeast amino acid transporters, followed by measuring growth rates. Despite most of them have been described before in other experimental contexts, the authors could show that many of them have a broader substrate range than initially thought.

Moving to the evolution experiments, the authors used the OrthoRep system to perform random mutagenesis of the transporter gene while it is actively expressed in yeast. The evolution experiments were conducted such that the medium would allow for poor/slow growth of cells expressing the wt transporters, but much better/faster growth if the amino acid transporter would mutate to efficiently take up a poorly transported (as in the case of citrulline and AGP1) or non-transported (as in case of Asp/Glu and PUT4) amino acid.

This way and using Sanger sequencing of plasmids isolated from faster-growing clones, the authors identified a number of mutations that were repeatedly present in biological replicates. When these mutations were re-introduced into the transporter using site-directed mutagenesis, faster growth on the said amino acids was confirmed. Growth phenotype data were attempted to be confirmed by uptake experiments using radioactive amino acids; however, the radioactive uptake data and growth-dependent analyses do not fully match, hinting at the existence of further parameters than only amino acid uptake alone to impact the growth rates.

When mapped to Alphafold prediction models on the transporters, the mutations mapped to the substrate permeation site, which suggests that the changes allow for more favourable molecular interactions with the newly transported amino acids.

Finally, the authors compared the growth rates of the evolved transporter variants with those of the wt transporter and found that some variants exhibit a somewhat diminished capacity to transport its original range of amino acids, while other variants were as fit as the wt transporter in terms of uptake of its original range of amino acids.

Based on these findings, the authors conclude that transporters can evolve novel substrates through generalist intermediates, either by increasing a weak activity or by establishing a new one.

Strengths:<br /> The study provides evidence in favour of an evolutionary model, wherein a transporter can "learn" to translocate novel substrates without "forgetting" what it used to transport before. This evolutionary concept has been proposed for enzymes before, and this study shows that it also can be applied to transporters. The concept behind the study is easy to understand, i.e. improving growth by uptake of more amino acids as nitrogen source. In addition, the study contains a large and extensive characterization of the transporter variants, including growth assays and radioactive uptake measurements.

Weaknesses:<br /> The authors took a genetic gain-of-function approach based on random mutagenesis of the transporter. While this has worked out for two transporters/substrate combinations, I wonder how comprehensive and general the insights are. In such approaches, it is difficult to know which mutation space is finally covered/tested. And information that can be gained from loss-of-function analyses is missed. The entire conclusions are grounded on a handful of variants analyzed. Accordingly, the outcome is somewhat anecdotal; in some cases, the fitness of the variants was changed and in others not. Highlighting the amino acid changes in the context of the structural models is interesting, but does not fully explain why the variants exhibit changed substrate ranges. Two important technical elements have not been studied in detail by the authors, but may well play a certain role in the interpretation of the results. Firstly, the authors did not quantify the amount of transporter being present on the cell surface; altered surface expression can impact uptake rates and thus growth rates. Secondly, the authors have not assessed whether overexpressing wt versus variant transporters has an impact on the growth rate per se. Overexpressing transporters from plasmids is quite a burden for the cells and often impacts growth rates. Variants may be more or less of a burden, an effect that may (or may also not) go hand in hand with increased/decreased surface production levels.

And finally, I was somewhat missing an evolutionary analysis of these transporters to gain insights into whether the identified substitutions also occurred during natural evolution under real-life conditions.

Reviewer #3 (Public Review):

The goal of the current manuscript is to investigate how changes in transporter substrate specificity emerge through experimental evolution. The authors investigate the APC family of amino acid transporters, a large family with many related transporters that together cover the spectrum of amino acid uptake in yeast.

The authors use a clever approach for their experimental evolutions. By deleting 10 amino acid uptake transporters in yeast, they develop a strain that relies on amino acid import by introducing APC transporters under nitrogen-limiting conditions. They can thus evolve transporters towards the transport of new substrates if no other nitrogen source is available. The main takeaway from the paper is that it is relatively easy for the spectrum of substrates in a particular transporter of this family to shift, as a number of single mutants are identified that modulate substrate specificity. In general, transporters evolved towards gain-of-function mutations (better or new activities) and also confer transport promiscuity, expanding the range of amino acids transported.

The data in the paper support the conclusions, in general, and the outcomes (evolution towards promiscuity) agree with the literature available for soluble enzymes. However, it is also a possibility that the design of these experiments selects for promiscuity among amino acids. The selections were designed such that yeast had access to amino acids that were already transported, with a greater abundance of the amino acid that was the target of selection. Under these conditions, it seems probable that the fittest variants will provide the yeast access to all amino acid substrates in the media, and unlikely that a specificity swap would occur, limiting the yeast to only the new amino acid.

The authors also examine the fitness costs of mutants, but only in the narrow context of growth on a single (original) amino acid under conditions of nitrogen limitation. Amino acid uptake is typically tightly controlled because some amino acids (or their carbon degradation products) are toxic in excess. This paper does not address or discuss whether there might be a fitness cost to promiscuous mutants in conditions where nitrogen is not limiting.

eLife assessment

By taking advantage of noise in gene expression, this important study introduces a new approach for detecting directed causal interactions between two genes without perturbing either. The main theoretical result is supported by a proof, although clearer statements are needed to ensure that there are no edge cases that can violate the theorem. Preliminary simulations and experiments on small circuits are presented, but the evidence remains incomplete because further investigations are needed to demonstrate the broad applicability and scalability of the method.

Reviewer #1 (Public Review):

Summary:<br /> This manuscript presents a method to infer causality between two genes (and potentially proteins or other molecules) based on the non-genetic fluctuations among cells using a version of the dual-reporter assay as a causal control, where one half of the dual-reporter pair is causally decoupled, as it is inactive. The authors propose a statistical invariant identity to formalize this idea.

Strengths:<br /> The paper outlines a theoretical formalism, which, if experimentally used, can be useful in causal network inference, which is a great need in the study of biological systems.

Weaknesses:<br /> The practical utility of this method may not be straightforward and potentially be quite difficult to execute. Additionally, further investigations are needed to provide evidence of the broad applicability of the method to naturally occurring systems and its scalability beyond the simple circuit in which it is experimentally demonstrated.

Reviewer #2 (Public Review):

Summary:<br /> This paper describes a new approach to detecting directed causal interactions between two genes without directly perturbing either gene. To check whether gene X influences gene Z, a reporter gene (Y) is engineered into the cell in such a way that (1) Y is under the same transcriptional control as X, and (2) Y does not influence Z. Then, under the null hypothesis that X does not affect Z, the authors derive an equation that describes the relationship between the covariance of X and Z and the covariance of Y and Z. Violation of this relationship can then be used to detect causality.

The authors benchmark their approach experimentally in several synthetic circuits. In four positive control circuits, X is a TetR-YFP fusion protein that represses Z, which is an RFP reporter. The proposed approach detected the repression interaction in two or three of the positive control circuits. The authors constructed sixteen negative control circuit designs in which X was again TetR-YFP, but where Z was either a constitutively expressed reporter or simply the cellular growth rate. The proposed method detected a causal effect in two of the sixteen negative controls, which the authors argue is not a false positive, but due to an unexpected causal effect. Overall, these pilot studies, albeit in simplified scenarios, provide encouraging results.

Strengths:<br /> The idea of a "no-causality control" in the context of detected directed gene interactions is a valuable conceptual advance that could potentially see play in a variety of settings where perturbation-based causality detection experiments are made difficult by practical considerations.

By proving their mathematical result in the context of a continuous-time Markov chain, the authors use a more realistic model of the cell than, for instance, a set of deterministic ordinary differential equations.

Caveats:<br /> The term "causally" is used in the main-text statement of the central theorem (Eq 2) without a definition of this term. This makes it difficult to fully understand the statement of the paper's central theorem without diving into the supplement.

The basic argument of theorem 1 appears to rely on establishing that x(t) and y(t) are independent of their initial conditions. Yet, there appear to be some scenarios where this property breaks down:

(1) Theorem 1 does not seem to hold in the edge case where R=beta=W=0, meaning that the components of interest do not vary with time, or perhaps vary in time only due to measurement noise. In this case x(t), y(t), and z(t) depend on x(0), y(0), and z(0). Since the distributions of x(0), y(0), and z(0) are unspecified, a counterexample to the theorem may be readily constructed by manipulating the covariance matrix of x(0), y(0), and z(0).

(2) A similar problem may occur when transition probabilities decay with time. For example, suppose that again R=0 and X are degraded by a protease (B), but this protease is subject to its own first-order degradation. The deterministic version of this situation can be written, for example, dx/dt=-bx and db/dt=-b. In this system, x(t) approaches x(0)exp(-b(0)) for large t. Thus, as above, x(t) depends on x(0). If similar dynamics apply to the Y and Z genes, we can make all genes depend on their initial conditions, thus producing a pathology analogous to the above example.

The reviewer does not know when such examples may occur in (bio)physical systems. Nevertheless, since one of the advantages of mathematics is the ability to correctly identify the domain of validity for a claim, the present work would be strengthened by "building a fence" around these edge cases, either by identifying the comprehensive set of such edge cases and explicitly prohibiting them in a stated assumption set, or by pointing out how the existing assumptions already exclude them.

eLife assessment

This study presents important findings on long-lived proteins in the mouse ovary and oocytes. Convincing evidence using two-generation stable isotope-based metabolic labelling of mice in combination with mass spectrometry analysis describes a resource of enriched long-lived macromolecules in oocytes and somatic cells. Mitochondrial, cytoskeletal, and myosin proteins were identified as long-lived. While this is a unique resource with significant value in the understanding of female reproductive aging, the results would be strengthened if supported by an orthogonal validation and a more in-depth mechanistic explanation of the relationship between mitochondrial and cytoskeletal protein stability and aging.

Reviewer #1 (Public Review):

Summary:

This manuscript by Bomba-Warczak describes a comprehensive evaluation of long-lived proteins in the ovary using transgenerational radioactive labelled 15N pulse-chase in mice. The transgenerational labeling of proteins (and nucleic acids) with 15N allowed the authors to identify regions enriched in long-lived macromolecules at the 6 and 10-month chase time points. The authors also identify the retained proteins in the ovary and oocyte using MS. Key findings include the relative enrichment in long-lived macromolecules in oocytes, pregranulosa cells, CL, stroma, and surprisingly OSE. Gene ontology analysis of these proteins revealed enrichment for nucleosome, myosin complex, mitochondria, and other matrix-type protein functions. Interestingly, compared to other post-mitotic tissues where such analyses have been previously performed such as the brain and heart, they find a higher fractional abundance of labeled proteins related to the mitochondria and myosin respectively.

Strengths:

A major strength of the study is the combined spatial analyses of LLPs using histological sections with MS analysis to identify retained proteins.

Another major strength is the use of two chase time points allowing assessment of temporal changes in LLPs associated with aging.

The major claims such as an enrichment of LLPs in pregranulosa cells, GCs of primary follicles, CL, stroma, and OSE are soundly supported by the analyses, and the caveat that nucleic acids might differentially contribute to this signal is well presented.

The claims that nucleosomes, myosin complex, and mitochondrial proteins are enriched for LLPs are well supported by GO enrichment analysis and well described within the known body of evidence that these proteins are generally long-lived in other tissues.

Weaknesses:

One small potential weakness is the lack of a mechanistic explanation of if/why turnover may be accelerating at the 6-10 month interval compared to 1-6.

A mild weakness is the open-ended explanation of OSE label retention. This is a very interesting finding, and the claims in the paper are nuanced and perfectly reflect the current understanding of OSE repair. However, if the sections are available and one could look at the spatial distribution of OSE signal across the ovarian surface it would interesting to note if label retention varied by regions such as the CLs or hilum where more/less OSE division may be expected.

Reviewer #2 (Public Review):

Summary:

The manuscript by Bomba-Warczak et al. applied multi-isotope imaging mass spectrometry (MIMS) analysis to identify the long-lived proteins in mouse ovaries during reproductive aging, and found some proteins related to cytoskeletal and mitochondrial dynamics persisting for 10 months.

Strengths:

The manuscript provides a useful dataset about protein turnover during ovarian aging in mice.

Weaknesses:

The study is pretty descriptive and short of further new findings based on the dataset. In addition, some results such as the numbers of follicles and ovulated oocytes in aged mice are not consistent with the published literature, and the method for follicle counting is not accurate. The conclusions are not fully supported by the presented evidence.

Reviewer #3 (Public Review):

Summary:

In this study, Bomba-Warczak et al focused on reproductive aging, and they presented a map for long-lived proteins that were stable during reproductive lifespan. The authors used MIMS to examine and show distinct molecules in different cell types in the ovary and tissue regions in a 6 month mice group, and they also used proteomic analysis to present different LLPs in ovaries between these two timepoints in 6-month and 10-month mice. The authors also examined the LLPs in oocytes in the 6-months mice group and indicated that these were nuclear, cytoskeleton, and mitochondria proteins.

Strengths:

Overall, this study provided basic information or a 'map' of the pattern of long-lived proteins during aging, which will contribute to the understanding of the defects caused by reproductive aging.

Weaknesses:

The 6-month mice were used as an aged model; no validation experiments were performed with proteomics analysis only.

Author Response

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

eLife assessment

This study presents a valuable finding on the immunophenotypes of cancer treatment-related pneumonitis. The evidence supporting the claims of the authors is solid, although the inclusion of controls, as suggested by one of the reviewers, strengthened the study. The work will be of interest to cancer immunologists.

Response: We are thankful for the editor's recognition of the contribution our study makes to understanding the immunophenotypes associated with cancer treatment-related pneumonitis. We agree that the inclusion of control data is pivotal for benchmarking biomarkers. While our initial study design was constrained by the availability of BALF from healthy individuals within clinical settings, we addressed this limitation by incorporating scRNA-seq data from healthy control and COVID-19 BALF cells sourced from the GSE145926 dataset. This additional analysis has provided a baseline for comparison, revealing that CD16 is expressed in a minority of T cells in healthy BALF, specifically 1.0% of CD4+ T cells and 1.6% of CD8+ T cells. The inclusion of this data as Figures 6H and 6I in our manuscript offers a robust context for the significant increase in CD16-expressing T cells observed in patients with PCP, thus enhancing the robustness of our study's conclusions.

Author response image 1.

Reviewer #1 (Recommendations For The Authors):

Many thanks for giving me the opportunity to review your paper. I really enjoyed the way you carried out this work - for example, your use of a wide panel of markers and the use of two analytical methods - you have clearly given great thought to bias avoidance. I also greatly appreciated your paragraph on the limitations, as there are several, but you do not 'over-sell' your conclusions so there is no issue here for me.

To improve the piece, there are a few typos (eg 318 - specific to alpha-myosin) and I was briefly confused about the highlighted clusters in Figure 4. Perhaps mention why they are highlighted when they first appear in 4D instead of E?

Response: We have corrected the typos, and we have rearranged the sequence of Figures 3E and 3F, as well as 4D and 4E, to ensure a logical flow. Citrus-generated violin plots are now presented prior to the heatmap of the clusters, which better illustrates the progression of our analysis and the derivation of the clusters.

In terms of improvements to the data, obviously it would have been ideal if you had had some sort of healthy control as a point of reference for all cohorts, but working in the field I understand the difficulties in getting healthy BAL. It would be worth your while however trying to find more supportive data in the literature in general. There are studies which assess various immune markers in healthy BAL eg https://journal-inflammation.biomedcentral.com/articles/10.1186/1476-9255-11-9. and so I think it is worth looking wrt the main findings. For example, are CD16+ T cells seen in healthy BAL or any other conditions (at present the COVID study is being over-relied on)? Could these cells be gamma deltas? (gamma deltas frequently express CD8 and CD16, and can switch to APC like phenotypes).

Response: We are grateful for the reviewer's consideration of the practical challenges associated with collecting BALF from healthy individuals. Alternatively, we have supplemented our analysis with single-cell RNA sequencing data from BALF cells of healthy controls, as found in existing literature (Nature Medicine 2020; 26: 842-844). We have accessed to GSE145926 and downloaded data of BALF cells from healthy control (n=3) and severe COVID19 (n=6). The filtered gene-barcode matrix was first normalized using ‘NormalizeData’ methods in Seurat v.4 with default parameters. The top 2,000 variable genes were then identified using the ‘vst’ method in Seurat FindVariableFeatures function. Then PCA and UMAP was performed. T cells were identified as CD2 >1 and CD3E >1, and FCGR3A expression was explored using an expression threshold of 0.5. Violin plots and bar plots were generated by ggplot function.

Regarding the pivotal finding of increased CD16-expressing T cells in patients with PCP, the scRNA-seq data mining indicates that CD16 is expressed by a minority of T cells in healthy BALF—1.0% of CD4+ T cells and 1.6% of CD8+ T cells. These figures, now incorporated into our revised manuscript as Figures 6H and 6I, substantiate our findings. These cells could be gamma delta T cells, but we could not confirm it with the limited data. We will investigate in the future study. The main text has been updated to reflect these findings.

Author response image 2.

I would agree with your approach of not going down the transcript route, so just focus on protein expression.

I think you need to mention more about the impact of ICI on PD1 expression - in the methods you lose one approach owing to low T cell expression (132) but in the discussion you mention ICI induced high expression (311) as previously reported. This apparent contradiction needs an explanation.

Response: We acknowledge the need for clarification regarding the impact of ICIs on PD-1 expression. In the methods section, the low detection of PD-1 expression on T cells in patients treated with nivolumab was indeed noted; this was due to the competitive nature of the PD-1 detection antibody EH12.2 with nivolumab. As reported by Suzuki et al. (International Immunology 2020; 32: 547-557), T cells from patients with ICI-induced ILD, including those treated with nivolumab, exhibit upregulated PD-1 expression, where the PD-1 detection antibody (clone: MIH4). Conversely, as outlined by Yanagihara et al. (BBRC 2020; 527: 213-217), the PD-1 detection antibody clone EH12.2 conjugated with 155Gd (#3155009B) used in our study is unable to detect PD-1 when patients are under nivolumab treatment due to competitive inhibition. The absence of a metal-conjugated PD-1 antibody with the MIH4 clone presented a limitation in our study. Ideally, we would have conjugated the MIH4 antibody with 155Gd for our analysis, which is a refinement we aim to incorporate in future research. We have now included this discussion in our manuscript to clarify the contradiction between the methodological limitations and the high PD-1 expression induced by ICIs, as reported in the literature. This addition will guide readers through the nuances of antibody selection and its implications for detecting PD-1 expression in the context of ICI treatment.

Finally, since you have the severity data, it would be good to assess all the significantly different clusters against this metric, as you have done for CD16+ T cells. Not only may this reveal more wrt the impact of other immune populations, but it'll also give a point of reference for the CD16+ T cell data.

Response: Thank you for the suggestion to assess all significantly different clusters against the disease severity metric. We have expanded our analysis to include a thorough correlation study between the disease severity and intensity of various T-cell markers. Notably, we observed that intensity of CCR7 expression correlates with the disease severity. Although the precise biological significance of this correlation remains to be elucidated, it may suggest a role for CCR7+ T cells in the pathogenesis or progression of the disease. We have considered the potential implications of this finding and included it as Supplementary Figure 5. We have also discussed this observation in the discussion section.

Author response image 3.

Overall though I think this is a really nice study, with a potentially very significant finding in linking CD16+ T cells with severity. Congratulations.

Response: We would like to thank the reviewer’s heartful comments on our manuscript.

Reviewer #2 (Recommendations For The Authors):

General:

1) The fact that this is a retrospective study should be indicated earlier in the paper.

Response: Now we have mentioned the retrospective nature of the study in the method section as follows: In this retrospective study, patients who were newly diagnosed with PCP, DI-ILD, and ICI-ILD and had undergone BALF collection at Kyushu University Hospital from January 2017 to April 2022 were included. The retrospective study was approved by the Ethics Committee of Kyushu University Hospital (reference number 22117-00).

2) tSNE and UMAP are dimensionality reduction techniques that don't cluster the cells, the authors should specify what clustering algorithm was used subsequently (e.g FlowSOM)

Response: The cluster was determined manually by their expression pattern.

3) With regards to the role of CD16 in a potential exacerbated cytotoxicity in the fatal PCP case, the authors could measure the levels of C3a related proteins in patient serum to link to a common immunopathogenic pathway with COVID.

Response: We did not collect serum from the patients in this study as our research protocol was approved by the Ethics committee for the use of BALF only. However, we agree with your assessment that the measurement of serum C3a levels would be informative. In future studies, we will incorporate the measurement of serum C3a levels to provide more comprehensive insights into the impact of C3a on immune function. Thank you for your valuable feedback and for helping us to improve the quality of our research.

Line-specific:

101 The authors should provide some information on how the cryopreservation of the BALF was carried out.

Response: Upon collection, BALF samples were immediately centrifuged at 300 g for 5 minutes to pellet the cells. The resultant cell pellets were then resuspended in Cellbanker 1 cryopreservation solution (Takara, catalog #210409). This suspension was aliquoted into cryovials and gradually frozen to –80ºC using a controlled rate freezing method to ensure cell viability. The samples were stored at –80ºC until required for experimental analysis. We have added the information in the method section.

Fig 3B: It would be very helpful if the authors could add a supplementary figure with marker expression on the UMAP projection.

Response: We have added Supplementary Figure 4 with marker expression on the UMAP projection in Figure 3B.

Fig 4A: Same as Fig 3B

Response: We have added Supplementary Figure 5 with marker expression on the UMAP projection in Figure 4A.

Fig 5B: Same as Fig 3B

Response: We have added Supplementary Figure 6 with marker expression on the tSNE projection in Figure 5B.

266 Authors should state if the data is not shown with regards to differences in myeloid cell fractions

430 Marker intensity is not shown in panel D

Re: Corrected as follows: “Citrus network tree visualizing the hierarchical relationship of each marker between identified T cell ~”

446 The legend says patients have IPF, CTD-ILD, sarcoidosis but the figure shows PCP, DI-ILD, ICI-ILD.

Re: Corrected.

451 What do the authors mean in "Graphical plots represent individual samples"? Panel B is a dot plot of all samples.

Response: Corrected as “Dot plots represent ~”.

472 What do the authors mean in "Graphical plots represent individual samples"? Panel C is a dot plot of all samples.

Response: Corrected as “Dot plots represent ~”.

Reviewer #3 (Recommendations For The Authors):

An important thing is to add comparisons against healthy donors, at least. A common baseline is needed to firmly establish any biomarkers.

Response: We acknowledge the reviewer's concern regarding the comparison with healthy donors. Although our study did not initially include BALF collection from healthy controls due to the constraints of clinical practice, we recognize the importance of a control baseline to validate biomarkers. To address this, we have integrated scRNA-seq data from healthy control BALF cells available in public datasets (Nature Medicine 2020; 26: 842-844), accessed from GSE145926. This dataset includes BALF cells from healthy controls (n=3) alongside severe COVID-19 patients (n=6). Data mining confirmed that CD16 expression is in a minority of T cells in healthy BALF—1.0% of CD4+ T cells and 1.6% of CD8+ T cells. We have included this comparative data in our manuscript as Figures 6H and 6I to provide context for the observed increase in CD16-expressing T cells in PCP patients, which substantiates our findings.

Author response image 4.

Data analysis needs to go deeper. There are several other tools on Cytobank alone that would allow a more quantitative analysis of the data. Fold changes in marker expressions would be very important as measurements of phenotypic changes.

Response: We thank the reviewer for their constructive feedback on the depth of our data analysis. We acknowledge the value of a more quantitative approach, including the use of fold change measurements to assess phenotypic alterations, and recognize the potential insights such tools on Cytobank could provide. Due to the scope and limited space of the current study, we have focused our analysis on the most pertinent findings relevant to our research questions. We believe the present analysis serves the immediate objectives of this study. However, we agree that further quantitative analysis would enhance the understanding of the data. We have expanded our analysis to include a thorough correlation study between the disease severity of PCP and intensity of various T-cell markers. Notably, we observed that intensity of CCR7 expression correlates with the disease severity of PCP. Although the precise biological significance of this correlation remains to be elucidated, it may suggest a role for CCR7+ T cells in the pathogenesis or progression of the disease. We have considered the potential implications of this finding and included it as Supplementary Figure 5. We have also discussed this observation in the discussion section. We aim to consider these approaches in future work to build upon the foundation laid by this study. Your suggestions are invaluable and will be kept at the forefront as we plan subsequent research phases.

Author response image 5.

Reviewer #1 (Public Review):

Cytotoxic agents and immune checkpoint inhibitors are the most commonly used and efficacious treatments for lung cancers. However their use brings two significant pulmonary side-effects; namely Pneumocystis jirovecii infection and resultant pneumonia (PCP), and interstitial lung disease (ILD). To observe the potential immunological drivers of these adverse events, Yanagihara et al. analysed and compared cells present in the bronchoalveolar lavage of three patient groups (PCP, cytotoxic drug-induced ILD [DI-ILD], and ICI-associated ILD [ICI-ILD]) using mass cytometry (64 markers). In PCP, they observed an expansion of the CD16+ T cell population, with the highest CD16+ T proportion (97.5%) in a fatal case, whilst in ICI-ILD, they found an increase in CD57+ CD8+ T cells expressing immune checkpoints (TIGIT+ LAG3+ TIM-3+ PD-1+), FCRL5+ B cells, and CCR2+ CCR5+ CD14+ monocytes. Given the fatal case, the authors also assessed for, and found, a correlation between CD16+ T cells and disease severity in PCP, postulating that this may be owing to endothelial destruction. Although n numbers are relatively small (n=7-9 in each cohort; common numbers for CyTOF papers), the authors use a wide panel (n=65) and two clustering methodologies giving greater strength to the conclusions. The differential populations discovered using one or two of the analytical methods are robust: whole population shifts with clear and significant clustering. These data are an excellent resource for clinical disease specialists and pan-disease immunologists, with a broad and engaging contextual discussion about what they could mean.

Strengths:

• The differences in immune cells in BAL in these specific patient subgroups is relatively unexplored.

• This is an observational study, with no starting hypothesis being tested.

• Two analytical methods are used to cluster the data.

• A relatively wide panel was used (64 markers), with particular strength in the alpha beta T cells and B cells.

• Relevant biomarkers, beta-D-glucan and KL-6 were also analysed

• Appropriate statistics were used throughout.

• Numbers are low (7 cases of PCP, 9 of DI-ILD, and 9 of ICI-ILD) but these are difficult samples to collect and so in relative terms, and considering the use of CyTOF, these are good numbers.

• Beta-D-glucan shows potential as a biomarker for PCP (as previously reported) whilst KL-6 shows potential as a biomarker for ICI-ILD (not reported before). Interestingly, KL-6 was not seen to be increased in DI-ILD patients.

• Despite the relatively low n numbers and lack of matching there are some clear differentials. The CD4/CD8+CD16+HLA-DR+CXCR3+CD14- T cell result is striking - up in PCP (with EM CD4s significantly down) - whilst the CD8 EMRA population is clear in ICI-ILD and 'non-exhausted' CD4s, with lower numbers of EMRA CD8s in DI-ILD.

• The authors identify 17/31 significantly differentiated clusters of myeloid cells, eg CD11bhi CD11chi CD64+ CD206+ alveolar macrophages with HLA-DRhi in PCP.

• With respect to B cells, the authors found that FCRL5+ B cells were more abundant in patients with ICI-ILD compared to those with PCP and DI-ILD, suggesting these FCRL5+ B cells may have a role in irAE.

• One patient's extreme CD16+ T cell (97.5% positive) and death, led the authors to consider CD16+ T cells as an indicator of disease severity in PCP. This was then tested and found to be correct.

• Authors discuss results in context of literature leading them to suggest that CD16+ T cells may target endothelial cells and wonder if anti-complement therapy may be efficacious in PCP.

• Great discussion on auto-reactive T cell clones where the authors suggest that in ICI-ILD CD8s may react against healthy lung, driving ILD.

• An observation of CXCR3 in different CD8 populations in ICI-ILD and PCP lead the authors to hypothesise on the chemoattractants in the microenvironment.

• Excellent point suggesting CD57 may not always be a marker of senescence on T cells - reflective of growing change within the community.

• Well considered suggestion that FCRL5+ B cells may be involved in ICI-ILD driven autoimmunity.

• The authors discuss the main weaknesses in the discussion and stress that the findings detailed in the paper "demonstrate a correlation rather than proof of causation".

• Figures and legends are clear and pleasing to the eye.

Weaknesses:

• This is an observational study, with no starting hypothesis being tested.

• Only patients who were able to have a lavage taken have been recruited.

• One set of analysis wasn't carried out for one subgroup (ICI-ILD) as PD1 expression was negative owing to the use of nivolumab.

• Some immune cell subsets wouldn't be picked up with the markers and gating strategies used; e.g. NK cells.

• Some immune cells would be disproportionately damaged by the storage, thawing and preparation of the samples; e.g. granulocytes.

• Numbers are low (7 cases of PCP, 9 of DI-ILD, and 9 of ICI-ILD), sex, age and adverse event matching wasn't performed, and treatment regimen are varied and 'suspected' (suggesting incomplete clinical data) - but these are difficult samples to collect. These numbers drop further for some analyses e.g. T cell clustering owing to factors such as low cell number.

• The disease comparisons are with each other, there is no healthy control.

• Samples are taken at one time point.

• The discussion on probably the stand out result - the CD16+ T cells in PCP - relies on two papers - leading to a slightly skewed emphasis on one paper on CD16+ cells in COVID. There are other papers out there that have observed CD16+ T cells in other conditions. It is also worth being in mind that given the markers used, these CD16+ T cell may be gamma deltas.

• The discussion on ICI patient consistently showing increased PD1, could have been greater, as given the ICI is targeting PD1, one would expect the opposite as commented on, and observed, in the methods section.

Reviewer #2 (Public Review):

Yanagihara and colleagues investigated the immune cell composition of bronchoalveolar lavage fluid (BALF) samples in a cohort of patients with malignancy undergoing chemotherapy and with with lung adverse reactions including Pneumocystis jirovecii pneumonia (PCP) and immune-checkpoint inhibitors (ICIs) or cytotoxic drug induced interstitial lung diseases (ILDs). Using mass cytometry, their aim was to characterize the cellular and molecular changes in BAL to improve our understanding of their pathogenesis and identify potential biomarkers and therapeutic targets. In this regard, the authors identify a correlation between CD16 expression in T cells and the severity of PCP and an increased infiltration of CD57+ CD8+ T cells expressing immune checkpoints and FCLR5+ B cells in ICI-ILD patients.

The conclusions of this paper are mostly well supported by data, but some aspects of the data analysis need to be clarified and extended.

1) The authors should elaborate on why different set of markers were selected for each analysis step. E.g., Different set of markers were used for UMAP, CITRUS and viSNE in the T cell and myeloid analysis.

2) The authors should state if a normality test for the distribution of the data was performed. If not, non-parametric tests should be used.

3) The authors should explore the correlation between CD16 intensity and the CTCAE grade in T cell subsets such as EMRA CD8 T cells, effector memory CD4, etc as identified in Figure 1B.

4) The authors could use CITRUS to better assess the B cell compartment.

Reviewer #3 (Public Review):

The authors collected BALF samples from lung cancer patients newly diagnosed with PCP, DI-ILD or ICI-ILD. CyTOF was performed on these samples, using two different panels (T-cell and B-cell/myeloid cell panels). Results were collected, cleaned-up, manually gated and pre-processed prior to visualisation with manifold learning approaches t-SNE (in the form of viSNE) or UMAP, and analysed by CITRUS (hierarchical clustering followed by feature selection and regression) for population identification - all using Cytobank implementation - in an attempt to identify possible biomarkers for these disease states. By comparing cell abundances from CITRUS results and qualitative inspection of a small number of marker expressions, the authors claimed to have identified an expansion of CD16+ T-cell population in PCP cases and an increase in CD57+ CD8+ T-cells, FCRL5+ B-cells and CCR2+ CCR5+ CD14+ monocytes in ICI-ILD cases.

By the authors' own admission, there is an absence of healthy donor samples and, perhaps as a result of retrospective experimental design, also an absence of pre-treatment samples. The entire analysis effectively compares three yet-established disease states with no common baseline - what really constitutes a "biomarker" in such cases? The introduction asserts that "y characterizing the cellular and molecular changes in BAL from patients with these complications, we aim to improve our understanding of their pathogenesis and identify potential therapeutic targets" (lines 82-84). Given these obvious omissions, no real "changes" have been studied in the paper. These are very limited comparisons among three, and only these three, states.

Even assuming more thorough experimental design, the data analysis is unfortunately too shallow and has not managed to explore the wealth of information that could potentially be extracted from the results. CITRUS is accessible and convenient, but also make a couple of big assumptions which could affect data analysis - 1) Is it justified to concatenate all FCS files to analyse the data in one batch / small batches? Could there be batch effects or otherwise other biological events that could confuse the algorithm? 2) With a relatively small number of samples, and after internal feature selection of CITRUS, is the regression model suitable for population identification or would it be too crude and miss out rare populations? There are plenty of other established methods that could be used instead. Have those methods been considered?

Colouring t-SNE or UMAP (e.g. Figure 6C) plots by marker expression is useful for quick identification of cell populations but it is not a quantitative analysis. In a CyTOF analysis like this, it is common to work out fold changes of marker expressions between conditions. It is inadequate to judge expression levels and infer differences simply by looking at colours.

The relatively small number of samples also mean that most results presented in the paper are not statistical significant. Whilst it is understandable that it is not always possible to collect a large number of patient samples for studies like this, having several entire major figures showing "n.s." (e.g. Figures 3A, 4B and 5C), together with limitations in the comparisons themselves and inadequate analysis, make the observations difficult to be convincing, and even less so for the single fatal PCP case where N = 1.

It would also be good scientific practice to show evidence of sample data quality control. Were individual FCS files examined? Did the staining work? Some indication of QC would also be great.

This dataset generated and studied by the authors have the potential to address the question they set out to answer and thus potentially be useful for the field. However, in the current state of presentation, more evidence and more thorough data analysis are needed to draw any conclusions, or correlations, as the authors would like to frame them.

eLife assessment

This study presents a useful inventory of immune signatures that are correlated with cancer treatment-related pneumonitis. The data were collected and analysed using solid and validated methodology and can be used as a starting point for further functional studies.

Reviewer #2 (Public Review):

Yanagihara and colleagues investigated the immune cell composition of bronchoalveolar lavage fluid (BALF) samples in a cohort of patients with malignancy undergoing chemotherapy and with with lung adverse reactions including Pneumocystis jirovecii pneumonia (PCP) and immune-checkpoint inhibitors (ICIs) or cytotoxic drug induced interstitial lung diseases (ILDs). Using mass cytometry, their aim was to characterize the cellular and molecular changes in BAL to improve our understanding of their pathogenesis and identify potential biomarkers and therapeutic targets. In this regard, the authors identify a correlation between CD16 expression in T cells and the severity of PCP and an increased infiltration of CD57+ CD8+ T cells expressing immune checkpoints and FCLR5+ B cells in ICI-ILD patients.

The conclusions of this paper are mostly well supported by data, but some aspects of the data analysis need to be clarified and extended.

1) The authors should elaborate on why different set of markers were selected for each analysis step. E.g., Different set of markers were used for UMAP, CITRUS and viSNE in the T cell and myeloid analysis.

2) The authors should state if a normality test for the distribution of the data was performed. If not, non-parametric tests should be used.

3) The authors should explore the correlation between CD16 intensity and the CTCAE grade in T cell subsets such as EMRA CD8 T cells, effector memory CD4, etc as identified in Figure 1B.

4) The authors could use CITRUS to better assess the B cell compartment.

Reviewer #3 (Public Review):

The authors collected BALF samples from lung cancer patients newly diagnosed with PCP, DI-ILD or ICI-ILD. CyTOF was performed on these samples, using two different panels (T-cell and B-cell/myeloid cell panels). Results were collected, cleaned-up, manually gated and pre-processed prior to visualisation with manifold learning approaches t-SNE (in the form of viSNE) or UMAP, and analysed by CITRUS (hierarchical clustering followed by feature selection and regression) for population identification - all using Cytobank implementation - in an attempt to identify possible biomarkers for these disease states. By comparing cell abundances from CITRUS results and qualitative inspection of a small number of marker expressions, the authors claimed to have identified an expansion of CD16+ T-cell population in PCP cases and an increase in CD57+ CD8+ T-cells, FCRL5+ B-cells and CCR2+ CCR5+ CD14+ monocytes in ICI-ILD cases.

By the authors' own admission, there is an absence of healthy donor samples and, perhaps as a result of retrospective experimental design and practical clinical reasons, also an absence of pre-treatment samples. The entire analysis effectively compares three yet-established disease states with no common baseline - what really constitutes a "biomarker" in such cases? These are very limited comparisons among three, and only these three, states.

By including a new scRNA-Seq analysis using publicly available dataset, the authors addressed this fundamental problem. Though more thorough and numerical analysis would be appreciated for a deeper and more impactful analysis, this is adequate for the intended objectives of the study.

Author Response

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

Reviewer #1:

Summary:

This paper performs fine-mapping of the silkworm mutants bd and its fertile allelic version, bdf, narrowing down the causal intervals to a small interval of a handful of genes. In this region, the gene orthologous to mamo is impaired by a large indel, and its function is later confirmed using expression profiling, RNAi, and CRISPR KO. All these experiments are convincingly showing that mamo is necessary for the suppression of melanic pigmentation in the silkworm larval integument. The authors also use in silico and in vitro assays to probe the potential effector genes that mamo may regulate. Strengths: The genotype-to-phenotype workflow, combining forward (mapping) and reverse genetics (RNAi and CRISPR loss-of-function assays) linking mamo to pigmentation are extremely convincing.

Response: Thank you very much for your affirmation of our work. The reviewer discussed the parts of our manuscript that involve evolution sentence by sentence. We have further refined the description in this regard and improved the logical flow. Thank you again for your help.

Weaknesses:

1) The last section of the results, entitled "Downstream target gene analysis" is primarily based on in silico genome-wide binding motif predictions.

While the authors identify a potential binding site using EMSA, it is unclear how much this general approach over-predicted potential targets. While I think this work is interesting, its potential caveats are not mentioned. In fact the Discussion section seems to trust the high number of target genes as a reliable result. Specifically, the authors correctly say: "even if there are some transcription factor-binding sites in a gene, the gene is not necessarily regulated by these factors in a specific tissue and period", but then propose a biological explanation that not all binding sites are relevant to expression control. This makes a radical short-cut that predicted binding sites are actual in vivo binding sites. This may not be true, as I'd expect that only a subset of binding motifs predicted by Positional Weight Matrices (PWM) are real in vivo binding sites with a ChIP-seq or Cut-and-Run signal. This is particularly problematic for PWM that feature only 5-nt signature motifs, as inferred here for mamo-S and mamo-L, simply because we can expect many predicted sites by chance.

Response: Thank you very much for your careful work. The analysis and identification of transcription factor-binding sites is an important issue in gene regulation research. Techniques such as ChIP-seq can be used to experimentally identify the binding sites of transcription factors (TFs). However, reports using these techniques often only detect specific cell types and developmental stages, resulting in a limited number of downstream target genes for some TFs. Interestingly, TFs may regulate different downstream target genes in different cell types and developmental stages.

Previous research has suggested that the ZF-DNA binding interface can be understood as a “canonical binding model”, in which each finger contacts DNA in an antiparallel manner. The binding sequence of the C2H2-ZF motif is determined by the amino acid residue sequence of its α-helical component. Considering the first amino acid residue in the α-helical region of the C2H2-ZF domain as position 1, positions -1, 2, 3, and 6 are key amino acids for recognizing and binding DNA. The residues at positions -1, 3, and 6 specifically interact with base 3, base 2, and base 1 of the DNA sense sequence, respectively, while the residue at position 2 interacts with the complementary DNA strand (Wolfe SA et al., 2000; Pabo CO et al., 2001). Based on this principle, the binding sites of C2H2-ZF have good reference value. For the 5-nt PWM sequence, we referred to the study of D. melanogaster, which was identified by EMSA (Shoichi Nakamura et al., 2019). In the new version, we have rewritten this section.

Pabo CO, Peisach E, Grant RA. Design and selection of novel Cys2His2 zinc finger proteins. Annu Rev Biochem. 2001;70:313-340.

Wolfe SA, Nekludova L, Pabo CO. DNA recognition by Cys2His2 zinc finger proteins. Annu Rev Biophys Biomol Struct. 2000;29:183-212.

Nakamura S, Hira S, Fujiwara M, et al. A truncated form of a transcription factor Mamo activates vasa in Drosophila embryos. Commun Biol. 2019;2:422. Published 2019 Nov 20.

2) The last part of the current discussion ("Notably, the industrial melanism event, in a short period of several decades ... a more advanced self-regulation program") is flawed with important logical shortcuts that assign "agency" to the evolutionary process. For instance, this section conveys the idea that phenotypically relevant mutations may not be random. I believe some of this is due to translation issues in English, as I understand that the authors want to express the idea that some parts of the genome are paths of least resistance for evolutionary change (e.g. the regulatory regions of developmental regulators are likely to articulate morphological change). But the language and tone is made worst by the mention that in another system, a mechanism involving photoreception drives adaptive plasticity, making it sound like the authors want to make a Lamarckian argument here (inheritance of acquired characteristics), or a point about orthogenesis (e.g. the idea that the environment may guide non-random mutations).

Because this last part of the current discussion suffers from confused statements on modes and tempo of regulatory evolution and is rather out of topic, I would suggest removing it.

In any case, it is important to highlight here that while this manuscript is an excellent genotype-to-phenotype study, it has very few comparative insights on the evolutionary process. The finding that mamo is a pattern or pigment regulatory factor is interesting and will deserve many more studies to decipher the full evolutionary study behind this Gene Regulatory Network.

Response: Thank you very much for your careful work. In this part of the manuscript, we introduced some assumptions that make the statement slightly unconventional. The color pattern of insects is an adaptive trait. The bd and bdf mutants used in the study are formed spontaneously. As a frequent variation and readily observable phenotype, color patterns have been used as models for evolutionary research (Wittkopp PJ et al., 2011). Darwin's theory of natural selection has epoch-making significance. I deeply believe in the theory that species strive to evolve through natural selection. However, with the development of molecular genetics, Darwinism’s theory of undirected random mutations and slow accumulation of micromutations resulting in phenotype evolution has been increasingly challenged.

The prerequisite for undirected random mutations and micromutations is excessive reproduction to generate a sufficiently large population. A sufficiently large population can contain sufficient genotypes to face various survival challenges. However, it is difficult to explain how some small groups and species with relatively low fertility rates have survived thus far. More importantly, the theory cannot explain the currently observed genomic mutation bias. In scientific research, every theory is constantly being modified to adapt to current discoveries. The most famous example is the debate over whether light is a particle or a wave, which has lasted for hundreds of years. However, in the 20th century, both sides seemed to compromise with each other, believing that light has a wave‒particle duality.

In summary, we have rewritten this section to reduce unnecessary assumptions.

Wittkopp PJ, Kalay G. Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat Rev Genet. 2011;13(1):59-69.

Minor Comment:

The gene models presented in Figure 1 are obsolete, as there are more recent annotations of the Bm-mamo gene that feature more complete intron-exon structures, including for the neighboring genes in the bd/bdf intervals. It remains true that the mamo locus encodes two protein isoforms.

An example of the Bm-mamo locus annotation, can be found at: https://www.ncbi.nlm.nih.gov/gene/101738295 RNAseq expression tracks (including from larval epidermis) can be displayed in the embedded genome browser from the link above using the "Configure Tracks" tool.

Based on these more recent annotations, I would say that most of the work on the two isoforms remains valid, but FigS2, and particularly Fig.S2C, need to be revised.

Response: Thank you very much for your careful work. In this study, we referred to the predicted genes of SilkDB, NCBI and Silkbase. In different databases, there are varying degrees of differences in the number of predicted genes and the length of gene mRNA. Because the SilkDB database is based on the first silkworm genome, it has been used for the longest time and has a relatively large number of users. In the revised manuscript, we have added the predicted genes of NCBI and Silkbase in Figure S1.

Author response image 1.

The predicted genes and qPCR analysis of candidate genes in the responsible genomic region for bd mutant. (A) The predicted genes in SilkDB;(B) the predicted genes in Genbak;(C) the predicted genes in Silkbase;(D) analysis of nucleotide differences in the responsible region of bd;(E) investigation of the expression level of candidate genes.

Reviewer #2 (Public Review):

Summary:

The authors tried to identify new genes involved in melanin metabolism and its spatial distribution in the silkworm Bombyx mori. They identified the gene Bm-mamo as playing a role in caterpillar pigmentation. By functional genetic and in silico approaches, they identified putative target genes of the Bm-mamo protein. They showed that numerous cuticular proteins are regulated by Bm-mamo during larval development.

Strengths:

• preliminary data about the role of cuticular proteins to pattern the localization of pigments

• timely question

• challenging question because it requires the development of future genetic and cell biology tools at the nanoscale

Response: Thank you very much for your affirmation of our work. The reviewer's familiarity with the color patterns of Lepidoptera is helpful, and the recommendation raised has provided us with very important assistance. This has allowed us to make significant progress with our manuscript.

Weaknesses:

• statistical sampling limited

• the discussion would gain in being shorter and refocused on a few points, especially the link between cuticular proteins and pigmentation. The article would be better if the last evolutionary-themed section of the discussion is removed.

A recent paper has been published on the same gene in Bombyx mori (https://www.sciencedirect.com/science/article/abs/pii/S0965174823000760) in August 2023. The authors must discuss and refer to this published paper through the present manuscript.

Response: Thank you very much for your careful work. First, we believe that competitive research is sometimes coincidental and sometimes intentional. Our research began in 2009, when we began to configure the recombinant population. In 2016, we published an article on comparative transcriptomics (Wu et al. 2016). The article mentioned above has a strong interest in our research and is based on our transcriptome analysis for further research, with the aim of making a preemptive publication. To discourage such behavior, we cannot cite it and do not want to discuss it in our paper.

Songyuan Wu et al. Comparative analysis of the integument transcriptomes of the black dilute mutant and the wild-type silkworm Bombyx mori. Sci Rep. 2016 May 19:6:26114. doi: 10.1038/srep26114.

Reviewer #1 (Recommendations For The Authors):

1) please consider using a more recent annotation model of the B. mori genome to revise your Result Section 1, Fig.1, and Fig. S2. https://www.ncbi.nlm.nih.gov/gene/101738295

Specifically, you used BGIM_ gene models, while the current annotation such as the one above featured in the NCBI database provides more accurate intron-exon structures without splitting mamo into tow genes. I believe this can be done with minor revisions of the figures, and you could keep the BGIM_ gene names for the text.

Response: Thank you very much for your careful work. The GenBank of NCBI (National Center for Biotechnology Information) is a very good database that we often use and refer to in this research process. Our research started in 2009, so we mainly referred to the SilkDB database (Jun Duan et al., 2010), although other databases also have references, such as NCBI and Silkbase (https://silkbase.ab.a.u-tokyo.ac.jp/cgi-bin/index.cgi). Because the SilkDB database was constructed based on the first published silkworm genome data, it has been used for the longest time and has a relatively large number of users. Recently, researchers are still using these data (Kejie Li et al., 2023).

The problem with predicting the mamo gene as two genes (BGIBMGA012517 and BGIBMGA012518) in SilkDB is mainly due to the presence of alternative splicing of the mamo gene. BGIBMGA012517 corresponds to the shorter transcript (mamo-s) of the mamo gene. Due to the differences in sequencing individuals, sequencing methods, and methods of gene prediction, there are differences in the number and sequence of predicted genes in different databases. We added the pattern diagram of predicted genes from NCBI and Silkbase, and the expression levels of new predicted genes are shown in Supplemental Figure S1.

Jun Duan et al., SilkDB v2.0: a platform for silkworm (Bombyx mori) genome biology. Nucleic Acids Res. 2010 Jan;38(Database issue): D453-6. doi: 10.1093/nar/gkp801. Kejie Li et al., Transcriptome analysis reveals that knocking out BmNPV iap2 induces apoptosis by inhibiting the oxidative phosphorylation pathway. Int J Biol Macromol. 2023 Apr 1;233:123482. doi: 10.1016/j.ijbiomac.2023.123482. Epub 2023 Jan 31.

Author response image 2.

The predicted genes and qPCR analysis of candidate genes in the responsible genomic region for bd mutant. (A) The predicted genes in SilkDB;(B) the predicted genes in Genbak;(C) the predicted genes in Silkbase;(D) analysis of nucleotide differences in the responsible region of bd;(E) investigation of the expression level of candidate genes.

2) As I mentioned in my public review, I strongly believe the interpretation of the PWM binding analyses require much more conservative statements taking into account the idea that short 5-nt motifs are expected by chance. The work in this section is interesting, but the manuscript would benefit from a quite significant rewrite of the corresponding Discussion section, making it that the in silico approach is prone to the identification of many sites in the genomes, and that very few of those sites are probably relevant for probabilistic reasons. I would recommend statements such as "Future experiments assessing the in vivo binding profile of Bm-mamo (eg. ChIP-seq or Cut&Run), will be required to further understand the GRNs controlled by mamo in various tissues".

Response: Thank you very much for your careful work. Previous research has suggested that the ZF-DNA binding interface can be understood as a “canonical binding model”, in which each finger contacts DNA in an antiparallel manner. The binding sequence of the C2H2-ZF motif is determined by the amino acid residue sequence of its α-helical component. Considering the first amino acid residue in the α-helical region of the C2H2-ZF domain as position 1, positions -1, 2, 3, and 6 are key amino acids for recognizing and binding DNA. The residues at positions -1, 3, and 6 specifically interact with base 3, base 2, and base 1 of the DNA sense sequence, respectively, while the residue at position 2 interacts with the complementary DNA strand (Wolfe SA et al., 2000; Pabo CO et al., 2001). Based on this principle, the prediction of DNA recognition motifs of C2H2-type zinc finger proteins currently has good accuracy.

The predicted DNA binding sequence (GTGCGTGGC) of the mamo protein in Drosophila melanogaster was highly consistent with that of silkworms. In addition, in D. melanogaster, the predicted DNA binding sequence of mamo, the bases at positions 1 to 7 (GTGCGTG), was highly similar to the DNA binding sequence obtained from EMSA experiments (Seiji Hira et al., 2013). Furthermore, in another study on the mamo protein of Drosophila melanogaster, five bases (TGCGT) were used as the DNA recognition core sequence of the mamo protein (Shoichi Nakamura et al., 2019). In the JASPAR database (https://jaspar.genereg.net), there are also some shorter (4-6 nt) DNA recognition sequences; for example, the DNA binding sequence of Ubx is TAAT (ID MA0094.1) in Drosophila melanogaster. However, we used longer DNA binding motifs (9 nt and 15 nt) of mamo to study the 2 kb genomic regions near the predicted gene. Over 70% of predicted genes were found to have these feature sequences near them. This analysis method is carried out with common software and processes. Due to sufficient target proteins, the accessibility of DNA, the absence of suppressors, the suitability of ion environments, etc., zinc finger protein transcription factors are more likely to bind to specific DNA sequences in vitro than in vivo. Using ChIP-seq or Cut&Run techniques to analyze various tissues and developmental stages in silkworms can yield one comprehensive DNA-binding map of mamo, and some false positives generated by predictions can be excluded. Thank you for your suggestion. We will conduct this work in the next research step. In addition, for brevity, we deleted the predicted data (Supplemental Tables S7 and S8) that used shorter motifs.

Pabo CO, Peisach E, Grant RA. Design and selection of novel Cys2His2 zinc finger proteins. Annu Rev Biochem. 2001;70:313-340.

Wolfe SA, Nekludova L, Pabo CO. DNA recognition by Cys2His2 zinc finger proteins. Annu Rev Biophys Biomol Struct. 2000;29:183-212.

Anton V Persikov et al., De novo prediction of DNA-binding specificities for Cys2His2 zinc finger proteins. Nucleic Acids Res. 2014 Jan;42(1):97-108. doi: 10.1093/nar/gkt890. Epub 2013 Oct 3.

Seiji Hira et al., Binding of Drosophila maternal Mamo protein to chromatin and specific DNA sequences. Biochem Biophys Res Commun. 2013 Aug 16;438(1):156-60. doi: 10.1016/j.bbrc.2013.07.045. Epub 2013 Jul 20.

Shoichi Nakamura et al., A truncated form of a transcription factor Mamo activates vasa in Drosophila embryos. Commun Biol. 2019 Nov 20;2: 422. doi: 10.1038/s42003-019-0663-4. eCollection 2019.

3) In my opinion, the last section of the Discussion needs to be completely removed ("Notably, the industrial melanism event, in a short period of several decades ... a more advanced self-regulation program"), as it is over-extending the data into evolutionary interpretations without any support. I would suggest instead writing a short paragraph asking whether the pigmentary role of mamo is a Lepidoptera novelty, or if it could have been lost in the fly lineage.

Below, I tried to comment point-by-point on the main issues I had.

Wu et al: Notably, the industrial melanism event, in a short period of several decades, resulted in significant changes in the body color of multiple Lepidoptera species(46). Industrial melanism events, such as changes in the body color of pepper moths, are heritable and caused by genomic mutations(47).

Yes, but the selective episode was brief, and the relevant "carbonaria" mutations may have existed for a long time at low-frequency in the population.

Response: Thank you very much for your careful work. Moth species often have melanic variants at low frequencies outside industrial regions. Recent molecular work on genetics has revealed that the melanic (carbonaria) allele of the peppered moth had a single origin in Britain. Further research indicated that the mutation event causing industrial melanism of peppered moth (Biston betularia) in the UK is the insertion of a transposon element into the first intron of the cortex gene. Interestingly, statistical inference based on the distribution of recombined carbonaria haplotypes indicates that this transposition event occurred in approximately 1819, a date highly consistent with a detectable frequency being achieved in the mid-1840s (Arjen E Van't Hof, et al., 2016). From molecular research, it is suggested that this single origin melanized mutant (carbonaria) was generated near the industrial development period, rather than the ancient genotype, in the UK. We have rewritten this part of the manuscript.

Arjen E Van't Hof, et al., The industrial melanism mutation in British peppered moths is a transposable element. Nature. 2016 Jun 2;534(7605):102-5. doi: 10.1038/nature17951.

Wu et al: If relying solely on random mutations in the genome, which have a time unit of millions of years, to explain the evolution of the phenotype is not enough.

What you imply here is problematic for several reasons.

First, as you point out later, some large-effect mutations (e.g. transpositions) can happen quickly.

Second, it's unclear what "the time units of million of years" means here... mutations occur, segregate in populations, and are selected. The speed of this process depends on the context and genetic architectures.

Third, I think I understand what you mean with "to explain the evolution of the phenotype is not enough", but this would probably need a reformulation and I don't think it's relevant to bring it here. After all, you used loss-of-function mutants to explain the evolution of artificially selected mutants. The evolutionary insights from these mutants are limited. Random mutations at the mamo locus are perfectly sufficient here to explain the bd and bdf phenotypes and larval traits.

Response: Thank you very much for your careful work. Charles Darwin himself, who argued that “natural selection can act only by taking advantage of slight successive variations; she can never take a leap, but must advance by the shortest and slowest steps” (Darwin, C. R. 1859). This ‘micromutational’ view of adaptation proved extraordinarily influential. However, the accumulation of micromutations is a lengthy process, which requires a very long time to evolve a significant phenotype. This may be only a proportion of the cases. Interestingly, recent molecular biology studies have shown that the evolution of some morphological traits involves a modest number of genetic changes (H Allen Orr. 2005).

One example is the genetic basis analysis of armor-plate reduction and pelvic reduction of the three-spined stickleback (Gasterosteus aculeatus) in postglacial lakes. Although the marine form of this species has thick armor, the lake population (which was recently derived from the marine form) does not. The repeated independent evolution of lake morphology has resulted in reduced armor plate and pelvic structures, and there is no doubt that these morphological changes are adaptive. Research has shown that pelvic loss in different natural populations of three-spined stickleback fish occurs by regulatory mutations deleting a tissue-specific enhancer (Pel) of the pituitary homeobox transcription factor 1 (Pitx1) gene. The researchers genotyped 13 pelvic-reduced populations of three-spined stickleback from disparate geographic locations. Nine of the 13 pelvic-reduced stickleback populations had sequence deletions of varying lengths, all of which were located at the Pel enhancer. Relying solely on random mutations in the genome cannot lead to such similar mutation forms among different populations. The author suggested that the Pitx1 locus of the stickleback genome may be prone to double-stranded DNA breaks that are subsequently repaired by NHEJ (Yingguang Frank Chan et al., 2010).

The bd and bdf mutants used in the study are formed spontaneously. Natural mutation is one of the driving forces of evolution. Nevertheless, we have rewritten the content of this section.

Darwin, C. R. The Origin of Species (J. Murray, London, 1859).

H Allen Orr. The genetic theory of adaptation: a brief history. Nat Rev Genet. 2005 Feb;6(2):119-27. doi: 10.1038/nrg1523.

Yingguang Frank Chan et al., Adaptive evolution of pelvic reduction in sticklebacks by recurrent deletion of a Pitx1 enhancer. Science. 2010 Jan 15;327(5963):302-5. doi: 10.1126/science.1182213. Epub 2009 Dec 10.

Wu et al: Interestingly, the larva of peppered moths has multiple visual factors encoded by visual genes, which are conserved in multiple Lepidoptera, in the skin. Even when its compound eyes are covered, it can rely on the skin to feel the color of the environment to change its body color and adapt to the environment(48). Therefore, caterpillars/insects can distinguish the light wave frequency of the background. We suppose that perceptual signals can stimulate the GRN, the GRN guides the expression of some transcription factors and epigenetic factors, and the interaction of epigenetic factors and transcription factors can open or close the chromatin of corresponding downstream genes, which can guide downstream target gene expression.

This is extremely confusing because you are bringing in a plastic trait here. It's possible there is a connection between the sensory stimulus and the regulation of mamo in peppered moths, but this is a mere hypothesis. Here, by mentioning a plastic trait, this paragraph sounds as if it was making a statement about directed evolution, especially after implying in the previous sentence that (paraphrasing) "random mutations are not enough". To be perfectly honest, the current writing could be misinterpreted and co-opted by defenders of the Intelligent Design doctrine. I believe and trust this is not your intention.

Response: Thank you very much for your careful work. The plasticity of the body color of peppered moth larvae is very interesting, but we mainly wanted to emphasize that their skin shows the products of visual genes that can sense the color of the environment by perceiving light. Moreover, these genes are conserved in many insects. Human skin can also perceive light by opsins, suggesting that they might initiate light–induced signaling pathways (Haltaufderhyde K et al., 2015). This indicates that the perception of environmental light by the skin of animals and the induction of feedback through signaling pathways is a common phenomenon. For clarity, we have rewritten this section of the manuscript.

Haltaufderhyde K, Ozdeslik RN, Wicks NL, Najera JA, Oancea E. Opsin expression in human epidermal skin. Photochem Photobiol. 2015;91(1):117-123.

Wu et al: In addition, during the opening of chromatin, the probability of mutation of exposed genomic DNA sequences will increase (49).

Here again, this is veering towards a strongly Lamarckian view with the environment guiding specific mutation. I simply cannot see how this would apply to mamo, nothing in the current article indicates this could be the case here. Among many issues with this, it's unclear how chromatin opening in the larval integument may result in heritable mutations in the germline.

Response: Thank you very much for your careful work. Previous studies have shown that there is a mutation bias in the genome; compared with the intergenic region, the mutation frequency is reduced by half inside gene bodies and by two-thirds in essential genes. In addition, they compared the mutation rates of genes with different functions. The mutation rate in the coding region of essential genes (such as translation) is the lowest, and the mutation rates in the coding region of specialized functional genes (such as environmental response) are the highest. These patterns are mainly affected by the traits of the epigenome (J Grey Monroe et al., 2022).

In eukaryotes, chromatin is organized as repeating units of nucleosomes, each consisting of a histone octamer and the surrounding DNA. This structure can protect DNA. When one gene is activated, the chromatin region of this gene is locally opened, becoming an accessible region. Research has found that DNA accessibility can lead to a higher mutation rate in the region (Radhakrishnan Sabarinathan et al., 2016; Schuster-Böckler B et al., 2012; Lawrence MS et al., 2013; Polak P et al., 2015). In addition, the BTB-ZF protein mamo belongs to this family and can recruit histone modification factors such as DNA methyltransferase 1 (DMNT1), cullin3 (CUL3), histone deacetylase 1 (HDAC1), and histone acetyltransferase 1 (HAT1) to perform chromatin remodeling at specific genomic sites. Although mutations can be predicted by the characteristics of apparent chromatin, the forms of mutations are diverse and random. Therefore, this does not violate randomness. For clarity, we have rewritten this section of the manuscript.

J Grey Monroe, Mutation bias reflects natural selection in Arabidopsis thaliana. Nature. 2022 Feb;602(7895):101-105.

Sabarinathan R, Mularoni L, Deu-Pons J, Gonzalez-Perez A, López-Bigas N. Nucleotide excision repair is impaired by binding of transcription factors to DNA. Nature. 2016;532(7598):264-267.

Schuster-Böckler B, Lehner B. Chromatin organization is a major influence on regional mutation rates in human cancer cells. Nature. 2012;488(7412):504-507.

Lawrence MS, Stojanov P, Polak P, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013;499(7457):214-218.

Polak P, Karlić R, Koren A, et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature. 2015;518(7539):360-364.

Mathew R, Seiler MP, Scanlon ST, et al. BTB-ZF factors recruit the E3 ligase cullin 3 to regulate lymphoid effector programs. Nature. 2012;491(7425):618-621.

Wu et al: Transposon insertion occurs in a timely manner upstream of the cortex gene in melanic pepper moths (47), which may be caused by the similar binding of transcription factors and opening of chromatin.

No, we do not think that the peppered moth mutation is Lamarckian at all, as seems to be inferred here (notice that by mentioning the peppered moth twice, you are juxtaposing a larval plastic trait and then a purely genetic wing trait, making it even more confusing). Also, the "in a timely manner" is superfluous, because all the data are consistent with a chance mutation being eventually picked up by strong directional mutation. The mutation and selection did NOT occur at the same time.

Response: Thank you very much for your careful work. The insertion of one transposon into the first intron of the cortex gene of industrial melanism in peppered moth occurred in approximately 1819, which is similar to the time of industrial development in the UK (Arjen E Van't Hof, et al., 2016). In multiple species of Heliconius, the cortex gene is the shared genetic basis for the regulation of wing coloring patterns. Interestingly, the SNP of the cortex, associated with the wing color pattern, does not overlap among different Heliconius species, such as H. erato dephoon and H. erato favorinus, which suggests that the mutations of this cortex gene have different origins (Nadeau NJ et al., 2016). In addition, in Junonia coenia (van der Burg KRL et al., 2020) and Bombyx mori (Ito K et al., 2016), the cortex gene is a candidate for regulating changes in wing coloring patterns. Overall, the cortex gene is an evolutionary hotspot for the variation of multiple butterfly and moth wing coloring patterns. In addition, it was observed that the variations in the cortex are diverse in these species, including SNPs, indels, transposon insertions, inversions, etc. This indicates that although there are evolutionary hotspots in the insect genome, this variation is random. Therefore, this is not completely detached from randomness.

Arjen E Van't Hof, et al., The industrial melanism mutation in British peppered moths is a transposable element. Nature. 2016 Jun 2;534(7605):102-5. doi: 10.1038/nature17951.

Nadeau NJ, Pardo-Diaz C, Whibley A, et al. The gene cortex controls mimicry and crypsis in butterflies and moths. Nature. 2016;534(7605):106-110.

van der Burg KRL, Lewis JJ, Brack BJ, Fandino RA, Mazo-Vargas A, Reed RD. Genomic architecture of a genetically assimilated seasonal color pattern. Science. 2020;370(6517):721-725.

Ito K, Katsuma S, Kuwazaki S, et al. Mapping and recombination analysis of two moth colour mutations, Black moth and Wild wing spot, in the silkworm Bombyx mori. Heredity (Edinb). 2016;116(1):52-59.

Wu et al: Therefore, we proposed that the genetic basis of color pattern evolution may mainly be system-guided programmed events that induce mutations in specific genomic regions of key genes rather than just random mutations of the genome.

While the mutational target of pigment evolution may involve a handful of developmental regulator genes, you do not have the data to infer such a strong conclusion at the moment.

The current formulation is also quite strong and teleological: "system-guided programmed events" imply intentionality or agency, an idea generally assigned to the anti-scientific Intelligent Design movement. There are a few examples of guided mutations, such as the adaptation phase of gRNA motifs in bacterial CRISPR assays, where I could see the term ""system-guided programmed events" to be applicable. But it is irrelevant here.

Response: Thank you very much for your careful work. The CRISPR-CAS9 system is indeed very well known. In addition, recent studies have found the existence of a Cas9-like gene editing system in eukaryotes, such as Fanzor. Fanzor (Fz) was reported in 2013 as a eukaryotic TnpB-IS200/IS605 protein encoded by the transposon origin, and it was initially thought that the Fz protein (and prokaryotic TnpBs) might regulate transposon activity through methyltransferase activity (Saito M et al., 2023). Fz has recently been found to be a eukaryotic CRISPR‒Cas system. Although this system is found in fungi and mollusks, it raises hopes for scholars to find similar systems in other higher animals. However, before these gene-editing systems became popular, zinc finger nucleases (ZFNs) were already being studied as a gene-editing system in many species. The mechanism by which ZFN recognizes DNA depends on its zinc finger motif (Urnov FD et al., 2005). This is consistent with the mechanism by which transcription factors recognize DNA-binding sites.

Furthermore, a very important evolutionary event in sexual reproduction is chromosome recombination during meiosis, which helps to produce more abundant alleles. Current research has found that this recombination event is not random. In mice and humans, the PRDM9 transcription factors are able to plan the sites of double-stranded breaks (DSBs) in meiosis recombination. PRDM9 is a histone methyltransferase consisting of three main regions: an amino-terminal region resembling the family of synovial sarcoma X (SSX) breakpoint proteins, which contains a Krüppel-associated box (KRAB) domain and an SSX repression domain (SSXRD); a PR/SET domain (a subclass of SET domains), surrounded by a pre-SET zinc knuckle and a post-SET zinc finger; and a long carboxy-terminal C2H2 zinc finger array. In most mammalian species, during early meiotic prophase, PRDM9 can determine recombination hotspots by H3K4 and H3K36 trimethylation (H3K4me3 and H3K36me3) of nucleosomes near its DNA-binding site. Subsequently, meiotic DNA DSBs are formed at hotspots through the combined action of SPO11 and TOPOVIBL. In addition, some proteins (such as RAD51) are involved in repairing the break point. In summary, programmed events of induced and repaired DSBs are widely present in organisms (Bhattacharyya T et al., 2019).

These studies indicate that on the basis of randomness, the genome also exhibits programmability.

Saito M, Xu P, Faure G, et al. Fanzor is a eukaryotic programmable RNA-guided endonuclease. Nature. 2023;620(7974):660-668.

Urnov FD, Miller JC, Lee YL, et al. Highly efficient endogenous human gene correction using designed zinc-finger nucleases. Nature. 2005;435(7042):646-651.

Bhattacharyya T, Walker M, Powers NR, et al. Prdm9 and Meiotic Cohesin Proteins Cooperatively Promote DNA Double-Strand Break Formation in Mammalian Spermatocytes [published correction appears in Curr Biol. 2021 Mar 22;31(6):1351]. Curr Biol. 2019;29(6):1002-1018.e7.

Wu et al: Based on this assumption, animals can undergo phenotypic changes more quickly and more accurately to cope with environmental changes. Thus, seemingly complex phenotypes such as cryptic coloring and mimicry that are highly similar to the background may have formed in a short period. However, the binding sites of some transcription factors widely distributed in the genome may be reserved regulatory interfaces to cope with potential environmental changes. In summary, the regulation of genes is smarter than imagined, and they resemble a more advanced self-regulation program.

Here again, I can agree with the idea that certain genetic architectures can evolve quickly, but I cannot support the concept that the genetic changes are guided or accelerated by the environment. And again, none of this is relevant to the current findings about Bm-mamo.

Response: Thank you very much for your careful work. Darwin's theory of natural selection has epoch-making significance. I deeply believe in the theory that species strive to evolve through natural selection. However, with the development of molecular genetics, Darwinism’s theory of undirected random mutations and slow accumulation of micromutations resulting in phenotype evolution has been increasingly challenged.

The prerequisite for undirected random mutations and micromutations is excessive reproduction to generate a sufficiently large population. A sufficiently large population can contain sufficient genotypes to face various survival challenges. However, it is difficult to explain how some small groups and species with relatively low fertility rates have survived thus far. More importantly, the theory cannot explain the currently observed genomic mutation bias. In scientific research, every theory is constantly being modified to adapt to current discoveries. The most famous example is the debate over whether light is a particle or a wave, which has lasted for hundreds of years. However, in the 20th century, both sides seemed to compromise with each other, believing that light has a wave‒particle duality.

Epigenetics has developed rapidly since 1987. Epigenetics has been widely accepted, defined as stable inheritance caused by chromosomal conformational changes without altering the DNA sequence, which differs from genetic research on variations in gene sequences. However, an increasing number of studies have found that histone modifications can affect gene sequence variation. In addition, both histones and epigenetic factors are essentially encoded by genes in the genome. Therefore, genetics and epigenetics should be interactive rather than parallel. However, some transcription factors play an important role in epigenetic modifications. Meiotic recombination is a key process that ensures the correct separation of homologous chromosomes through DNA double-stranded break repair mechanisms. The transcription factor PRDM9 can determine recombination hotspots by H3K4 and H3K36 trimethylation (H3K4me3 and H3K36me3) of nucleosomes near its DNA-binding site (Bhattacharyya T et al., 2019). Interestingly, mamo has been identified as an important candidate factor for meiosis hotspot setting in Drosophila (Winbush A et al., 2021).

Bhattacharyya T, Walker M, Powers NR, et al. Prdm9 and Meiotic Cohesin Proteins Cooperatively Promote DNA Double-Strand Break Formation in Mammalian Spermatocytes [published correction appears in Curr Biol. 2021 Mar 22;31(6):1351]. Curr Biol. 2019;29(6):1002-1018.e7.

Winbush A, Singh ND. Genomics of Recombination Rate Variation in Temperature-Evolved Drosophila melanogaster Populations. Genome Biol Evol. 2021;13(1): evaa252.

Reviewer #2 (Recommendations For The Authors):

Major comments

• A recent paper has been published on the same gene in Bombyx mori (https://www.sciencedirect.com/science/article/abs/pii/S0965174823000760) in August 2023. The authors must discuss and refer to this published paper through the present manuscript.

Response: Thank you very much for your careful work. First, we believe that competitive research is sometimes coincidental and sometimes intentional. Our research began in 2009, when we began to configure the recombinant population. In 2016, we published an article on comparative transcriptomics (Wu et al. 2016). The article mentioned above has a strong interest in our research and is based on our transcriptome analysis for further research, with the aim of making a preemptive publication.

To discourage such behavior, we cannot cite it and do not want to discuss it in our paper.

Songyuan Wu et al. Comparative analysis of the integument transcriptomes of the black dilute mutant and the wild-type silkworm Bombyx mori. Sci Rep. 2016 May 19:6:26114. doi: 10.1038/srep26114.

• line 52-54. The numerous biological functions of insect coloration have been thoroughly investigated. It is reasonable to expect more references for each function.

Response: Thank you very much for your careful work. We have made the appropriate modifications.

Sword GA, Simpson SJ, El Hadi OT, Wilps H. Density-dependent aposematism in the desert locust. Proc Biol Sci. 2000;267(1438):63-68. … Behavior.

Barnes AI, Siva-Jothy MT. Density-dependent prophylaxis in the mealworm beetle Tenebrio molitor L. (Coleoptera: Tenebrionidae): cuticular melanization is an indicator of investment in immunity. Proc Biol Sci. 2000;267(1439):177-182. … Immunity.

N. F. Hadley, A. Savill, T. D. Schultz, Coloration and Its Thermal Consequences in the New-Zealand Tiger Beetle Neocicindela-Perhispida. J Therm Biol. 1992;17, 55-61…. Thermoregulation.

Y. G. Hu, Y. H. Shen, Z. Zhang, G. Q. Shi, Melanin and urate act to prevent ultraviolet damage in the integument of the silkworm, Bombyx mori. Arch Insect Biochem. 2013; 83, 41-55…. UV protection.

M. Stevens, G. D. Ruxton, Linking the evolution and form of warning coloration in nature. P Roy Soc B-Biol Sci. 2012; 279, 417-426…. Aposematism.

K. K. Dasmahapatra et al., Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature.2012; 487, 94-98…. Mimicry.

Gaitonde N, Joshi J, Kunte K. Evolution of ontogenic change in color defenses of swallowtail butterflies. Ecol Evol. 2018;8(19):9751-9763. Published 2018 Sep 3. …Crypsis.

B. S. Tullberg, S. Merilaita, C. Wiklund, Aposematism and crypsis combined as a result of distance dependence: functional versatility of the colour pattern in the swallowtail butterfly larva. P Roy Soc B-Biol Sci.2005; 272, 1315-1321…. Aposematism and crypsis combined.

• line 59-60. This general statement needs to be rephrased. I suggest remaining simple by indicating that insect coloration can be pigmentary, structural, or bioluminescent. About the structural coloration and associated nanostructures, the authors could cite recent reviews, such as: Seago et al., Interface 2009 + Lloyd and Nadeau, Current Opinion in Genetics & Development 2021 + "Light as matter: natural structural colour in art" by Finet C. 2023. I suggest doing the same for recent reviews that cover pigmentary and bioluminescent coloration in insects. The very recent paper by Nishida et al. in Cell Reports 2023 on butterfly wing color made of pigmented liquid is also unique and worth to consider.

Response: Thank you very much for your careful work. We have made the appropriate modifications.

Insect coloration can be pigmentary, structural, or bioluminescent. Pigments are mainly synthesized by the insects themselves and form solid particles that are deposited in the cuticle of the body surface and the scales of the wings (10, 11). Interestingly, recent studies have found that bile pigments and carotenoid pigments synthesized through biological synthesis are incorporated into body fluids and passed through the wing membranes of two butterflies (Siproeta stelenes and Philaethria diatonica) via hemolymph circulation, providing color in the form of liquid pigments (12). The pigments form colors by selective absorption and/or scattering of light depending on their physical properties (13). However, structural color refers to colors, such as metallic colors and iridescence, generated by optical interference and grating diffraction of the microstructure/nanostructure of the body surface or appendages (such as scales) (14, 15). Pigment color and structural color are widely distributed in insects and can only be observed by the naked eye in illuminated environments. However, some insects, such as fireflies, exhibit colors (green to orange) in the dark due to bioluminescence (16). Bioluminescence occurs when luciferase catalyzes the oxidation of small molecules of luciferin (17). In conclusion, the color patterns of insects have evolved to be highly sophisticated and are closely related to their living environments. For example, cryptic color can deceive animals via high similarity to the surrounding environment. However, the molecular mechanism by which insects form precise color patterns to match their living environment is still unknown.

• RNAi approach. I have no doubt that obtaining phenocopies by electroporation might be difficult. However, I find the final sampling a bit limited to draw conclusions from the RT-PCR (n=5 and n=3 for phenocopies and controls). Three control individuals is a very low number. Moreover, it would nice to see the variability on the plot, using for example violin plots.

Response: Thank you very much for your careful work. In the RNAi experiment, we injected more than 20 individuals in the experimental group and control group. We have added the RNAi data in Figure 4.

Author response table 1.

• Figure 6. Higher magnification images of Dazao and Bm-mamo knockout are needed, as shown in Figure 5 on RNAi.

Response: Thank you very much for your careful work. We have added enlarged images.

Author response image 3.

• Phylogenetic analysis/Figure S6. I am not sure to what extent the sampling is biased or not, but if not, it is noteworthy that mamo does not show duplicated copies (negative selection?). It might be interesting to discuss this point in the manuscript.

Response: Thank you very much for your careful work. mamo belongs to the BTB/POZ zinc finger family. The members of this family exhibit significant expansion in vertebrates. For example, there are 3 members in C. elegans, 13 in D. melanogaster, 16 in Bombyx mori, 58 in M. musculus and 63 in H. sapiens (Wu et al, 2019). These members contain conserved BTB/POZ domains but vary in number and amino acid residue compositions of the zinc finger motifs. Due to the zinc finger motifs that bind to different DNA recognition sequences, there may be differences in their downstream target genes. Therefore, when searching for orthologous genes from different species, we required high conservation of their zinc finger motif sequences. Due to these strict conditions, only one orthologous gene was found in these species.

• Differentially-expressed genes and CP candidate genes (line 189-191). The manuscript would gain in clarity if the authors explain more in details their procedure. For instance, they moved from a list of 191 genes to CP genes only. Can they say a little bit more about the non-CP genes that are differentially expressed? Maybe quantify the number of CPs among the total number of differentially-expressed genes to show that CPs are the main class?

Response: Thank you very much for your careful work. The nr (Nonredundant Protein Sequence Database) annotations for 191 differentially expressed genes in Supplemental Table S3 were added. Among them, there were 19 cuticular proteins, 17 antibacterial peptide genes, 6 transporter genes, 5 transcription factor genes, 5 cytochrome genes, 53 enzyme-encoding genes and others. Because CP genes were significantly enriched in differentially expressed genes (DEGs), previous studies have found that BmorCPH24 can affect pigmentation. Therefore, we first conducted an investigation into CP genes.

• Interaction between Bm-mamo. It is not clear why the authors chose to investigate the physical interaction of Bm-mamo protein with the putative binding site of yellow, and not with the sites upstream of tan and DDC. Do the authors test one interaction and assume the conclusion stands for the y, tan and DDC?

Response: Thank you very much for your careful work. In D. melanogaster, the yellow gene is the most studied pigment gene. The upstream and intron sequences of the yellow gene have been identified as containing multiple cis-regulatory elements. Due to the important pigmentation role of the yellow gene and its variable cis-regulatory sequence among different species, it has been considered a research model for cis-regulatory elements (Laurent Arnoult et al. 2013, Gizem Kalay et al. 2019, Yaqun Xin et al. 2020, Yann Le Poul et al. 2020). We use yellow as an example to illustrate the regulation of the mamo gene. We added this description to the discussion.

Laurent Arnoult et al. Emergence and diversification of fly pigmentation through evolution of a gene regulatory module. Science. 2013 Mar 22;339(6126):1423-6. doi: 10.1126/science.1233749.

Gizem Kalay et al. Redundant and Cryptic Enhancer Activities of the Drosophila yellow Gene. Genetics. 2019 May;212(1):343-360. doi: 10.1534/genetics.119.301985. Epub 2019 Mar 6.

Yaqun Xin et al. Enhancer evolutionary co-option through shared chromatin accessibility input. Proc Natl Acad Sci U S A. 2020 Aug 25;117(34):20636-20644. doi: 10.1073/pnas.2004003117. Epub 2020 Aug 10.

Yann Le Poul et al. Regulatory encoding of quantitative variation in spatial activity of a Drosophila enhancer. Sci Adv. 2020 Dec 2;6(49):eabe2955. doi: 10.1126/sciadv.abe2955. Print 2020 Dec.

• Please note that some controls are missing for the EMSA experiments. For instance, the putative binding-sites should be mutated and it should be shown that the interaction is lost.

Response: Thank you very much for your careful work. In this study, we found that the DNA recognition sequence of mamo is highly conserved across multiple species. In D. melanogaster, studies have found that mamo can directly bind to the intron of the vasa gene to activate its expression. The DNA recognition sequence they use is TGCGT (Shoichi Nakamura et al. 2019). We chose a longer sequence, GTGCGTGGC, to detect the binding of mamo. This binding mechanism is consistent across species.

• Figure 7 and supplementary data. How did the name of CPs attributed? According to automatic genome annotation of Bm genes and proteins? Based on Drosophila genome and associated gene names? Did the authors perform phylogenetic analyses to name the different CP genes?

Response: Thank you very much for your careful work. The naming of CPs is based on their conserved motif and their arrangement order on the chromosome. In previous reports, sequence identification and phylogenetic analysis of CPs have been carried out in silkworms (Zhengwen Yan et al. 2022, Ryo Futahashi et al. 2008). The members of the same family have sequence similarity between different species, and their functions may be similar. We have completed the names of these genes in the text, for example, changing CPR2 to BmorCPR2.

Zhengwen Yan et al. A Blueprint of Microstructures and Stage-Specific Transcriptome Dynamics of Cuticle Formation in Bombyx mori. Int J Mol Sci. 2022 May 5;23(9):5155.

Ningjia He et al. Proteomic analysis of cast cuticles from Anopheles gambiae by tandem mass spectrometry. Insect Biochem Mol Biol. 2007 Feb;37(2):135-46.

Maria V Karouzou et al. Drosophila cuticular proteins with the R&R Consensus: annotation and classification with a new tool for discriminating RR-1 and RR-2 sequences. Insect Biochem Mol Biol. 2007 Aug;37(8):754-60.

Ryo Futahashi et al. Genome-wide identification of cuticular protein genes in the silkworm, Bombyx mori. Insect Biochem Mol Biol. 2008 Dec;38(12):1138-46.

• Discussion. I think the discussion would gain in being shorter and refocused on the understudied role of CPs. Another non-canonical aspect of the discussion is the reference to additional experiments (e.g., parthogenesis line 290-302, figure S14). This is not the place to introduce more results, and it breaks the flow of the discussion. I encourage the authors to reshuffle the discussion: 1) summary of their findings on mamo and CPs, 2) link between pigmentation mutant phenotypes, pigmentation pattern and CPs, 3) general discussion about the (evo-)devo importance of CPs and link between pigment deposition and coloration. Three important papers should be mentioned here:

1) Matsuoka Y and A Monteiro (2018) Melanin pathway genes regulate color and morphology of butterfly wing scales. Cell Reports 24: 56-65... Yellow has a pleiotropic role in cuticle deposition and pigmentation.

2) https://arxiv.org/abs/2305.16628... Link between nanoscale cuticle density and pigmentation

3) https://www.cell.com/cell-reports/pdf/S2211-1247(23)00831-8.pdf... Variation in pigmentation and implication of endosomal maturation (gene red).

Response: Thank you very much for your careful work. We have rewritten the discussion section.

1) We have summarized our findings.

Bm-mamo may affect the synthesis of melanin in epidermis cells by regulating yellow, DDC, and tan; regulate the maturation of melanin granules in epidermis cells through BmMFS; and affect the deposition of melanin granules in the cuticle by regulating CP genes, thereby comprehensively regulating the color pattern in caterpillars.

2) We describe the relationship among the pigmentation mutation phenotype, pigmentation pattern, and CP.

Previous studies have shown that the lack of expression of BmorCPH24, which encodes important components of the endocuticle, can lead to dramatic changes in body shape and a significant reduction in the pigmentation of caterpillars (53). We crossed Bo (BmorCPH24 null mutation) and bd to obtain F1(Bo/+Bo, bd/+), then self-crossed F1 and observed the phenotype of F2. The lunar spots and star spots decreased, and light-colored stripes appeared on the body segments, but the other areas still had significant melanin pigmentation in double mutation (Bo, bd) individuals (Fig. S13). However, in previous studies, introduction of Bo into L (ectopic expression of wnt1 results in lunar stripes generated on each body segment) (24) and U (overexpression of SoxD results in excessive melanin pigmentation of the epidermis) (58) strains by genetic crosses can remarkably reduce the pigmentation of L and U (53). Interestingly, there was a more significant decrease in pigmentation in the double mutants (Bo, L) and (Bo, U) than in (Bo, bd). This suggests that Bm-mamo has a stronger ability than wnt1 and SoxD to regulate pigmentation. On the one hand, mamo may be a stronger regulator of the melanin metabolic pathway, and on the other hand, mamo may regulate other CP genes to reduce the impact of BmorCPH24 deficiency.

3) We discussed the importance of (evo-) devo in CPs and the relationship between pigment deposition and coloring.

CP genes usually account for over 1% of the total genes in an insect genome and can be categorized into several families, including CPR, CPG, CPH, CPAP1, CPAP3, CPT, CPF and CPFL (68). The CPR family is the largest group of CPs, containing a chitin-binding domain called the Rebers and Riddiford motif (R&R) (69). The variation in the R&R consensus sequence allows subdivision into three subfamilies (RR-1, RR-2, and RR-3) (70). Among the 28 CPs, 11 RR-1 genes, 6 RR-2 genes, 4 hypothetical cuticular protein (CPH) genes, 3 glycine-rich cuticular protein (CPG) genes, 3 cuticular protein Tweedle motif (CPT) genes, and 1 CPFL (like the CPFs in a conserved C-terminal region) gene were identified. The RR-1 consensus among species is usually more variable than RR-2, which suggests that RR-1 may have a species-specific function. RR-2 often clustered into several branches, which may be due to gene duplication events in co-orthologous groups and may result in conserved functions between species (71). The classification of CPH is due to their lack of known motifs. In the epidermis of Lepidoptera, the CPH genes often have high expression levels. For example, BmorCPH24 had a highest expression level, in silkworm larvae epidermis (72). The CPG protein is rich in glycine. The CPH and CPG genes are less commonly found in insects outside the order Lepidoptera (73). This suggests that they may provide species specific functions for the Lepidoptera. CPT contains a Tweedle motif, and the TweedleD1 mutation has a dramatic effect on body shape in D. melanogaster (74). The CPFL members are relatively conserved in species and may be involved in the synthesis of larval cuticles (75). CPT and CPFL may have relatively conserved functions among insects. The CP genes are a group of rapidly evolving genes, and their copy numbers may undergo significant changes in different species. In addition, RNAi experiments on 135 CP genes in brown planthopper (Nilaparvata lugens) showed that deficiency of 32 CP genes leads to significant defective phenotypes, such as lethal, developmental retardation, etc. It is suggested that the 32 CP genes are indispensable, and other CP genes may have redundant and complementary functions (76). In previous studies, it was found that the construction of the larval cuticle of silkworms requires the precise expression of over two hundred CP genes (22). The production, interaction, and deposition of CPs and pigments are complex and precise processes, and our research shows that Bm-mamo plays an important regulatory role in this process in silkworm caterpillars. For further understanding of the role of CPs, future work should aim to identify the function of important cuticular protein genes and the deposition mechanism in the cuticle.

Minor comments - Title. At this stage, there is no evidence that Bm-mamo regulates caterpillar pigmentation outside of Bombyx mori. I suggest to precise 'silkworm caterpillars' in the title.

Response: Thank you very much for your careful work. We have modified the title.

• Abstract, line 29. Because the knowledge on pigmentation pathway(s) is advanced, I would suggest writing 'color pattern is not fully understood' instead of 'color pattern is not clear'.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 29. I suggest 'the transcription factor' rather than 'a transcription factor'.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 30. If you want to mention the protein, the name 'Bm-mamo' should not be italicized.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 30. 'in the silkworm'.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 31. 'mamo' should not be italicized.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 31. 'in Drosophila' rather 'of Drosophila'.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 32. Bring detail if the gamete function is conserved in insects? In all animals?

Response: Thank you very much for your careful work. The sentence was changed to “This gene has a conserved function in gamete production in Drosophila and silkworms and evolved a pleiotropic function in the regulation of color patterns in caterpillars.”

• Introduction, line 51. I am not sure what the authors mean by 'under natural light'. Please rephrase.

Response: Thank you very much for your careful work. We have deleted “under natural light”.

• line 43. I find that the sentence 'In some studies, it has been proven that epidermal proteins can affect the body shape and appendage development of insects' is not necessary here. Furthermore, this sentence breaks the flow of the teaser.

Response: Thank you very much for your careful work. We have deleted this sentence.

• line 51-52. 'Greatly benefit them' should be rephrased in a more neutral way. For example, 'colours pattern have been shown to be involved in...'.

Response: Thank you very much for your careful work. We have modified to “and the color patterns have been shown to be involved in…”

• line 62. CPs are secreted by the epidermis, but I would say that CPs play their structural role in the cuticle, not directly in the epidermis. I suggest rephrasing this sentence and adding references.

Response: Thank you very much for your careful work. We have modified “epidermis” to “cuticle”.

• line 67. Please indicate that pathways have been identified/reported in Lepidoptera (11). Otherwise, the reader does not understand if you refer to previous biochemical in Drosophila for example.

Response: Thank you very much for your careful work. We have modified this sentence. “Moreover, the biochemical metabolic pathways of pigments used for color patterning in Lepidoptera…have been reported.”

• line 69. Missing examples of pleiotropic factors and associated references. For example, I suggest adding: engrailed (Dufour, Koshikawa and Finet, PNAS 2020) + antennapedia (Prakash et al., Cell Reports 2022) + optix (Reed et al., Science 2011), etc. Need to add references for clawless, abdominal-A.

Response: Thank you very much for your careful work. We have made modifications.

• line 76. The simpler term moth might be enough (instead of Lepidoptera).

Response: Thank you very much for your careful work. We have modified this to “insect”.

• line 96. I would simplify the text by writing "Then, quantitative RT-PCR was performed..."

Response: Thank you very much for your careful work. We have modified this sentence.

• line 112. 'Predict' instead of 'estimate'?

Response: Thank you very much for your careful work. We have modified this sentence.

• line 113. I would rather indicate the full name first, then indicate mamo between brackets.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 144. The Perl script needs to be made accessible on public repository.

Response: Thank you very much for your careful work.

• line 147-150. Too many technical details here. The details are already indicated in the material and methods section. Furthermore, the details break the flow of the paragraph.

Response: Thank you very much for your careful work. We have modified this section.

• line 152. Needs to make the link with the observed phenotypes in Figure 1. Just needs to state that RNAi phenocopies mimic the mutant alleles.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 153-157. Too many technical details here. The details are already indicated in the material and methods section. Furthermore, the details break the flow of the paragraph.

Response: Thank you very much for your careful work. We have simplified this paragraph.

• line 170. Please rephrase 'conserved in 30 species' because it might be understood as conserved in 30 species only, and not in other species.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 182. Maybe explain the rationale behind restricting the analysis to +/- 2kb. Can you cite a paper that shows that most of binding sites are within 2kb from the start codon?

Response: Thank you very much for your careful work. We have modified this sentence.

• line 182. '14,623 predicted genes'.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 183. '10,622 genes'

Response: Thank you very much for your careful work. We have modified this sentence.

• line 183. Redundancy. Please remove 'silkworm' or 'B. mori'.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 187. '10,072 genes'

Response: Thank you very much for your careful work. We have modified this sentence.

• line 188. '9,853 genes'

Response: Thank you very much for your careful work. We have modified this sentence.

• line 200. "Therefore, the differential...in caterpillars" is a strong statement.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 204. Remove "The" in front of eight key genes. Also, needs a reference... maybe a recent review on the biochemical pathway of melanin in insects.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 220. This sentence is too general and vague. Please explicit what you mean by "in terms of evolution". Number of insect species? Diversity of niche occupancy? Morphological, physiological diversity?

Response: Thank you very much for your careful work. We have modified this sentence.

• line 285. The verb "believe" should be replaced by a more neutral one.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 354-355. This sentence needs to be rephrased in a more objective way.

Response: Thank you very much for your careful work. We have rewritten this sentence.

• line 378. Missing reference for MUSCLE.

Response: Thank you very much for your careful work. We have modified this sentence.

• line 379. Pearson model?

Response: Thank you very much for your careful work. We have modified this sentence.

• line 408. "The CRISPRdirect online software was used...".

Response: Thank you very much for your careful work. We have modified this sentence.

• Figure 1. In the title, I suggest indicating Dazao, bd, bdf as it appears in the figure. Needs to precise 'silkworm larval development'.

Response: Thank you very much for your careful work. We have modified this figure title.

• Figure 3. In the title, is the word 'pattern' really necessary? In the legend, please indicate the meaning of the acronyms AMSG and PSG.

Response: Thank you very much for your careful work. We have modified this figure legend.

• Figure S7A. Typo 'Znic finger 1', 'Znic finger 2', 'Znic finger 3',

Response: Thank you very much for your careful work. We have fixed these typos. .

eLife assessment

This important study identifies the gene mamo as a new regulator of pigmentation in the silkworm Bombyx mori, a function that was previously unsuspected based on extensive work on Drosophila where the mamo gene is involved in gamete production. The evidence supporting the role of Bm-nano in pigmentation is convincing, including high-resolution linkage mapping of two mutant strains, expression profiling, and reproduction of the mutant phenotypes with state-of-the-art RNAi and CRISPR knock-out assays. While the discussion about genetic changes being guided or accelerated by the environment is extremely speculative and has little relevance for the findings presented, the work will be of interest to evolutionary biologists and geneticists studying color patterns and evolution of gene networks.

Reviewer #1 (Public Review):

Summary: This papers performs fine-mapping of the silkworm mutants bd and its fertile allelic version, bdf, narrowing down the causal intervals to a small interval of a handful of genes. In this region, the gene orthologous to mamo is impaired by a large indel, and its function is later confirmed using expression profiling, RNAi, and CRISPR KO. All these experiments are convincingly showing that mamo is necessary for the suppression of melanic pigmentation in the silkworm larval integument.

The authors also use in silico and in vitro assays to probe the potential effector genes that mamo may regulate.

Strengths: The genotype-to-phenotype workflow, combining forward (mapping) and reverse genetics (RNAi and CRISPR loss-of-function assays) linking mamo to pigmentation are extremely convincing.

This revision is a much improved manuscript and I command the authors for many of their edits.

I find the last part of the discussion, starting at "It is generally believed that changes in gene expression patterns are the result of the evolution of CREs", to be confusing.<br /> In this section, I believe the authors sequentially:<br /> - emphasize the role of CRE in morphological evolution (I agree)<br /> - emphasize that TF, and in particular their own CRE, are themselves important mutational targets of evolution (I agree, but the phrasing need to insist the authors are here talking about the CRE found at the TF locus, not the CRE bound by the TF).<br /> - use the stickleback Pel enhancer as an example, which I think is a good case study, but the authors also then make an argument about DNA fragility sites, which is hard to connect with the present study.<br /> - then continue on "DNA fragility" using the peppered moth and butterfly cortex locus. There is no evidence of DNA fragility at these loci, so the connection does not work. "The cortex gene locus is frequently mutated in Lepidoptera", the authors say. But a more accurate picture would be that the cortex locus is repeatedly involved in the generation of color pattern variants. Unlike for Pel fragile enhancer, we don't know if the causal mutations at this locus are repeatedly the same, and the haplotypes that have been described could be collateral rather than causal. Overall, it is important to clarify the idea that mutation bias is a possible factor explaining "genetic hotspots of evolution" (or genetic parallelism sensu 10.1038/nrg3483), but it is also possible that many genetic hotspots are repeated mutational targets because of their "optimal pleiotropy" (e.g. hub position in GRNs, such as mamo might be), or because of particularly modular CRE region that allow fine-tuning. Thus, I find the "fragility" argument misleading here. In fact the finding that "bd" and "bdf" alleles are different in nature is against the idea of a fragility bias (unless the authors can show increased mutation rates at this locus in a wild silkmoth species?). These alleles are also artificially-selected ie. they increased in frequency by breeding rather than natural selection in the wild, so while interesting for our understand of the genotype-phenotype map, they are not necessarily representative of the mutations that may underlie evolution in the wild.<br /> - Curiously, the last paragraph ("Some research suggests that common fragile sites...") elaborate on the idea that some sites of the genome are prone to mutation. The connection with mamo and the current article are extremely thin. There is here an attempt to connect meiotic and mitotic breaks to Bm-mamo, but this is confusing : it seems to propose Bm-mamo as a recruiter of epigenetic modulators that may drive higher mutation rates elsewhere. Not only I am not convinced by this argument without actual data, but this would not explain how the mutations at the Bm-mamo itself evolved.

On a more positive note, I find it fascinating that the authors identified a TF that clearly articulates or orchestrate larval pattern development, and that when it is deleted, can generate healthy individuals. In other words, while it is a TF with many targets, it is not too pleiotropic. This idea, that the genetically causal modulators of developmental evolution are regulatory genes, has been described elsewhere (e.g. Fig 4c in 10.1038/s41576-020-0234-z, and associated refs). To me, the beautiful findings about Bm-mamo make sense in the general, existing framework that developmental processes and regulatory networks "shape" the evolutionary potential and trajectories of organisms. There is a degree of "programmability" in the genomes, because some loci are particularly prone to modulate a given type of trait. Here, Bm-mamo, as a potentially regulator of both CPs and melanin pathway genes, appear to be a potent modulator of epithelial traits. Claiming that there are inherent mutational biases behind this is unwarranted.

Author Response

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

Reviewer #1 (Public Review):

In 2019, Wilkinson and colleagues (PMID: 31142833) managed to break the veil in a 20-year open question on how to properly culture and expand Hematopoietic Stem Cells (HSCs). Although this study is revolutionizing the HSC biology field, several questions regarding the mechanisms of expansion remain open. Leveraging on this gap, Zhang et al.; embarked on a much-needed investigation regarding HSC self-renewal in this particular culturing setting.

The authors firstly tacked the known caveat that some HSC membrane markers are altered during in vitro cultures by functionally establishing EPCR (CD201) as a reliable and stable HSC marker (Figure 1), demonstrating that this compartment is also responsible for long-term hematopoietic reconstitution (Figure 3). Next in Figure 2, the authors performed single-cell omics to shed light on the potential mechanisms involved in HSC maintenance, and interestingly it was shown that several hematopoietic populations like monocytes and neutrophils are also present in this culture conditions, which has not been reported. The study goes on to functionally characterize these cultured HSCs (cHSC). The authors elegantly demonstrate using state-of-the-art barcoding strategies that these culturing conditions provoke heterogeneity in the expanding HSC pool (Figure 4). In the last experiment (Figure 5), it was demonstrated that cHSC not only retain their high EPCR expression levels but upon transplantation, these cells remain more quiescent than freshly-isolated controls.

Taken together, this study independently validates that the proposed culturing system works and provides new insights into the mechanisms whereby HSC expansion takes place.

Most of the conclusions of this study are well supported by the present manuscript, some aspects regarding experimental design and especially the data analysis should be clarified and possibly extended.

1) The first major point regards the single-cell (sc) omics performed on whole cultured cells (Figure 2):

a. The authors claim that both RNA and ATAC were performed and indeed some ATAC-seq data is shown in Figure 2B, but this collected data seems to be highly underused.

We appreciate the opportunity to clarify our analytical approach and the rationale behind it. In our study, we employed a novel deep learning framework, SAILERX, for our analysis. This framework is specifically designed to integrate multimodal data, such as RNAseq and ATACseq. The advantage of SAILERX lies in its ability to correct for technical noise inherent in sequencing processes and to align information from different modalities. Unlike methods that force a hard alignment of modalities into a shared latent space, SAILERX allows for a more refined integration. It achieves this by encouraging the local structures of the two modalities, as measured by pairwise similarities.

To put it more simply, SAILERX combines RNAseq and ATACseq data, ensuring that the unique characteristics of each data type are respected and used to enhance the overall biological picture, rather than forcing them into a uniform framework.

While it is indeed possible to analyze the ATAC-seq and RNA-seq modalities separately, and we acknowledge the potential value in such an approach, our primary objective in this study was to highlight the relatively low content of HSCs in cultures. This finding is a key point of our work, and the multiome data support this from a molecular point of view.

The Seurat object we provide was created to facilitate further analysis by interested researchers. This object simplifies the exploration of both the ATAC-seq and RNA-seq data, allowing for additional investigations that may be of interest to the scientific community. We hope this explanation clarifies our methodology and its implications.

b. It's not entirely clear to this reviewer the nature of the so-called "HSC signatures"(SF2C) and why exactly these genes were selected. There are genes such as Mpl and Angpt1 which are used for Mk-biased HSCs. Maybe relying on other HSC molecular signatures (PMID: 12228721, for example) would not only bring this study more into the current field context but would also have a more favorable analysis outcome. Moreover reclustering based on a different signature can also clarify the emergence of relevant HSC clusters.

In our study, the selection of the HSC signature in our work was based on well-referenced datasets on well-defined HSPCs, as detailed in the "v. HSC signature" section of our methods. This signature was projected also to another single-cell RNA sequencing dataset generated from ex vivo expanded HSC culture (PMID: 35971894, see Author response image 1 below), demonstrating again an association primarily to the most primitive cells (at least based on gene expression).

Author response image 1.

Projection of "our" HSC signature on scRNAseq data from independent work.

In further response to the suggestion here, we have also examined the molecular signature of HSCs referenced in PMID: 12228721 but also of another HSC signature from PMID: 26004780 in our data (Author response image 2). While these signatures do indeed enrich for cells that fall in the cluster of molecularly defined HSCs, our analysis indicates that neither of them significantly improves the identification of HSCs in our dataset compared to the signature we originally used. This finding reinforces our confidence in the appropriateness of our chosen HSC signature for this study.

Author response image 2.

Projection of alternative HSC signatures onto the SAILERX UMAP.

Regarding the specific genes Mpl and Angpt1, we respectfully oppose the view that these genes are exclusively associated with MK-biased HSCs. There is substantial evidence supporting the broader role of Mpl in regulating HSCs, regardless of any particular "lineage bias". Similarly, while Angpt1 has been less extensively studied, its role in HSCs, as examined in PMID: 25821987, suggests a more general association with HSCs rather than a specific impact on MKs. Therefore, we maintain that it is more accurate to consider these genes as HSC-associated rather than restricted to MK-biased HSCs.

Finally, addressing the comment on reclustering based on different signatures, we would like to clarify that the clustering process is independent of the projection of signatures. The clustering aims to identify cell populations based on their overall molecular profiles, and while signatures can aid in characterizing these populations, they do not influence the clustering process itself.

c. The authors took the hard road to perform experiments with the elegant HSC-specific Fgd5-reporter, and they claim in lines 170-171 that it "failed to clearly demarcate in our single-cell multimodal data". This seems like a rather vague statement and leads to the idea that the scRNA-seq experiment is not reliable. It would be interesting to show a UMAP with this gene expression regardless and also potentially some other HSC markers.

We understand the concerns raised about our statement on the performance of the Fgd5-reporter in our multimodal data analysis. Our aim was not to suggest that single-cell molecular data are unreliable. Instead, we intended to point out specific challenges associated with scRNA sequencing, notably the high rates of dropout. Regarding the specific example of Fgd5, it appears this transcript is not efficiently captured by 10x technology. Our previous 10x scRNA-seq experiments on cells from the Fgd5 reporter strain (Säwén et al., eLife 2018; Konturek-Ciesla et al., Cell Rep. 2023) support this observation. Despite cells being sorted as Fgd5-reporter positive, many showed no detectable transcripts.

We consider it pertinent to note that our study integrates ATAC-seq data in conjunction with single-cell molecular data. We believe that this integration, coupled with the analytical methods we have employed, potentially offers a way to address some of the limitations typically associated with scRNA sequencing. However, in assessing frequencies, we observe that the number of candidate HSCs identified via single-cell molecular data is substantially higher compared to those identified through flow cytometry, the latter which we demonstrate correlate functionally with genuine long-term repopulating activity.

With respect to Fgd5, as depicted in our analysis below, there appears to be an enrichment of cells in the cluster identified as HSCs, as well as a significant representation in the cycling cell cluster (Author response image 3). Regarding the projection of other individual genes, the Seurat object we have provided allows for such projections to be readily performed. This offers an opportunity for further exploration and validation of our findings by interested researchers.

Author response image 3.

Feature plot depicting Fgd5 expression in the SAILERX UMAP.

2) During the discussion and in Figure 4, the authors ponder and demonstrate that this culturing system can provoke divert HSC close expansion, having also functional consequences. This a known caveat from the original system, but in more recent publications from the original group (PMID: 36809781 and PMID: 37385251) small alterations into the protocol seem to alleviate clone selection. It's intriguing why the authors have not included these parameters at least in some experiments to show reproducibility or why these studies are not mentioned during the discussion section.

Thank you for pointing out the recent publications (PMID: 36809781 and PMID: 37385251) that discuss modifications to the HSC culturing system. We appreciate the opportunity to address why these were not included in our discussion or experiments.

Firstly, it is important to note that these papers were published after the submission of our manuscript. In fact, one of the studies (PMID: 36809781) references the preprint version of our work on Biorxiv. This timing meant that we were unable to consider these studies in our initial manuscript or incorporate any of their findings into our experimental designs.

Furthermore, as strong advocates for the peer-review system, we prioritize references that have undergone this rigorous process. Preprints, while valuable for early dissemination of research findings, do not offer the same level of scrutiny and validation as peer-reviewed publications. Our approach was to rely on the most relevant and rigorously reviewed literature available to us at the time of submission. This included, most notably, the original and ground-breaking work by Wilkinson et al., which provided a foundational basis for our research.

We acknowledge that the field of HSC research is rapidly evolving, and new findings, such as those mentioned, are continually emerging. These new studies undoubtedly contribute valuable insights into HSC culturing systems and their optimization. However, given the timing of their publication relative to our study, we were not able to include them in our analysis or discussion.

3) In this reviewer's opinion, the finding that transplanted cHSC are more quiescent than freshly isolated controls is the most remarkable aspect of this manuscript. There is a point of concern and an intriguing thought that sprouts from this experiment. It is empirical that for this experiment the same HSC dose is transplanted between both groups. This however is technically difficult since the membrane markers from both groups are different. Although after 8 weeks chimerism levels seem to be the same (SF5D) for both groups, it would strengthen the evidence if the author could demonstrate that the same number of HSCs were transplanted in both groups, likely by limiting dose experiments. Finally, it's interesting that even though EE100 cells underwent multiple replication rounds (adding to their replicative aging), these cells remained more quiescent once they were in an in vivo setting. Since the last author of this manuscript has also expertise in HSC aging, it would be interesting to explore whether these cells have "aged" during the expansion process by assessing whether they display an aged phenotype (myeloid-skewed output in serial transplantations and/or assisting their transcriptional age).

We thank the reviewer for the insightful observations regarding the quiescence of transplanted cultured HSCs. We appreciate the opportunity to clarify the experimental design and its implications, particularly in the context of HSC aging.

The primary aim of comparing cKit-enriched bone BM cells with cultured cells was to investigate if ex vivo activated HSCs exhibit a similar proliferation pattern to in vivo quiescent HSCs post-transplantation. This comparison was crucial for evaluating the similarity between in vitro cultured and "unmanipulated" HSC behavior. While we acknowledge the technical challenge of transplanting equivalent HSC doses between groups due to differing membrane markers, our study design focused on assessing stem cell activity post-culture. This was quantitatively evaluated by calculating the repopulating units (detailed in Table 1 and Fig S4G), rather than through a limiting dilution assay. There exists a plethora of literature demonstrating the correlation between these assays, although of course the limiting dilution assay is designed to provide a more exact output.

Regarding the intriguing aspect of HSC aging in the context of ex vivo expansion, our observations indicate that both the subfraction of ex vivo expanded cells (Fig 3 and Fig S3) and the entire cultured population (Fig 4B, Fig 5B, Fig S4A, and Fig S5B) maintain long-term multilineage reconstitution capacity post-transplantation. This suggests that the PVA-culture system does not lead to apparent signs of "HSC aging," despite the cells undergoing active self-renewal in vitro. This is further supported by our serial transplantation experiments, where cultured cells continued to demonstrate multilineage capacity rather than any evident myeloid-biased reconstitution 16 weeks post-second transplantation (see Author response image 4 below).

Author response image 4.

Serial transplantation behavior of ex vivo expanded HSCs. 5 million whole BM cells from primary transplantation were transplanted together with 5 million competitor whole BM cells. The control group was transplanted with 100 cHSCs freshly isolated from BM for the primary transplantation. Mann-Whitney test was applied and the asterisks indicate significant differences. , p < 0.05; , p < 0.01; ***, p < 0.0001. Error bars denote SEM.

However, we recognize the complexity of defining HSC aging and the potential for the culture system to influence certain aspects of this process. The association of aging signature genes with HSC primitiveness and young signature genes with differentiation presents an interesting dichotomy. Our analysis of a native dataset on young mice and the projection of aged signatures onto our multiome data (as shown below for a set of genes known to be induced at higher levels in aged HSCs (f.i. Wahlestedt et al., Nature Comm 2017), aging scRNAseq data from PMID: 36581635) does not directly indicate that the culture system promotes HSC aging compared to aged Lin-Sca+Kit+ cells. Yet, we do not rule out the possibility that culturing may influence other facets of the HSC aging process.

In conclusion, while our current data do not provide direct evidence of induced HSC aging through the culture system, this remains a compelling area for future research. The potential impact of ex vivo culture on aspects of the HSC aging process warrants further exploration, and we appreciate your suggestion in this regard.

Author response image 5.

No evident signs of "molecular aging" following ex vivo expansion of HSCs. Young and aged scRNAseq data from PMID: 36581635 were integrated and explored from the perspective of known genes associating to HSC aging. The top row depicts contribution to UMAPs from young and aged cells (two left plots), cell cycle scores of the cells, and the expression of EPCR and CD48 as examples markers for primitive and more differentiated cells, respectively. The expression of the HSC aging-associated genes Wwtr1, Cavin2, Ghr, Clu and Aldh1a1 was then assessed in the data as well as in the SAILERX UMAP of cultured HSCs (bottom row).

Reviewer #2 (Public Review):

Summary:

In this study, Zhang and colleagues characterise the behaviour of mouse hematopoietic stem cells when cultured in PVA conditions, a recently published method for HSC expansion (Wilkinson et al., Nature, 2019), using multiome analysis (scRNA-seq and scATACseq in the same single cell) and extensive transplantation experiments. The latter are performed in several settings including barcoding and avoiding recipient conditioning. Collectively the authors identify several interesting properties of these cultures namely: 1) only very few cells within these cultures have long-term repopulation capacity, many others, however, have progenitor properties that can rescue mice from lethal myeloablation; 2) single-cell characterisation by combined scRNAseq and scATACseq is not sufficient to identify cells with repopulation capacity; 3) expanded HSCs can be engrafted in unconditioned host and return to quiescence.

The authors also confirm previous studies that EPCRhigh HSCs have better reconstitution capability than EPCRlow HSCs when transplanted.

Strengths:

The major strength of this manuscript is that it describes how functional HSCs are expanded in PVA cultures to a deeper extent than what has been done in the original publication. The authors are also mindful of considering the complexities of interpreting transplantation data. As these PVA cultures become more widely used by the HSC community, this manuscript is valuable as it provides a better understanding of the model and its limitations.

Novelty aspects include:

• The authors determined that small numbers of expanded HSCs enable transplantation into non-conditioned syngeneic recipients.

• This is to my knowledge the first report characterising the output of PVA cultures by multiome. This could be a very useful resource for the field.

• They are also the first to my knowledge to use barcoding to quantify HSC repopulation capacity at the clonal level after PVA culture.

• It is also useful to report that HSCs isolated from fetal livers do expand less than their adult counterparts in these PVA cultures.

Weaknesses:

• The analysis of the multiome experiment is limited. The authors do not discuss what cell types, other than functional or phenotypic HSCs are present in these cultures (are they mostly progenitors or bona fide mature cells?) and no quantifications are provided.

The primary objective of our manuscript was to characterize the features of HSCs expanded from ex vivo culture. In this context, our analysis of the single cell multiome sequencing data was predominantly centered on elucidating the heterogeneity of cultures, along with subsequent in vivo functional analysis. This focus is reflected in our comparisons between the molecular features of ex vivo cultured candidate HSCs (cHSCs) and "fresh/unmanipulated" HSCs, as illustrated in Figures 2D-E of our manuscript.

Our findings provide substantial evidence that ex vivo expanded cells share significant similarities with HSCs isolated from the BM in terms of molecular features, differentiation potential, heterogeneity, and in vivo stem cell activity/function. This suggests that the ex vivo culture system closely mimics several aspects of the in vivo environment, thereby broadening the potential applications of this system for HSC research.

Regarding the presence of other cell types in the cultures, it is important to note that most cells did not express mature lineage markers, suggesting their immature status. However, we acknowledge the presence of some mature lineage marker-positive cells within the cultures. These cells are represented by the endpoints in our SAILERX UMAP, indicating a progression from immature to more differentiated states within the culture system.

While the main emphasis of our study was on HSCs, we understand the importance of acknowledging and briefly discussing the presence and characteristics of other cell types in the cultures. This aspect provides a more comprehensive understanding of the culture system and its impact on cellular heterogeneity, although it was for the most part beyond the scope of our studies.

• Barcoding experiments are technically elegant but do not bring particularly novel insights. We respectfully disagree with the view that our barcoding experiments do not offer novel insights. We believe that the application of barcoding technology in our study represents a significant advancement over previous methods, both in terms of quantitative rigor and ethical considerations.

In the foundational work by Wilkinson et al., clonal assessments were indeed performed, but these were limited in scope and largely served as proof of concept. Our use of barcoding technology, on the other hand, allowed for a comprehensive quantitative assessment of the expansion potential of HSC clones. This technology enabled us to rigorously quantify the number of HSC clones capable of undergoing at least three self-renewing divisions (e.g. those clones present in 5 separate animals), while also revealing the heterogeneity in their expansion potential.

One alternative approach could have been to culture single HSCs and distribute the progeny among multiple mice for analysis. However, when considering the sheer number of mice that would be required for such an experiment for quantitative assessments, it becomes evident that viral barcoding is a far superior method. Not only does it provide a more efficient and scalable approach to assessing clonal expansion, but it also significantly reduces the number of animals required for the study, aligning with the principles of ethical research and animal welfare.

In conclusion, we assert that the barcoding experiments conducted in our study are not only technically robust but also yield novel quantitative insights into the dynamics of HSC clones within expansion cultures. These insights have value not only for current research but also hold potential implications for future applications.

• The number of mice analysed in certain experiments is fairly low (Figures 1 and 5).

We would like to clarify our approach in the context of the 3R (replacement, refinement, and reduction) policy, which guides ethical considerations in animal research.

In alignment with the 3R principles, our study was designed to minimize the use of experimental animals wherever possible. For most experiments, including those presented in Figures 1 and 5, we adopted a standard of using five mice per group. Based on the effect sizes we observed, we concluded that this sample size was appropriate for most parts of our study.

Specifically for Figure 5, we used two animals per time point, totaling seven animals per treatment group. It is important to note that we did not monitor the same animals over time but used different animals at each time point, as mice had to be sacrificed for the type of analyses conducted. Despite the seemingly small sample size, the results we obtained were remarkably consistent across groups. This consistency provided strong evidence that ex vivo activated HSCs return to a more quiescent state after being transplanted into unconditioned recipients. Given the clear and consistent nature of these results, we determined that including more animals for the purpose of additional statistical analysis was not necessary.

Our approach reflects a balance between adhering to ethical standards in animal research and ensuring the scientific validity and reliability of our findings. We believe that the sample sizes chosen for our experiments are justified by the consistent and significant results we obtained, which contribute meaningfully to our understanding of HSC behavior post-transplantation.

• The manuscript remains largely descriptive. While the data can be used to make useful recommendations to future users working with PVA cultures and in general with HSCs, those recommendations could be more clearly spelled out in the discussion.

We fully agree that many aspects of our study are indeed descriptive, which is reflective of the exploratory and foundational nature of this type of research.

We have strived to provide clear and direct recommendations for researchers interested in utilizing the PVA culture system, which we believe are evident throughout our manuscript:

1) Utility of Viral Delivery in HSC Research: Our research, particularly through the use of barcoding experiments, underscores the effectiveness of viral delivery methods in HSC studies. While barcoding itself is a significant tool, it is the underlying process of viral delivery that truly exemplifies the potential of this approach. Our work shows that the culture system is highly conducive to maintaining HSC activity, which is critical for genetic manipulation. This is evident not only in our current study but also in our previous work that included for transient delivery methods (Eldeeb et al., Cell Reports 2023).

2) Non-conditioned transplantation: Our findings suggest that non-conditioned transplantation can be a valuable method in studying both normal and malignant hematopoiesis. This approach can complement genetic lineage tracing models, providing a more native and physiological context for hematopoietic research. We state this explicitly in our discussion.

3) Integration with recent technical advances: The combination of the PVA culture system with recent developments in transplantation biology, genome engineering, and single-cell technologies holds significant promise. This integration is likely to yield exciting discoveries with relevance to both basic and clinically oriented hematopoietic research. This is the end statement of our discussion.

While our manuscript is in a way tailored to those with experience in HSC research, we have made a concerted effort to ensure that the content is accessible and informative to a broader audience, including those less familiar with this area of study. Our intention is to provide a resource that is both informative for experts in the field and approachable for newcomers.

• The authors should also provide a discussion of the other publications that have used these methods to date.

We would like to clarify that the scope of literature on the specific methods we employed, particularly in the context of our research objectives, is not extensive. Most of the existing references on these methods come from a relatively narrow range of research groups. In preparing our manuscript, we tried to be comprehensive yet selective in our citations to maintain focus and relevance. Our referencing strategy was guided by the aim to include literature that was most directly pertinent to our study's methodologies and findings.

Overall, the authors succeeded in providing a useful set of experiments to better interpret what type of HSCs are expanded in PVA cultures. More in-depth mining of their bioinformatic data (by the authors or other groups) is likely to highlight other interesting/relevant aspects of HSC biology in relation to this expansion methodology.

We are grateful for the overall positive assessment of our work and the recognition of its contributions to understanding HSC expansion in PVA cultures.

We agree that every study, including ours, has its limitations, particularly regarding the scope and depth of exploration. It is challenging to cover every aspect comprehensively in a single study. Our research aimed to provide a foundational understanding of HSCs in PVA cultures, and we are pleased that this goal appears to have been met.

We also concur with your point on the potential for further in-depth mining of our bioinformatic data. Our hope is that this data can serve as a resource (or at least a starting point) for other investigators.

In conclusion, we hope that our responses have adequately addressed your queries and clarified any concerns. We are committed to contributing to the growth of knowledge in HSC research and look forward to the advancements that our study might enable, both within our team and the wider scientific community.

Reviewer #1 (Recommendations For The Authors):

1) In Line 150, the R packages can/should be mentioned just in the method section;

We have moved this text to the methods section.

2) In Figure F3C adding a legend next to the plot would assist the reader in identifying which populations are referred to, as the same color pellet is used for other panels;

We have now adjusted the figure legend position to make it more clear for the reader.

3) In Figure 4D, for the pre-culture experiments 1000 cHSCs were used and then in the post-culture 1200 cHSCs were used. Can the authors justify the different numbers?

The decision to use 1000 cHSCs in the pre-culture experiments and 1200 cHSCs in the post-culture experiments was not based on a specific rationale favoring one cell number over the other. In our Method section, we have detailed our experimental design, which was structured to provide robust and reliable readouts of HSC behavior and characteristics in different conditions.

We consider the two cell numbers – 1000 and 1200 – to be quite similar in the context of our experimental aims. Since the readouts here are based on clonal assessments, this slight difference in cell numbers is unlikely to significantly impact the overall conclusions drawn from these experiments. The primary focus of our study was on qualitative aspects of HSC behavior and function, rather than on quantitative differences that might arise from small variations in initial cell numbers.

4) In SF5F it would help readers if a line plot (per group) was also shown together with the dot plots. Moreover, applying statistics to the trend lines (Wilcoxon, for example) would strengthen the argument that cHSCs divide less than control cells.

We would like to clarify that the data presented in SF5F were derived from different animals at each respective time point. As such, the data points at each time point represent independent measurements from separate animals, rather than a continuous measurement from the same set of animals over time. Therefore, creating a line plot that connects each time point within a group would inadvertently convey a misleading impression of a longitudinal study on the same animals, which is not reflective of the actual experimental design. Instead, the dot plot format was chosen as it more accurately depicts the independent and discrete nature of the measurements at each time point. Our current data presentation method was selected to provide the most accurate and transparent representation of our findings.

Reviewer #2 (Recommendations For The Authors):

Listed below are recommendations to further improve this manuscript:

Major Comments

1) Fig 1: the authors showed that EPCRhigh HSCs have better reconstitution capability than EPCRlow HSCs via bone marrow transplantation. Additionally, mice receiving cultured EPCRhigh SLAM LSK cells were more efficiently radioprotected than those receiving PVA expanded EPCRlow SLAM LSK.

a. In addition to Fig.1F, authors should show the lineage distributions and chimerism of mice receiving cultured EPCRhigh and EPCRlow SLAM LSK respectively.

We have indeed analyzed the lineage distribution in these experiments, and our findings indicate no statistically significant differences between the groups (see graph in Author response image 6). This suggests that the cultured EPCRhigh and EPCRlow SLAM LSK cells do not preferentially differentiate into specific lineages in a way that would impact the overall interpretation of our results.

Author response image 6.

Regarding the chimerism in peripheral blood (PB) lineages, Fig. 1F in our manuscript currently shows the PB myeloid chimerism. We chose to focus on this parameter as it most directly relates to our study's objectives. We did here not transplant with competitor cells, and in most cases, the chimerism levels reached 100% for lineages other than T cells (T cells being more radioresistant). Based on our analysis, including data on chimerism in other PB lineages would not significantly enhance the understanding of the functional capacity of the transplanted cells, as the myeloid chimerism data already provides a robust indicator of their engraftment and functional potential.

We believe that our current presentation of data in Fig. 1F, along with the additional analyses provided in the results section, offers a comprehensive understanding of the behavior and potential of the cultured EPCRhigh and EPCRlow SLAM LSK cells.

b. Fig1F: only 5 mice were used in each group. Could this result occur by chance? Testing with Fisher's exact test with the data provided results in p=0.16. The authors should consider adding more animals or adding the p-value above (or from another relevant test) for readers' consideration.

We acknowledge the point that only five mice were used in each group and understand the concern regarding the robustness of our findings.

As correctly noted, applying Fisher's exact test to the data in Fig. 1F results in a p-value which does not reach the conventional threshold for statistical significance. However, one might also consider the analysis of the KM survival curve, which associated with a p-value of 0.0528 (Fig. 1F, left graph below; Gehan-Breslow-Wilcoxon test). A similar test on the single-cell culture transplantation experiment (Fig. 1E, right graph below) also demonstrated statistical significance (p-value = 0.0485).

While these p-values meet (or are very close to) the conventional criteria for statistical significance (p<0.05), we have chosen to place greater emphasis on effect sizes rather than strictly on p-values. This decision is based on our belief that effect sizes provide a more direct and meaningful measure of the biological impact observed in our experiments. We find that the effect sizes observed are compelling and consistent with the overall narrative of our study.

Author response image 5.

2) The characterisation of the multiome experiment is highly underdeveloped.

a. From an experimental point of view, it is not clear how the PVA culture for this experiment was started. Are there technical/biological replicates? Have several PVA cultures been pooled together?

We have included these details in the revised text to ensure a comprehensive understanding of our experimental setup.

b. Fig2B: The authors should present more data as to how each of the clusters was annotated (bubble plot of marker genes used for annotation?) and importantly the percentage of cells in each of the clusters. It is particularly relevant to note what % is the cluster annotated as HSCs and compare that to the % of phenotypic HSCs and the % repopulating HSCs calculated in the transplantation experiments.

In our study, the annotation of clusters was primarily based on reference genes for cell types from prior works in the field, such as from our recent work (Konturek-Ciesla et al., Cell Reports 2023). Additionally, we employed transcription factor (TF) motifs to assign identities to these clusters. This approach is relatively standard in the field, and we believe it provides a robust framework for our analysis. We included information on some of the key TF motifs used to guide our annotations.

Regarding the assignment of a percentage to cells within the HSC cluster, we initially had reservations about the utility of this measure. This is because the transcriptional identity of HSCs might not align precisely with their identity based on candidate HSC protein markers. There are complexities related to transcriptional continuums that could influence the interpretation of such data. However, acknowledging your request for this information, we have now included the percentage of cells in the HSC cluster in Fig. 2B for reference.

We also wish to highlight that when isolating EPCR+ cells, which encompasses a range of CD48 expression, clustering becomes much less distinct, as shown in Fig. 2E. Most of these cells do not demonstrate long-term functional HSC activity in a transplantation setting (as presented in Figure 3). This observation underscores the challenges in deducing HSC identity based solely on molecular data and reinforces the importance of functional validation.

c. Are there any mature cells in these PVA cultures? The annotations presented in the table under the UMAP are vague: Are cluster 4 monocytes or monocytes progenitors? Same for clusters 0,1 and 7 - are these progenitors or more mature cells? How were HPCs (cluster 3) distinguished from cHSCs (cluster 5)?

We agree with your observation that the annotations for certain clusters, such as clusters 4, 0, 1, and 7, as well as the distinction between HPCs (cluster 3) and cHSCs (cluster 5), appear vague. This vagueness to some extent stems from the challenges inherent in comparing cultured cells to their counterparts isolated directly from animals. Most reference data defining cell types are derived from cells in their native state, and less is known about how these definitions translate to the progeny of HSPCs cultured in vitro.

In our study, we used the expression of reference genes and enriched transcription factor motifs to annotate clusters. This method, while useful, has its limitations in precisely defining the maturation stage of cells in culture. The enrichment of lineage-defining factors at the ends of the UMAP suggests the presence of more mature cells, whereas the lack of lineage marker expression in the majority of cells implies a general lack of terminal differentiation.

This issue is not necessarily unique to the culture situation, as similar challenges in cell type annotation are encountered in other contexts, such as the analysis of granulocyte-macrophage progenitors in bone marrow, where a vast range of cell types and clusters are identified (e.g., PMID: 26627738). To try to address these challenges, we employed an approach detailed in the methods section under the header "iv. ATAC processing and cluster annotation." We assessed marker genes for clusters using Enrichr for cell types, relying on databases designed to provide gene expression identities to defined cell types. This methodology informed our references to the clusters.

In summary, while our annotations provide a general overview of the cell types present in the cultures, we acknowledge the complexities and limitations in precisely defining these types, particularly in distinguishing between progenitors and more mature cells. We hope this explanation clarifies our approach and the considerations behind our cluster annotations, but at the same time feel that the alternative approaches have their own drawbacks.

d. What is the meaning of the trajectories presented in Figure 2C? In the absence of a comparison to i) what is observed either when HSCs are cultured in control/non-expanding conditions ii) an in vivo landscape of differentiation in mouse bone marrow; this analysis does not bring any relevant piece of information.

We understand the perspective on comparisons to control conditions and in vivo differentiation landscapes. However, we respectfully disagree with the viewpoint that the analysis that we have performed does not bring relevant information.

The trajectory analysis in Figure 2C is intended to provide insights into the cell types generated in our PVA cultures and the potential differentiation pathways they may follow. This kind of analysis is particularly valuable in the context of understanding how in vitro cultures can support HSC maintenance and differentiation, which is a topic of significant interest in the field. For instance, studies like PMID: 31974159 have highlighted the importance of combining in vitro HSC cultures with molecular investigations.

While we acknowledge that our analysis would benefit from a direct comparison to control or non-expanding conditions, as well as to an in vivo differentiation landscape, we believe that the information provided by our current analysis still holds substantial value. It offers a glimpse into the possible cellular dynamics and differentiation routes within our culture system, which can be a valuable reference point for other investigators working with similar systems.

Regarding the confidence in computed differentiation trajectories, we recognize that this is an area where caution is warranted. Computational approaches to define cell differentiation pathways have inherent limitations and should be interpreted within the context of their assumptions and the data available. This challenge is not unique to our work but is a broader issue in the field of computational biology.

In conclusion, while we agree that additional comparative analyses could further enrich our findings, we maintain that the trajectory analysis presented in Figure 2C contributes meaningful insights into cell differentiation in our PVA culture system. We believe these insights are of interest and value to researchers exploring the complex interplay of HSC maintenance and differentiation in vitro.

3) The addition of barcoding experiments is appreciated. However, it is already known that upon transplantation clonal output is highly heteroegeneous, with a small number of clones predominating over others. This is particularly the case after myeloablation conditioning.

a. The "pre-culture" experimental design makes sense. The "post-culture" one is however ambiguous in terms of result interpretation. The authors observe fewer clones contributing to a large proportion of the graft (>5%) than in the "pre-culture" setting. Their interpretation is that expanded HSCs are functionally more homogeneous than the input HSCs. However, in the pre-culture experiment, there are 19 days of expansion during which there will be selection pressures over culture plus ongoing differentiation. In the post-culture experiment, there is no time for such pressures to be exerted. Therefore the conclusion drawn by the authors is not the only conclusion. I would encourage the authors to compare the "pre-culture" experiment to an experiment in which cHSCs are in culture for 48h, then barcoded, and then transplanted. This would be much more informative and would allow a proper comparison of expanded HSCs vs input HSCs.

We understand the perspective that a shorter culture period would reduce the influence of selection pressures and differentiation, potentially allowing for a more direct comparison between expanded HSCs and input HSCs. However, we would like to point out that similar experiments have been conducted in the past, as referenced in our work (PMID: 28224997) and others (PMID: 21964413). These studies have demonstrated a significant heterogeneity in the reconstituting clones when barcoding is done early and cells are transplanted directly.

In light of previous research, we are confident that our methodology — tracking the fates of candidate HSC clones throughout the culture period and assessing the outcomes of individual cells from these expanding clones — yields significant and pertinent insights. We want to highlight the significance of barcoding cells late in the culture, a strategy that allows us to barcode cells that have already been subjected to potential selection pressures within the culture environment. Our primary objective is to investigate the effects of these selection pressures on the subsequent in vivo behavior of the cells that emerge from this process. By focusing on this aspect, we aim to deepen the understanding of how in vitro culture conditions influence the functional characteristics and heterogeneity of HSCs after expansion. We believe this approach provides a unique perspective on the adaptive changes HSCs undergo during culture and their implications for transplantation efficacy and HSC biology. Our study thus addresses a critical question in the field: how do the conditions and selection pressures inherent to in vitro culture impact the quality and behavior of HSCs upon their return to an in vivo environment?

b. Another experiment the authors may consider is barcoding in unconditioned recipients as there the bottleneck of selecting specific clones should be lower. In addition, this could nicely complement the return to quiescence observed in Figure 5 (see point below)

We agree that this experiment could provide valuable insights, particularly in understanding how different selection pressures might affect HSC clones in various transplantation contexts. It would indeed be a worthwhile complement to our observations in Figure 5 regarding the return to quiescence of HSCs post-transplantation.

However, we would like to point out that our study already includes a substantial amount of data and analyses aimed at addressing specific research questions within this defined scope. The addition of an experiment with barcoding in unconditioned recipients, while undoubtedly relevant and interesting, would extend beyond the boundaries we set for this particular study.

4) Figure 5D-F, only 2 animals per condition were tested, so the experiment is underpowered for any statistics. How about cell viability of cHSC after in vitro culture? The authors have also not tested whether there is a difference in cell viability post-transplant between EE100 and control. In addition, comparing cell cycle profiles of donor EPCR+ HSCs in these transplanted mice would provide additional evidence to support the conclusion.

Regarding the sample size, we acknowledge that only two animals per condition were used in these experiments, which limits the statistical power for robust quantitative analysis. This decision was guided by ethical considerations to minimize animal use, in line with the 3Rs principle (Replacement, Reduction, Refinement). Despite the small sample size, we believe that the strong trends observed in these experiments are indicative and consistent with our broader findings, although we recognize the limitations in terms of statistical generalization. At the same time, as we have written in the public response: "Specifically for Figure 5, we used two animals per time point, totaling seven animals per treatment group. It is important to note that we did not monitor the same animals over time but used different animals at each time point, as mice had to be sacrificed for the type of analyses conducted."

In the context of post-transplant analysis, conducting separate viability assessments on transplanted cells is not typically informative. This is because non-viable cells would naturally be eliminated through biological processes such as phagocytosis soon after transplantation. Therefore, any post-transplant viability analysis would not provide meaningful insights into the engraftment potential or behavior of the transplanted cells.

However, it is important to note that in all our cell isolation and analysis protocols, we routinely include viability markers. This practice ensures that the cell populations we study and report on are indeed viable. Including these markers is a standard part of our methodology and contributes to the accuracy and reliability of our data.

Regarding the comparison of cell cycle profiles, we chose to focus on the cell trace assay as a means to monitor and track cell division history, which directly addresses the central theme here - informing on the proliferation and quiescence dynamics of transplanted HSCs. While comparing cell cycle profiles could perhaps offer an additional layer of information, we did not deem it essential for our core objectives.

5) Several publications have used these PVA cultures and made comments on their strengths and limitations. They do not overlap with this study but should be discussed here for completeness (for example Che et al, Cell Reports, 2022; Becker et al., Cell Stem Cell, 2023; Igarashi, Blood Advances, 2023).

See comments to reviewer 1.

Minor Comments

Figure 1C: should add in the legend that this is in peripheral blood.

Figure 2C: typo in the title.

Figure 3A: typo in "equivalent". We thank the reviewer for catching these errors, which we have now corrected.

Figure 3B and 3C: symbol colours of EPCRhighCD48+ and EPCR- are too similar to distinguish the 2 groups easily. We highly recommend using contrasting colours.

For easier visualization, we have changed the symbol types and colors in our revised version.

Fig3B and S3A-B: authors should show statistical significance in comparing the 4 fractions. We have now added this information.

In the discussion, the authors rightly point out a paper that described EPCR+ HSCs. There are other papers that also looked at EPCR intensity (high vs low), for example, Umemoto et al., EMBO J, 2022.

While we acknowledge the relevance of the paper you mentioned, we faced constraints in the number of references we could include. Therefore, we prioritized citing the original demonstration of EPCR as an HSC marker, particularly focusing on the work by the Mulligan laboratory, which established that cells expressing the highest levels of EPCR exhibit the most potent HSC activity. We believe this reference most directly supports the core focus of our study and provides the necessary context for our findings.

eLife assessment

This study presents a valuable dissection on how functional HSCs are expanded in PVA cultures. The functional and multi-omic analyses provided are convincing, although the additional data and their analysis provided during revision could have been included in the test to assist readers and to strengthen the published manuscript. Nevertheless, the present work will be of value for stem cell biologists interested in HSC regulation.

Reviewer #1 (Public Review):

In 2019, Wilkinson and colleagues (PMID: 31142833) managed to break the veil on a 20-year open question on how to properly culture and expand Hematopoietic Stem Cells (HSCs). Although this study is revolutionizing the HSC biology field, several questions regarding the mechanisms of expansion remain open. Leveraging on this gap, Zhang et al.; embarked on a much-needed investigation regarding HSC self-renewal in this particular culturing setting.

The authors firstly tacked the known caveat that some HSC membrane markers are altered during in vitro cultures by functionally establishing EPCR (CD201) as a reliable and stable HSC marker (Figure 1), demonstrating that this compartment is also responsible for long-term hematopoietic reconstitution (Figure 3). Next in Figure 2, the authors performed single-cell omics to shed light into the potential mechanisms involved into HSC maintenance, and interestingly it was shown that several hematopoietic populations like monocytes and neutrophils are also present in this culture conditions, which has not been reported. The study goes on to functionally characterize these cultured HSCs (cHSC). The authors elegantly demonstrate using state-of-the-art barcoding strategies that these culturing conditions provoke heterogeneity in the expanding HSC pool (Figure 4). In the last experiment (Figure 5), it was demonstrated that cHSC not only retain their high EPCR expression levels but upon transplantation these cells remain more quiescent than freshly-isolated controls.

Taken together, this study independently validates that the proposed culturing system works and provide new insights into the mechanisms whereby HSC expansion takes place.

Following a first round of comments, the authors provided a comprehensive point-by-point response to the different points raised by reviewers, which significantly helps on better understanding some of the decisions taken by the authors. However, it is surprising that the current manuscript is practically unchanged compared to the previous version. Effectively, all major comments raised by reviewers are address in the response letter rather than incorporated into a truly updated version, which would be of great benefit for readers.

Further comments:<br /> 1. It is highly appreciated that the authors provide a comprehensive and cohesive explanations on i) the rationale for employing SAILERX for single-cell RNA and ATAC-seq, ii) data on HSC signature projected on independent scRNA-seq datasets and iii) further context on the Fgd5 expression limitations. These are important snippets of information which do not only further validate this manuscript's data but also provide context within the HSC biology field.<br /> However, I do not fully agree with the author statement "our primary objective in this study was to highlight the relatively low content of HSCs in cultures" (page 1, response to Reviewers) justifying why single-cell genome-wise approaches were used. As the authors are aware HSCs are defined by functional characterization rather than transcriptional/chromatin accessibility profiles, so it seems odd that this was the rationale to perform omics for this purpose. More importantly, the authors had gone through the lengths of already performing this costly and time-consuming experiment, but miss out on the opportunity to take a deeper dive into the molecular characteristics that could explain divergent behavior between freshly-isolated and cultured HSCs. It would be extremely relevant to the HSC biology community to understand, for example, if these two HSC populations have differences in enhancer accessibility (if the data quality allows), which could provide an upstream explanation for differences in transcription (is also not explored in this manuscript version).

2. It intriguing that the authors acknowledge that there are already more recent versions of this expansion protocol (page 2, response to Reviewers) and provided a convoluted explanation on why these were not included in the original manuscript. Both papers (PMID: 36809781 and PMID: 37385251) have now been published in respected peer-reviewed journals and provide insights which are pertinent for this work. Yet, the authors decided not to discuss these findings. It is understandable that repeating experiments with these updated conditions is outside of the scope of this manuscript, but it would be relevant to discuss how these recent advances in the protocol impact the work presented in this manuscript.

3. Regarding the previous comment on how cultured HSC are related to HSC aging, I highly appreciate both data on serial transplantation and also on scRNA-seq.

Reviewer #2 (Public Review):

Summary:<br /> In this study, Zhang and colleagues characterise the behaviour of mouse hematopoietic stem cells when cultured in PVA conditions, a recently published method for HSC expansion (Wilkinson et al., Nature, 2019), using multiome analysis (scRNA-seq and scATACseq in the same single cell) and extensive transplantation experiments. The latter are performed in several settings including barcoding and avoiding recipient conditioning. Collectively the authors identify several interesting properties of these cultures namely: 1) only very few cells within these cultures have long-term repopulation capacity, many others however have progenitor properties which can rescue mice from lethal myeloablation; 2) single cell characterisation by combined scRNAseq and scATACseq is not sufficient to identify cells with repopulation capacity; 3) expanded HSCs can be engrafted in unconditioned host and return to quiescence.

The authors also confirm previous studies that EPCRhigh HSCs have better reconstitution capability than EPCRlow HSCs when transplanted.

Strengths:<br /> The major strength of this manuscript is that it describes how functional HSCs are expanded in PVA cultures to a deeper extent that what has been done in the original publication. The authors are also mindful of considering the complexities of interpreting transplantation data. As these PVA cultures become more widely used by the HSC community, this manuscript is valuable as it provides a better understanding of the model and its limitations.

Novelty aspects include:<br /> • The authors determined that small numbers of expanded HSCs enable transplantation into non-conditioned syngeneic recipients.<br /> • This is to my knowledge the first report characterising output of PVA cultures by multiome. This could be a very useful resource to the field.<br /> • They are also the first to my knowledge to use barcoding to quantify HSC repopulation capacity at the clonal level after PVA culture.<br /> • It is also useful to report that HSCs isolated from fetal livers do expand less than their adult counterparts in these PVA cultures.

Weaknesses:<br /> • The analysis of the multiome experiment is limited. The authors do not discuss what cell types, other than functional or phenotypic HSCs are present in these cultures (are they mostly progenitors or bona fide mature cells?) and no quantifications are provided. It seems nonetheless that most cells in these cultures do not acquire differentiation markers. In addition, the functional experiments demosntrate very few retain transplantation capacity. Future works will have to investigate the nature of the bulk of the other cells in these cultures.<br /> • Barcoding experiments are technically elegant but do not bring particularly novel insights.<br /> • Number of mice analysed in certain experiments is fairly low (Figure 1 and 5).<br /> • The manuscript remains largely descriptive. While the data can be used to make useful recommendations to future users working with PVA cultures and in general with HSCs, those recommendations could be more clearly spelled out in the discussion.<br /> • The authors could have provided discussion of the other publications/preprints which have used these methods to date. This would have been useful for researchers who have not used this technique.

Overall, the authors succeeded in providing a useful set of experiments to better interpret what type of HSCs are expanded in PVA cultures. More in depth mining of their bioinformatic data (by the authors or other groups) is likely to highlight other interesting/relevant aspects of HSC biology in relation to this expansion methodology.

eLife assessment

In an important study that will be of interest to the mechanistic membrane transport community, the authors capture the first cryo-EM structure of the inward facing melbiose transporter MelB, a well-studied model transporter from the major facilitator (MFS) superfamily. Cryo-EM experiments and supporting biophysical experiments provide solid evidence for transporter conformational changes.

Reviewer #1 (Public Review):

Summary:

The current study reports a cryo-EM structure of MFS transporter MelB trapped in an inward-facing state by a conformationally selective nanobody. The authors compare this structure to previously-resolved crystal structures of outward-facing MelB. Additionally, the authors report H/D exchange/ mass spec experiments that identify accessible residues in the protein.

Strengths:

The authors overcame very significant technical challenges to solve the first inward-facing structure of the small, model MFS transporter MelB by cryo-EM. The use of conformation-trapping nanobodies (which had been reported previously by this group) is particularly nice.

Weaknesses:

The authors highlight the use of HDX experiments as a measurement of protein conformational dynamics. However, the experiment instead measures the accessibility of different residues. An ideal experiment would trap the transporter in inward- and outward states, but only the inward conformation is trapped here. The outward-facing conformation is instead an ensemble of outward and occluded conformations. It seems obvious that this will be more dynamic than the nanobody-trapped inward state.

Reviewer #3 (Public Review):

Summary:

The manuscript authored by Lan Guan and colleagues reveals the structure of the cytosol-facing conformation of the MelB sodium/Li coupled permease using the nab-Fab approach and cryoEM for structure determination. The study reveals the conformational transitions in the melB transport cycle and allows understanding of the role of sugar and ion specificities within this transporter.

Strengths:

The study employs a very exciting strategy of transferring the CDRS of a conformation specific nano body to the nab-fab system to determine the inward-open structure of MelB. The resolution of the structure is reasonable enough to support the major conclusions of the study. This is a well-executed study.

Reviewer #1 (Public Review):

Summary: The current study reports a cryo-EM structure of MFS transporter MelB trapped in an inward-facing state by a conformationally selective nanobody. The authors compare this structure to previously-resolved crystal structures of outward-facing MelB. Additionally, the authors report H/D exchange/ mass spec experiments that identify accessible residues in the protein.

Strengths:

The authors overcame very significant technical challenges to solve the first inward-facing structure of the small, model MFS transporter MelB by cryo-EM. The use of conformation-trapping nanobodies (which had been reported previously by this group) is particularly nice.

Weaknesses:

Maps and coordinates were not provided by the authors, which presents a gap in this assessment.

The authors highlight the use of HDX experiments as a measurement of protein conformational dynamics. However, this experiment does not measure the conformational dynamics of the transporter, since in these experiments exchange is not initiated by ligand addition or another trigger. The experiment instead measures the accessibility of different residues, and of course, a freely-exchanging sodium bound transporter would have more exchangeable positions than when a conformation-trapping nanobody is bound. It is not clear what new mechanistic information this provides, since this property of the nanobody has already been established.

Based on the evidence presented, it is somewhat speculative that the structure represents the EIIa-bound regulatory state.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Hariharan and colleagues present an elegant study regarding the mechanistic basis of sugar transport by the prototypical Na+-coupled transporter MelB. The authors identified a nanobody (Nb 725) that reduces melibiose binding but not Na+ binding. In vitro (ITC) experiments suggest that the conformation targeted by this nanobody is different from the published outward-open structures. They go on to solve the structure of this other conformational by cryo-EM using the Nanobody grafted with a fiducial marker and enhancer and, as predicted, capture a new conformation of MelB, namely the inward-open conformation. Through MD simulations and ITC measurements, they demonstrate that such state has a reduced affinity for sugar but that Na+ binding is mostly unaffected. A detailed observation and comparison between previously published structures in the outward-open conformation and this new conformational intermediate allows to strengthen and develop the mobile barrier hypothesis underpinning sugar transport. The conformational transition to the inward-facing state leads to the formation of a barrier on the extracellular side that directly affects the amino acid arrangement of the sugar binding site, leading to a decreased affinity that drives the direction of transport. In contrast, the Na+ binding remains the same. This structural data is complemented with dynamic insights from HDX-MS experiments conducted in the presence and absence of the Nb. These measurements highlight the overall protective effect of nanobody binding, consistent with the stabilization of one conformational intermediate.

Strengths:

The experimental strategy to isolate this elusive conformational intermediate is smart and well-executed. The biochemical and biophysical data were obtained in a lipid system (nanodiscs), which allows dismissing questions about detergent induced artefacts. The new conformation observed is of great interest and allows to have a better mechanistic understanding of ion-coupled sugar transport. The comparison between the two structures and the mobile barrier mechanism hypothesis is convincingly depicted and tested.

Weaknesses:

This is excellent experimental work. My recommendations stem mostly from concerns regarding the interpretation of the observed results. In particular, I am somewhat puzzled by the important role the authors give to the regulatory protein EIIa with little structural or biophysical data to back up their claims. The hypothesis that the conformation captured by the Nb is physiologically and functionally equivalent to that caused by EIIa binding is definitely a worthy hypothesis, but it is not an experimental result.

Evidence in support could include a structure with EIIa bound. Since it does not bind at the same location as the Nb, it seems feasible. Or, the authors could have performed HDX-MS in the presence of EIIa to determine if the effect is similar to that of Nb_725 binding. In the absence of these experiments, discussion about EIIa should be limited. Along the same lines, I find it misleading to put in the abstract a sentence such as "It is the first structure of a major facilitator superfamily (MFS) transporter with experimentally determined cation binding, and also a structure mimicking the physiological regulatory state of MelB under the global regulator EIIAGlc of the glucose-specific phosphoenolpyruvate:phosphotransferase system." None of this is supported by the experimental work presented in this article: the Na+ is modelled (with great confidence, but still) and whether this structure mimics the physiological state of MelB bound to EIIa is not known. The results of the paper are strong and interesting enough per se, and there is no need to inflate them with hypothesis that belongs to the discussion section.

I also note that the HDX-MS experiments do not distinguish between two conformational states, but rather an ensemble of states vs one state.

Reviewer #3 (Public Review):

Summary:

The manuscript authored by Lan Guan and colleagues reveals the structure of the cytosol-facing conformation of the MelB sodium/Li coupled permease using the nab-Fab approach and cryoEM for structure determination. The study reveals the conformational transitions in the melB transport cycle and allows understanding the role of sugar and ion specificities within this transporter.

Strengths:

The study employs a very exciting strategy of transferring the CDRS of a conformation specific nano body to the nab-fab system to determine the inward-open structure of MelB. The resolution of the structure is reasonable enough to support the major conclusions of the study. This is overall a well-executed study.

Weaknesses:

The authors seem to have mixed up the exothermic and endothermic aspects of ITC binding in their description. Positive heats correspond to endothermic heat changes in ITC and negative heat changes correspond to exothermic heats. The authors seem to suggest the opposite. This is consistently observed throughout the manuscript.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

It is somewhat speculative that the structure represents the EIIa-bound regulatory state. There's a strong enough case that it should be analyzed in the discussion, but I don't think it is firmly established. Therefore, the title of the paper should be changed.

Our answer: Thank you for the comment. We have changed the title to “Mobile barrier mechanisms for Na+-coupled symport in an MFS sugar transporter”

Reading through the manuscript, it was challenging to distinguish what is new in the current manuscript and what has been done previously. There were a lot of parts where it was hard for me to identify the main point of the current study among all the details of previous studies. It would also benefit from shortening. For example:

-Page 6: Nb725 binding has already been characterized extensively in the very nice JBC paper earlier this year. It's important to test 725-4 for binding, but since it doesn't change the binding interaction, and probably wouldn't be expected to, the entire section could be written more succinctly. The main point, which is that 725-4 behaves like 725, is lost among all the details

Our answer: Thanks for this instructive suggestion. We have shortened the description in this section.

-Page 9-10. I don't understand what summarizing all of the results from the previous D59C studies adds to the current story. It's important because it provides an indication of the substrate binding site, but its mechanism of action does not seem relevant to the current work.

Our answer: We have shortened the description of the sugar-binding site and moved the previous Fig. 3b to supplementary figure sFig. 11. According to your comment about showing the location of the binding sites, which is also suggested by Reviewer #2, we modified Fig. 3 and added two panels to map the location of the bound Na+ in the inward-facing structure and the bound sugar in the outward-facing structure.

The sugar-binding site identified in the published structure is critical to construct the mobile barrier mechanism. The sugar-binding residues identified in the published structure provided essential data to support the conclusion that the sugar-binding pocket is broken in the inward-facing structure. Thus, this published structure is mechanistically relevant to the current study.

-Page 12. Too much summary of the previous outward structure. Since this is already part of the literature, it would be more efficient to reference the previous data when it is important to interpret the new data (or show as a figure).

Our answer: The introduction of the previous sugar-binding sit is important for the detailed comparison between the two states as discussed above, but we agree with this reviewer and have significantly shortened the paragraph by moving the detailed description into the legend to the sFig. 11.

-Instead of providing the PDB ID in figures of the current structure, just say "current work" or similar. Then it is obvious you are not citing a previous structure.

Our answer: To distinguish clearly the new data and published results, the citation of the cryoEM structure [PDP ID 8T60] has been completely removed from the main text but kept in sTable 1.

-An entire panel of Figure 3 is dedicated to ligand binding in a previous outward-facing structure.

Showing it in the overlay would be sufficient.

Our answer: It is the first time for us to show a structure with a bound-Na+. Fig. 3 also illustrates the spatial relationship between the sugar-binding pocket and the cation-binding pocket since both binding sites are determined now. As stated above, according to two reviewers’ comments, we have modified the Figures and the Fig. 3d is the overlay.

Please increase the size of the font in all figures. It should be 6-8 point when printed on a standard sheet of paper. Labels in Figure 3, distances in Figure 4, and everything in Figure 5 is hard to see.

Our answer: Thank you for the comments and the enlargement of the figure size and label font in all figures have been made.

Figure 2: would be helpful to show Figure S8 in the main text, orienting the reader to the approximate location of substrate binding. What is known about the EIIA-Glc binding interface? Has anyone probed this by mutagenesis? Where are these residues on the overall structure, and are they somewhere other than the nanobody interface?

Our answer: Thank you for this comment. We have added a panel for orienting the readers about the substrate location in MelB in Figure 3c. The sFig. 8 actually focuses on the details of Nb interactions with MelB. Our current data strongly supported the notion that the Nb-bound MelBSt structure mimics the EIIAGlc-bound MelB but is not structurally resolved, so we have tuned down our statement on EIIAGlc. There is one study suggesting the C-terminal tail helix may be involved in the EIIAGlc binding, which has been added to the discussion.

Can Figure 5 be split into 2 figures and simplified?

Our answer: thanks for the suggestion. We have split it into Figs. 5b and 6 and also moved the peptide mapping to the Fig 5a.

What is the difference between cartoon and ribbon rendering?

Our answer: Ribbon: illustrating the structure; cartoon: highlighting the positions with statistically significant protection or deprotection. The statistically significant changes are implied by the ribbon representation; Sphere: not covered by labeled peptides.

Can the panels showing the kinetic data be enlarged? I don't think they need to surround the molecule. An array underneath would be fine.

Our answer: We have enlarged all figures and labels. The placement of selected plots around the model could clearly show the difference in deuterium uptake rates between the transmembrane domain and extra-membrane regions. We will maintain this arrangement.

Do colors in panel A correspond with colors in panel B?

Our answer: The color usage in both are different. Now the two panels have been separated.

Do I understand correctly that in the HDX experiments, negative values indicate positions that exchange more quickly in the nanobody-free protein relative to the nanobody-bound protein?

Our answer: Your understanding is correct.

I assume some of this is due to the protein changing conformation, but some of it might be due to burial at the nanobody-binding interface. Can those peptides be indicated?

Our answer: Thank you for this comment. We have marked the peptide carrying the Nb-binding residues on uptake plots in Figs.6 and Extended Fig. 1. There are only three Nb-binding residues covered by many overlapping peptides. Most are not covered, either not carried by the labeled peptides (Tyr205, Ser206, and Ser207) or with insignificant changes (Pro132 and Thr133), except for Asp137, Lys138, and Arg141 which are presented in 8 labeled peptides.

Few buried positions in the outward-facing state are expected to be solvent in the inward-facing state; unfortunately, inward-facing state they are buried by Nb binding.

Make figure legends easier to interpret by removing non-essential methods details (like buffer conditions).

Our answer: We removed the detailed method descriptions in most figure legends. Thank you.

Check throughout for typos.

ie page 9 Lue Leu

Page 9 like likely

Our answer: We have corrected them. Thank you!

Reviewer #2 (Recommendations For The Authors):

I have mostly minor questions/remarks.

• Why not do the hdx-ms experiments in the presence of sugar? That would give a proper distinction between two conformational states, instead of an ensemble of states vs one state.

Our answer: MelB conformation induced by sugar is also multiple states, and likely most are outward-facing states and occluded intermediate states. This is also supported by the new finding of an inward state with low sugar affinity. The ideal design should be one inward and one outward to understand the inward-outward transition. We have not identified an outward-facing mutant while we can obtain the inward by the Nb. WT MelBSt with bound Na+ favors the outward-facing state. Although our design is not ideal, we do have one state vs a predominant outward-facing WT with bound Na+.

Minor comments:

• Fig 5 is misleading as the peptide number does not match with the amino acid sequence. I would suggest putting a heat map with coverage on top. Or showing deuterium uptake per peptide. See examples below.

Our answer: The peptide number should not match with sequence number. We have 155 overlapping peptides that cover the entire amino acid sequence including the 10-His tag, and there are 60 residues with no data because they are not covered by a labeled peptide. The residue positions that are covered by peptides are estimated by bars on the top. The cylinder length does not correspond to the length of the transmembrane helix, just for mapping purposes.

• Can the authors explain how they found that the Nbs bind to the cytoplasmic side (before obtaining the structure)?

Our answer: Our in vivo two-hybrid assay between the Nb and MelBSt indicated their interaction on the cytoplasmic surface of MelBSt, which is further confirmed by the melibiose fermentation and transport assay, where the transport activities were completely inhibited by intracellularly coexpressed Nb and MelBSt. Thanks for raising this question.

• The authors use the word "substrate" indifferently for sugar and Na+ binding, which is a bit confusing. Technically, only sugar is the substrate and Na+ is a ligand, or cotransported-ion, that powers the reaction of transport. This might sound like nit-picking but it can lead to misunderstandings (at some point I thought two sugars were transported, and then I was looking for the second Na+ binding site).

Our answer: We used to call the sugar and Na as co-substrate but we agree with this comment.

We have changed by using substrate for the cargo sugar and coupling cation for the driving cation.

• Abstract "only the inner barrier" - the is missing.

Thanks. We have corrected this.

• p.3 intro "and identified that the positive cooperativity of cation and melibiose, " something is missing.

Thanks again. We missed the “as the core symport mechanism”.

• P.6 Nb275_4 instead of Nb725_4

Thank you very much for your careful reading.

• P.7. Also, affinity affinities

Thank you very much. We changed to “; and also, the -NPG affinity decreased by 21~32-fold for both Nbs”

• P.8 " contains 417 MelBSt residues (positions 2-210, 219-355, and 364-432). This does not sum up to 417 residues.

Thanks for your critical reading. We changed 364-432 to 262-432.

• p.9 Lue 54

We have corrected it to Leu54.

• I find fig.3 hard to read. Can the authors show the Na+ binding pockets and sugar binding pockets within the structure? Especially figure 3b. why are the residues in different colors?

Our answer: We have moved Fig 3b into sFig. 11. We colored the residues in the previous Fig 3B to match the hosting helices. We have added two panels to show the location of both sugar and Na in the molecular. Thank you for your comments.

• Fig4 bcef. Colored circles at the end of the helices. What are they for?

Our answer: We revised the legend. “The paired helices involved in either barrier formation were highlighted in the same colored circles.”

• 86% coverage includes the his-tag - it would be good to clarify that.

Our answer: Yes, it includes the 10-His tag.

• Fig.7 - anti clockwise cycle of transport is counter-intuitive.

Our answer: We have re-arranged. Our model was constructed originally to explain efflux due to limited information at the earlier state. Now more data are available allowing us to explain inflow and active transport.

• Where are all the uptake plots per peptide for the HDX-MS data?

Our answer: We have added the course raw data and prepared all uptake plots for all 71 peptides with statistically significant changes as an Extended Fig. 1.

• P.22 protein was concentrated to 50 mg/mL. Really? That is a lot.

This is correct. We can even concentrate MelBSt protein to greater than 50 mg/ml.

• Have the authors looked into the potential role of lipids in regulating the conformational transition? Since the structure was obtained in nanodiscs, have they observed some unexplained densities? The role of lipid-protein interactions in regulating such transitions was observed for several transporters including MFS (Gupta K, et al. The role of interfacial lipids in stabilizing membrane protein oligomers. Nature. 2017 10.1038/nature20820. Martens C, et al. Direct protein-lipid interactions shape the conformational landscape of secondary transporters. Nat Commun. 2018 10.1038/s41467-018-06704-1.). Furthermore, I see the authors have already observed lipid specific functional regulation of MelB (ref: Hariharan, P., et al BMC Biol 16, 85 (2018). https://doi.org/10.1186/s12915-018-0553-0). A few words about this previous work, and even commenting on the absence of lipid-protein interactions in this current work is worthwhile.

Our answer: Thanks for this very relevant comment. We paid attention to the unmodelled densities. There is one with potential but it is challenging to model it. We have added a sentence “There is no unexplained density that can be clearly modeled by lipids.” in the method to address this concern.

Reviewer #3 (Recommendations For The Authors):

1) In the following sentence, the authors report high errors for the Kd value. The anti-Fab Nb binding to NabFab was two-fold poorer than Nb725_4 at a Kd value of 0.11 {plus minus} 0.16 μM. The figure however indicates that the error value is 0.016 µM. Pls correct.

Our answer: Thank you. You are correct. The error has been corrected. 0.16 ± 0.02 uM. In this revised manuscript, we present the data in nM units.

2) Is the stoichiometry of the MelB:Na+ symport clearly known in this transporter. It can be mentioned in the discussion with appropriate references.

Our answer: Yes, the stoichiometry of unity has been clearly determined, which was included in the second paragraph of the previous version.

3) In the last section of results, the authors seem to suggest a greater movement within their Cterminal helical bundle compared to N-terminal helices. Is there evidence to suggest an asymmetry in the rocker switch between the two states of the transporter?

Our answer: Our structural data revealed that the C-terminal bundle is more dynamic compared with the N-terminal bundle where hosts the residues for specific binding of galactoside and Na+. The HDX data showed that the most dynamic regions are the structurally unresolved C-terminal tail by either method, the conserved tail helix and the middle-loop helix. transmembrane helices are relatively less dynamic with similar distributions on both transmembrane bundles. Since the most dynamic regions are peripheral element associated with the C-terminal domain, it might give a wrong impression. With regard to the symmetric or asymmetric movement, which will certainly affect the dynamic interactions between the transporter and the lipids, we favor the notion that MelBSt performs symmetric movement during the rocker switch between inward and outward states at the least cost for the protein-lipids interaction.

4) Figure 1. Are the thermograms exothermic or endothermic? clarify

Our answer: In our thermograms, all positive peaks are exothermic due to the direct detection of the heat release by the TA instrument. We clarified this in Method and now we stress this in figure legends to avoid confusion.

5) Figure 4a,d. Please put in a membrane bilayer and depict cytosolic and extracellular compartments for clarity.

Thank you. We have added a bilayer and labeled the sidedness in this figure and other related figures.

6) Fig 7. Melibiose symport cannot be referred to as Melibiose efflux transport in the legend as the latter refers to antiport. Pls rectify.

Our answer: Influx and efflux are conventionally used to describe the direction of movement of a substrate. The use of symport and antiport indicates the directions of the coupling reaction for the cargo and cation. For the symporter MelB, melibiose efflux means that sugar with the coupled cation moves out, which is driven by the melibiose concentration. During the steady state of melibiose active transport, efflux rate = influx rate.

7) Page 11 "A common feature of carrier transporters". The authors can use either carriers or transporters. Need not use both simultaneously.

Sorry for overlooking this. We have deleted carriers. Thank you very much for your time.

8) Several typos were noticed in this manuscript. some are listed below. pls correct.

Page 4- last paragraph "Furthermore"

We have corrected it. Thank you again!

Page 7 - second para one repharse "affinity reduced by 21~32 fold/units.." pls clarify

Added 21~32 fold.

Page 9 - "so it is highly likely that inward-open conformation" pls correct.

We have corrected to “likely”.

Fig. S9c - correct the spelling "Distance".

We have corrected to “Distance”

Author Response

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

Reviewer #1 (Public Review):

Summary:

The manuscript by Xia et al. investigated the mechanisms underlying Glucocorticoid-induced osteonecrosis of the femoral head (GONFH). The authors observed that abnormal osteogenesis and adipogenesis are associated with decreased β-catenin in the necrotic femoral head of GONFH patients, and that the inhibition of β-catenin signalling leads to abnormal osteogenesis and adipogenesis in GONFH rats. Of interest, the deletion of β-catenin in Col2-expressing cells rather than in Osx-expressing cells leads to a GONFH-like phenotype in the femoral head of mice.

Strengths:

A strength of the study is that it sets up a Col2-expressing cell-specific β-catenin knockout mouse model that mimics the full spectrum of osteonecrosis phenotype of GONFH. This is interesting and provides new insights into the understanding of GONFH. Overall, the data are solid and support their conclusions.

Reviewer #1 (Recommendations For The Authors):

1) Fig. 1I should be quantified and presented as bar graphs to make it consistent with other data, and the significance should be shown.

Reply: Thanks for your comments. We have provided the quantitative bar graph in the new version.

2) Fig. 2H, beta-catenin, ALP and FABP4 should be labled below the X axis. Moreover, the pattern of Fig. 2H is different from other bar graphs and the dots for individual samples are missing, so I could not judge the N values for the experiments. N values should also be provided for Fig. 3.

Reply: Thanks for your comments. We have added the labels of beta-catenin, ALP and FABP4 below the X axis in Fig. 2H. The modes of quantitative bar graphs were changed to show the N values in the each experiment.

3) Fig. 4 shows the fate mapping of Col2+ cells and Osx+ cells in the femoral head. In this regard, the authors presented images for Col2-expressing cells at all the indicated time points, i.e. 1, 3, 6, and 9 months, but only presented images for Osx-expressing cells for 1 month while those for 3, 6, and 9 months are missing.

Reply: Thanks for your comments. Here, we showed that the expression of Osx+ cells in the femoral head were total different with Col2+ cells at the age of 3, 6 month, further indicating they were two different progenitor lineage cells.

Author response image 1.

4) Some experiments may need to be described in more detail" e.g., ABH/Orange G staining, biomechanical testing, μCT analysis, et al.

Reply: Thanks for your comments. We have provided more information of experiment procedures.

5) This study proposed that Col2-expressing cells play a key role in the progression of GONFH, did the authors use Col2+ cells for the in vitro experiments?

Reply: As in vitro experiments could not reflect the location of Col2-expressing cells in the femoral head, therefore here we applied in vivo lineage tracing study. After as long as 9 month of linage trace, we thoroughly showed the self-renew ability and osteogenic commitment of Col2+ cells, as well as its space variation in the femoral head with age. Conditional knockout of β-catenin caused that Col2+ cells trans-differentiated into adipogenic cells instead of osteogenic cells, which directly clarified the mechanism of Col2+ cells leading to GONFH-like phenotype in mice.

6) A few typo errors, such as Line 13, "contribute" should be "contributes"; Line 118, "reveled" should be "revealed".

Reply: We have revised the grammar errors in the new manuscript.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors reported a study to uncover that β-catenin inhibition disrupting the homeostasis of osteogenic/adipogenic differentiation contributes to the development of Glucocorticoid-induced osteonecrosis of the femoral head (GONFH). In this study, they first observed abnormal osteogenesis and adipogenesis associated with decreased β-catenin in the necrotic femoral head of GONFH patients, but the exact pathological mechanisms of GONFH remain unknown. They then performed in vivo and in vitro studies to further reveal that glucocorticoid exposure disrupted osteogenic/adipogenic differentiation of bone marrow stromal cells (BMSCs) by inhibiting β-catenin signaling in glucocorticoid-induced GONFH rats, and specific deletion of β-catenin in Col2+ cells shifted BMSCs commitment from osteoblasts to adipocytes, leading to a full spectrum of disease phenotype of GONFH in adult mice.

Strengths:

This innovative study provides strong evidence supporting that β-catenin inhibition disrupts the homeostasis of osteogenic/adipogenic differentiation that contributes to the development of GONFH. This study also identifies an ideal genetically modified mouse model of GONFH. Overall, the experiment is logically designed, the figures are clear, and the data generated from humans and animals is abundant supporting their conclusions.

Weaknesses:

There is a lack of discussion to explain how the Wnt agonist 1 works. There are several types of Wnt ligands. It is not clear if this agonist only targets Wnt1 or other Wnts as well. Also, why Wnt agonist 1 couldn't rescue the GONFH-like phenotype in β-cateninCol2ER mice needs to be discussed.

Reply: Thanks for your constructive comments. Wnt agonist 1 is a cell-permeating activator of the Wnt signaling pathway that induces transcriptional activity dependent on β-catenin (PMID: 25514428,18624906). In the present study, we aim to demonstrate that activation of β-catenin signaling could alleviate the phenotype of rat GONFH, thus only β-catenin and downstream targets (RUNX2, ALP, PPAR-γ, FABP4) expressions were detected after Wnt agonist 1 intervention. Conditional knockout β-catenin in Col2+ cells lead to an mouse GONFH-like phenotype. Wnt agonist 1 couldn't rescue this GONFH-like, as it did not activate β-catenin signaling. We have discussed them in the new version.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors are trying to delineate the mechanism underlying the osteonecrosis of the femoral head.

Strengths:

The authors provided compelling in vivo and in vitro data to demonstrate Col2+ cells and Osx+ cells were differentially expressed in the femoral head. Moreover, inducible knockout of β-catenin in Col2+ cells but not Osx+ cells lead to a GONFH-like phenotype including fat accumulation, subchondral bone destruction, and femoral head collapse, indicating that imbalance of osteogenic/adipogenic differentiation of Col2+ cells plays an important role in GONFH pathogenesis. Therefore, this manuscript provided mechanistic insights into osteonecrosis as well as potential therapeutic targets for disease treatment.

Weaknesses:

However, additional in-depth discussion regarding the phenotype observed in mice is highly encouraged.

Reply: Thanks for your comments. Inducible knockout of β-catenin in Col2+ cells but not Osx+ cells lead to a GONFH-like phenotype. Lineage tracing data showed Col2+ cells and Osx+ cells were different cell populations, and we have discussed the potential mechanism caused the different phenotypes between β-cateninCol2ER mice and β-cateninOsxER mice.

1) Why did the authors use dexamethasone in the cellular experiments but methylprednisolone to induce the GONFH rat model?

Reply: Thanks for the comments. Here, we applied a dexamethasone (DEX)-treated BMSC model in vitro and a methylprednisolone (MPS)-induced rat model in vivo for GONFH study based on the published literatures (PMID: 37317020, 29662787, 29512684,35126710, 32835568).

2) Both bone damage and fat accumulation were observed in 3-month-old and 6-month-old β-cateninCol2ER mice, but the femoral head collapse (the feature of GONFH at the late stage) only occurred in the older β-catenin Col2ER mice. This interesting observation needs to be discussed. Reply: Thanks for the comments. Bone damage caused a poor mechanical support is the key to femoral head collapse. Despite of similar trabecular bone loss and fat accumulation in the 3-month-old and 6-month-old β-cateninCol2ER mice, the older mice also presented extensive subchondral bone destruction. Integrated subchondral bone provided a well mechanical support for femoral head morphology, therefore femoral head collapse were occurred in the older β-cateninCol2ER mice.

3) In the Materials and Methods, detailed information on the reagents should be provided.

Reply: We have provided detailed information of the important reagents.

4) As shown in Figure 4, β-cateninOsxER mice at 3 months of age did not show differences in lipid droplet area and empty lacunae rate, but there was a decrease in bone area. The authors should at least provide some necessary discussion of this phenomenon.

Reply: Thanks for your comments. In the present study, we found few lipid droplet and empty lacuna but a significant decrease of bone mass in the femoral heads of β-cateninOsxER mice. Previous studies showed that specific knockout of β-catenin in Osx-expressing cells promoted osteoclast formation and activity, leading to the bone mass loss (PMID: 29124436, 34973494). We discussed this phenomenon in the new version.

eLife assessment

This study presents valuable findings on the mechanism of glucocorticoid-induced osteonecrosis of the femoral head. The data were collected and analyzed using solid, validated methodology and can be used as a starting point for functional studies of development of glucocorticoid-induced osteonecrosis. This paper would be of interest to cell biologists and biophysicists working on potential pharmacological treatments for glucocorticoid-induced osteonecrosis.

Reviewer #1 (Public Review):

Summary:

The manuscript by Xia et al. investigated the mechanisms underlying Glucocorticoid-induced osteonecrosis of the femoral head (GONFH). The authors observed that abnormal osteogenesis and adipogenesis is associated with decreased β-catenin in the necrotic femoral head of GONFH patients and inhibition of β-catenin signaling leads to abnormal osteogenesis and adipogenesis in GONFH rats. Of interest, deletion of β-catenin in Col2-expressing cells rather than in osx-expressing cells leads to a GONFH-like phenotype in femoral head of mice.

Strengths:

A strength of the study is that it sets up a Col2-expressing cell-specific β-catenin knockout mouse model that mimics full spectrum of osteonecrosis phenotype of GONFH. This is interesting and provides new insights into the understanding of GONFH. Overall, the data are solid and support their conclusions.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors reported a study to uncover that β-catenin inhibition disrupting the homeostasis of osteogenic/adipogenic differentiation contributes to the development of Glucocorticoid-induced osteonecrosis of the femoral head (GONFH). In this study, they first observed abnormal osteogenesis and adipogenesis associated with decreased β-catenin in the necrotic femoral head of GONFH patients, but the exact pathological mechanisms of GONFH remain unknown. They then performed in vivo and in vitro studies to further revealed that glucocorticoid exposure disrupted osteogenic/adipogenic differentiation bone marrow stromal cells (BMSCs) by inhibiting β-catenin signaling in glucocorticoid-induced GONFH rats, and specific deletion of β-catenin in Col2+ cells shifted BMSCs commitment from osteoblasts to adipocytes, leading to a full spectrum of disease phenotype of GONFH in adult mice.

Strengths:

This innovative study provides strong evidence supporting that β-catenin inhibition disrupts the homeostasis of osteogenic/adipogenic differentiation that contributes to the development of GONFH. This study also identifies an ideal genetic modified mouse model of GONFH. Overall, the experiment is logically designed, the figures are clear, and the data generated from humans and animals is abundant supporting their conclusions.

Weaknesses:

Lack of the discussion to explain how the Wnt agonist 1 works. There are several types of Wnt ligands. It is not clear if this agonist only targets Wnt1 or other Wnts as well? Also, why Wnt agonist 1 couldn't rescue the GONFH-like phenotype in β-cateninCol2ER mice needs to be discussed.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors are trying to delineate the mechanism underlying the osteonecrosis of the femoral head.

Strengths:

The authors provided compelling in vivo and in vitro data to demonstrate Col2+ cells and Osx+ cells were differentially expressed in the<br /> the femoral head. Moreover, inducible knockout of β-catenin in Col2+ cells but not<br /> Osx+ cells lead to a GONFH-like phenotype including fat accumulation, subchondral<br /> bone destruction and femoral head collapse, indicating that imbalance of osteogenic/adipogenic differentiation of Col2+ cells play an important role in GONFH pathogenesis. Therefore, this manuscript provided the mechanistic insights of osteonecrosis as well as potential therapeutic target for disease treatment.

Weaknesses: Additional in depth discussion regarding the phenotype observed in mice is highly encouraged.

Author Response

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

We thank the reviewers and the editors for their constructive and critical comments/ suggestions regarding our paper. We have since extensively revised the manuscript accordingly, including the addition of new experimental data. Hope the readers, reviewers, and editors are now satisfied with the quality and significance of the revised paper.

Our responses to the eLife assessment and the reviewers’ comment as well as the details of the revisions are described below.

Wang et al present a useful manuscript that builds modestly on the group's previous publication on KLF1 (EKLF) K47R mice focused on understanding how Eklf mutation confers anticancer and longevity advantages in vivo (Shyu et al., Adv Sci (Weinh). 2022). The data demonstrates that Eklf (K74R) imparts these advantages in a background, age, and gender independent manner, not the consequence of the specific amino acid substitution, and transferable by BMT. However, the authors overstate the meaning of these results and the strength of evidence is incomplete, since only a melanoma model of cancer is used, it is unclear why only homozygous mutation is needed when only a small fraction of cells during BMT confer benefit, they do not show EKLF expression in any cells analyzed, and the PD-1 and PDL-1 experiments are not conclusive. The definitive mechanism relative to the prior publication from this group on this topic remains unclear.

The issues in the assessment by the editor on our paper were also brought up by the reviewers. We have taken care of them by carrying out new experiments as well as rewriting of the paper to highlight the rationales and novel aspects of the current study, as described below in our responses to the three reviewers.

Public Reviews:

Reviewer #1 (Public Review):

The authors Wang et al. present a study of a mouse model K74R that they claim can extend the life span of mice, and also has some anti-cancer properties. Importantly, this mechanism seems to be mediated by the hematopoietic system, and protective effects can be transferred with bone marrow transplantation.

The authors need to be more specific in the title and abstract as to what is actually novel in this manuscript (a single tumor model), and what relies on previously published data (lifespan). Because many of these claims derive from previously published data, and the current manuscript is an extension of previously published work. The authors need to be more specific as to the actual data they present (they only use the B16 melanoma model) and the actual novelty of this manuscript.

Especially experiments on life span are published and not sufficiently addressed in this actual paper, as the title would suggest.

Indeed important to point out the novelty of this paper in comparison to the previous paper. First, we have modified the title, the abstract, and the text so to emphasize that the extended lifespan as well as tumor resistance could be transferred by from Eklf(K74R) mice to WT mice by a single transplantation of the Eklf(K74R) bone marrow mononuclear cells (BMT) to the WT mice at their young age (2 months).

We now also provide several new experimental data including the one demonstrating that Eklf(K74R) mice are resistant to tumorigenesis of hepatocellular carcinoma as well (new Fig. 1E). These points are elaborated in more details below in my responses to the reviewers’ comments/ suggestions.

Reviewer #2 (Public Review):

The manuscript by Wang et al. follows up on the group's previous publication on KLF1 (EKLF) K47R mice and reduced susceptibility to tumorigenesis and increased life span (Shyu et al., Adv Sci (Weinh). Sep 2022;9(25):e2201409. doi:10.1002/ advs.202201409). In the current manuscript, the authors have described the dependence of these phenotypes on age, gender, genetic background, and hematopoietic translation of bone marrow mononuclear cells. Considering the current study is centered on the phenotypes described in the previous study, the novelty is diminished. Further, there are significant conceptual concerns in the study that make the inferences in the manuscript far less convincing. Major concerns are listed below:

1) The authors mention more than once in the manuscript that KLF1 is expressed in range of blood cells including hematopoietic stem cells, megakaryocytes, T cells and NK cells. In the case of megakaryocytes, studies from multiple labs have shown that while EKLF is expressed megakaryocyte-erythroid progenitors, EKLF is important for the bipotential lineage decision of these progenitors, and its high expression promotes erythropoiesis, while its expression is antagonized during megakaryopoiesis. In the case of HSCs, the authors reference to their previous publication for KLF1's expression in these cells- however, in this study nor in the current study, there is no western blot documented to convincingly show that KLF1 protein is expressed at detectable levels in these cells. For T cells, the authors have referenced a study which is based on ectopic expression of KLF1. For NK cells, the authors reference bioGPS: however, upon inspection, this is also questionable.

2) The current study rests on the premise that KLF1 is expressed in HSCs, NK cells and leukocytes, and the references cited are not sufficient to make this assumption, for the reasons mentioned in the first point. Therefore, the authors will have to show both KLF1 mRNA and protein levels in these cells, and also compare them to the expression levels seen in KLF1 wild type erythroid cells along with knockout erythroid cells as controls, for context and specificity.

Regarding the novelties of the current story. Besides demonstration of the independence of the healthy longevity characteristics on age, gender, and genetic background, as exemplified by the tumor resistance, another novelty of the current study is that the healthy longevity characteristics, in particular the tumor resistance and extended lifespan, could be transferred by one-time long-term transplantation of the Eklf(K74R) bone marrow mononuclear cells from young Eklf(K74R) mice to young WT mice. Also, since submission of the last version of the paper, we have carried out new experiments, including the characterization of the anti-cancer capability of NK cells (new Fig. 6) as well as assay of the tumor-resistance of Eklf(K74R) mice to hepatocellular carcinoma (new Fig. 1E), etc.

We have also modified the title, Abstract, and different parts of the text to highlight the novelties of the current study.

As to the expression of EKLF in different hematopoietic blood cell types, we have now added a paragraph in Result (p.6 and p.7) describing what have been known in literature in relation to our data presented in the paper. Importantly, following the reviewer’s comments, we have since carried out Western blot analysis of EKLF expression in NK, T, and B cells (p. 6, p.7 and new Fig. S4B). Also noted is that the level of EKLF in B cells is very low and only could be detected by RT-qPCR (Fig. S4C) and RNA-Seq (Bio-GPS database)

3) To get to the mechanism driving the reduced susceptibility to tumorigenesis and increased life span phenotypes in EKLF K74R mice, the authors report some observations- However, how these observations are connected to the phenotypes is unclear.

a. For example, in Figure S3, they report that the frequency of NK1.1+ cells is higher in the mutant mice. The significance of this in relation to EKLF expression in these cells and the tumorigenesis and life span related phenotypes are not described. Again, as mentioned in the second point, KLF1 protein levels are not shown in these cells.

b. In Figure 4, the authors show mRNA levels of immune check point genes, PD-1 and PD-l1 are lower in EKLF K74R mice in PB, CD3+ T cells and B220+ B cells. Again, the questions remain on how these genes are regulated by EKLF, and whether and at what levels EKLF protein is expressed in T cells and B cells relative to erythroid cells. Further, while the study they reference for EKLF's role in T cells is based on ectopic expression of EKLF in CD4+ T cells, in the current study, CD3+ T cells are used. Also, there are no references for the status of EKLF in B cells. These details are not discussed in the manuscript.

Regarding this part of the questions and comments by the reviewer.

First, we have since assayed the effect of the K74R substitution of EKLF on the in vitro cancer cell-killing ability of NK cells (termed NK1.1 cells in the previous version). The data showed that NK(K74R) cells have higher ability than the WT NK cells (new Fig. 6). This property together with the higher expression level of NK(K74R) cells in 24 month-old Eklf (K74R) mice than NK cells in 24 month-old WT mice would contribute to the higher tumor-resistance of the Eklf (K74R) mice. This point is also addressed on p. 8 andp.9.

Second, as stated in previous sections, we have since carried out comparative Western blot analysis of the expression of EKLF protein in NK, CD3 T, and B cells of the WT and Eklf(K74R) mice, respectively (please see the new Fig. S4B). Also, description regarding what are known in literature in relation to our data on the expression of EKLF protein/ Eklf mRNA in different types of hematopoietic blood cells is now included in the Result (please see p.6 and p.7). Notably though, the level of EKLF protein in B cells was too low to be detected by WB (Fig. S4B).

4) The authors perform comparative proteomics in the leukocytes of EKLF K74R and WT mice as shown in Figure S5. What is the status of EKLF levels in the mutant lysate vs wild type lysates based on this analysis? More clarity needs to be provided on what cells were used for this analysis and how they were isolated since leukocytes is a very broad term.

The leukocytes used by us were isolated from the peripheral blood after removal of red blood cells, as described in the Materials and Methods.

Also, the Western blot analysis of EKLF expression in the lysates of leukocytes/ white blood cells (WBC) has been shown previously, now presented in the new Figure S4A.

5) In the discussion the authors make broad inferences that go beyond the data shown in the manuscript. They mention that the tumorigenesis resistance and long lifespan is most likely due to changes in transcription regulatory properties and changes in global gene expression profile of the mutant protein relative to WT leukocytes. And based on reduced mRNA levels of Pd-1 Pd-l1 genes in the CD3+ T cells and B220+ B cells from mutant mice, they "assert" that EKLF is an upstream regulator of these genes and regulates the transcriptomes of a diverse range of hematopoietic cells. The lack of a ChIP assay to show binding of WT EKLF on genes in these cells and whether this binding is reduced or abolished in the mutant cells, make the above statements unsubstantiated.

We have since carried out ChIP-PCR analysis of EKLF-binding in the Pd-1 promoter (new Fig. S5). The data showed that EKLF was bound on the CACCC box at -103 of the promoter in WT CD3+T as well as in CD3+T(K74R) cells. This result is discussed on p.7.

6) Where westerns are shown, the authors need to show the molecular weight ladder, and where qPCR data are shown for EKLF, it will be helpful to show the absolute levels and compare these levels to those in erythroid cells, along the corresponding EKLF knock out cells as controls.

We have since included the molecular weight markers by the side of Western blots in Fig. S4. Also, we have added a new figure (Fig.S4C) showing the comparison of the expression levels of Eklf mRNA in B cells and CD3+ T cells to the mouse erythroleukemia (MEL) cells, as analyzed by RT-qPCR.

Also, as indicated now in the Material and Methods section, the specificity of the primers used for RT-qPCR quantitation of mouse Eklf mRNA has been validated before by comparative analysis of wild type and EKLF-knockout mouse erythroid cells (Hung et al., IJMS, 2020).

7) Figure S1D does not have a figure legend. Therefore, it is unclear what the blot in this figure is showing. In the text of the manuscript where they reference this figure, they mention that the levels of the mutant EKLF vs WT EKLF does not change in peripheral blood, while in the figure they have labeled WBCs for the blot, and the mRNA levels shown do seem to decrease in the mutant compared to WT peripheral blood.

We apologize for this ignorance on our side. The data shown in the original Fig. SID (new Fig. S4A) are from Western blot analysis of EKLF protein and RT-qPCR analysis of Eklf mRNA in leukocytes/ white blood cells (WBC) isolated from the peripheral blood samples. We have now added back the figure legend and also rewritten the corresponding description in the text on p.6.

Reviewer #3 (Public Review):

Hung et al provide a well-written manuscript focused on understanding how Eklf mutation confers anticancer and longevity advantages in vivo. The work is fundamental and the data is convincing although several details remain incompletely elucidated. The major strengths of the manuscript include the clarity of the effect and the appropriate controls. For instance, the authors query whether Eklf (K74R) imparts these advantages in a background, age, and gender dependent manner, demonstrating that the findings are independent. In addition, the authors demonstrate that the effect is not the consequence of the specific amino acid substitution, with a similar effect on anticancer activity. Furthermore, the authors provide some evidence that PD-1 and PDL-1 are altered in Eklf (K74R) mice.

Here we thank the encouraging comments by this reviewer.

Finally, they demonstrate that the effects are transferrable with BMT. Several weaknesses are also evidence. For instance, only melanoma is tested as a model of cancer such that a broad claim of "anti-cancer activity" may be somewhat of an overreach.

We have now included new data showing that the Eklf(K74R) mice also carry a higher anti-cancer ability against hepatocellular carcinoma than the WT mice (new Fig. 1E).

It is also unclear why a homozygous mutation is needed when only a small fraction of cells during BMT can confer benefit. It is also difficult to explain how transplanted donor Eklf (K74R) HSCs confer anti-melanoma effect 7 and 14 days after BMT.

First, these two observations not necessarily conflict with each other. It is likely that homozygosity, but not heterozygosity, of the K74R substitution in EKLF allows one or more types of hematopoietic blood cells to gain new functions, e.g. the higher cancer cell- killing capability of NK(K74R) cells (new Fig. 6), that help the mice to live long and healthy. Also, the data in Fig. 2D indicated that as low as 20% of the blood cells carrying homozygous Eklf(K74R) alleles in the recipient mice upon BMT could be sufficient to confer the mice a higher anti-cancer capability, likely in part due to cells such as NK(K74R). These points are now clarified in Discussion (p.9 and p.10).

Second, we think the NK(K74R) cells contributed a significant part to the anti-cancer capability of the transplanted Eklf(K74R) blood in the recipient WT mice. As documented in some literature, e.g. Ferreira et al., Journal of Molecular Medicine (2019), the hematopoietic lineage of the NK cells would be fully reconstituted as early as 2 weeks after BMT. Of course, there could be other still unknown factors/ cells that also contribute to the tumor-resistance of the recipient mice at 7 day following BMT. This point is now touched upon on p.8 and p.9.

Furthermore, it would be useful to see whether there are virulence marker alterations in the melanoma loci in WT vs Eklf (K74R) mice.

As responded in the Public Reviews, we will analyze this in future together with other types of tumors in a separate study.

Finally, the data in Fig 4c is difficult to interpret as decreased PD-1 and PDL-1 after knockdown of EKLF in vitro is not a useful experiment to corroborate how mutation without changing EKLF expression impacts immune cells. The work is impactful as it provides evidence that healthspan and lifespan may be modulated by specific hematological mutation but the mechanism by which this occurs is not completely elucidated by this work.

As described in a previous section, we have since also carried out ChIP-qPCR analysis of the binding of WT EKLF and EKLF (K74R) on the Pd-1 promoter (new Fig. S5).

Reviewer #1 (Recommendations For The Authors):

The authors present interesting melanoma model data but need to tone down their claim of multiple effects of their model system. It needs to be clear what is new and what is previously known.

As respond in the Public Reviews, we have since added new data on the tumor resistance of the Eklf(K74R) mice to hepatocellular carcinoma (new Fig. 1E). We have also modified the title as well as highlighted the novel points in the Abstract and text of the revised draft.

Reviewer #2 (Recommendations For The Authors):

In addition to the major concerns listed in the public review, the minor concerns that the authors could address are listed below:

1) Will be helpful to describe why was the pulmonary melanoma focus assay chosen for metastasis assay?

We now describe on p. 4 the rationale behind the initial choice of this assay for analysis of the anti-cancer capability of the Eklf(K74R) mice. Also, we have since included data from experiment using the subcutaneous cancer cell inoculation assay for comparative analysis of the anti-hepatocellular carcinoma capability of Eklf(K74R) and WT mice (Fig. 1E and p.5).

2) Reference #61 for B16-F10-luc cells cited in the methods does not have details on the generation of these cells. What these cells are and why this model was chosen needs to be described.

Sorry about not providing this information before. We now describe the generation of B16F10-luc cells in the Material and Methods section (p.13). The rationale of choosing the B16-F10 cells for the pulmonary lung foci assay is also added on p.4.

3) The DNA binding consensus site for EKLF needs to be expanded in the introduction.

This part has been taken care of now on p.13.

Reviewer #3 (Recommendations For The Authors):

Hung et al provide a well-written manuscript focused on understanding how Eklf mutation confers anticancer and longevity advantages in vivo. The work is fundamental and the data is convincing although several details remain incompletely elucidated.

1) Only melanoma is tested as a model of cancer such that a broad claim of "anti-cancer activity" may be somewhat of an overreach. The authors, therefore, need to provide evidence of a second type of malignancy to which Eklf mutation confers anticancer and longevity advantages or temper the claims in the discussion that the effect still needs to be tested in non-melanoma cancer models to determine the broad anti-cancer effect.

As responded in the Public Reviews, we have since shown that Eklf(K74R) mice also exhibited a higher resistance to the carcinogenesis of hepatocellular carcinoma (new Fig. 1E).

2) Why is a homozygous mutation needed when only a small fraction of cells during BMT can confer benefit of Eklf mutation? Is there evidence that the cellular effect is binary but only a few such cells are needed? This is confusing and requires further clarification.

As responded in the Public Reviews, these two observations not necessarily conflict with each other. It is likely that homozygosity, but not heterozygosity, of the K74R substitution in EKLF allows one or more types of hematopoietic blood cells to gain new functions, e.g. the higher cancer cell- killing capability of NK(K74R) cells (new Fig. 6), that help the mice to live long and healthy. Also, the data in Fig. 2D indicated that as low as 20% of the blood cells carrying homozygous Eklf(K74R) alleles in the recipient mice upon BMT could be sufficient to confer the mice a higher anti-cancer capability, likely in part due to cells such as NK(K74R). This point is now clarified in Discussion (p.9).

3) BMT typically requires at least 3-4 weeks to reconstitute the marrow compartment but the authors are able to see effects of Eklf mutation as early as 7 days following BMT. This is surprising and brings into question the mechanism of effect.

As responded in the Public Reviews, we think the NK(K74R) cells contributed a significant part to the anti-cancer capability of the transplanted Eklf(K74R) blood in the recipient WT mice. As documented in some literature, e.g. Ferreira et al., Journal of Molecular Medicine (2019), the hematopoietic lineage of the NK cells would be fully reconstituted as early as 2 weeks after BMT. Of course, there could be other still unknown factors/ cells that also contribute to the tumor-resistance of the recipient mice at 7 day following BMT (please see discussion of this point on p. 9).

4) It would be useful to see whether there are virulence marker alterations in the melanoma loci in WT vs Eklf (K74R) mice.

As responded in the Public Reviews, we will analyze this in future together with other types of tumors in a separate study.

5) The data in Fig 4c is difficult to interpret as decreased PD-1 and PDL-1 after knockdown of EKLF in vitro is not a useful experiment to corroborate how mutation WITHOUT changing EKLF expression impacts immune cells.

Indeed, the RNAi knockdown experiment only demonstrated a positive regulatory role of EKLF in Pd1/Pd-l1 gene expression. We have followed the reviewer’s suggestion and carried out ChIP-qPCR analysis and shown that the factor is bound on the Pd-1 promoter in both WT CD3+T cells and CD3+T(K74R) cells (new Fig. S5). We briefly discuss these data on p.7 in relation to the possible effect of K74R substitution of EKLF on Pd-1 expression.

We have now further clarified this point on p. 7.

eLife assessment

This useful manuscript focuses on understanding how an Eklf mutation confers anticancer and longevity advantages in vivo. The data demonstrate that Eklf (K74R) imparts such advantages in a background and age independent manner in both female and male mice, and that the benefits are transferable by bone marrow transplantation. Despite added data since a previous version, the paper unfortunately remains incomplete, as it is still unclear whether Eklf affects resistance to malignant progression/metastasis by modulating Pd1 or Pdl1, or by increasing NK cells. The authors provide evidence that supports in principle both mechanisms, and they do not resolve which mechanism is primarily involved.

Reviewer #1 (Public Review):

The authors Wang et al. present a study of a mouse model K74R that they claim can extend the life span of mice, and also has some anti-cancer properties in some standrad models of melanoma and hepatocellular carcinoma. Importantly, this mechanism seems to be mediated by the hematopoietic system, and protective effects can be transferred with bone marrow transplantation.

The authors have now adapted their manuscript reflecting the novelties of these studies. Overall, the study is a continuation and also corroboration of previous work, without clinical data yet. The authors have now expanded their work to a second mouse model, which strengthens their data.

Reviewer #2 (Public Review):

The manuscript by Wang et al., follows up on the group's previous publication on KLF1 (EKLF) K47R mice and reduced susceptibility to tumorigenesis and increased life span (Shyu et al., Adv Sci (Weinh). Sep 2022;9(25):e2201409. doi:10.1002/advs.202201409). In the current manuscript, the authors have described these phenotypes in the context of age, gender, genetic background, and hematopoietic transplantation of bone marrow mononuclear cells. Despite the revisions, significant conceptual concerns still remain in the study that make the inferences in the manuscript less convincing. Major concerns are listed below.

Major concerns:

1. The authors mention more than once in the manuscript that KLF1 is expressed in range of blood cells including hematopoietic stem cells, megakaryocytes, T cells and NK cells. In the case of megakaryocytes, studies from multiple labs have shown that while EKLF is expressed megakaryocyte-erythroid progenitors, EKLF is important for the bipotential lineage decision of these progenitors, and its high expression promotes erythropoiesis, while its expression is antagonized during megakaryopoiesis. In the case of HSCs, the authors reference to their previous publication for KLF1's expression in these cells- however, in this study nor in the current study, there is no western blot documented to convincingly show that KLF1 protein is expressed at detectable levels in these cells. For T cells, the authors have referenced a study which is based on ectopic expression of KLF1. For NK cells, the authors reference bioGPS: however, upon inspection, this is also questionable. As part of the revision, the authors have provided western blots in supplemental figure S4. However, these blots are difficult to interpret, since the EKLF bands for NK cells, and T cells are very faint and since the positive control EKLF band from MEL erythroid cell lysates is oversaturated, to interpret the data clearly. Therefore, although a quantification is shown, the representative blot included for EKLF protein levels is not convincing.

2. The current study rests on the premise that KLF1 is expressed in HSCs, NK cells and leukocytes, and the references cited are not sufficient to make this assumption, for the reasons mentioned in the first point. Therefore, the authors were asked to show both KLF1 mRNA and protein levels in these cells, and also compare them to the expression levels seen in KLF1 wild type erythroid cells along with knockout erythroid cells as controls, for context and specificity. The authors have now included western blots and mRNA levels and have compared it to MEL erythroid cells. This data raises additional questions. Overall, the mRNA levels in CD3+ T cells and B220+ B cells are approximately 3000 fold lower than MEL erythroid cells. Based on the information provided, although unclear, the assumption is that the MEL cell extracts are from undifferentiated cells. Therefore, this raises questions on the inference that the healthy aging phenotype is a result of cell intrinsic effects, since EKLF expression in these cells of interest is extremely low. This also allows for the consideration for potential systemic/indirect effects.

3. In the discussion, the authors make broad inferences that go beyond the data shown in the manuscript. For example, they mention that the tumorigenesis resistance and long lifespan is most likely due to changes in transcription regulatory properties and changes in global gene expression profile of the mutant protein relative to WT leukocytes. And based on reduced mRNA levels of Pd-1 Pd-l1 genes in the CD3+ T cells and B220+ B cells from mutant mice, they "assert" that EKLF is an upstream regulator of these genes and regulates the transcriptomes of a diverse range of hematopoietic cells. The authors were asked to perform a ChIP assay to show whether WT EKLF binds on these genes in these cells, and whether this binding is reduced or abolished in the mutant cells, to substantiate the above statements. The authors have now included a ChIP assay in Figure S5. The data on WT EKLF and K74R EKLF on Pd-1 promoter shows that both forms of EKLF bind at similar levels. Therefore, the mechanism remains unclear, and there is insufficient discussion on how their data support cell intrinsic differences in transcriptional regulation between WT and mutant EKLF.

Reviewer #3 (Public Review):

Hung et al provide a well-written manuscript focused on understanding how Eklf mutation confers anticancer and longevity advantages in vivo.

The authors were responsive to the reviewers comments in some aspects. However, the manuscript continues to suffer from significantly overstated claims that are not mitigated in the revision. While additional data has been added, it is unclear how this new data provides clarity to the overall premise of this observational study. Importantly, the authors have added a second model of hepatocellular carcinoma with findings that are consistent with the melanoma model previously reported. In addition, they make more clear that the previously published manuscript on this subject was use of older donors for BMT while now they use younger donors. This is at best incremental. It remains unclear whether Eklf exerts its effect on resistance to malignant progression / metastasis by modulating Pd1 or Pdl1 vs. increasing NK cells as the authors provide evidence of both and do not resolve which mechanism is primarily involved. Finally, there is no evidence that Eklf mutation confers "an anti-disease and anti-aging" effect as at best the data provides evidence of resistance to malignant progression / metastasis in melanoma and hepatoma models.

The work is impactful as it provides evidence of anticancer effect of a specific hematological mutation but the mechanism by which this occurs is not completely elucidated by this work.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

Congratulations on the very nice structure! In my opinion, which you can feel free to take or leave, this would work better as a short report focused on the improvement of the structure relative to the current published model. To my mind, while the functional and dimerization studies are supportive of the cryo-EM studies (specifically, the purified protein is functional, and does tend to dimerize in various membrane mimetics), these experiments don't provide a lot of new mechanistic insight on their own. The dimerization, in particular, could be developed further.

Response: Thank you for the comments. We have chosen to stick with the current article format. That the protein is dimeric is exciting in our view and we are working to further define the functional significance of this formation.

Reviewer #2 (Recommendations For The Authors):

Ln 48. Abstract. "highlighting feature of the complex interface" sounds a bit vague. I was wondering if the authors considered including more specific findings here.

Response: This sentence has been removed.

Ln 149 and elsewhere. The authors refer to the previously published structure of HiSiaQM as "low resolution". It may just be me and likely not the intention of the authors, but this comes across as an attempt to diminish the validity of this previous work from another group, which is not necessary. I would recommend rewording these parts slightly, even if it is just to say "lower resolution" instead of "low resolution".

Response: It was not our intention to diminish the excellent work published by another group, we have changed “low resolution” to “lower resolution” throughout.

Ln 160. The authors state that the inward-open conformation is likely "the resting state of the transporter". I think this statement should be modified slightly to acknowledge that this is only true under these conditions, i.e. in the absence of the bilayer, membrane potential and chemical gradients.

Response: We have edited this as follows “That we observe the inward-open conformation without either a bound P-subunit or fiducial marker, suggests that this is the resting state of the transporter under experimental conditions (in the absence of a membrane bilayer, membrane potential and chemical gradients).”

Ln 202. I'm not convinced that the use of the word "probable" is appropriate here; "possible" would likely fit better in the absence of compelling evidence that this dimer forms in a bacterial cell membrane with physiological levels of HiSiaQM expression.

Response: We have changed “probable” to “possible”.

The authors show an SEC trace for DDM solubilised protein, which is a single peak, whereas the LMNG extracted protein has 2 distinctly different elution profiles depending on the LMNG concentration. Was the same phenomenon observed when varying the DDM concentration?

Response: We observed significantly more aggregation with DDM than L-MNG, so it was infrequently used and considerably less well characterised. In one purification, moderately higher DDM shifted the elution peak to be slightly later but retained a similar profile. Overall, we did not observe the same phenomenon of distinctly different elution profiles with DDM, but we have limited data.

Ln 245. The two positions cited as important for the elevator-type mechanism are the fusion helix and the dimer interface. However, there is no evidence that the dimer interface observed in this work has any relevance to the transport mechanism. To make this statement, the interface would need to be disrupted and the effects on transport evaluated.

Response: This has been edited as follows. “Evident in our cryo-EM maps are well-defined phospholipid densities associated with areas of HiSiaQM that may be important for the function of an elevator-type mechanism (Figure 4), but require further testing.”

Ln 257. The authors state that the lipids form "specific and strong interactions" with the protein, but without knowing the identity of the lipids present, it is difficult to say anything about the specificity of this interaction. I think the authors could consider rewording this. Response: We have edited this by removing the term “specific” and describing the lipid interactions only as strong interactions.

Ln 270. The authors identify a lipid-binding site and residues that likely interact with the headgroup. It would be interesting if the authors could speculate on the purpose of this lipid binding site and how it could affect transport. The residues are not conserved, which the authors suggest reflects the variety of lipid compositions in different bacteria. Are the authors suggesting that this lipid binding site is a general feature for all fused TRAP transporters and that the identity of the lipid changes depending on the species?

Response: Yes, we speculate that the lipid binding site may be a general feature for fused TRAP transporters. We have added speculation about this binding site, specifically that “the fusion helix and concomitant lipid molecule may provide a more structurally rigid scaffold than a Q-M heterodimer, i.e., PpSiaQM, although how this impacts the elevator transition requires further testing” at Line 283.

Though we believe that a binding pocket is likely found in a number of fused TRAPs (based on sequence and Alphafold predictions, e.g., FnSiaQM and AaSiaQM), we have now acknowledged that some fusions may not necessarily bind a lipid molecule here, by stating “While this binding pocket is likely found in a number of fused TRAPs (based on sequence predictions, e.g., FnSiaQM and AaSiaQM in Supplementary Figure 8), it is not clear whether they also bind lipids here without experimental data” at Line 290.

Ln 306. The authors state that the HiSiaPQM has a 10-fold higher transport activity than PpSiaPQM. Unless the transport assays were performed in parallel (to mitigate small changes in experimental set-up) and the reconstitution efficiency for each proteoliposome preparation was carefully analysed, it is very difficult for this to be a meaningful comparison. Even if the amount of protein incorporated into the proteoliposomes is quantified (e.g. by evaluating protein band intensity when the proteoliposomes are analysed using SDS-PAGE), this does not account for an inactive protein that was incorporated, nor the proportion of the protein that was incorporated in the inside-out orientation, which would be functionally silent in these assays. I'm not suggesting these assays actually need to be performed, but I think the text should be modified to reflect what can actually be compared.

Response: We agree with the reviewer that a meaningful comparison is difficult to make without a careful analysis of the reconstitution efficiency and have modified the text to reflect this. We have altered the paragraph beginning at Line 319 to the following: “The fused HiSiaPQM system appears to have a higher transport activity than the non-fused PpSiaPQM system. With the same experimental setup used for PpSiaPQM (5 M Neu5Ac, 50 M SiaP) (33), the accumulation of [3H]-Neu5Ac by the fused HiSiaPQM is ~10-fold greater. Although this difference may reflect the reconstitution efficiency of each proteoliposome preparation, it is possible that it has evolved as a result of the origins of each transporter system—P. profundum is a deep-sea bacterium and as such the transporter is required to be functional at low temperatures and high pressures… ”

Ln 335. "S298A did not show an effect on growth when mutated to alanine previously." Suggest changing "S298A" here to "S298".

Response: This has been changed.

Ln 340. In addition to PpSiaQM, the large cavity was also presumably observed in the lower resolution structure of HiSiaQM?

Response: The cavity is detectable in the lower resolution structure (7qe5), though very poorly defined by the density. Furthermore, the AlphaFold model fitted to this density has positioned sidechains inside the cavity, which we consider very likely to be an error (in comparison to our structures, VcINDY and our estimates of the volume required to house sialic acid). The cavity is generally much better defined by the structures we have referenced.

Ln 345. Reference missing after "previously reported"? Response: This has been added. Measuring the affinity for the P-to-QM interaction is very useful, but it would have enhanced the study if some of the residues identified as important for this interaction (detailed on p.13) had been tested for their contributions to binding using this approach.

Response: We do aim to perform this assay with these mutants in the future, but are also developing parallel assays to further test this interaction in different membrane mimetics.

Ln 436. As stated previously, it is more accurate to say that "this is the most stable conformation" under these conditions.

Response: We have edited this to say “The ‘elevator down’ (inward-facing) conformation is preferred in experimental conditions”. We have also changed the last sentence of this paragraph to say “However, the dimeric structures we have presented have no other proteins bound, yet exist stably in the elevator down state, suggesting this is the most stable conformation in experimental conditions, where there is no membrane bilayer, membrane potential, or chemical gradient present.”

Ln 438. "Lipids associated with HiSiaQM are structurally and mechanistically important." This conclusion is not supported by the data presented; there is no evidence that the bound lipids influence the mechanism at all. The lipids observed are certainly interestingly placed and one could speculate about their relevance, but this statement of fact is not supported. Therefore, their importance to the mechanism needs to be tested or this conclusion needs to be substantially softened.

Response: We have softened this statement by changing it to “Lipids have strong interactions with HiSiaQM and are likely to be important for the transport mechanism.”

Reviewer #3 (Recommendations For The Authors):

The fact that HiSiaQM samples consist of a mixture of compact monomer and dimer is clear, from Fig. S5 and S6. However, the analysis displayed in Fig 3 and Fig S4 would require more explanation. To my understanding, it requires the values of the sedimentation and diffusion coefficients. It could be good to provide the experimental values of D, and explain a little more about the method in the material and method section.

Response: Yes, the analysis requires the experimental diffusion coefficients. These have been added to the Figure 3 and S4 legends and more detail has been added to the method section.

In addition, I am puzzled when reading, in the legend of Fig 3, considerations that peak 2 could not correspond to a monomer or trimer: do these sentences correspond to other mathematical solutions, or is a given frictional ratio considered, or do they refer to Fig. S5 analysis?

We can see where this confusion could arise from. These sentences do not correspond to a given frictional ratio or the Fig. S5 analysis (this is a separate, complementary analysis). For peak 2 not existing as a monomer is strictly a physical justification – with pure protein and an observed peak smaller than peak 2, a monomer is not possible for peak 2. For peak 2 not existing as a trimer is a mathematical solution using the s and D coefficients. The solutions identify that an unreasonably low amount of detergent would be bound to a trimer (32 molecules for L-MNG or 0 for DDM) to exist at those s and D values so we have ruled the trimer out. Reassuringly, the complementary analysis in Fig. S5/S6 agrees with the monomer-dimer outputs from the s and D analysis. We have adjusted the text in the legends of Fig. 3 and S4 to better convey these points.

Reviewer #1 (Public Review):

Summary:<br /> TRAP transporters are an unusual class of secondary active transporters that utilize periplasmic binding proteins to deliver their substrates. This paper contributes a new 3 Å structure of the Haemophilus influenzae TRAP transporter. The structure joins two other recent cryo-EM structures of TRAP transporters, including a lower resolution structure of the same H. influenzae protein (overall 4.7 Å), and a ~3 Å structure of a homologue from P. profundum. In addition to reporting a higher resolution cryo-EM structure, the authors also recapitulate protein activity in a reconstituted system, investigate protein oligomerization using analytic ultracentrifugation, and evaluate interactions and function in "mix and match" configurations with periplasmic subunits from other homologues.

Strengths:<br /> The strength of the paper is that the better resolution cryo-EM data permits sidechain assignment, the identification of bound lipids, and the identification of sodium ions. It is important to get this structure out there, since the resolution passes an important threshold for model building accuracy. The current structure nicely explains a lot of prior mutagenesis data on the H. influenzae TRAP. This is also the first structure of a TRAP protein to be solved without a fiducial, although the overall structure is not very different than those solved with fiducials.

Weaknesses:<br /> The experiments examining the monomer/dimer equilibrium appear somewhat preliminary. The biological or mechanistic importance of oligomerization is not established, so these experiments are inherently of limited scope. Moreover, cryo-EM datasets exhibit both parallel and antiparallel dimers, the latter of which are clearly not biologically relevant. It is probably impossible to distinguish these in the AUC experiments, which makes interpretation of these experiments more difficult.

Similarly, the importance of the lipid binding sites observed in cryo-EM aren't experimentally established (for example by mutating the binding site) and it is thus unknown whether they are important for function (as the authors acknowledge).

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the membrane component of the sialic acid-specific TRAP transporter, SiaQM (HiSiaQM), from H. influenzae, is structurally characterized. TRAP transporters are substrate binding protein (SBP)-dependent secondary-active transporters, and HiSiaQM is the most comprehensively studied member of this family. While all previous work on fused TRAP transporter membrane proteins suggests that they are monomeric (including the previous structural characterization of HiSiaQM by a different group), a surprising finding from this work is the observation that HiSiaQM can form higher oligomers, consistent with it being a dimer. These higher oligomeric states were initially observed after extraction of the protein with LMNG detergent, but were also observed in DDM detergent, amphipol and nanodiscs using analytical ultracentrifugation (AUC). Structural characterization of dimeric HiSiaQM revealed 2 arrangements, a parallel and antiparallel arrangements, the latter of which is unlikely to be physiologically relevant.

The higher resolution of this new structure of HiSiaQM (2.2-2.7 Å compared to 4.7 Å for the previous structure) facilitated the assignment of bound lipids at the dimer interface and a lipid molecule embedded in each of the protomers; allowed for a clearer refinement of the Na+ and putative substrate binding sites, which differ slightly from the previous structure; and produced better modelled side chains for the residues involved in the SBP:HiSiaQM interaction. The authors developed a useful AUC-based assay to determine the affinity for this interaction revealing an affinity of 65 µM. Finally, the authors make the very interesting observation that a sialic acid specific SBP from a different TRAP transporter can utilize HiSiaQM for transport, contrary to previous observations, revealing for the first time that TRAP membrane components can recognize multiple SBPs.

Overall, this is a well written and presented manuscript detailing some interesting new observations about this interesting protein family. One of the main findings, that the protein can form a dimer, is supported by data, but the physiological relevance of this is questionable, and the possibility that this is artefactual has not been ruled out. Conclusions regarding the mechanistic importance of the lipid bindings sites is not currently supported by the data.

Strengths:<br /> The main strength of this work is the increased resolution of HiSiaQM, which allows for much more precise assignment of side chains and their orientation. This will be of importance for subsequent mechanistic studies on the contributions of these residues to Na+ and sialic acid binding and conformational changes.<br /> The observation of the lipids, especially the lipid embedded near the fusion helix, is an intriguing observation, which lays the groundwork for future work to understand the lipid-dependence of these transporters.<br /> The development of the AUC-based approach to measure SBP affinity for the membrane component will likely prove be useful to future studies.

Weaknesses:<br /> One of the main results from this work is the observation that HiSiaQM can form a dimer. Two arrangements were observed, parallel and antiparallel, the latter of which is almost certainly physiologically irrelevant as it would preclude essential interactions with the extracytoplasmic substrate binding protein. As acknowledged by the author, this non-physiological arrangement is likely a consequence of protein preparation (overexpression, extraction, purification, etc.). However, if one dismisses the antiparallel arrangement as non-relevant and an artefact of protein preparation, it is difficult for the parallel arrangement to maintain its credibility, as it was also processed in the same way. This is especially true when one considers that there is only 100 Å2 buried surface area in the parallel arrangement that does not involve any sidechains; it is difficult to envisage this as a specific interaction, e.g. compared to related proteins that have ~2000 Å2 buried surface area. Unless this dimerization is observed in a bacterial membrane at physiological protein concentrations, it is difficult to rule out the possibility that the observed dimerization is merely an artefact caused by the expression, purification and concentration of the protein.

The manuscript contains some excellent structural analysis of this protein, whose higher resolution reveals some new and interesting insight. However, a weakness of the current work is a lack of validation of these observations using other approaches. For example, lipid interactions are observed in the structure that the authors claim is mechanistically important. However, without disrupting these interactions to look at the effect on transport, this conclusion is not supported. Similarly, the authors use their structure to predict residues that are important for the SBP:membrane protein interaction, and they develop an AUC-based binding assay to study this interaction, but they do not test their predictions using this approach.

Reviewer #3 (Public Review):

Summary:<br /> The manuscript reports new molecular characterization of the Haemophilus influenza tripartite ATP-independent periplasmic (TRAP) transporter of N-acetylneuraminate (Neu5Ac). This membrane transporter is important for the virulence of the pathogen. H. influenza lacks Neu5Ac biosynthetic pathway, and utilizes the TRAP transporter to import it. Neu5Ac is used as a nutrient source but also as a protection from human immune response. The transporter is composed of two fused membrane subunits, HiSiaQM, and one soluble, periplasmic subunit HiSiaP. HiSiaP, by binding to the substrate Neu5Ac, changes its conformation, allowing its binding to HiSiaQM, followed by Neu5Ac and Na+ transport to the cytoplasm. The combination of structural, biophysical and biochemical approaches provides a solid basis for describing the functioning of the Haemophilus influenza Neu5Ac TRAP transporter, which is essential for the pathogen virulence.

Strengths:<br /> The paper describes the electron microscopy structure of HiSiaQM, thanks to its solubilization in L-MNG followed by exchange to amphipol or nanodisc. In these conditions, HiSiaQM consists in a mixture of monomers and dimers, as characterized by analytical ultracentrifugation. The cryo-EM analysis shows two types of dimers: one in an antiparallel configuration, which is artifactual, and a parallel one, which may be physiologically relevant. Cryo-EM on the dimers allows high resolution (≈ 3 Å) structure determination. The structure is the first one of a fused SiaQM, and is the first obtained without megabody. The work highlights structural elements (fusion helix, lipids) that could modulate transport. The authors checked the functionality of the purified HiSiaQM, which, after reconstitution in liposome, displays a significantly larger Neu5Ac transport activity compared to the non-fused PpSiaQM homolog. The work identifies Na+ binding sites, and the putative Neu5Ac binding site. From analytical ultracentrifugation using fluorescently labelled HiSiaP, the authors show that HiSiaP is able to interact with HiSiaQM monomer and dimer, with a low but physiologically relevant affinity. HiSiaP interaction with HiSiaQM was modelled using AlphaFold2, and discussed in view of published activity on mutants, and new transport activity assays using SiaQM and SiaP from different organisms. In conclusion, the combination of structural, biophysical and biochemical approaches provides a solid basis for describing the functioning of this TRAP fused transporter.

Weakness: This work evidences in vitro a HiSiaQM dimer, whose in vivo relevance is not ascertained. However, the authors are very careful, they do not to over-interpret their data, and their conclusions regarding the transporter structure and function are valid irrespective of its state of association.

Author Response

eLife assessment

This useful study uses a mouse model of pancreatic cancer to examine mitochondrial mass and structure in atrophying muscle along with aspects of mitochondrial metabolism in the same tissue. Most relevant are the solid transcriptomics and proteomics approaches to map out related changes in gene expression networks in muscle during cancer cachexia.

Response: We very much appreciate the positive feedback from the editors on our article and are delighted to have it published in eLife. Our sincere thanks to the Reviewers for their positive feedback on our work, and for their insightful and constructive comments.

Reviewer #1 (Public Review):

Summary:

This important study provides a comprehensive evaluation of skeletal muscle mitochondrial function and remodeling in a genetically engineered mouse model of pancreatic cancer cachexia. The study builds upon and extends previous findings that implicate mitochondrial defects in the pathophysiology of cancer cachexia. The authors demonstrate that while the total quantity of mitochondria from skeletal muscles of mice with pancreatic cancer cachexia is similar to controls, mitochondria were elongated with disorganized cristae, and had reduced oxidative capacity. The mitochondrial dysfunction was not associated with exercise-induced metabolic stress (insufficient ATP production), suggesting compensation by glycolysis or other metabolic pathways. However, mitochondrial dysfunction can lead to increased production of ROS/oxidative stress and would be expected to interfere with carbohydrate and lipid metabolism, events that are linked to cancer-induced muscle loss. The data are convincing and were collected and analyzed using state-of-the-art techniques, with unbiased proteomics and transcriptomics analyses supporting most of their conclusions.

Additional Strengths:

The authors utilize a genetically engineered mouse model of pancreatic cancer which recapitulates key aspects of human PDAC including the development of cachexia, making the model highly appropriate and translational.

The authors perform transcriptomic and proteomics analyses on the same tissue, providing a comprehensive analysis of the transcriptional networks and protein networks changed in the context of PDAC cachexia.

Weaknesses:

The authors refer to skeletal muscle wasting induced by PDAC as sarcopenia. However, the term sarcopenia is typically reserved for the loss of skeletal muscle mass associated with aging.

Response: We agree that the term sarcopenia initially refers to aged muscle, but its use has spread to other fields, including oncology (for example, in this article, which we quote: Mintziras I et al. Sarcopenia and sarcopenic obesity are significantly associated with poorer overall survival in patients with pancreatic cancer: Systematic review and meta-analysis. Int J Surg 2018;59:19-26). Actually, the term sarcopenia is now widely used in the literature and in the clinic to describe the loss of muscle mass and strength in cancer patients (see for example, this recent review: Papadopetraki A. et al. The Role of Exercise in Cancer-Related Sarcopenia and Sarcopenic Obesity. Cancers 2023;15;5856).

In Figure 2, the MuRF1 IHC staining appears localized to the extracellular space surrounding blood vessels and myofibers-which causes concern as to the specificity of the antibody staining. MuRF1, as a muscle-specific E3 ubiquitin ligase that degrades myofibrillar proteins, would be expected to be expressed in the cytosol of muscle fibers.

Response: We agree that MuRF1 IHC staining was also observed in the extracellular space, which was a surprise, for which we have no explanation to date.

Disruptions to skeletal muscle metabolism in PDAC mice are predicted based on mitochondrial dysfunction and the transcriptomic and proteomics data. The manuscript could therefore be strengthened by additional measures looking at skeletal muscle metabolites, or linking the findings to previous work that has looked at the skeletal muscle metabolome in related models of PDAC cachexia (Neyroud et al., 2023).

Response: We agree that our omics data could be strengthened by additional measures looking at skeletal muscle metabolites. It's an excellent suggestion to parallel the transcriptomic and proteomic data we obtained on the gastrocnemius muscle with the metabolomic data obtained by Neyroud et al. on the same muscle. These authors used another mouse model of PDAC than our KIC GEMM model, namely the allograft model implanting KPC cells (derived from the pancreatic tumor of KPC mice, another PDAC GEMM model) into syngeneic recipient mice. They carried out a proteomic study on the tibialis anterior muscle and a metabolomic study on the gastrocnemius muscle. Proteomics data identified in particular a KPC-induced reduction in the relative abundance of proteins annotating to oxidative phosphorylation, consistently with our data showing reduced mitochondrial activity pathways. Metabolomic data showed reduced abundance of many amino acids as expected, and of intermediates of the mitochondrial TCA cycle (malate and fumarate) in KPC-atrophied muscle consistently with reduced mitochondrial metabolic pathways that we illustrated. In contrast, metabolites that were increased in abundance included those related to oxidative stress and redox homeostasis, which is not surprising regarding the profound oxidative stress affecting atrophied muscle. Finally, we noted in Neyroud's metabolomic data the dysregulation of certain lipids and nucleotides in atrophied muscle, which is very interesting to relate to our study describing alterations in lipid and nucleotide metabolic pathways.

Reviewer #2 (Public Review):

The present work analyzed the mitochondrial function and bioenergetics in the context of cancer cachexia induced by pancreatic cancer (PDAC). The authors used the KIC transgenic mice that spontaneously develop PDAC within 9-11 weeks of age. They deeply characterize bioenergetics in living mice by magnetic resonance (MR) and mitochondrial function/morphology mainly by oxygraphy and imaging on ex vivo muscles. By MR they found that phosphocreatine resynthesis and maximal oxidative capacity were reduced in the gastrocnemius muscle of tumor-bearing mice during the recovery phase after 6 minutes of 1 Hz electrical stimulation while pH was reduced in muscle during the stimulation time. By oxygraphy, the authors showed a decrease in basal respiration, proton leak, and maximal respiration in tumor-bearing mice that was associated with the decrease of complex I, II, and IV activity, a reduction of OXPHOS proteins, mitochondrial mass, mtDNA, and to several morphological alterations of mitochondrial shape. The authors performed transcriptomic and proteomic analyses to get insights into mitochondrial defects in the muscles of PDAC mice. By IPA analyses on transcriptomics, they found an increase in the signature of protein degradation, atrophy, and glycolysis and a downregulation of muscle function. Focusing on mitochondria they showed a downregulation mainly in OXPHOS, TCA cycle, and mitochondrial dynamics genes and upregulation of glycolysis, ROS defense, mitophagy, and amino acid metabolism. IPA analysis on proteomics revealed major changes in muscle contraction and metabolic pathways related to lipids, protein, nucleotide, and DNA metabolism. Focusing on mitochondria, the protein changes mainly were related to OXPHOS, TCA cycle, translation, and amino acid metabolism.

The major strength of the paper is the bioenergetics and mitochondrial characterization associated with the transcriptomic and proteomic analyses in PDAC mice that confirmed some published data of mitochondrial dysfunction but underlined some novel metabolic insights such as nucleotide metabolism.

There are minor weaknesses related to some analyses on mitochondrial proteins and to the fact that proteomic and transcriptomic comparison may be problematic in catabolic conditions because some gene expression is required to maintain or re-establish enzymes/proteins that are destroyed by the proteolytic systems (including the autophagy proteins and ubiquitin ligases). The authors should consider the following points.

Point 1. The authors used the name sarcopenia as synonymous with muscle atrophy. However, sarcopenia clearly defines the disease state (disease code: ICD-10-CM (M62.84)) of excessive muscle loss and force drop during ageing (Ref: Anker SD et al. J Cachexia Sarcopenia Muscle 2016 Dec;7(5):512-514.). Therefore, the word sarcopenia must be used only when pathological age-related muscle loss is the subject of study. Sarcopenia can be present in cancer patients who also experience cachexia, however since the age of tumor-bearing mice in this study is 7-9 weeks old, the authors should refrain from using sarcopenia and instead replace it with the words muscle atrophy/ muscle wasting/muscle loss.

Response: This issue has also been raised by the Reviewer #1. We agree that the term sarcopenia historically refers to aged muscle, but it is also used in oncology (for example, in this article, which we quote: Mintziras I et al. Sarcopenia and sarcopenic obesity are significantly associated with poorer overall survival in patients with pancreatic cancer: Systematic review and meta-analysis. Int J Surg 2018;59:19-26). Actually, the term sarcopenia is now widely used in the literature and in the clinic to describe the loss of muscle mass and strength in cancer patients (see for example, this recent review: Papadopetraki A. et al. The Role of Exercise in Cancer-Related Sarcopenia and Sarcopenic Obesity. Cancers 2023;15;5856).

Point 2. Most of the analyses of mitochondrial function are appropriate. However, the methodological approach to determining mitochondrial fusion and fission machinery shown in Fig. 5F is wrong. The correct way is to normalize the OPA1, MFn1/2 on mitochondrial proteins such as VDAC/porin. In fact, by loading the same amount of total protein (see actin in panel 5F) the difference between a normal and a muscle with enhanced protein breakdown is lost. In fact, we should expect a decrease in actin level in tumor-bearing mice with muscle atrophy while the blots clearly show the same level due to the normalization of protein content. Moreover, by loading the same amount of proteins in the gel, the atrophying muscle lysates become enriched in the proteins/organelles that are less affected by the proteolysis resulting in an artefactual increase. The correct way should be to lyse the whole muscle of control and tumor-bearing mice in an identical volume and to load in western blot the same volume between control cachectic muscles. Alternatively, the relative abundance of mitochondrial shaping proteins related to mitochondrial transmembrane or matrix proteins (mito mass) should compensate for the loading normalization. Because the authors showed elongated mitochondria despite mitophagy genes being up, fragmentation may be altered. Moreover, DNM1l gene is suppressed and therefore DRP1 protein must be analyzed. Finally, OPA 1 protein has different isoforms due to the action of proteases like OMA1, and YME1L that elicit different functions being the long one pro-fusion while the short ones do not. The authors must quantify the long and short isoforms of OPA1.

Response: We acknowledge that our analysis of a minor set of proteins involved in mitochondrial dynamics by Western blotting (Figure 5F) is basic and could have been improved. We thank the Reviewer for all the suggestions, which will be very useful in future projects studying the subject in greater depth and according to the molecular characteristics of each player in mitochondrial fusion, fission, mitophagy and biogenesis.

Point 3. The comparison of proteomic and transcriptomic profiles to identify concordance or not is problematic when atrophy programs are induced. In fact, most of the transcriptional-dependent upregulation is to preserve/maintain/reestablish enzymes that are consumed during enhanced protein breakdown. For instance, the ubiquitin ligases when activated undergo autoubiquitination and proteasome degradation. The same happens for several autophagy-related genes belonging to the conjugation system (LC3, Gabarap), the cargo recognition pathways (e.g. Ubiquitin, p62/SQSTM1) and the selective autophagy system (e.g. BNIP3, PINK/PARKIN) and metabolic enzymes (e.g. GAPDH, lipin). Finally, in case identical amounts of proteins have been loaded in mass spec the issues rise in point 2 of selective enrichment should be considered. Therefore, when comparing proteomic and transcriptomic these issues should be considered in discussion.

Response: We fully agree with the Reviewer that seeking concordance between transcriptomic and proteomic data in the case of an organ affected by a high level of proteolysis is a difficult business. Another major difficulty we discussed in the Discussion section of the article is the fact that there is no concordance between RNA and protein level for a good proportion of proteins, for multiple reasons, so each level of omics has to be interpreted independently to give information on the pathophysiology of the organ studied.

eLife assessment

This useful study uses a mouse model of pancreatic cancer to examine mitochondrial mass and structure in atrophying muscle along with aspects of mitochondrial metabolism in the same tissue. Most relevant are the solid transcriptomics and proteomics approaches to map out related changes in gene expression networks in muscle during cancer cachexia.

Reviewer #1 (Public Review):

Summary:<br /> This important study provides a comprehensive evaluation of skeletal muscle mitochondrial function and remodeling in a genetically engineered mouse model of pancreatic cancer cachexia. The study builds upon and extends previous findings that implicate mitochondrial defects in the pathophysiology of cancer cachexia. The authors demonstrate that while the total quantity of mitochondria from skeletal muscles of mice with pancreatic cancer cachexia is similar to controls, mitochondria were elongated with disorganized cristae, and had reduced oxidative capacity. The mitochondrial dysfunction was not associated with exercise-induced metabolic stress (insufficient ATP production), suggesting compensation by glycolysis or other metabolic pathways. However, mitochondrial dysfunction can lead to increased production of ROS/oxidative stress and would be expected to interfere with carbohydrate and lipid metabolism, events that are linked to cancer-induced muscle loss. The data are convincing and were collected and analyzed using state-of-the-art techniques, with unbiased proteomics and transcriptomics analyses supporting most of their conclusions.

Additional Strengths:<br /> The authors utilize a genetically engineered mouse model of pancreatic cancer which recapitulates key aspects of human PDAC including the development of cachexia, making the model highly appropriate and translational.

The authors perform transcriptomic and proteomics analyses on the same tissue, providing a comprehensive analysis of the transcriptional networks and protein networks changed in the context of PDAC cachexia.

Weaknesses:<br /> The authors refer to skeletal muscle wasting induced by PDAC as sarcopenia. However, the term sarcopenia is typically reserved for the loss of skeletal muscle mass associated with aging.

In Figure 2, the MuRF1 IHC staining appears localized to the extracellular space surrounding blood vessels and myofibers-which causes concern as to the specificity of the antibody staining. MuRF1, as a muscle-specific E3 ubiquitin ligase that degrades myofibrillar proteins, would be expected to be expressed in the cytosol of muscle fibers.

Disruptions to skeletal muscle metabolism in PDAC mice are predicted based on mitochondrial dysfunction and the transcriptomic and proteomics data. The manuscript could therefore be strengthened by additional measures looking at skeletal muscle metabolites, or linking the findings to previous work that has looked at the skeletal muscle metabolome in related models of PDAC cachexia (Neyroud et al., 2023).

Reviewer #2 (Public Review):

The present work analyzed the mitochondrial function and bioenergetics in the context of cancer cachexia induced by pancreatic cancer (PDAC). The authors used the KIC transgenic mice that spontaneously develop PDAC within 9-11 weeks of age. They deeply characterize bioenergetics in living mice by magnetic resonance (MR) and mitochondrial function/morphology mainly by oxygraphy and imaging on ex vivo muscles. By MR they found that phosphocreatine resynthesis and maximal oxidative capacity were reduced in the gastrocnemius muscle of tumor-bearing mice during the recovery phase after 6 minutes of 1 Hz electrical stimulation while pH was reduced in muscle during the stimulation time. By oxygraphy, the authors showed a decrease in basal respiration, proton leak, and maximal respiration in tumor-bearing mice that was associated with the decrease of complex I, II, and IV activity, a reduction of OXPHOS proteins, mitochondrial mass, mtDNA, and to several morphological alterations of mitochondrial shape. The authors performed transcriptomic and proteomic analyses to get insights into mitochondrial defects in the muscles of PDAC mice. By IPA analyses on transcriptomics, they found an increase in the signature of protein degradation, atrophy, and glycolysis and a downregulation of muscle function. Focusing on mitochondria they showed a downregulation mainly in OXPHOS, TCA cycle, and mitochondrial dynamics genes and upregulation of glycolysis, ROS defense, mitophagy, and amino acid metabolism. IPA analysis on proteomics revealed major changes in muscle contraction and metabolic pathways related to lipids, protein, nucleotide, and DNA metabolism. Focusing on mitochondria, the protein changes mainly were related to OXPHOS, TCA cycle, translation, and amino acid metabolism.

The major strength of the paper is the bioenergetics and mitochondrial characterization associated with the transcriptomic and proteomic analyses in PDAC mice that confirmed some published data of mitochondrial dysfunction but underlined some novel metabolic insights such as nucleotide metabolism.

There are minor weaknesses related to some analyses on mitochondrial proteins and to the fact that proteomic and transcriptomic comparison may be problematic in catabolic conditions because some gene expression is required to maintain or re-establish enzymes/proteins that are destroyed by the proteolytic systems (including the autophagy proteins and ubiquitin ligases). The authors should consider the following points.

Point1. The authors used the name sarcopenia as synonymous with muscle atrophy. However, sarcopenia clearly defines the disease state (disease code: ICD-10-CM (M62.84)) of excessive muscle loss and force drop during ageing (Ref: Anker SD et al. J Cachexia Sarcopenia Muscle 2016 Dec;7(5):512-514.). Therefore, the word sarcopenia must be used only when pathological age-related muscle loss is the subject of study. Sarcopenia can be present in cancer patients who also experience cachexia, however since the age of tumor-bearing mice in this study is 7-9 weeks old, the authors should refrain from using sarcopenia and instead replace it with the words muscle atrophy/ muscle wasting/muscle loss.

Point2. Most of the analyses of mitochondrial function are appropriate. However, the methodological approach to determining mitochondrial fusion and fission machinery shown in Fig. 5F is wrong. The correct way is to normalize the OPA1, MFn1/2 on mitochondrial proteins such as VDAC/porin. In fact, by loading the same amount of total protein (see actin in panel 5F) the difference between a normal and a muscle with enhanced protein breakdown is lost. In fact, we should expect a decrease in actin level in tumor-bearing mice with muscle atrophy while the blots clearly show the same level due to the normalization of protein content. Moreover, by loading the same amount of proteins in the gel, the atrophying muscle lysates become enriched in the proteins/organelles that are less affected by the proteolysis resulting in an artefactual increase. The correct way should be to lyse the whole muscle of control and tumor-bearing mice in an identical volume and to load in western blot the same volume between control cachectic muscles. Alternatively, the relative abundance of mitochondrial shaping proteins related to mitochondrial transmembrane or matrix proteins (mito mass) should compensate for the loading normalization. Because the authors showed elongated mitochondria despite mitophagy genes being up, fragmentation may be altered. Moreover, DNM1l gene is suppressed and therefore DRP1 protein must be analyzed. Finally, OPA 1 protein has different isoforms due to the action of proteases like OMA1, and YME1L that elicit different functions being the long one pro-fusion while the short ones do not. The authors must quantify the long and short isoforms of OPA1.

Point3. The comparison of proteomic and transcriptomic profiles to identify concordance or not is problematic when atrophy programs are induced. In fact, most of the transcriptional-dependent upregulation is to preserve/maintain/reestablish enzymes that are consumed during enhanced protein breakdown. For instance, the ubiquitin ligases when activated undergo autoubiquitination and proteasome degradation. The same happens for several autophagy-related genes belonging to the conjugation system (LC3, Gabarap), the cargo recognition pathways (e.g. Ubiquitin, p62/SQSTM1) and the selective autophagy system (e.g. BNIP3, PINK/PARKIN) and metabolic enzymes (e.g. GAPDH, lipin). Finally, in case identical amounts of proteins have been loaded in mass spec the issues rise in point 2 of selective enrichment should be considered. Therefore, when comparing proteomic and transcriptomic these issues should be considered in discussion.

Author Response

We thank the editors and reviewers for taking the time to provide a critical assessment of our manuscript. We are delighted our work was found to have merit, and will revise the manuscript based on their valuable input.

eLife assessment

This important study explores the physicochemical properties of SARS-CoV-2 N proteins with mutations that have been found in variants of concern but for which there is limited knowledge of their contribution to the biological activity of such variants. The evidence presented is solid; however, this study could be considerably improved by a more extensive analysis of LLPS in R203K/G204R and in the P31L mutants, as well as a more quantitative analysis of the LLPS droplets.

Reviewer #1 (Public Review):

The study is highly interesting and the applied methods are target-oriented. The biophysical characterization of viable N-protein species and several representative N-protein mutants is supported by the data, including polarity, hydrophobicity, thermodynamic stability, CD spectra, particle size, and especially protein self-association. The physicochemical parameters for viable N-protein and related coronavirus are described for comparison in detail. However, the conclusion becomes less convincing that the interaction of peptides or motifs was judged by different biophysical results, with no more direct data about peptide interaction. Additionally, the manuscript could benefit from more results involving peptide interaction to support the author's opinions or make expression more accurate when concerning the interaction of motifs. Although the authors put a lot of effort into the study, there are still some questions to answer.

Reviewer #2 (Public Review):

Summary:<br /> This work focuses on the biochemical features of the SARS-CoV-2 Nucleocapsid (N) protein, which condenses the large viral RNA genome inside the virus and also plays other roles in the infected cell. The N protein of SARS-CoV-2 and other coronaviruses is known to contain two globular RNA-binding domains, the NTD and CTD, flanked by disordered regions. The central disordered linker is particularly well understood: it contains a long SR-rich region that is extensively phosphorylated in infected cells, followed by a leucine-rich helical segment that was shown previously by these authors to promote N protein oligomerization.

In the current work, the authors analyze 5 million viral sequence variants to assess the conservation of specific amino acids and general sequence features in the major regions of the N protein. This analysis shows that disordered regions are particularly variable but that the general hydrophobic and charge character of these regions are conserved, particularly in the SR and leucine-rich regions of the central linker. The authors then construct a series of N proteins bearing the most prevalent mutations seen in the Delta and Omicron variants, and they subject these mutant proteins to a comprehensive array of biophysical analyses (temperature sensitivity, circular dichroism, oligomerization, RNA binding, and phase separation).

Strengths:<br /> The results include a number of novel findings that are worthy of further exploration. Most notable are the analyses of the previously unstudied P31L mutation of the Omicron variant. The authors use ColabFold and sedimentation analysis to suggest that this mutation promotes the self-association of the disordered N-terminal region and stimulates the formation of N protein condensates. Although the affinity of this interaction is low, it seems likely that this mutation enhances viral fitness by promoting N-terminal interactions. The work also addresses the impact of another unstudied mutation, D63G, that is located on the surface of the globular NTD and has no significant effect on the properties analyzed here, raising interesting questions about how this mutation enhances viral fitness. Finally, the paper ends with studies showing that another common mutant, R203K/G204R, disrupts phase separation and might thereby alter N protein function in a way that enhances viral fitness.

Weaknesses:<br /> In general, the results in the paper confirm previous ideas about the role of N protein regions. The key novelty of the paper lies in the identification of point mutations, notably P13L, that suggest previously unsuspected functions of the N-terminal disordered region in protein oligomerization. The paper would benefit from further exploration of these possibilities.

Reviewer #3 (Public Review):

Nguyen, Zhao, et al. used bioinformatic analysis of mutational variants of SARS-CoV-2 Nucleocapsid (N) protein from the large genomic database of SARS-CoV-2 sequences to identify domains and regions of N where mutations are more highly represented and computationally determined the effects of these mutations on the physicochemical properties of the protein. They found that the intrinsically disordered regions (IDRs) of N protein are more highly mutated than structured regions and that these mutations can lead to higher variability in the physical properties of these domains. These computational predictions are compared to in vitro biophysical experiments to assess the effects of identified mutations on the thermodynamic stability, oligomeric state, particle formation, and liquid-liquid phase separation of a few exemplary mutants.

The paper is well-written and easy to follow, and the conclusions drawn are supported by the evidence presented. The analyses and conclusions are interesting and will be of value to virologists, cell biologists, and biophysicists studying SARS-CoV-2 function and assembly. It would be nice if some further extrapolation or comments could be made regarding the effects of the observed mutations on the in vivo behavior and properties of the virus, but I appreciate that this is much higher-order than could be addressed with the approaches employed here.

Author Response

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

Reviewer #1 (Recommendations for The Authors):

Major comments:

1) The immunolabeling data in Figure S4 shows no change in puncta number but reduced puncta size in Kit KO. sIPSC data show reduced frequency but little change in amplitude. These data would seem contradictory in that one suggests reduced synaptic strength, but not number, and the other suggests reduced synapse number, but not strength. How do the authors reconcile these results?

Regarding the synaptic puncta, In Kit KO (or KL KO), we have not detected an overt reduction in the average VGAT/Gephyrin/Calbindin positive puncta density or puncta size per animal. With respect to puncta size, only in the Kit KO condition, and only when individual puncta are assessed does this modest (~10%) difference in size become statistically significant. In the revision, we eliminate this figure and focus on the per animal averages.

We interpret that the reduction in sIPSC and mIPSC frequency likely stems from a decreased proportion of functional synapse sites. The number of MLIs, their action potential generation, the density of synaptic puncta, and the ability of direct stimulation to evoke release and equivalent postsynaptic currents, are all similar in Control vs Kit KO. It is therefore feasible that a reduced frequency of postsynaptic inhibitory events is due to a reduced ability of MLI action potentials to invade the axon terminal, and/or an impaired ability for depolarization to drive (e.g. coordinated calcium flux) transmitter release. That is, while the number of MLIs and their synapses appear similar, the reduced mIPSC frequency suggests that there is a reduced proportion of, or probability that, Kit KO synapse sites that function properly.

2) Related to point 1, it would be helpful to see immunolabeling data from Kit ligand KO mice? Do these show the same pattern of reduced puncta size but no change in number?

Although we have not added a figure, we have now added experiments and a corresponding analysis in the manuscript. As we had previously for Kit KO, we now for KL KO conducted IHC for VGAT, Gephyrin, and Calbindin, and we analyzed triple-positive synaptic puncta in the molecular layer of Pcp2 Cre KL KO mice and Control (Pcp2 Cre negative, KL floxed homozygous) mice. We did not find a gross reduction in the average synaptic puncta size or density, or in the PSD-95 pinceau size. From this initial analysis, it appears that the presynaptic hypotrophy is more notable in the receptor than in the ligand knockout. We speculate that this is perhaps because the Kit receptor may have basal activity in the absence of Kit ligand, that Kit may serve a presynaptic scaffolding role that is lost in the receptor (but not the ligand) knockout, or simply that the embryonic timing of the Pax2 Cre vs Pcp2 Cre recombination events is more relevant to pinceaux development, especially as basket cells are born primarily prenatally.

3) The data using KL overexpression in PC (figure 4E,F) are intriguing, but puzzling. The reduction in sIPSC frequency and amplitude in the control PC is much greater than seen in the Kit or KL KO. The interpretation of these data, "Thus, KL-Kit levels may not set the number of MLI:PC release sites, but may instead influence the proportion of synapses that are functional for neurotransmission (Figure 4G)" is not clear and the reasoning here should be explained in more detail, perhaps in the discussion.

We have attempted to clarify this portion of the manuscript by eliminating the cartoon of the proposed model, and by revising and adding to the discussion. Either MLI Kit KO or PC KL KO seems to preserve the absolute number of MLI:PC anatomical synapse sites (IHC) but to reduce the proportion of those synapse that are contributing to neurotransmission (mIPSC). We speculate that sparse PC KL overexpression (OX) may either 1) weaken inhibition to surrounding control PCs by either diminishing KL OX PC to KL Control PC inhibition, and/or 2) act retrogradely through MLI Kit to potentiate MLI:MLI inhibition, reducing the MLI:PC inhibition at neighboring Control PCs.

Minor comments:

1) In the first sentence of the results, should "Figure 1A, B" be "Figure C, D"?

Yes, corrected.

2) The top of page 6 states "the mean mIPSC amplitude was ~10% greater in PC KL KO than in control", this does not appear to be the case in Figure 3E. control and KL KO look very similar here.

In this portion of the text citing the modest 10% increase in mIPSC amplitude, we are referring to the average amplitude of all individual mIPSC events in the PC KL KO condition; in the figure referred to by the reviewer (3E), we are instead referring to the average of all mIPSC event amplitudes per KL KO PC. Because of the dramatic difference in sample size for individual events vs cells, this modest difference rises to statistical, if not biological, significance. We include this individual event analysis only to suggest that, since we in fact saw a slightly higher event amplitude in the KL KO condition, it is unlikely that a reduced amplitude would have been a technical reason that we detected a lower event frequency.

3) Figure 3 D, duration, y-axis should be labelled "ms"

Event duration is no longer graphed or referenced. This has been replaced with total inhibitory charge.

Reviewer #2 (Recommendations For The Authors):

Methods:

• Pax2-Cre line: embryonal Cre lines sometimes suffer from germline recombination. Was this evaluated, and if yes, how?

The global loss of Kit signaling is incompatible with life, as seen from perinatal lethality in other Kit Ligand or Kit mutant mouse lines or other conditional approaches. Furthermore, a loss of Kit signaling in germ cells impedes fertility. Thus, while not explicitly ruled out, since conditional Pax2 Cre mediated Kit KO animals were born, survived, and produced offspring in normal ratios, we do not suspect that germline recombination was a major issue in this specific study.

• Include rationale for using different virus types in different studies (AAV vs. Lenti).

This rationale is now included and reflects the intention to achieve infection sparsity in the smaller and less dense tissue of perinatal mouse brains.

• How, if at all, was blinding performed for histological and electrophysiological experiments?

It was not possible for electrophysiology to be conducted blinded for the Kit KO experiments, owing to the subjects’ hypopigmentation. However, whenever feasible, resultant microscopy images or electrophysiological data sets were analyzed by Transnetyx Animal ID, and the genotypes unmasked after analysis.

• Provide justification for limiting electrophysiology recordings to lobule IV/V and why MLIs in the middle third of the molecular layer were prioritized when inhibition of PCs is dominated by large IPSCs from basket cells. Why were 2 different internals used for recording IPSCs and EPSCs in PCs and MLIs? While that choice is justified for action potential recordings, it provides poor voltage control in PC voltage clamp. Both IPSCs and EPSCs could have been isolated pharmacologically using a CsCl internal.

The rationale for regional focus has been added to the text. For MLI action potential recordings, we opted to sample the middle third of the molecular layer so that we would not be completely biased to either classic distal stellate vs proximal basket subtypes. It is our hope, in future optogenetic interrogations, to simultaneously record the dynamics of all MLI subtypes in a more unbiased way. With respect to internal solutions, we initially utilized a cesium chloride internal to maximize our ability to resolve differences in GABAA mediated currents, which was the hypothesis-driven focus of our study. While we agree that utilizing a single internal and changing the voltage clamp to arrive at per-cell analysis of Excitatory/Inhibitory input would have been most informative, our decision to utilize pharmacological methods was driven by our experience that achieving adequate voltage clamp across large Purkinje cells was often problematic, particularly in adult animals.

Introduction:

In the introduction, the authors state that inactivating Kit contributes to neurological dysfunction - their examples highlight neurological, psychiatric, and neurodevelopmental conditions.

The language has been changed.

General:

Using violin plots illustrates the data distribution better than bar graphs/SEM.

We have included violin plots throughout, and we have changed p values to numeric values, both in the interest of presenting the totality of the data more clearly.

Synapses 'onto' PCs sounds more common than 'upon' PCs.

We have changed the wording throughout.

Figure 1:

1F - there seems to be an antero-posterior gradient of Kit expression.

Though not explicitly pursued in the manuscript, it is possible that such a gradient may reflect differences in the timing of the genesis and maturation of the cerebellum along the AP axis. Regional variability is however now briefly addressed as a motivator for focused studies within lobules IV/V.

E doesn't show male/female ratios but only hypopigmentation.

This language has been corrected.

Figure 2 and associated supplementary figures:

2A/B: The frequency of sIPSCs is very high in PCs, making the detection of single events challenging. How was this accomplished? Please add strategy to the methods.

We have added methodological detail for electrophysiology analysis.

How were multi-peak events detected and analyzed? 'Duration' is not specified - do the authors refer to kinetics? If so, report rise and decay. It is likely impossible to show individual aligned sIPSCs with averages superimposed, given that sIPSCs strongly overlap. Alternatively, since no clear baseline can be determined in between events, and therefore frequency, amplitude, and kinetics quantification is near-impossible, consider plotting inhibitory charge.

Given the heterogeneity of events, we now do not refer to individual event kinetics. As suggested, we have now included an analysis of the total inhibitory charge transferred by all events during the recording epoch.

S2: Specify how density, distribution, and ML thickness were determined in methods. How many animals/cells/lobules?

For consistency with viral injections and electrophysiology, the immunohistochemical analysis was restricted to lobule IV/V. This is clearer in the revision and detail is added in the methods.

S3:

S3B: the labels of Capacitance and Input resistance are switched.

This has been corrected.

How were these parameters determined? Add to methods.

Added

In the previous figure the authors refer to 'frequency', in this figure to 'rate' - make consistent

This has been corrected.

D: example does not seem representative. Add amplitude of current pulse underneath traces.

We added new traces from nearer the group means and we now include the current trace.

F/G example traces (aligned individual events + average) are necessary.

We added example traces near the relevant group means for each condition.

Statement based on evoked IPCSs that 'synapses function normally' is a bit sweeping and can only be fully justified with paired recordings. Closer to the data would be the release probability of individual synapses is similar between control and Kit KO.

Paired recordings in both Kit Ligand and Kit receptor conditional knockout conditions is indeed an informative aim of future studies should support permit. For now, we have clarified the language to be more in line with the reviewer’s welcome suggestion.

S4:

Histological strategy cannot unambiguously distinguish MLI-PC and PC-PC synapses. Consider adding this confound to the text.

We have added this confound to the discussion.

The observation that the pinceau is decreased in size could have important implications for ephaptic coupling of MLI and PC and could be mentioned.

We agree and have added this notion to the discussion.

Y-label is missing in B.

Corrected.

Figure 3 and associated supplementary figures:

In the text, change PC-Cre to L7-Cre or Pcp2-Cre.

Changed

How do the authors explain a reduction in frequency, amplitude, and duration of sIPSCs in the KL KO but not in the Kit KO? Add to the discussion

We now address this apparent discordance in the discussion. Pax2 Cre mediates recombination weeks ahead of Pcp2 Cre. We therefore suspect that postnatal PC KL KO may be more phenotypic than embryonic MLI Kit KO because there is less time for developmental compensation. A future evaluation of the impact of postnatal Kit KO would be informative to this end.

As in Figure 2, plotting the charge might be more accurate.

We now plot total charge transfer.

Are the intrinsic properties in KL KO PCs altered? (Spontaneous firing, capacitance, input resistance).

We have added to the text that we found no difference in capacitance or input resistance between Purkinje cells from KL floxed homozygous Control animals versus those from KL floxed homozygous, PCP2 Cre positive KL KO animals. We plan to characterize both basal and MLI modulated PC firing in a future manuscript, especially since Pcp2 Cre mediated KL KO seems more phenotypic than Pax2 Cre mediated Kit KO, we agree that this seems a better testbed for investigating differences in both the basal, and the MLI-mediated modulations in, PC firing.

3D-F - Example traces would be desirable (see above, analogous to Fig. 2).

More example traces have been added.

Figure 4: 'In vivo mixtures' sounds unusual. Consider revision (e.g., 'to sparsely delete KL').

Changed

The observation that control PC sIPSC frequency is lower in KL OX PCs than in sham is interesting. This observation would be consistent with overall inhibitory synapse density being preserved. This could be evaluated with immunohistochemistry. For how far away from the injection area does this observation hold true?

Because we have now analyzed and failed to find an overt (per animal average) change in synaptic puncta size or density in the whole animal Control vs PCP2 Cre mediated KL KO conditions, we do not have confidence that it is feasible to pursue this IHC strategy in the sparse viral-mediated KL KO or OX conditions. To the reviewer’s valid point however, we intend to probe the spatial extent/specificity of the sparse phenomenon when we are resourced to complement the KL/Kit manipulations with transgenic methods for evaluating MLI-PC synapses specifically, potentially by GRASP or related methods that would not be confounded by PC-PC synapses. Transgenic MLI access would also facilitate determining the spatial extent to which opto-genetically activated MLIs evoke equivalent responses in Control vs KL manipulated PCs.

Y-legend in D clipped.

Corrected

Existing literature suggests that MLI inhibition regulates the regularity of PC firing - this could be tested in Kit and KL mutants.

For now, based upon transgenic animal availability, we have now included an evaluation of PC firing in the (Pax2 Cre mediated) Kit KO condition. PC average firing frequency, mean ISI, and ISI CV2 were not significantly different across genotypes. A KS test of individual ISI durations for Control vs Kit KO did reveal a difference (p<0.0001). We have added a supplementary figure (S6) with this data. It is possible that in the more phenotypic PC KL KO condition that we may find a difference in these PC spiking patterns of PC firing, however, we are also eager to test in future studies whether postnatal KL or Kit KO impairs the ability of MLI activation to produce pauses or other alterations in PC firing or in PF-PC mediated plasticity.

Reviewer #3 (Recommendations For The Authors):

Reference to Figure 1A in the Results section is slightly inaccurate. Kit gene modifications are illustrated in Figures 1A, B. Where Figure 1A shows Kit distribution. Please rephrase. Relatedly, the reference to Figs 1B - D are shifted in the results section, and 1E is skipped.

We have changed the text.

Please show cumulative histograms for frequency too for consistency with amplitude (e.g. Fig 2).

We have instead, for reasons outlined by other reviewers, documented total charge transfer for both Kit KO and KL KO experiments where sIPSC events were analyzed.

Fig S3: include example traces of PPR.

This is now included.

Include quantifications of GABAergic synapse density in Fig S4.

This is now included.

Include inset examples of KO in Fig S4A.

This is now included.

Add average puncta size graphs along Figure S4B. The effect apparent in the histogram of S4B is small and statistics using individual puncta as n values (in the 20,000s) therefore misleading.

Per animal analysis is now instead included in the figure and text.

Figure S4B y axis label blocked.

Corrected

Include quantification referenced in "As PSD95 immunoreactivity faithfully follows multiple markers of pinceaux size 40, we quantified PSD95 immunoreactive pinceau area and determined that pinceaux area was decreased by ~50% in Kit KO (n 26 Control vs 43 Kit KO, p<0.0001, two-tailed t-test)."

We added a graph of per animal averages, instead of in text individual pinceau areas.

Include antibody dilutions in the methods.

Added.

It's unclear from the text where the Mirow lab code comes from.

Detail has now been added in text.

Typo in methods "The Kit tm1c alle was bred...".

Corrected

Typo in Figure S4 legend "POSD-95 immuno-reactivity".

Corrected

Reviewer #1 (Public Review):

This manuscript from Zaman et al., investigates the role of cKit and Kit ligand in inhibitory synapse function at molecular layer interneuron (MLI) synapses onto cerebellar Purkinje cells (PC). cKit is a receptor tyrosine kinase expressed in multiple tissues, including select populations of neurons in the CNS. cKIt is activated by Kit ligand, a transmembrane protein typically expressed at the membrane of connected cells. A strength of this paper is the use of cell-specific knockouts of cKit and Kit ligand, in MLIs and PCs, respectively. In both cases, the frequency of spontaneous or miniature (in the presence of TTX) IPSCs was reduced. This suggests either a reduction in the number of functional inhibitory release sites or reduced release probability. IPSCs evoked by electrical stimulation in the molecular layer showed no change in paired-pulse ratio, indicating release probability is not changed in the cKit KO, and favoring a reduction in the number of release sites. Changes in IPSC amplitude were more subtle, with some analyses showing a decrease and others not. These data suggest that disruption of the cKit-Kit ligand complex reduces the number of functional synapses with only minor changes in synapse strength.

Reviewer #2 (Public Review):

In their study, Zaman et al. demonstrate that deletion of either the receptor tyrosine kinase Kit from cerebellar interneurons or the kit ligand (KL) from Purkinje cells reduces the inhibition of Purkinje cells. They delete Kit or KL at different developmental time points, illustrating that Kit-KL interactions are not only required for developmental synapse formation but also for synapse maintenance in adult animals. The study is interesting as it highlights a molecular mechanism for the formation of inhibitory synapses onto Purkinje cells.

The tools generated, such as the floxed Kit mouse line and the virus for Kit overexpression, may have broader applications in neuroscience and beyond.

One general weakness is that Kit expression is not limited to molecular layer interneurons but also extends to the Purkinje layer and Golgi interneurons. But this expression does not conflict with the principal conclusions, as Purkinje layer interneurons form few or no synapses onto Purkinje cells.

In summary, the data support the hypothesis that the interaction between Kit and KL between cerebellar Molecular Layer Interneurons and Purkinje Cells plays a crucial role in promoting the formation and maintenance of inhibitory synapses onto PCs. This study provides valuable insights that could inform future investigations on how this mechanism contributes to the dynamic regulation of Purkinje cell inhibition across development and its impact on mouse behavior.

Reviewer #3 (Public Review):

Summary: Bidirectional transsynaptic signaling via cell adhesion molecules and cell surface receptors contributes to the remarkable specificity of synaptic connectivity in the brain. Zaman et al., investigates how the receptor tyrosine kinase Kit and its trans-cellular kit ligand regulate molecular layer interneuron (MLI)- Purkinje cell (PC) connectivity in the cerebellum. Presynaptic Kit is specific for MLIs, and forms a trans-synaptic complex with Kit ligand in postsynaptic PC cells. The authors begin by generating Kit cKOs via an EUCOMM allele to enable cell-type specific Kit deletion. They cross this Kit cKO to the MLI-specific driver Pax2-Cre and conduct validation via Kit IHC and immunoblotting. Using this system to examine the functional consequences of presynaptic MLI Kit deletion onto postsynaptic PC cells, they record spontaneous and miniature synaptic currents from PC cells and find a selective reduction in IPSC frequency. Deletion of Kit ligand from postsynaptic PC cells also results in reduced IPSC frequency, together supporting that this trans-synaptic complex regulates GABAergic synaptic formation or maturation. The authors then show that sparse Kit ligand overexpression in PCs decreases neighboring uninfected control sIPSCs in a potential competitive manner.

Strengths: Overall, the study addresses an important open question, the data largely supports the authors conclusions, the experiments appear well-performed, and the manuscript is well-written. I just have a few suggestions to help shore up the author's interpretations and improve the study.

Weaknesses:

The strong decrease in sIPSC frequency and amplitude in control uninfected cells in Figure 4 is surprising and puzzling. The competition model proposed is one possibility, and I think the authors need to do additional experiments to help support or refute this model. The authors can conduct similar synaptic staining experiments as in Fig S4 but in their sparse infection paradigm, comparing synapses on infected and uninfected cells. Additional electrophysiological parameters in the sparse injection paradigm, such as mIPSCs or evoked IPSCs, would also help support their conclusions.

The authors should validate KL overexpression and increased cell surface levels using their virus to support their overexpression conclusions.

Author Response

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

First of all, we'd like to thank the three reviewers for their meticulous work that enable us to present now an improved manuscript and substantial changes were made to the article following reviewers' and editors' recommendations. We read all their comments and suggestions very carefully. Apart from a few misunderstandings, all comments were very pertinent. We responded positively to almost all the comments and suggestions, and as a result, we have made extensive changes to the document and the figures. This manuscript now contains 16 principal figures and 15 figure supplements.

The number of principal figures is now 16 (1 new figure), and additional panels have been added to certain figures. On the other hand, we have added 7 additional figures (supplement figures) to answer the reviewers' questions and/or comments.

Main figures

▪ Figures 1, 4, 5, 10, 11, 12, 13, 14: unchanged ▪ Figure 7 and 8 were switched.

▪ Figure 2: we added panel F in response to reviewer 3's and request for sperm defect statistics

▪ Figure 3: the contrast in panel B has been taken over to homogenize colors

▪ Figure 6: This figure was recomposed. The WB on testicular extract was suppressed and we present a new WB allowing to compare the presence of CCDC146 in the flagella fraction. Using an anti-HA Ab, we demonstrate that the protein is localized in the flagella in epididymal sperm. Request of the 3 reviewers.

▪ Figure 7 (old 8): to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum. Moreover, the WB was removed and is now presented in figure 6 (improved as requested).

▪ Figure 8. Was old figure 7

▪ Figure 9: figure 9 was recomposed and improved for increased clarity as suggested by reviewer 2 and 3.

▪ Figure 16 was before appendix 11

Figure supplements and supplementary files

▪ Figure 1-Figure supplement 1 New. Sperm parameters of the 2 patients. requested by editor (remark #1) by the reviewer 1 (Note #3)

▪ Figure 2-Figure supplement 1 new. Sperm parameters of the line 2 (KO animals) requested by the reviewer 1 (Note #5)

▪ Figure 4-Figure supplement 1 New. Experiment to evaluate the specificity of the human CCDC146 antibody. Minimal revision request and reviewer 1 note #8

▪ Figure 6-Figure supplement 1 New. Figure recomposed; Asked by reviewer 2 note #4 and reviewer 3

▪ Figure 8-Figure supplement 1 New. We now provide new images to show the non-specific staining of the midpiece of human sperm by secondary Abs in ExM experiments; Asked by reviewer 2

▪ Figure 10-Figure supplement 1 New. We added new images to show the non-specific staining of the midpiece of mouse sperm by secondary Abs in IF (panel B). Rewiever 1 note #9 and reviewer 2 note #5

▪ Figure 12-Figure supplement 1 New. Control requested by reviewer 3 Note #23

▪ Figure 13-Figure supplement 1 New. We provide a graph and a statistical analysis demonstrating the increase of the length of the manchette in the Ccdc146 KO. Requested by editor and reviewer 3 Note 24

▪ Figure 15-Figure supplement 1 New. Control requested by reviewer 2. Minor comments

▪ Figure supplementary 1 New. Answer to question requested by reviewer 2 note #1

All the reviewers' and editors’ comments have been answered (see our point to point response) and we resubmit what we believe to be a significantly improved manuscript. We strongly hope that we meet all your expectations and that our manuscript will be suitable for publication in "eLife". We look forward to your feedback,

Point by point answer

Please note that there has been active discussion of the manuscript and the summarize points below is the minimal revision request that the reviewers think the authors should address even under this new review model system. It was the reviewers' consensus that the manuscript is prepared with a lot of oversights - please see all the minor points to improve your manuscript.

All minimal revision requests have been addressed

Minimal revision request

1) Clinical report/evaluation of the two patients should be given as it was not described even in their previous study as well as full description of CCDC146.

We provide now a new Figure 1-figure supplement 1 describing the patients sperm parameters

2) Antibody specificity should be provided, especially given two of the reviewers were not convinced that the mid piece signal is non-specific as the authors claim. As both KO and KI model in their hands, this should be straightforward.

To validate the specificity of the Antibody, we transfected HEK cells with a human DDK-tagged CCDC146 plasmid and performed a double immunostaining with a DDK antibody and the CCDC146 antibody. We show that both staining are superimposable, strongly suggesting that the CCDC146 Ab specifically target CCDC146. This experiment is now presented in Figure 4-Figure supplement 1. Next, to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum.

3) The authors should improve statistical analysis to support their experimental results for the reader can make fair assessment. Combined with clear demonstration of ab specificity, this lack of statistical analysis with very few sample number is a major driver of dampening enthusiasm towards the current study.

Several statistical analyses were carried out and are now included:

1) distribution of the HA signal in mouse sperm cells (see point 2 Figure 7 panel B)

2) quantification and statistical analyses of the defect observed in Ccdc146 KO sperm (figure 2 panel E)

3) Quantification and statistical analyses of the length of the manchette in spermatids 13-15 steps (Figure 13-Figure supplement 1 new)

4) The authors need to clarify (peri-centriolar vs. centriole)

In figure 4A, we have clearly shown that the protein colocalizes with centrin, a centriolar core protein in somatic cells. This colocalization strongly suggests that CCDC146 is therefore a centriolar protein, and this is now clearly indicated lines 211-212. However, its localization is not restricted to the centrioles and a clear staining was also observed in the pericentriolar material (PCM). The presence of a protein in PCM and centriole was already described, and the best example is maybe gamma-tubulin (PMID: 8749391).

or tone down (CCDC146 to be a MIP) of their claim/description.

Concerning its localization in sperm, we agree with the reviewer that our demonstration that CCDC146 is MIP would deserve more results. Because of that, we have toned down the MIP hypothesis throughout the manuscript. See lines 491495

Testis-specific expression of CCDC146 as it is not consistent with their data.

We have also modified our claim concerning the testis-expression of CCDC146. Line 176

Reviewer #1 (Recommendations For The Authors):

Major comments

1) As described in general comments, this study limits how the CCDC146 deficiency impairs abnormal centriole and manchette formation. The authors should explain their relationship in developing germ cells.

In fact, there are limited information about the relationship between the manchette and the centriole. However, few articles have highlighted that both organelles share molecular components. For instance, WDR62 is required for centriole duplication in spermatogenesis and manchette removal in spermiogenesis (Commun Biol. 2021; 4: 645. doi: 10.1038/s42003-021-02171-5). Another study demonstrates that CCDC42 localizes to the manchette, the connecting piece and the tail (Front. Cell Dev. Biol. 2019 https://doi.org/10.3389/fcell.2019.00151). These articles underline that centrosomal proteins are involved in manchette formation and removal during spermiogenesis and support our results showing the impact of CCDC146 lack on centriole and manchette biogenesis. This information is now discussed. See lines 596-603

2) The authors generated knock-in mouse model. If then, are the transgene can rescue the MMAF phenotype in CCDC146-null mice? This reviewer strongly suggest to test this part to clearly support the pathogenicity by CCDC146.

We indeed wrote that we created a “transgenic mice”, which was misleading. We actually created a CCDC16 knock-in expressing a tagged-protein. The strain was actually made by CRISPR-Cas9 and a sequence coding for the HA-tag was inserted just before the first amino acid in exon 2, leading to the translation of an endogenous HA-tagged CCDC146 protein. We have removed the word transgenic from the text and made changes accordingly (see lines 250-253). We can therefore not use this strain to rescue the MMAF phenotype as suggested by the reviewer.

3) Although the authors cite the previous study (Coutton et al., 2019), the study does not describe any information for CCDC146 and clinical information for the patients. The authors must show the results for clinical analysis to clarify the attended patients are MMAF patients without other phenotypic defects.

We have now inserted a table, indicating all sperm parameters for the patients harboring a mutation in the CCDC146 gene (Figure 1-Figure supplement 1) and is now indicated lines 159-160

4) The authors describe CCDC146 expression is dominant in testes, However, the level in testis is only moderate in human (Supp Figure 1). Thus, this description is not suitable.

In Figure 1-figure supplement 2 (old FigS1), the median of expression in testis is around 12 in human, a value considered as high expression by the analysis software from Genevestigator. However, for mouse, it is true that the level of expression is medium. We assumed that reviewer’s comment concerned testis expression in mouse. To take into account this remark, we changed the text accordingly. See line 176.

5) Although the authors mentioned that two mice lines are generated, only one line information is provided. Authors must include information for another line and provide basic characterization results to support the shared phenotype within the lines.

We now provide a revised Figure 2-figure supplement 1CD, presenting the second line and the corresponding text in the main text is found lines 178-183.

6) In somatic cells, the CCDC146 localizes at both peri-centriole and microtubule but its intracellular localization in sperm is distinguished. The authors should explain this discrepancy.

The multi-localization of a centriolar protein is already discussed in detail in discussion lines 520-526. We have written:

“Despite its broad cellular distribution, the association of CCDC146 with tubulin-dependent structures is remarkable. However, centrosomal and axonemal localizations in somatic and germ cells, respectively, have also been reported for CFAP58 [37, 55], thus the re-use of centrosomal proteins in the sperm flagellar axoneme is not unheard of. In addition, 80% of all proteins identified as centrosomal are found in multiple localizations (https://www.proteinatlas.org/humanproteome/subcellular/centrosome). The ability of a protein to home to several locations depending on its cellular environment has been widely described, in particular for MAP. The different localizations are linked to the presence of distinct binding sites on the protein…. “

7) Authors mention CCDC146 is a centriolar protein in the title and results subtitle. However, the description in results part depicts CCDC146 is a peri-centriolar protein, which makes confusion. Do the authors claim CCDC146 is centrosomal protein?

In figure 4A, we have clearly shown that the protein colocalizes with centrin, a centriolar core protein. This colocalization strongly suggests that CCDC146 is therefore a centriolar protein in somatic cells, and is now clearly indicated lines 211-212. However, its localization is not restricted to the centrioles and a clear staining was also observed in the pericentriolar material (PCM). The presence of a protein in PCM and centriole was already described and the best example is maybe gamma-tubulin (PMID: 8749391).

8) Verification of the antibody against CCDC146 must be performed and shown to support the observed signal are correct. 2nd antibody only signal is not proper negative control.

It is a very important remark. The commercial antibody raised against human CCDC146 was validated in HEK293-cells expressing a DDK-tagged CCDC146 protein. Cells were co-marked with anti-DDK and anti-CCDC146 antibodies. We have a perfect colocalization of the staining. This experiment is now presented in Figure 4-figure supplement 1 and presented in the text (lines 206-208).

9) In human sperm, conventional immunostaining reveals CCDC146 is detected from acrosome head and midpiece. However, in ExM, the signal at acrosome is not detected. How is this discrepancy explained? The major concern for the ExM could be physical (dimension) and biochemical (properties) distortion of the sample. Without clear positive and negative control, current conclusion is not clearly understood. Furthermore, it is unclear why the authors conclude the midpiece signal is non-specific. The authors must provide experimental evidence.

Staining on acrosome should always be taken with caution in sperm. Indeed, numerous glycosylated proteins are present at the surface of the plasma membrane regarding the outer acrosomal membrane for sperm attachment and are responsible for numerous nonspecific staining. Moreover, this acrosomal staining was not observed in mouse sperm, strongly suggesting that it is not specific.

Concerning the staining in the midpiece observed in both conventional and Expansion microscopy, it also seems to be nonspecific and associated with secondary Abs.

For IF, we now provide new images showing clearly the nonspecific staining of the midpiece when secondary Ab were used alone (see Figure 10-figure supplement 1B).

For ExM, we provide new images in Figure 8-figure supplement 1B (POC5 staining) showing a staining of the midpiece (likely mitochondria), although POC5 was never described to be present in the midpiece. Both experiments (CCDC146 and POC5 staining by ExM) shared the same secondary Ab and the midpiece signal was likely due to it.

Moreover, we now provide new images (figure 7C) in ExM on mouse sperm showing no staining in the midpiece and demonstrating that the punctuated signal is present all along the flagellum. Finally, we would like to underline that we now provide new IF results, using an anti-HA conjugated with alexafluor 488 and confirming the ExM results.

These points are now discussed lines 498-502 for acrosome and lines 503-511 for midpiece staining.

10) For intracellular localization of the CCDC146 in mouse sperm, the authors should provide clear negative control using WT sperm which do not carry the transgene.

This experiment was performed.

To avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum.

11) Current imaging data do not clearly support the intracellular localization of the CCDC146. Although western blot imaging reveal that CCDC146 is detected from sperm flagella, this is crude approach. Thus, this reviewer highly recommends the authors provide more clear experimental evidence, such as immuno EM.

We provide now a WB comparing the presence of the protein in the flagellum and in the head fractions; see new figure 6. We show that CCDC146 is only present in the flagellum fraction; The detection of the band appeared very quickly at visualization and became very strong after few minutes, demonstrating that the protein is abundant in the flagella. It is important to note that epididymal sperm do not have centrioles and therefore this signal is not a centriolar signal. We also now provide new statistical analyses showing that the immuno-staining observed in the principal piece is very specific (Figure 7B). Altogether, these results demonstrate unequivocally the intracellular localization of CCDC146 in the flagellum. This point is now discussed lines 480-489

12) Although sarkosyl is known to dissociate tubulin, it is not well understood and accepted that the enhanced detection of CCDC146 by the detergent indicates its microtubule inner space. Sperm axoneme to carry microtubule is also wrapped peri-axonemal components with structural proteins, which are even not well solubilized by high concentration of the ionic detergent like SDS.

We agree with the reviewer that the solubilization of the protein by sarkozyl is not a proof of the presence of the protein inside microtubule. Taking into account this point, the MIP hypothesis was toned down and we now discuss alternative hypothesis concerning these results; See discussion lines 490-497

13) SEM image is not suitable to explain internal structure (line 317-323).

We agree with the reviewers and changes were made accordingly. See lines 354-357

Minor comments

1) In main text, supplementary figures are cited "Supp Figure". And the corresponding legends are written in "Appendix - Figure". Please unify them.

Done Labelled now “Figure X-figure supplement Y”

2) Line 159, "exon 9/19" is not clear.

We have written now exons 9 and indicated earlier that the gene contains 19 exons

3) Line 188, "positive cells" are vague.

Positive was changed by “fluorescent”

4) Representative TUNEL assay image for knockout testes were not shown in Supp Figure 3B.

It was a mistake now Figure 2-figure supplement 2C

5) Please provide full description for "IF" and "AB" when described first.

Done

6) Line 262, It is unclear what is "main piece".

Changed to principal piece

7) Line 340, Although the "stage" information might be applicable, this is information for "seminiferous tubule" rather than "spermatid". This reviewer suggests to provide step information rather than stage information.

We agree with the reviewer that there was a confusion between “stage” and “step”. We change to step spermatids

8) Line 342, Step 1 is not correct in here.

OK corrected. now steps 13-15 spermatids

9) Line 803, "C." is duplicated.

Removed

10) Figure 3A, it will be good to mark the defective nuclei which are described in figure legends.

These cells are now indicated by white arrow heads

11) Figure 5, Please provide what MT stands for.

Now explained in the legend of figure 5

12) Figure 6. Author requires clear blot images for C. In addition, Panel B information is not correct. If the blot was performed using HA antibody, then how "WT" lane shows bands rather than "HA" bands?

The reviewer is correct. It was a mistake; The figure was recomposed and improved.

Reviewer #2 (Recommendations For The Authors):

Overall, editing oversights are present throughout the manuscript, which has made the review process quite difficult. Some repetitive figures can be removed to streamline to grasp the overall story easier. Some claims are not fully supported by evidence that need to tone down. Some figures not referenced in the main text need to be mentioned at least once.

All figures are now referenced in the text

Major comments:

1) 163-164 - Please clarify the claim that there is going to be an absence of the protein or nonfunctional protein, especially for the patient with a deletion that could generate a truncated protein at two third size of the full-length protein. Similarly, 35% of the protein level is present for the patient with a nonsense mutation. Some in silico structural analysis or analysis of conserved domains would be beneficial to support these claims.

Both mutations are predicted to produce a premature stop codons: p.Arg362Ter and p.Arg704serfsTer7, leading either to the complete absence of the protein in case of non-sense mediated mRNA decay or to the production of a truncated protein missing almost two third or one fourth of the protein respectively. CCDC146 is very well conserved throughout evolution (Figure supplementary 1), including the 3’ end of the protein which contains a large coil-coil domain (Figure 1B). In view of the very high degree of conservation, it is most likely that the 3’ end of the protein, absent in both subjects, is critical for the CCDC146 function and hence that both mutations are deleterious. This explanation is now added to the discussion. see lines 439-448

2) 173, 423 - Please clearly state a rationale of your mouse model design (i.e., why a mouse model that recapitulate human mutation is not generated) as the truncations identified in human patients are located further towards the C-terminus, and it is not clear whether truncated proteins are present, and if so, they could still be functional. Basically, the current mouse model supports the causality of the human mutations.

This is an important question, which goes beyond the scope of this article, and raises the question of how to confirm the pathogenicity of mutations identified by high-throughput sequencing. The production of KO or KI animals is an important tool to help confirm one’ suspicions but the first element to take into consideration is the nature of the genetic data.

Here we had two patients with homozygous truncating variants. In human, it is well established that the presence of premature stop codons usually induces non-sense mediated mRNA decay (NMD), inducing the complete absence of the protein or a strong reduction in protein production. In the unlikely absence of NMD in our two patients, the identified variants would induce the production of proteins missing 60% and 30% of their C terminal part. Often (and it is particularly true for structural proteins) the production of abnormal proteins is more deleterious than the complete absence of the protein (and it is most likely the purpose of NMD, to limit the production of abnormal “toxic” proteins). For these reasons, to try to recapitulate the most likely consequences of the human variants, without risking obtaining an even more severe effect, we decided to introduce a stop codon in the first exon in order to remove the totality of the protein in the KO mice.

The second element is to interpret the phenotype of the KO animals. Here, the human sperm phenotype is perfectly recapitulated in the KO mice.

Overall, we have strong genetic arguments in human and the reproduction of the phenotype in KO mice confirming the pathogenicity of the variants identified in men.

This point is now discussed see lines 433-438

3) Figure 6A - the labelling is misleading as it seems to suggest that the specific cells were isolated from the testes for RT-PCR.

We have modified the labelling to avoid any confusion.

Figure 6B -Signal of HA-tag is shown in WT, not in transgenic. Please check the order of the labels. Figure 6C - This blot is NOT a publication-quality figure. The bands are very difficult to observe, especially in lane D18. Because it is one of the important data of this study, replacing this figure is a must.

The figure has been completely remade, including new results. See new figure 6. Figure 6C was suppressed.

4) Supplementary fig 6 is also not a publication-level figure, and the top part seems largely unnecessary (already in the figure legend).

The figure has been completely remade as well (now Figure 6-Figure Supplement 1).

5) 261/267- The conclusion that mitochondrial staining in the flagellum (in both mice and humans) is non-specific is not convincing. Supplementary fig 8 shows that the signal from secondary only IF possibly extends beyond the midpiece - but it is hard to determine as no mitochondrial-specific staining is present. Either need to tone down the conclusion or provide supporting experimental evidence.

First, to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum. These experiments are now described lines 271-279

Second, we provide new images of the signal obtained with secondary Abs only that shows more clearly that the secondary Ab gave a non-specific staining (Figure 10-Figure supplement 1B). This point is discussed lines 503-511

6) Figure 9 A - Please relate the white line to Fig. 9B label in X-axis. The information from Fig 9A+D and 9E+F are redundant. The main text nor the figure legends indicate why these specific two sperm were chosen for quantification and demonstrating the outcomes. One of them could be moved to supplementary information or removed, or the two could be combined.

As suggested by the reviewer, we have combined the two sperm to demonstrate that CCDC146 staining is mostly located on microtubule doublets. Moreover, the figure was recomposed to make it clearer.

Minor comments:

All of the supplementary figures are referred to as Supp Fig X in the text, however, they are actually titled Appendix - Figure X. This needs to be consistent.

The figures are now referred as figure supplement x in both text and figures

Line 125 - edit spacing.

We think this issue (long internet link) will be curated later and more efficiently by the journal, during the step of formatting necessary for publication.

144 - With which to study  with which we studied?

We made the change as suggested.

151 - Supp Fig 1 - the text says that the gene is highly transcribed in human and mouse testes, but the information in the figure states that the level in mouse tissues is "medium"

We have corrected this mistake in the text; See line 176

165 - The two mutations are most likely deleterious. Please specifically mention what analyses done to predict the deleterious nature to support these claims.

Both variants, c.1084C>T and c.2112del, are extremely rare in the general population with a reported allele frequency of 6.5x10-5 and 6.5x10-06 respectively in gnomAD v3. Moreover, these variants are annotated with a high impact on the protein structure (MoBiDiC prioritization algorithm (MPA) score = 10, DOI: 10.1016/j.jmoldx.2018.03.009) and predicted to induce each a premature termination codon, p.(Arg362Ter) and p.(Arg704SerfsTer7) respectively, leading to the production of a truncated protein. This information is now given line 164-169

196-200/Figure 4 - As serum starved cells/basal body (B) are not mentioned in the main text, as is, Fig 4A would be sufficient/is relevant to the text. Please make the text reflect the contents of the whole figure, or re/move to supplement.

We agree with the reviewer that the full description of the figure should be in the text. We added two sentences to describe figure 4B see lines 217-218.

224 - spermatozoa (plural) fits better here, not spermatozoon

OK changed accordingly

236 - According to the figure legend, 6B is only showing data from the epididymal sperm, not postnatal time points; should be referencing 6C. Alignment of Marker label

As indicated above, the figure has been completely remade, including new results. See new figure 6. Figure 6C was suppressed. The corresponding text was changed accordingly see lines 249-266

255-256 - Referenced figure 7B3, however, 7B3 only shows tubulin staining, so no CCDC146 can be observed. Did authors mean to reference fig 7B as a whole?

Sorry for this mistake. We agree and the text is now figure 8B6 (figure 7 and 8 were switched)

305 - "of tubules" - I presume it is meant to be microtubules?

Yes; The text was changed as suggested

317-321 - a diagram of HTCA would be useful here

We have added a reference where HTCA diagram is available see line 363. Moreover, a TEM view of HTCA is presented figure 12A

322/Fig 11A - an arrow denoting the damage might be useful, as A1 and A3 look similar. The size of the marker bar is missing. Please update the information on figure legend.

Concerning, the comparison between A1 and A3, the take home message is that there is a great variability in the morphological damages. This point is now underlined in the corresponding text. We updated the size of the marker bar as suggested (200 nm). See line 365-367

323 - Please mark where capitulum is in the figure

Capitulum was changed for nucleus

Since Fig 11B2 is not referenced in the main text, it does not seem to add anything to the data, and could be removed/moved to supplement.

We added a sentence to describe figure 11B2 line 370

342-343 - manchette in step I is not seen clearly - the figure needs to be annotated better. However, DPY19L2 is absent in step I in the KO, but the main text does not reflect that - why is that?

We do not understand the remark of the reviewer “manchette in step I is not seen clearly”. The figure shows clearly the manchette (red signal) in both WT and KO (Figure 13 D1/D2).

For steps 13-15 WT spermatids, the size of the manchette decreases and become undetectable. In KO spermatids, the shrinkage of the manchette is hampered and in contrast continue to expand (Figure 13D2). We also provide a new Figure 13-figure supplement 1 for other illustrations of very long manchettes and a statistical analysis. In the meantime, the acrosome is strongly remodeled, as shown in figure 16-new, with detached acrosome (panel H). This morphological defect may induce a loss of the DPY19L2 staining (Figure 13 D2 stage I-III). This explanation is now inserted in the text line 396399

Figure 15B and 15C only show KO, corresponding images from the WT should be present for comparison.

WT images are now provided in Figure 1-figure supplement 1 new

Figure 12 - Figure 12 - JM?.

JM was removed. It does not mean anything

Figure 12C and Supplementary Fig 10 - structures need to be labelled, as it is unclear what is where

Done

338 - text mentions step III, but only sperm from step VII are shown in Figure 13

As suggested by reviewer 3, we changed stage by step. The text was modified to take into account this remark see lines 388-396

360 - This is likely supposed to say Supp Figure 11E-G, not 13??

Yes, it is a mistake. Corrected

388 Typo "in a in a".

Yes, it is a mistake. Corrected

820 - Fig 3 legend - in KO spermatid nuclei were elongated - could this be labelled by arrows? I am not convinced this phenotype is that different from the WT.

In fact, the nuclei of elongating KO spermatids are elongated and also very thin, a shape not observed in the WT; We have added arrow heads and modified the text to indicate this point line 200.

836 - Figure 5 legend says that in yellow is centrin, but that is not true for 5A, where the figure shows labelling for y-tubulin (presumably, according to the figure itself).

We have modified the text of the legend to take into account the remark

837- 5A supposedly corresponds to synchronized HEK293T cells, but the reasoning behind using synchronized cells is not mentioned at all in the main text; furthermore, how this synchronization is achieved is not explained in materials and methods (serum starvation? Thymidine block?).

Yes, figure 5A was obtained with synchronized cells. We have added one paragraph in the MM section. For cell synchronization experiments, cells underwent S-phase blockade with thymidine (5 mM, SigmaAldrich) for 17 h followed by incubation in a control culture medium for 5 h, then a second blockade at the G2-M transition with nocodazole (200 nM, Sigma-Aldrich) for 12 h. Cells were then fixed with cold methanol at different times for IF labelling. See line 224 for changes made in the result section and lines 700-704 for changes made in the MM section.

845- figure legend says that the RT-PCR was done on CCDC146-HA tagged mice, but the main text does not reflect that.

We made changes and the description of the KI is now presented before (line 240) the RT-PCR experiment (line 257).

949 - it is likely supposed to say A2, not B1 (B1 does not exist in Fig 15)

Yes, it is a mistake. Corrected

971 - Appendix Fig 3 legend - I believe that the description for B and C are swapped.

Yes, it is a mistake. Corrected

Furthermore, some questions to address in A would be: Which cross sections were from which animal/points? How many per animal? Were they always in the same location?

Yes, we have a protocol for arranging and orienting all testes in the same way during the paraffin embedding phase. The cross-sections are therefore not taken at random, and we can compare sections from the same part of the testis. The number of animals was already indicated in the figure legend (see line 1128)

Reviewer #3 (Recommendations For The Authors):

1) There are a number of grammatical and orthographical errors in the text. Careful proofreading should be performed.

We have sent the manuscript to a professional proofreader

2) The author should also check for redundancies between the introduction and the discussion.

The discussion has modified to take into account reviewers’ remarks. Nevertheless, we did our best to avoid redundancies between introduction and discussion.

3) Can the authors provide a rationale why they have chosen to tag their gene with an HA tag for localisation? One would rather think of fluorescent proteins or a Halo tag.

Because the functional domains of the protein are unknown, adding a fluorescent protein of 24 KDa may interfere with both the localization and the function of CCDC146. For this reason, we choose a small tag of only 1.1 KDa, to limit as such as possible the risk of interfering with the structure of the protein. This rational is now indicated in the manuscript lines 251-254. It is worth to note, that the tagged-strain shows no sperm defect, demonstrating that the HA-tag does not interfere with CCDC146 function.

4) In the abstract, line 53, "provide evidence" is not the right term for something that is just suggestive. The term "suggests" would be more appropriate.

The text was modified to take into account this remark

5) Line 74: "genetic deficiency" sounds strange here, do the authors mean simply "mutation"?

Infertility may be due to several genetic deficiency such as chromosomal defects (XXY (Klinefelter syndrome)), microdeletion of the Y chromosome or mutations in a single gene. Therefore, mutation is too restrictive. Nevertheless, we modified the sentence which is now “…or a genetic disorder including chromosomal or single gene deficiencies”

6) Lines 163-164: the authors describe the mutations (premature stop mutations) and say that they could either lead to complete absence of the gene product, or the expression of a truncated protein. Did they test this, for example, with some immuno blot analyses?

As stated above, unfortunately, we were unable to verify the presence of RNA-decay in these patients for lack of biological material.

7) Line 184 and Fig 2E: the sperm head morphologies should be quantitatively assessed.

We provide now a full statistical analysis of the observed defects: see new panel in Figure 2 F

8) Fig 3: The annotation should be more precise - KO certainly means CDCC146-KO. The colours of the IH panels is different, which attracts attention but is clearly a colour-adjustment artefact. Colours should be adjusted for the panels to look comparable. It would be also helpful to add arrowheads into the figure to point at the phenotypes that are highlighted in the text.

We have added Ccdc146 KO in all figures. We have added arrow heads to point out the spermatids showing a thin and elongated nucleus. Concerning adjustment of colors, we attempted to make images of panel B comparable. See new figure 3.

9) Fig 6A: the authors use RT PCR to determine expression dynamics of their gene of interested, and use actin (apparently) as control. However, actin and CDCC146 expression levels follow the same trend. How is the interpreted?

The reviewer did not understand the figure. The orange bars do not correspond to actin expression and the grey bars to Ccdc146 expression but both bars represent the mRNA expression levels of Ccdc146 relative to Actb (orange) and Hprt (grey) expression in CCDC146-HA mouse pups’ testes. We tested two housekeeping genes as reference to be sure that our results were not distorted by an unstable expression of a housekeeping gene. We did not see significant difference between both house keeping genes. Actin was not used.

10) In line 235, the authors suggest posttranslational modifications of their protein as potential cause for a slightly different migration in SDS PAGE as predicted from the theoretical molecular weight. This is not necessarily the case, some proteins do migrate just differently as predicted.

We have changed the text accordingly and now provide alternative explanation for the slightly different migration. See lines 258-259

11) The annotation of Fig 6 panels is problematic. First, why do the authors write "Laemmli" as description of the gel? It would be more helpful to write what is loaded on the gel, such as "sperm". Second, in panels B and C it would be helpful to add the antibodies used. It is not clear why there is a signal in the WT lane of panel B, but not in the HA lane (supposing an anti-HA antibody is used: why has WT a specific HA band?). In panel C, it is not clear why the blot that has so beautifully shown a single band in panel B suddenly gives such a bad labelling. Can the authors explain this? Also, they cut off the blot, likely because to too much background, but this is bad practice as full blots should be shown. In the current state, the panel C does not allow any clear conclusion. To make it conclusive, it must be repeated.

Several mistakes were present in this figure. This figure was recomposed. The WB on testicular extract was suppressed and we now present a new WB allowing to compare the presence of CCDC146 in the flagella and head fractions from WT and HA-CCDC146 sperm. Using an anti-HA Ab, we demonstrate that in epididymal sperm the protein is localized in the flagella only. See new figure 6. The corresponding text was changed accordingly.

12) The authors have raised an HA-knockin mouse for CDCC146, which they explained by the unavailability of specific antibodies. However, in Fig 7, they use a CDCC146 antibody. Can they clarify?

The commercial Ab work for HUMAN CCDC146 but not for MOUSE CCDC146. We have added few words to make the situation clearer, we have added the following information “the commercial Ab works for human CCDC146 only”. See line 240

13) In Fig 7A (line 258), the authors hypothesise that they stain mitochondria - why not test this directly by co-staining with mitochondria markers?

We chose another solution to resolve this question:

To avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the whole flagellum.

14) It seems that in both, Fig 7 and 8, the authors use expansion microscopy to localise CDCC146 in sperm tails. However, the staining differs substantially between the two figures. How is this explained?

In figure 8 we used the commercial Ab in human sperm, whereas in figure 7 we used the anti-HA Abs in mouse sperm. Because the antibodies do not target the same part of the CCDC146 protein (the tag is placed at the N-terminus of the protein, and the HPA020082 Ab targets the last 130 amino acids of the Cter), their accessibility to the antigenic site could be different. However, it is important to note that both antibodies target the flagellum. This explanation is now inserted see lines 304-312

15) Fig 8D and line 274: the authors do a fractionation, but only show the flagella fraction. Why?

Showing all fractions of their experiment would have underpinned the specific enrichment of CDCC146 in the flagella fraction, which is what they aim to show. Actually, given the absence of control proteins, the fact that the band in the flagellar fraction appears to be weaker than in total sperm, one could even conclude that there is more CDCC146 in another (not analysed) fraction of this experiment. Thus, the experiment as it stands is incomplete and does not, as the authors claim, confirm the flagellar localisation of the protein.

We agree with the reviewer’s remark. We provide now new results showing both flagella and nuclei fractions in new figure 6A. This experiment is presented lines 253-256

16) Line 283, Fig 9D,F: The description of the microtubules in this experiment is not easy to understand. Do the authors mean to say that the labelling shows that the protein is associated with doublet microtubules, but not with the two central microtubules? They should try to find a clearer way to explain their result.

As suggested by reviewer 2, we have changed the figure to make it clearer. The text was changed accordingly. See new figure 9 and new corresponding legend lines 1006.

17) Fig 9G - how often could the authors observe this? Why is the axoneme frayed? Does this happen randomly, or did the authors apply a specific treatment?

Yes, it happens randomly during the fixation process.

18) Line 300 and Fig 10A - the authors talk about the 90-kDa band, but do say anything about what they think this band is representing.

We have now added the following sentence lines 340-342: “This band may correspond to proteolytic fragment of CCDC146, the solubilization of microtubules by sarkosyl may have made CCDC146 more accessible to endogenous proteases.”

19) Fig 11A, lines 321-322: the authors write that the connecting piece is severely damaged. This is not obvious for somebody who does not work in sperm. Perhaps the authors could add some arrow heads to point out the defects, and briefly describe them in the text.

We realized from your remark that our message was not clear. In fact, there is a great variability in the morphological damages of the HTCA. For instance, the HTCA of Ccdc146 KO sperm presented in figure 10A2 is quite normal, whereas that in figure 10A4 is completely distorted. This point is now underlined in the corresponding text. See lines 367-369

We also added the size of the marker bar (200 nm), which were missing in the figure’s legend.

20) Line 323: it will be important to name which tubulin antibody has been used to identify centrioles, as they are heavily posttranslationally modified.

The different types of anti-tubulin Abs are described in the corresponding figure’s legend

21) Fig 11B - phenotypes must be quantified to make these observations meaningful.

We agree that a quantification would improve the message. However, testicular sperm are obtained by enzymatic separation of spermatogenic cells and the number of testicular sperm are very low. Moreover, not all sperm are stained. Taking these two points into account, it seems to us that quantification could be difficult to analyze. For this reason, the quantification was not done; however, it is important to note that these defects were not observed in WT sperm, demonstrating that these defects are cased by the lack of CCDC146. We have added a sentence to underline this point; See lines 374-375

22) Line 329: Figure 12AB - is this a typo - should it read Figure 12B?

We have split the panel A in A1 and A2 and changed the text accordingly. See line 378

23) Why are there not wildtype controls in Fig 12B, C?

We provide now as Figure 12-figure supplement 1, a control image for fig 12B. For figure 12C, the emergence of the flagellum from the distal centriole in WT is already shown in Fig 12A1

24) Fig 13: the authors write that the manchette is "clearly longer and wider than in WT cells" (lines 342-343). How can they claim this without quantitative data?

We now provide a statistical analysis of the length of the manchette. See figure 13-figure supplement 1A. We also provide a new a new image illustrating the length of the manchette in Ccdc146 KO spermatids; See Figure 13-figure supplement 1B.

eLife assessment

This study presents valuable information that demonstrates CCDC146 as a novel cause of male infertility that play key role in microtubule-associated structures. The evidence supporting the claims of the authors is solid using combination of human and mouse genetics, biochemical and imaging approaches. This paper would be of interest to cell and developmental biologists working on genes involved in spermatogenesis and male infertility.

Reviewer #1 (Public Review):

Here, Muronova et al., demonstrate the physiological importance of a centriole and microtubule-associated protein, CCDC146, in sperm flagellar formation and male reproduction. This study identifies novel causal variants to cause male infertility and resolves the pathogenicity by the mutation with characterizing mouse models. Furthermore, the authors' claims are well supported by the biochemical and imaging approaches used in this study.

Reviewer #3 (Public Review):

Male infertility is an important health problem. Among pathologies with multiple morphological abnormalities of the flagellum (MMAF), only 50% of the patients have no identified genetic causes. It is thus primordial to find novel genes that cause the MMAF syndrome. In the current work, the authors follow up the identification of two patients with MMAF carrying a mutation in the CCDC146 gene. To understand how mutations in CCDC146 lead to male infertility, the authors generated two mouse models: a CCDC146-knockout mouse, and a knockin mouse in which the CCDC146 locus is tagged with an HA tag. Male CCDC146-knockout mice are infertile, which proves the causative role of this gene in the observed MMAF cases. Strikingly, animals develop no other obvious pathologies, thus underpinning the specific role of CCDC146 in male fertility. The authors have carefully characterised the subcellular roles of CCDC146 by using a combination of expansion and electron microscopy. They demonstrate that all microtubule-based organelles, such as the sperm manchette, the centrioles, as well as the sperm axonemes are defective when CCDC146 is absent. Their data show that CCDC146 is a microtubule-associated protein, and indicate, but do not prove beyond any doubt, that it could be a microtubule-inner protein (MIP).

This is a solid work that defines CCDC146 as a novel cause of male infertility. The authors have performed comprehensive phenotypic analysis to define the defects in CCDC146 knockout mice. The manuscript text is well written and easy to follow also for non-specialists. The introduction and discussion chapters contain important background information that allow to put the current work into the greater context of fertility research. Overall, this manuscript provides convincing evidence for CCDC146 being essential for male fertility and illustrates this with a large panel of phenotypic observations. Together, the work provides important first insights into the role of a so-far unexplored proteins, CCDC146, in spermatogenesis, thereby broadening the spectrum of genes involved in male infertility.

eLife assessment

This study provides useful insights into inter- and intra-site B cell receptor repertoire heterogeneity, noting that B cell clones from the tumour interact more with their draining lymph node than with the blood and that there is less mutation/expansion/activation of B cell clones in tumours. Unfortunately, the main claims are incomplete and only partially supported. This work could be of interest to an audience including medical biologists/immunologists and computational biologists across cancer specialities.

Reviewer #1 (Public Review):

Summary:

The authors attempt to fully characterise the immunoglobulin (Ig) heavy (H) chain repertoire of tumor-infiltrating B cells from three different cancer types by identifying the IgH repertoire overlap between these, their corresponding draining lymph nodes (DLNs), and peripheral B cells. The authors claim that B cells from tumors and DLNs have a closer IgH profile than those in peripheral blood and that DLNs are differentially involved with tumor B cells. The claim that tumor-resident B cells are more immature and less specific is made based on the characteristics of the CDR-H3 they express.

Strengths:

The authors show great expertise in developing in-house bioinformatics pipelines, as well as using tools developed by others, to explore the IgH repertoire expressed by B cells as a means of better characterising tumour-associated B cells for the future generation of tumour-reactive antibodies as a therapy.

Weaknesses:

This paper needs major editing, both of the text and the figures, because as it stands it is convoluted and extremely difficult to follow. The conclusions reached are often not obvious from the figures themselves. Sufficient a priori details describing the framework for their analyses are not provided, making the outcome of their results questionable and leaving the reader wondering whether the findings are on solid ground. The authors are encouraged to explain in more detail the premises used in their algorithms, as well as the criteria they follow to define clonotypes, clonal groups, and clonal lineages, which are currently poorly defined and are crucial elements that may influence their results and conclusions. Having excluded the IGHD gene segment from some of their analyses (at least those related to clonal lineage inference and phylogenetic trees), it is not well explained which region of CDR-H3 is responsible for the charge, interaction strength, and Kidera factors, since in some cases the authors mention that the central part of CDR-H3 consists of five amino acids and in others of seven amino acids. How can the authors justify that the threshold for CDR-H3 identity varies according to individual patient data?

Throughout the analyses, the reasons for choosing one type of cancer over another sometimes seem subjective and are not well justified in the text.

Overall, the narrative is fragmented. There is a lack of well-defined conclusions at the end of the results subheadings. The exact same paragraph is repeated twice in the results section. The authors have also failed to synchronise the actual number of main figures with the text, and some panels are included in the main figures that are neither described nor mentioned in the text (Venn diagram Fig. 2A and phylogenetic tree Fig. 5D). Overall, the manuscript appears to have been rushed and not thoroughly read before submission.

Reviewers are forced to wade through, unravel, and validate poorly explained algorithms in order to understand the authors' often bold conclusions.

Reviewer #2 (Public Review):

Summary:

The authors sampled the B cell receptor repertoires of Cancers, their draining lymph nodes, and blood. They characterized the clonal makeup of all B cells sampled and then analyzed these clones to identify clonal overlap between tissues and clonal activation as expressed by their mutation level and CDR3 amino acid characteristics and length. They conclude that B cell clones from the Tumor interact more with their draining lymph node than with the blood and that there is less mutation/expansion/activation of B cell clones in Tumors. These conclusions are interesting but hard to verify due to the under-sampling and short sequencing reads as well as confusion as to when analysis is across all individuals or of select individuals.

Strengths:

The main strength of their analysis is that they take into account multiple characteristics of clonal expansion and activation and their different modes of visualization, especially of clonal expansion and overlap. The triangle plots once one gets used to them are very nice.

Weaknesses:

The data used appears inadequate for the conclusions reached. The authors' sample size of B cells is small and they do not address how it could be sufficient. at such low sampling rates, compounded by the palsmablast bias they mention, it is unclear if the overlap trends they observe show real trends. Analyzing only top clones by size does not solve this issue. As it could be that the top 100 clones of one tissue are much bigger than those of another and that all overlap trends are simply because the clones are bigger in one tissue or the other. i.e there is equal overlap of clones with blood but blood is not sufficiently sampled given its greater diversity and smaller clones. Similarly, the read length (150bp X2) is too short missing FWR1 and CDR1 and often parts of FWR2 if CDR3 is long. As the authors themselves note (and as was shown in (Zhang 2015 - PMC4811607) this makes mutation analysis difficult. It also makes the identification of V genes and thus clonal identification ambiguous. This issue becomes especially egregious when clones are mutated. Finally, it is not completely clear when the analysis is of single individuals or across all individuals. If it is the former the authors did not explain how they chose the individuals analyzed and if the latter then it is not clear from the figures which measurements belong to which individual (i.e they are mixing measurements from different people). For all these reasons while the authors make many interesting suggestions about the potential relationships of B cell repertoires in cancer tissues and their draining lymph nodes and how to characterize and visualize them, it is hard to assess any of their conclusions and specific results.

Reviewer #3 (Public Review):

In multiple cancers, the key roles of B cells are emerging in the tumor microenvironment (TME). The authors of this study appropriately introduce that B cells are relatively under-characterised in the TME and argue correctly that it is not known how the B cell receptor (BCR) repertoires across tumors, lymph nodes, and peripheral blood relate. The authors therefore supply a potentially useful study evaluating the tumor, lymph node, and peripheral blood BCR repertoires and site-to-site as well as intra-site relationships. The authors employ sophisticated analysis techniques, although the description of the methods is incomplete. Among other interesting observations, the authors argue that the tumor BCR repertoire is more closely related to that of draining lymph node (dLN) than the peripheral blood in terms of clonal and isotype composition. Furthermore, the author's findings suggest that tumor-infiltrating B cells (TIL-B) exhibit a less mature and less specific BCR repertoire compared with circulating B cells. Overall, this is a potentially useful work that would be of interest to both medical and computational biologists working across cancer. However, there are aspects of the work that would have benefitted from further analysis and areas of the manuscript that could be written more clearly and proofread in further detail.

Major Strengths:

1. The authors provide a unique analysis of BCR repertoires across tumor, dLN, and peripheral blood. The work provides useful insights into inter- and intra-site BCR repertoire heterogeneity. While patient-to-patient variation is expected, the findings with regard to intra-tumor and intra-dLN heterogeneity with the use of fragments from the same tissue are of importance, contribute to the understanding of the TME, and will inform future study design.

2. A particular strength of the study is the detailed CDR3 physicochemical properties analysis which leads the authors to observations that suggest a less-specific BCR repertoire of TIL-B compared to circulating B cells.

Major Weaknesses:

1. The study would have benefitted from a deeper biological interpretation of the data. While given the low number of patients one can plausibly understand a reluctance to speculate about clinical details, there is limited discussion about what may contribute to observed heterogeneity. For example, for the analysis of three lymph nodes taken per patient which were examined for inter-LN heterogeneity, there is a lack of information regarding these lymph nodes. 'LN3' is deemed as exhibiting the most repertoire overlap with the tumor but there is no discussion as to why this may be the case.

2. At times the manuscript is difficult to follow. In particular, the 'Intra-LN heterogeneity' section follows the 'LN-LN heterogeneity in colorectal cancer' section and compares the overlap of LN fragments (LN11, LN21, LN31) with the tumor in two separate patients (Fig 6A). In the previous section (LN-LN), LN11, LN21, LN31 are names given to separate lymph nodes from the same patient. The fragments are referred to as 'LN2' and the nodes in the previous section are referred to similarly. This conflation of naming for nodes and fragments is confusing.

3. There is a duplicated paragraph in 'Short vs long trees' and the following section 'Productive involvement in hypermutation lineages depends on CDR3 characteristics.

eLife assessment

This paper provides an important assessment of competition dynamics allowing coexistence of the carnivore guild within a large national park. A solid dataset and multiple surveying techniques (camera traps and DNA metabarcoding) provide convincing evidence that spatial segregation represents the main strategy of coexistence, while species have a certain degree of temporal and dietary overlap. Altogether, the manuscript provides important information critical to the conservation and management agenda of the park.

Reviewer #1 (Public Review):

Summary:<br /> This work explored intra and interspecific niche partitioning along spatial, temporal, and dietary niche partitioning between apex carnivores and mesocarnivores in the Qilian Mountain National Park of China, using camera trapping data and DNA metabarcoding sequencing data. They conclude that spatial niche partitioning plays a key role in facilitating the coexistence of apex carnivore species, spatial and temporal niche partitioning facilitate the coexistence of mesocarnivore species, and spatial and dietary niche partitioning facilitate the coexistence between apex and mesocarnivore species. The information presented in this study is important for wildlife conservation and will contribute substantially to the current understanding of carnivore guilds and effective conservation management in fragile alpine ecosystems.

Strengths:<br /> Extensive fieldwork is evident in the study. Aiming to cover a large percentage of the Qilian Mountain National Park, the study area was subdivided into squares, as a geographical reference to distribute the sampling points where the camera traps were placed and the excreta samples were collected.

They were able to obtain many records in their camera traps and collected many samples of excreta. This diversity of data allowed them to conduct robust analyses. The data analyses carried out were adequate to obtain clear and meaningful results that enabled them to answer the research questions posed. The conclusions of this paper are mostly well supported by data.

The study has demonstrated the coexistence of carnivore species in the landscapes of the Qilian Mountains National Park, complementing the findings of previous studies. The information presented in this study is important for wildlife conservation and will contribute substantially to the current understanding of carnivore guilds and effective conservation management in fragile alpine ecosystems.

Weaknesses:<br /> It is necessary to better explain the methodology because it is not clear what is the total sampling effort. In methodology, they only claim to have used 280 camera traps, and in the results, they mention that there are 319 sampling sites. However, the total sampling effort (e.g. total time of active camera traps) carried out in the study and at each site is not specified.

Reviewer #2 (Public Review):

Summary:<br /> The study entitled "Different coexistence patterns between apex carnivores and mesocarnivores based on temporal, spatial, and dietary niche partitioning analysis in Qilian Mountain National Park, China" by Cong et al. addresses the compelling topic of carnivores' coexistence in a biodiversity hotspot in China. The study is interesting given it considers all three components affecting sympatric carnivores' distribution and co-occurrence, namely the temporal, the spatial, and the dietary partition within the carnivore guild. The authors have found that spatial co-occurrence is generally low, which represents the major strategy for coexistence, while there is temporal and dietary overlap. I also appreciated the huge sampling effort carried out for this study by the authors: they were able to deploy 280 camera trapping sites (which became 322 in the result section?) and collect a total of 480 scat samples. However, I have some concerns about the study on the non-consideration of the human dimension and potential anthropogenic disturbance that could affect the spatial and temporal distribution of carnivores, the choice of the statistical model to test co-occurrence, and the lack of clearly stated ecological hypotheses.

Strengths:<br /> The strengths of the study are the investigation of all three major strategies that can mitigate carnivores' coexistence, therefore, the use of multiple monitoring techniques (both camera trapping and DNA metabarcoding) and the big dataset produced that consists of a very large sampled area with a noteworthy number of camera tap stations and many scat samples for each species.

Weaknesses:<br /> I think that some parts of the manuscript should be written better and more clearly. A clear statement of the ecological hypotheses that could affect the partitioning among the carnivore guild is lacking. I think that the human component (thus anthropogenic disturbance) should have been considered more in the spatial analyses given it can influence the use of the environment by some carnivores. Additionally, a multi-species co-occurrence model would have been a more robust approach to test for spatial co-occurrence given it also considers imperfect detection.

Temporal and dietary results are solid and this latter in particular highlights a big predation pressure on some prey species such as the pika. This implies important conservation and management implications for this species, and therefore for the trophic chain, given that i) the pika population should be conserved and ii) a potential poisoning campaign against small mammals could be incredibly dangerous also for mesocarnivores feeding on them due to secondary poisoning.

Author Response

We appreciate the insightful and constructive feedback from the reviewers regarding our manuscript, "Gain neuromodulation mediates perceptual switches: evidence from pupillometry, fMRI, and RNN Modelling." The comments have provided us with a number of valuable perspectives that will undoubtedly strengthen the impact and clarity of our work.

We recognize the need for a more detailed and comparative analysis of the perceptual tasks used in our pupil and fMRI experiments. To address these points directly: the jittered intertrial intervals (ITIs) in the fMRI work were deemed necessary to effectively deconvolve the BOLD response (see Stottinger et al., 2018). In our fMRI work, each image was randomly preceded and followed by varying ITIs (2, 4, 6, and 8 seconds), ensuring an equitable distribution across sets and subjects. Importantly, our analysis of both fMRI and behavioral studies, including eye tracking data, indicates that perceptual switch behavior – the point at which switches occur – is consistent across modalities. If more predictive or preparatory activity were present in the fMRI version of the task, we would expect earlier switches or choices and altered reaction time distributions – neither of these signatures was observed in the original study (Stottinger et al., 2018). Importantly, this suggests that the additional time available in the fMRI experiments did not significantly alter behavioral outcomes. Thus, our findings suggest that despite the differences in timing and task structure, the behavioural responses remain consistent across both experimental setups. We will clarify this in the revised manuscript.

In response to the reviewer's comments on our computational model, particularly regarding the modelling of noradrenaline (NA) effects in the RNN, we agree that modelling gain as stationary is a substantial approximation. However, given the slow ramping of pupil diameter, which served as our proxy for gain, it is an approximation that we believe is justified: in the revised manuscript, we will run additional simulations to ensure the validity of this approximation. In addition, whilst we agree that the model is more complicated than is needed for the task, we opted for RNN modelling, in lieu of a simpler modelling approach, because we wanted to use RNN modelling as a method for both hypothesis testing and generation. To build the RNN, the only key elements of model structure we had to specify in advance were the inputs and the target outputs of the network. The solution the RNN arrived at, although involving many more parameters than a simpler model, was entirely determined by optimisation (i.e., not our a priori hypotheses). We feel that this strengthens the result considerably. Importantly, this approach also allowed us to be surprised by the results of the model – for instance, we did not anticipate that the effect of gain on the energy landscape to be primarily mediated by inhibitory gain. In the revised manuscript, we will integrate this line of thinking into the paper. We are also sensitive to the fact that this result is both counterintuitive and difficult to study in high-dimensional dynamical systems like RNNs. In revisions, we will provide further analysis of the RNN and build a 2D approximation to the RNN that can be studied on the phase plane to better conceptually illuminate the mechanisms at play.

Furthermore, we agree with the suggestion to consider alternative mechanisms that might contribute to perceptual switches, such as attention and top-down processing. While our study primarily focuses on LC-mediated gain modulation, we acknowledge the complexity of neural processes involved in perception and will expand our discussion to include these potential mechanisms. Furthermore, noting the importance of moderating the causal language used in our manuscript. We will revise our wording to more accurately reflect the correlational nature of our findings and ensure that our conclusions are firmly grounded in the data presented.

In conclusion, we are enthusiastic about the opportunity to refine our manuscript based on these valuable comments. In an updated version, we will address the overall points by providing clearer explanations of our methods, refining our figures for better readability, and ensuring that our conclusions are supported by robust analysis. We believe that these revisions will not only address the concerns raised but also significantly enhance the overall quality of our research. We thank the reviewers for their thorough and thoughtful critiques and look forward to submitting our revised manuscript.

eLife assessment

This valuable paper explores the idea that transient modulations of neural gain promote switches between distinct perceptual interpretations of ambiguous stimuli. Evidence for this idea is provided by pupillometry (an indirect proxy of neuromodulatory activity), fMRI, neural network modeling, and dynamical systems analyses. While this integrative approach is intriguing, the data analysis, as well as computational modeling, are incomplete, especially for supporting the many causal statements in the paper.

Reviewer #1 (Public Review):

Summary:<br /> This paper investigates the neural mechanisms underlying the change in perception when viewing ambiguous figures. Each possible percept is related to an attractor-like brain state and a perceptual switch corresponds to a transition between these states. The hypothesis is that these switches are promoted by bursts of noradrenaline that change the gain of neural circuits. The authors present several lines of evidence consistent with this view: pupil diameter changes during the time point of the perceptual change; a gain change in neural network models promotes a state transition; and large-scale fMRI dynamics in a different experiment suggests a lower barrier between brain states at the change point. However, some assumptions of the computational model seem not well justified and the theoretical analysis is incomplete. The paper would also benefit from a more in-depth analysis of the experimental data.

Strengths:<br /> The main strength of the paper is that it attempts to combine experimental measurements - from psychophysics, pupil measurements, and fMRI dynamics - and computational modeling to provide an emerging picture of how a perceptual switch emerges. This integrative approach is highly useful because the model has the potential to make the underlying mechanisms explicit and to make concrete predictions.

Weaknesses:<br /> A general weakness is that the link between the three parts of the paper is not very strong. Pupil and fMRI measurements come from different experiments and additional analysis showing that the two experiments are comparable should be included. Crucially, the assumptions underlying the RNN modeling are unclear and the conclusions drawn from the simulation may depend on those assumptions.

Main points:<br /> Perceptual tasks in pupil and fMRI experiments: how comparable are these two tasks? It seems that the timing is very different, with long stimulus presentations and breaks in the fMRI task and a rapid sequence in the pupil task. Detailed information about the task timing in the pupil task is missing. What evidence is there that the same mechanisms underlie perceptual switches at these different timescales? Quantification of the distributions of switching times/switching points in both tasks is missing. Do the subjects in the fMRI task show the same overall behavior as in the pupil task? More information is needed to clarify these points.

Computational model:<br /> 1. Modeling noradrenalin effects in the RNN: The pupil data suggests phasic bursts of NA would promote perceptual switches. But as I understand, in the RNN neuromodulation is modeled as different levels of gain throughout the trial. Making the neural gain time-dependent would allow investigation of whether a phasic gain change can explain the experimentally observed distribution of switching times.

2. Modeling perceptual switches: in the results, it is described that the networks were trained to output a categorical response, but the firing rates in Fig 2B do not seem categorical but rather seem to follow the input stimulus. The output signals of the network are not shown. If I understand correctly, a trivial network that would just represent the two input signals without any internal computation and relay them to the output would do the task correctly (because "the network's choice at each time point was the maximum of the two-dimensional output", p. 22). This seems like cheating: the very operation that the model should perform is to signal the change, in a categorical manner, not to represent the gradually changing input signals.

3. The mechanism of how increased gain leads to faster switches remains unclear to me. My first intuition was that increasing the gain of excitatory populations (the situation shown in Fig. 2E) in discrete attractor models would lead to deeper attractor wells and this would make it more difficult to switch. That is, a higher gain should lead to slower decisions in this case. However, here the switching time remains constant for a gain between 1 and 1.5. Lowering the gain, on the other hand, leads to slower switching. It is, of course, possible that the RNN behaves differently than classical point attractor models or that my intuition is incorrect (though I believe it is consistent with previous literature, e.g. Niyogi & Wong-Lin 2013 (doi:10.1371/journal.pcbi.1003099) who show higher firing rates - more stable attractors - for increased excitatory gain).

4. From the RNN model it is not clear how changes in excitatory and inhibitory gain lead to slower/faster switching. In order to better understand the role of inhibitory and excitatory gain on switching, I would suggest studying a simple discrete attractor model (a rate model, for example as in Wong and Wang 2006 or Roxin and Ledberg, Plos Comp. Bio 2008) which will allow to study these effects in terms of a very few model parameters. The Roxin paper also shows how to map rate models onto simplified one-dimensional systems such as the one in Fig S3. Setting up the model using this framework would allow for making much stronger, principled statements about how gain changes affect the energy landscape, and under which conditions increased inhibitory gain leads to faster switching.

One possibility is that increasing the excitatory gain in the RNN leads to saturated firing rates. If this is the reason for the different effects of excitatory and inhibitory gain changes, it should be properly explained. Moreover, the biological relevance of this effect should be discussed (assuming that saturation is indeed the explanation).

Alternative mechanisms:<br /> It is mentioned in the introduction that changes in attention could drive perceptual switches. A priori, attention signals originating in the frontal cortex may be plausible mechanisms for perceptual switches, as an alternative to LC-controlled gain modulation. Does the observed fMRI dynamics allow us to distinguish these two hypotheses? In any case, I would suggest including alternative scenarios that may be compatible with the observed findings in the discussion.

Reviewer #2 (Public Review):

Strengths<br /> - the study combines different methods (pupillometry, RNNs, fMRI).<br /> - the study combines different viewpoints and fields of the scientific literature, including neuroscience, psychology, physics, dynamical systems.<br /> - This combination of methods and viewpoints is rarely done, it is thus very useful.<br /> - Overall well-written.

Weaknesses<br /> - The study relies on a report paradigm: participants report when they identify a switch in the item category. The sequence corresponds to the drawing of an object being gradually morphed into another object. Perceptual switches are therefore behaviorally relevant, and it is not clear whether the effect reported correspond to the perceptual switch per se, or the detection of an event that should change behavior (participant press a button indicating the perceived category, and thus switch buttons when they identify a perceptual change). The text mentions that motor actions are controlled for, but this fact only indicates that a motor action is performed on each trial (not only on the switch trial); there is still a motor change confounded with the switch. As a result, it is not clear whether the effect reported in pupil size, brain dynamics, and brain states is related to a perceptual change, or a decision process (to report this change).

- The study presents events that co-occur (perceptual switch, change in pupil size, energy landscape of brain dynamics) but we cannot identify the causes and consequences. Yet, the paper makes several claims about causality (e.g. in the abstract "neuromodulatory tone ... causally mediates perceptual switches", in the results "the system flattening the energy landscape ... facilitated an updating of the content of perception").

- Some effects may reflect the expectation of a perceptual switch, rather than the perceptual switch per se. Given the structure of the task, participants know that there will be a perceptual switch occurring once during a sequence of morphed drawings. This change is expected to occur roughly in the middle of the sequence, making early switches more surprising, and later switches less surprising. Differences in pupil response to early, medium, and late switches could reflect this expectation. The authors interpret this effect very differently ("the speed of a perceptual switch should be dependent on LC activity").

- The RNN is far more complex than needed for the task. It has two input units that indicate the level of evidence for the two categories being morphed, and it is trained to output the dominant category. A (non-recurrent) network with only these two units and an output unit whose activity is a sigmoid transform of the difference in the inputs can solve the task perfectly. The RNN activity is almost 1-dimensional probably for this reason. In addition, the difficult part of the computation done by the human brain in this task is already solved in the input that is provided to the network (the brain is not provided with the evidence level for each category, and in fact, it does not know in advance what the second category will be).

- Basic fMRI results are missing and would be useful, before using elaborate analyses. For instance, what are the regions that are more active when a switch is detected?

- The use of methods from physics may obscure some simple facts and simpler explanations. For instance, does the flatter energy landscape in the higher gain condition reflect a smaller number of states visited in the state space of the RNN because the activity of each unit gets in the saturation range? If correct, then it may be a more straightforward way of explaining the results.

- Some results are not as expected as the authors claim, at least in the current form of the paper. For instance, they show that, when trained to identify which of two inputs u1 and u2 is the largest (with u2=1-u1, starting with u1=1 and gradually decreasing u1), a higher gain results in the RNN reporting a switch in dominance before the true switch (e.g. when u1=0.6 and u2=0.4), and vice et versa with a lower gain. In other words, it seems to correspond to a change in criterion or bias in the RNN's decision. The authors should discuss more specifically how this result is related to previous studies and models on gain modulation. An alternative finding could have been that the network output is a more (or less) deterministic function of its inputs, but this aspect is not reported.

Author Response

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

Reviewer #1 (Public Review):

In this manuscript, the authors explore the effects of DNA methylation on the strength of regulatory activity using massively parallel reporter assays in cell lines on a genome-wide level. This is a follow-up of their first paper from 2018 that describes this method for the first time. In addition to adding more indepth information on sequences that are explored by many researchers using two main methods, reduced bisulfite sequencing and sites represented on the Illumina EPIC array, they now show also that DNA methylation can influence changes in regulatory activity following a specific stimulation, even in absence of baseline effects of DNA methylation on activity. In this manuscript, the authors explore the effects of DNA methylation on the response to Interferon alpha (INFA) and a glucocorticoid receptor agonist (dexamethasone). The authors validate their baseline findings using additional datasets, including RNAseq data, and show convergences across two cell lines. The authors then map the methylation x environmental challenge (IFNA and dex) sequences identified in vitro to explore whether their methylation status is also predictive of regulatory activity in vivo. This is very convincingly shown for INFA response sequences, where baseline methylation is predictive of the transcriptional response to flu infection in human macrophages, an infection that triggers the INF pathways.

Thank you for your strong assessment of our work!

The extension of the functional validity of the dex-response altering sequences is less convincing.

We agree. We note that genes close to dex-specific mSTARR-seq enhancers tend to be more strongly upregulated after dex stimulation than those near shared enhancers, which parallels our results for IFNA (lines 341-344). However, there is unfortunately no comparable data set to the human flu data set (i.e., with population-based whole genome-bisulfite sequencing data before and after dex challenge), so we could not perform a parallel in vivo validation step. We have added this caveat to the revised manuscript (lines 555-557).

Sequences altering the response to glucocorticoids, however, were not enriched in DNA methylation sites associated with exposure to early adversity. The authors interpret that "they are not links on the causal pathway between early life disadvantage and later life health outcomes, but rather passive biomarkers". However, this approach does not seem an optimal model to explore this relationship in vivo. This is because exposure to early adversity and its consequences is not directly correlated with glucocorticoid release and changes in DNA methylation levels following early adversity could be related to many physiological mechanisms, and overall, large datasets and meta-analyses do not show robust associations of exposure to early adversity and DNA methylation changes. Here, other datasets, such as from Cushing patients may be of more interest.

Thank you for making these important points. We have expanded the set of caveats regarding the lack of enrichment of early adversity-reported sites in the mSTARR-data set (lines 527-533). Specifically, we note that the relationship between early adversity and glucocorticoid physiology is complex (e.g., Eisenberger and Cole, 2012; Koss and Gunnar, 2018) and that dex challenge models one aspect of glucocorticoid signaling but not others (e.g., glucocorticoid resistance). Nevertheless, we also see little evidence for enrichment of early adversity-associated sites in the mSTARR data set at baseline, independently of the dex challenge experiment (lines 483-485; Figure 4).

We also agree that large data sets (e.g., Houtepen et al., 2018; Marzi et al., 2018) and reviews (e.g., Cecil et al., 2020) of early adversity and DNA methylation in humans show limited evidence of associations between early adversity and DNA methylation levels. However, the idea that early adversity impacts downstream outcomes remains pervasive in the literature and popular science (see Dubois et al., 2019), which we believe makes tests like ours important to pursue. We also hope that our data set (and others generated through these methods) will be useful in interpreting other settings in which differential methylation is of interest as well—in line with your comment below. We have clarified both of these points in the revised manuscript (lines 520-522; 536-539).

Overall, the authors provide a great resource of DNA methylation-sensitive enhancers that can now be used for functional interpretation of large-scale datasets (that are widely generated in the research community), given the focus on sites included in RBSS and the Illumina EPIC array. In addition, their data lends support that differences in DNA methylation can alter responses to environmental stimuli and thus of the possibility that environmental exposures that alter DNS methylation can also alter the subsequent response to this exposure, in line with the theory of epigenetic embedding of prior stimuli/experiences. The conclusions related to the early adversity data should be reconsidered in light of the comments above.

Thank you! And yes, we have revised our discussion of early life adversity effects as discussed above.

Reviewer #1 (Recommendations For The Authors):

While the paper has a lot of strengths and provides new insight into the epigenomic regulation of enhancers as well as being a great resource, there are some aspects that would benefit from clarification.

a. It would be great to have a clearer description of how many sequences are actually passing QC in the different datasets and what the respective overlaps are in bps or 600bp windows. Now often only % are given. Maybe a table/Venn diagram for overview of the experiments and assessed sequences would help here. This concern the different experiments in the K652, A549, and Hep2G cell lines, including stimulations.

We now provide a supplementary figure and supplementary table providing, for each dataset, the number of 600 bp windows passing each filter (Figure 2-figure supplement 1; Supplementary File 9), as well as a supplementary figure providing an upset plot to show the number of assessed sequences shared across the experiments (Figure 2-figure supplement 2).

b. It would also be helpful to have a brief description of the main differences in assessed sequences and their coverage of the old (2018) and new libraries in the main text to be able better interpret the validation experiments.

We now provide information on the following characteristics for the 2018 data set versus the data set presented for the first time here: mean (± SD) number of CpGs per fragment; mean (± SD) DNA sequencing depth; and mean (± SD) RNA sequencing depth (lines 169-170 provide values for the new data set; in line 194, we reference Supplementary File 5, which provides the same values for the old data set). Notably, the coverage characteristics of analyzed windows in both data sets are quite high (mean DNA-seq read coverage = 94x and mean RNA-seq read coverage = 165x in the new data set at baseline; mean DNA-seq read coverage = 22x and mean RNA-seq read coverage = 54x in Lea et al. 2018).

c. Statements of genome-wide analyses in the abstract and discussion should be a bit tempered, as quite a number of tested sites do not pass QC and do not enter the analysis. From the results it seems like from over 4.5 million sequences, only 200,000 are entering the analysis.

The reason why many of the windows are not taken forward into our formal modeling analysis is that they fail our filter for RNA reads because they are never (or almost never) transcribed—not because there was no opportunity for transcription (i.e., the region was indeed assessed in our DNA library, and did not show output transcription, as now shown in Figure 2-figure supplement 1). We have added a rarefaction analysis (lines 715-722 in Materials and Methods) of the DNA fragment reads to the revised manuscript which supports this point. Specifically, it shows that we are saturated for representation of unique genomic windows (i.e., we are above the stage in the curve where the proportion of active windows would increase with more sequencing: Figure 1figure supplement 4). Similarly, a parallel rarefaction curve for the mSTARR-seq RNA-seq data (Figure 1-figure supplement 4) shows that we would gain minimal additional evidence for regulatory activity with more sequencing depth. We now reference these analyses in revised lines 179-184 and point to the supporting figure in line 182.

In other words, our analysis is truly genome-wide, based on the input sequences we tested. Most of the genome just doesn’t have regulatory activity in this assay, despite the potential for it to be detected given that the relevant sequences were successfully transfected into the cells.

d. Could the authors comment on the validity of the analysis if only one copy is present (cut-off for QC)?

We think this question reflects a misunderstanding of our filtering criteria due to lack of clarity on our part, which we have modified in the revision. We now specify that the mean DNA-seq sequencing depth per sample for the windows we subjected to formal modeling was quite high:

93.91 ± 10.09 SD (range = 74.5 – 113.5x) (see revised lines 169-170). In other words, we never analyze windows in which there is scant evidence that plasmids containing the relevant sequence were successfully transfected (lines 170-172).

Our minimal RNA-seq criteria require non-zero counts in at least 3 replicate samples within either the methylated condition or the unmethylated condition, or both (lines 166-168). Because we know that multiple plasmids containing the corresponding sequence are present for all of these windows—even those that just cross the minimal RNA-seq filtering threshold—we believe our results provide valid evidence that all analyzed windows present the opportunity to detect enhancer activity, but many do not act as enhancers (i.e., do not result in transcribed RNA). Notably, we observe a negligible correlation between DNA sequencing depth for a fragment, among analyzed windows, and mSTARR-seq enhancer activity (R2 = 0.029; now reported in lines 183-184). We also now report reproducibility between replicates, in which all replicate pairs have r > 0.89, on par with previously published STARR-seq datasets (e.g., Klein et al., 2020; Figure 1-figure supplement 6, pointed to in line 193).

e. While the authors state that almost all of the control sequences contain CpGs sites, could the authors also give information on the total number of CpG sites in the different subsets? Was the number of CpGs in a 600 bp window related to the effects of DNA methylation on enhancer activity?

We now provide the number of CpG sites per window in the different subsets in lines 282-284. As expected, they are higher for EPIC array sites and for RRBS sites because the EPIC array is biased towards CpG-rich promoter regions, and the enzyme typically used in the starting step of RRBS digests DNA at CpG motifs (but control sequences still contain an average of ~13 CpG sites per fragment). We also now model the magnitude of the effects of DNA methylation on regulatory activity as a function of number of CpG sites within the 600 bp windows. Consistent with our previous work in Lea et al., 2018, we find that mSTARR-seq enhancers with more CpGs tend to be repressed by DNA methylation (now reported in lines 216-219 and Figure 1figure supplement 11).

f. In the discussion, a statement on the underrepresented regions, likely regulatory elements with lower CG content, that nonetheless can be highly relevant for gene regulation would be important to put the data in perspective.

Thanks for this suggestion. We agree that regulatory regions, independent of CpG methylation, can be highly relevant, and now clarify in the main text that the “unmethylated” condition of mSTARR-seq is essentially akin to a conventional STARR-seq experiment, in that it assesses regulatory activity regardless of CpG content or methylation status (lines 128-130).

Consequently, our study is well-designed to detect enhancer-like activity, even in windows with low GC content. We now show with additional analyses that we generated adequate DNA-seq coverage on the transfected plasmids to analyze 90.2% of the human genome, including target regions with no or low CpG content (lines 148-149; 153-156; Supplementary file 2). As noted above, we also now clarify that regions dropped out of our formal analysis because we had little to no evidence that any transcription was occurring at those loci, not because sequences for those regions were not successfully transfected into cells (see responses above and new Figure 1-figure supplement 4 and Figure 2-figure supplement 1).

g. To control for differences in methylation of the two libraries, the authors sequence a single CpGs in the vector. Could the authors look at DNA methylation of the 600 bp windows at the end of the experiment, could DNA methylation of these windows be differently affected according to sequence? 48 hours could be enough for de-methylation or re-methylation.

We agree that variation in demethylation or remethylation depending on fragment sequence is possible. We now state this caveat in the main text (lines 158-159), and specify that genomic coverage of our bisulfite sequencing data across replicates are (unfortunately) too variable to perform reliable site-by-site analysis of DNA methylation levels before and after the 48 hour experiment (lines 1182-1185). Instead, we focus on a CpG site contained in the adapter sequence (and thus included in all plasmids) to generate a global estimate of per replicate methylation levels. We also now note that any de-methylation or re-methylation would reduce our power to detect methylation-dependent activity, rather than leading to false positives (lines 163-165).

h. The section on the method for correction for multiple testing should be more detailed as it is very difficult to follow. Why were only 100 permutations used, the empirical p-value could then only be <0.01? The description of a subsample of the N windows with positive Betas is unclear, should the permutation not include the actual values and thus all windows - or were the no negative Betas? Was FDR accounting for all elements and pairs?

We have now expanded the text in the Materials and Methods section to clarify the FDR calculation (lines 691, 695-699, 702, 706). We clarify that the 100 permutations were used to generate a null distribution of p-values for the data set (e.g., 100 x 17,461 p-values for the baseline data set), which we used to derive a false discovery rate. Because we base our evidence on FDRs, we therefore compare the distribution of observed p-values to the distribution of pvalues obtained via permutation; we do not calculate individual p-values by comparing an observed test statistic against the test statistics for permuted data for that individual window.

We compare the data to permutations with only positive betas because in the observed data, we observe many negative betas. These correspond to windows which have no regulatory activity (i.e., they have many more input DNA reads than RNA-seq reads) and thus have very small pvalues in a model testing for DNA-RNA abundance differences. However, we are interested in controlling the false discovery rate of windows that do have regulatory activity (positive betas). In the permuted data, by contrast and because of the randomization we impose, test statistics are centered around 0 and essentially symmetrical (approximately equally likely to be positive or negative). Retaining all p-values to construct the null therefore leads to highly miscalibrated false discovery rates because the distribution of observed values is skewed towards smaller values— because of windows with “significantly” no regulatory activity—compared to the permuted data. We address that problem by using only positive betas from the permutations.

i. The interpretation of the overlap of Dex-response windows with CpGs sites associated with early adversity should be revisited according to the points also mentioned in the public review and the authors may want to consider exploring additional datasets with other challenges.

Thank you, see our responses to the public review above and our revisions in lines (lines 555559). We agree that comparisons with more data sets and generation of more mSTARR-seq data in other challenge conditions would be of interest. While beyond the scope of this manuscript, we hope the resource we have developed and our methods set the stage for just such analyses.

Reviewer #2 (Public Review):

This work presents a remarkably extensive set of experiments, assaying the interaction between methylation and expression across most CpG positions in the genome in two cell types. To this end, the authors use mSTARR-seq, a high-throughput method, which they have previously developed, where sequences are tested for their regulatory activity in two conditions (methylated and unmethylated) using a reporter gene. The authors use these data to study two aspects of DNA methylation:

1) Its effect on expression, and 2. Its interaction with the environment. Overall, they identify a small number of 600 bp windows that show regulatory potential, and a relatively large fraction of these show an effect of methylation on expression. In addition, the authors find regions exhibiting methylation-dependent responses to two environmental stimuli (interferon alpha and glucocorticoid dexamethasone).

The questions the authors address represent some of the most central in functional genomics, and the method utilized is currently the best method to do so. The scope of this study is very impressive and I am certain that these data will become an important resource for the community. The authors are also able to report several important findings, including that pre-existing DNA methylation patterns can influence the response to subsequent environmental exposures.

Thank you for this generous summary!

The main weaknesses of the study are: 1. The large number of regions tested seems to have come at the expense of the depth of coverage per region (1 DNA read per region per replicate). I have not been convinced that the study has sufficient statistical power to detect regulatory activity, and differential regulatory activity to the extent needed. This is likely reflected in the extremely low number of regions showing significant activity.

We apologize for our lack of clarity in the previous version of the manuscript. Nonzero coverage for half the plasmid-derived DNA-seq replicates is a minimum criterion, but for the baseline dataset, the mean depth of DNA coverage per replicate for windows passing the DNA filter is quite high: 12.723 ± 41.696 s.d. overall, and 93.907 ± 10.091 s.d. in the windows we subjected to full analysis (i.e., windows that also passed the RNA read filter). We now provide these summary statistics in lines 148-149 and 169-170 and Supplementary file 5 (see also our responses to Reviewer 1 above). We also now show, using a rarefaction analysis, that our data set saturates the ability to detect regulatory windows based on DNA and RNA sequencing depth (new Figure 1-figure supplement 4; lines 179-184; 715-722).

2) Due to the position of the tested sequence at the 3' end of the construct, the mSTARR-seq approach cannot detect the effect of methylation on promoter activity, which is perhaps the most central role of methylation in gene regulation, and where the link between methylation and expression is the strongest. This limitation is evident in Fig. 1C and Figure 1-figure supplement 5C, where even active promoters have activity lower than 1. Considering these two points, I suspect that most effects of methylation on expression have been missed.

Thank you for pointing this out. We agree that we have not exhaustively detected methylationdependent activity in all promoter regions, given that not all promoter regions are active in STARR-seq. However, there is good evidence that some promoter regions can function like enhancers and thus be detected in STARR-seq-type assays (Klein et al., 2020). This important point is now noted in lines 187-189; an example promoter showing methylation-dependent regulatory activity in our dataset is shown in Figure 3E.

We also now clarify that Figure 1C shows significant enrichment of regulatory activity in windows that overlap promoter sequence (line 239). The y-axis is not a measure of activity, but rather the log-transformed odds ratio, with positive values corresponding to overrepresentation of promoter sequences in regions of mSTARR-seq regulatory activity. Active promoters are 1.640 times more likely to be detected with regulatory activity than expected by chance (p = 1.560 x 10-18), which we now report in a table that presents enrichment statistics for all ENCODE elements shown in Figure 1C for clarity (Supplementary file 4). Moreover, 74.1% of active promoters that show regulatory activity have methylation-dependent activity, also now reported in Supplementary file 4.

Overall, the combination of an extensive resource addressing key questions in functional genomics, together with the findings regarding the relationship between methylation and environmental stimuli makes this a key study in the field of DNA methylation.

Thank you again for the positive assessment!

Reviewer #2 (Recommendations For The Authors):

I suggest the authors conduct several tests to estimate and/or increase the power of the study:

1) To estimate the potential contribution of additional sequencing depth, I suggest the authors conduct a downsampling analysis. If the results are not saturated (e.g., the number of active windows is not saturated or the number of differentially active windows is not saturated), then additional sequencing is called for.

We appreciate the suggestion. We have now performed a downsampling/rarefaction curve analysis in which we downsampled the number of DNA reads, and separately, the number of RNA reads. We show that for both DNA-seq depth and RNA-seq depth, we are within the range of sequencing depth in which additional sequencing would add minimal new analysis windows in the dataset (Figure 1-figure supplement 4; lines 179-184; 715-722).

2) Correlation between replicates should be reported and displayed in a figure because low correlations might also point to too few reads. The authors mention: "This difference likely stems from lower variance between replicates in the present study, which increases power", but I couldn't find the data.

We now report the correlations between RNA and DNA replicates within the current dataset and within the Lea et al., 2018 dataset (Figure 1-figure supplement 6). The between-replicate correlations in both our RNA libraries and DNA libraries are consistently high (r ≥ 0.89).

3) The correlation between the previous and current K562 datasets is surprisingly low. Given that these datasets were generated in the same cell type, in the same lab, and using the same protocol, I expected a higher correlation, as seen in other massively parallel reporter assays. The fact that the correlations are almost identical for a comparison of the same cell and a comparison of very different cell types is also suspicious.

Thanks for raising this point. We think it is in reference to our original Figure 1-Figure supplement 6, for which we now provide Pearson correlations in addition to R2 values (now Figure 1-Figure supplement 8). We note that this is not a correlation in raw data, but rather the correlation in estimated effect sizes from a statistical model for methylation-dependent activity. We now provide Pearson correlations for the raw data between replicates within each dataset (Figure 1-Figure supplement 6), which for the baseline dataset are all r > 0.89 for RNA replicates and r > 0.98 for DNA replicates, showing that replicate reproducibility in this study is on par with other published studies (e.g., Klein et al., 2020 report r > 0.89 for RNA replicates and r > 0.91 for DNA replicates).

We do not know of any comparable reports in other MPRAs for effect size correlations between two separately constructed libraries, so it’s unclear to us what the expectation should be. However, we note that all effect sizes are estimated with uncertainty, so it would be surprising to us to observe a very high correlation for effect sizes in two experiments, with two independently constructed libraries (i.e., with different DNA fragments), run several years apart—especially given the importance of winner’s curse effects and other phenomena that affect point estimates of effect sizes. Nevertheless, we find that regions we identify as regulatory elements in this study are 74-fold more likely to have been identified as regulatory elements in Lea et al., 2018 (p < 1 x10-300).

4) The authors cite Johnson et al. 2018 to support their finding that merely 0.073% of the human genome shows activity (1.7% of 4.3%), but:

a. the percent cited is incorrect: this study found that 27,498 out of 560 million regions (0.005%) were active, and not 0.165% as the authors report.

We have modified the text to clarify the numerator and denominator used for the 0.165% estimate from Johnson et al 2018 (lines 175-176). The numerator is their union set of all basepairs showing regulatory activity in unstimulated cells, which is 5,547,090 basepairs. The denominator is the total length of the hg38 human genome, which is 3,298,912,062 basepairs.

Notably, the denominator (the total human genome) is not 560 million—while Johnson et al (2018) tested 560 million unique ~400 basepair fragments, these fragments were overlapping, such that the 560 million fragments covered the human genome 59 times (i.e., 59x coverage).

b. other studies that used massively parallel reporter assays report substantially higher percentages, suggesting that the current study is possibly underpowered. Indeed, the previous mSTARR-seq found a substantially larger percentage of regions showing regulatory activity (8%). The current study should be compared against other studies (preferably those that did not filter for putatively active sequences, or at least to the random genomic sequences used in these studies).

We appreciate this point and have double checked comparisons to Johnson et al., 2018 and Lea et al., 2018. Our numbers are not unusual relative to Johnson et al., 2018 (0.165%), which surveyed the whole genome. Also, in comparing to the data from Lea et al., 2018, when processed in an identical manner (our criteria are more stringent here), our values of the percent of the tested genome showing significant regulatory activity are also similar: 0.108% in the Lea et al., 2018 dataset versus 0.082% in the baseline dataset. Finally, our rarefaction analyses (see our responses above) indicate that we are not underpowered based on sequencing depth for RNA or DNA samples. We also note that there are several differences in our analysis pipeline from other studies: we use more technical replicates than is typical (compare to 2-5 replicates in Arnold et al., 2013; Johnson et al., 2018; Muerdter et al., 2018), we measure DNA library composition based on DNA extracted from each replicate post-transfection (as opposed to basing it on the pre-transfection library: [Johnson et al., 2018], and we use linear mixed models to identify regulatory activity as opposed to binomial tests [Johnson et al., 2018; Arnold et al., 2013; Muerdter et al., 2018].

I find it confusing that the four sets of CpG positions used: EPIC, RRBS, NR3C1, and random control loci, add up together to 27.3M CpG positions. Do the 600 bp windows around each of these positions sufficient to result in whole-genome coverage? If so, a clear explanation of how this is achieved should be added.

Thanks for this comment. Although our sequencing data are enriched for reads that cover these targeted sites, the original capture to create the input library included some off target reads (as is typical of most capture experiments, which are rarely 100% efficient). We then sequenced at such high depth that we ultimately obtained sequencing coverage that encompassed nearly the whole genome. We now clarify in the main text that our protocol assesses 27.3 million CpG sites by assessing 600 bp windows encompassing 93.5% of all genomic CpG sites (line 89), which includes off-target sites (line 149).

scatter plot showing the RNA to DNA ratios of the methylated (x-axis) vs unmethylated (y-axis) library would be informative. I expect to see a shift up from the x=y diagonal in the unmethylated values.

We have added a supplementary figure showing this information, which shows the expected shift upwards (Figure 1-figure supplement 9).

Another important figure missing is a histogram showing the ratios between the unmethylated and methylated libraries for all active windows, with the significantly differentially active windows marked.

We have added a supplementary figure showing this information (Figure 1-Supplementary Figure 10).

Perhaps I missed it, but what is the distribution of effect sizes (differential activity) following the various stimuli?

This information is provided in table form in Supplementary Files 3, 10, and 11, which we now reference in the Figure 2 legend (lines 365-366).

Minor changes

It is unclear what the lines connecting the two groups in Fig.3C represent, as these are two separate groups of regions.

We now clarify in the figure legend that values connected by a line are the same regions, not two different sets of regions. They show the correlation between DNA methylation and gene expression at mSTARR-seq-identified enhancers in individuals before and after IAV stimulation, separately for enhancers that are shared between conditions (left) versus those that are IFNAspecific (right). The two plots therefore do show two different sets of regions, which we have depicted to visualize the contrast in the effect of stimulation on the correlation on IFNA-specific enhancers versus shared enhancers. We have revised the figure legend to clarify these points (line 458-460).

L235-242 are unclear. Specifically - isn't the same filter mentioned in L241-242 applied to all regions?

Yes, the same filter for minimal RNA transcription was applied to all regions. We have modified the text (lines 264-265, 271, 275-277) to clarify that the enrichment analyses were performed twice, to test whether the target types were: 1) enriched in the dataset passing the RNA filter (i.e., the dataset showing plasmid-derived RNA reads in at least half the sham or methylated replicates; n = 216,091 windows) and 2) enriched in the set of windows showing significant regulatory activity (at FDR < 1%; n = 3,721 windows).

To improve cohesiveness, the section about most CpG sites associated with early life adversity not showing regulatory activity in K562s can be moved to the supplementary in my opinion.

Thank you for this suggestion. Because ELA and the biological embedding hypothesis (via DNA methylation) were major motivations for our analysis (see Introduction lines 42-48; 75-79), and we also discuss these results in the Discussion (lines 518-520), we have respectfully elected to retain this section in the main manuscript. We have added text in the Discussion explaining why we think experimental tests of methylation effects on regulation are relevant to the literature on early life adversity (lines 520-522), and have added discussion on limits to these analyses (lines 527-533).

References:

Arnold CD, Gerlach D, Stelzer C, Boryń ŁM, Rath M, Stark A (2013) Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science, 339, 1074-1077.

Cecil CA, Zhang Y, Nolte T (2020) Childhood maltreatment and DNA methylation: A systematic review. Neuroscience & Biobehavioral Reviews, 112, 392-409.

Dubois M, Louvel S, Le Goff A, Guaspare C, Allard P (2019) Epigenetics in the public sphere: interdisciplinary perspectives. Environmental Epigenetics, 5, dvz019.

Eisenberger NI, Cole SW (2012) Social neuroscience and health: neurophysiological mechanisms linking social ties with physical health. Nature neuroscience, 15, 669-674.

Houtepen L, Hardy R, Maddock J, Kuh D, Anderson E, Relton C, Suderman M, Howe L (2018) Childhood adversity and DNA methylation in two population-based cohorts. Translational Psychiatry, 8, 1-12.

Johnson GD, Barrera A, McDowell IC, D’Ippolito AM, Majoros WH, Vockley CM, Wang X, Allen AS, Reddy TE (2018) Human genome-wide measurement of drug-responsive regulatory activity. Nature communications, 9, 1-9.

Klein JC, Agarwal V, Inoue F, Keith A, Martin B, Kircher M, Ahituv N, Shendure J (2020) A systematic evaluation of the design and context dependencies of massively parallel reporter assays. Nature Methods, 17, 1083-1091.

Koss KJ, Gunnar MR (2018) Annual research review: Early adversity, the hypothalamic–pituitary– adrenocortical axis, and child psychopathology. Journal of Child Psychology and Psychiatry, 59, 327-346.

Marzi SJ, Sugden K, Arseneault L, Belsky DW, Burrage J, Corcoran DL, Danese A, Fisher HL, Hannon E, Moffitt TE (2018) Analysis of DNA methylation in young people: limited evidence for an association between victimization stress and epigenetic variation in blood. American journal of psychiatry, 175, 517-529.

Muerdter F, Boryń ŁM, Woodfin AR, Neumayr C, Rath M, Zabidi MA, Pagani M, Haberle V, Kazmar T, Catarino RR (2018) Resolving systematic errors in widely used enhancer activity assays in human cells. Nature methods, 15, 141-149.

eLife assessment

This important paper uses a genome-wide, massively parallel reporter assay to determine how CpG methylation affects regulatory sequences that control the expression of human genes. The authors provide compelling evidence that methylation not only influences baseline activity of regulatory sequences but also the magnitude of acute responses to environmental stimuli. The findings are of broad interest, and the extensive data set will likely become a key resource for the community.

Reviewer #1 (Public Review):

In this manuscript, the authors explore the effects of DNA methylation on the strength of regulatory activity using massively parallel reporter assays in cell lines on a genome-wide level. This is a follow-up of their first paper from 2018 that describes this method for the first time. In addition to adding more in depth information on sequences that are explored by many researchers using two main methods, reduced bisulfite sequencing and sites represented on the Illumina EPIC array, they now show also that DNA methylation can influence changes in regulatory activity following a specific stimulation, even in absence of baseline effects of DNA methylation on activity. In this manuscript, the authors explore the effects of DNA methylation on the response to Interferon alpha (INFA) and a glucocorticoid receptor agonist (dexamethasone). The author validate their baseline findings using additional datasets, including RNAseq data and show convergences across two cell lines. The authors then map the methylation x environmental challenge (IFNA and dex) sequences identified in vitro to explore whether their methylation status is also predictive of regulatory activity in vivo. This is very convincingly shown for INFA response sequences, where baseline methylation is predictive of the transcriptional response to flu infection in human macrophages, an infection that triggers the INF pathways. The extension of the functional validity of the dex-response altering sequences is less convincing. Sequences altering the response to glucocorticoids, however, were not enriched in DNA methylation sites associated with exposure to early adversity which the authors interpret that "they are not links on the causal pathway between early life disadvantage and later life health outcomes, but rather passive biomarkers. However, this approach does not seem an optimal model to explore this relationship in vivo. This is because exposure to early adversity and its consequences is not directly correlated with glucocorticoid release and changes in DNA methylation levels following early adversity could be related to many physiological mechanisms, and overall, large datasets and meta-analyses do not show robust associations of exposure to early adversity and DNA methylation changes. Here other datasets, such as from Cushing patients maybe of more interest.<br /> ***<br /> After revision, the authors have now discussed this issue carefully, so that this point is addressed.<br /> ***<br /> Overall, the authors provide a great resource of DNA methylation sensitive enhancers that can now be used for functional interpretation of large scale datasets (that are widely generated in the research community), given the focus on sites included in RBSS and the Illumina EPIC array. In addition, their data lends support that difference in DNA methylation can alter responses to environmental stimuli and thus of the possibility that environmental exposures that alter DNS methylation can also alter subsequent response to this exposure, in line with the theory of epigenetic embedding of prior stimuli/experiences. The conclusions related to the early adversity data should be reconsidered in light of the comments above.

Reviewer #2 (Public Review):

This work presents a remarkably extensive set of experiments, assaying the interaction between methylation and expression across most CpG positions in the genome in two cell types. To this end, the authors use mSTARR-seq, a high-throughput method, which they have previously developed, where sequences are tested for their regulatory activity in two conditions (methylated and unmethylated) using a reporter gene. The authors use these data to study two aspects of DNA methylation: 1. Its effect on expression, and 2. Its interaction with the environment. Overall, they identify a small number of 600 bp windows that show regulatory potential, and a relatively large fraction of these show an effect of methylation on expression. In addition, the authors find regions exhibiting methylation-dependent response to two environmental stimuli (interferon alpha and glucocorticoid dexamethasone).

The questions the authors address represent some of the most central in functional genomics, and the method utilized is currently the best method to do so. The scope of this study is very impressive and I am certain that these data will become an important resource for the community. The authors are also able to report several important findings, including that pre-existing DNA methylation patterns can influence the response to subsequent environmental exposures.

eLife assessment

This study investigates the role of the bile acid receptor TGR5 in adult hematopoiesis of the mouse model. The findings are potentially useful because the loss of TGR5 leads to dysregulation of bone marrow adipose tissue (BMAT) that has emerging regulatory functions. However, the study is still incomplete because the mechanism of TGR5 is not clear, the stromal cells expressing TGR5 have not been well defined, and there is not strong evidence for the role of TGR5 in recovery from transplant stress.

Reviewer #1 (Public Review):

Summary:<br /> Alonso-Calleja and colleagues explore the role of TGR5 in adult hematopoiesis at both steady state and post-transplantation. The authors utilize two different mouse models including a TGR5-GFP reporter mouse to analyze the expression of TGR5 in various hematopoietic cell subsets. Using germline Tgr5-/- mice it's reported that loss of Tgr5 has no significant impact on steady-state hematopoiesis, with a small decrease in trabecular bone fraction, associated with a reduction in proximal tibia adipose tissue, and an increase in marrow phenotypic adipocytic precursors. The authors further explored the role of stroma TGR5 expression in the hematopoietic recovery upon bone marrow transplantation of wild-type cells, although the studies supporting this claim are weak. Overall, while most of the hematopoietic phenotypes have negative results or small effects, the role of TGR5 in adipose tissue regulation is interesting to the field.

Strengths:<br /> • This is the first time the role of TGR5 has been examined in the bone marrow.<br /> • This paper supports further exploration of the role of bile acids in bone marrow transplantation and possible therapeutic strategies.

Weaknesses:<br /> • The authors fail to describe whether niche stroma cells or adipocyte progenitor cells (APCs) express TGR5.<br /> • Although the authors note a significant reduction in bone marrow adipose tissue in Tgr5-/- mice, they do not address whether this is white or brown adipose tissue especially since BA-TGR5 signaling has been shown to play a role in beiging.<br /> • In Figure 1, the authors explore different progenitor subsets but stop short of describing whether TGR5 is expressed in hematopoietic stem cells (HSCs).<br /> • Are there more CD45+ cells in the BM because hematopoietic cells are proliferating more due to a direct effect of the loss of Tgr5 or is it because there is just more space due to less trabecular bone?<br /> • In Figure 4 no absolute cell counts are provided to support the increase in immunophenotypic APCs (CD45-Ter119-CD31-Sca1+CD24-) in the stroma of Tgr5-/- mice. Accordingly, the absolute number of total stromal cells and other stroma niche cells such as MSCs, ECs are missing.<br /> • There are issues with the reciprocal transplantation design in Fig 4. Why did the authors choose such a low dose (250 000) of BM cells to transplant? If the effect is true and relevant, the early recovery would be observed independently of the setup and a more robust engraftment dataset would be observed without having lethality post-transplant. On the same note, it's surprising that the authors report ~70% lethality post-transplant from wild-type control mice (Fig 4E), according to the literature 200 000 BM cells should ensure the survival of the recipient post-TBI. Overall, the results even in such a stringent setup still show minimal differences and the study lacks further in-depth analyses to support the main claim.<br /> • Mechanistically, how does the loss of Tgr5 impact hematopoietic regeneration following sublethal irradiation?<br /> • Only male mice were used throughout this study. It would be beneficial to know whether female mice show similar results.

Reviewer #2 (Public Review):

Summary: In this manuscript, the authors examined the role of the bile acid receptor TGR5 in the bone marrow under steady-state and stress hematopoiesis. They initially showed the expression of TGR5 in hematopoietic compartments and that loss of TGR5 doesn't impair steady-state hematopoiesis. They further demonstrated that TGR5 knockout significantly decreases BMAT, increases the APC population, and accelerates the recovery upon bone marrow transplantation.

Strengths: The manuscript is well-structured and well-written.

Weaknesses: The mechanism is not clear, and additional studies need to be performed to support the authors' conclusion.

eLife assessment

This important study advances our understanding of why diabetes is a risk factor for more severe Covid-19 disease. The authors offer solid evidence that cathepsin L is more active in diabetic individuals, that this higher activity is recapitulated at the cellular level in the presence of high glucose, and that high glucose leads to higher cathepsin L maturation. While not all aspects of the relationship between diabetes and cathepsin L (e.g., effects of metabolic acidosis) have been investigated, the work should be of interest to researchers in diabetes, virology, and immunology.

Reviewer #1 (Public Review):

Summary:<br /> The study by He et al. investigates the relationship of an increased susceptibility of diabetes patients to COVID-19. The paper raises the possibility that hyperglycemia-induced cathepsin L maturation could be one of the driving forces in this pathology, suggesting that an increased activity of CTSL leads to accelerated virus infection rates due to an elevated processing of the SARS-CoV-2 spike protein.

In a clinical case-control study, the team found that the severity of corona infections was higher in diabetic patients, and their CTSL levels correlated well with the progression of the disease. They further showed an increase in CTSL activity in the long term as well as acute hyperglycemia. SARS-CoV-2 increasingly infected cells that were cultured in serum from diabetic patients, the same was observed using high glucose medium. No effect was observed in the medium with increased concentrations of insulin. CTSL knockout abolished the glucose-dependent increase in infection.

Increased glucose levels did not correlate with an increase in CTSL transcription. Rather He et al. could show that high glucose levels led to CTSL translocation from the ER into the lysosome. It was the glucose-dependent processing of the protease to its active form which promoted infection.

Strengths:<br /> It is a complete study starting from a clinical observation and ending on the molecular mechanism. A strength is certainly the wide selection of experiments. The clinical study to investigate the effect of glucose on CTSL concentrations in healthy individuals sets the stage for experiments in cell culture, animal models, and human tissue. The effect of CTSL knockout cell lines on glucose-induced SARS-CoV2 infection rates is convincing. Finally, the team used a combination of Western blots and confocal microscopy to identify the underlying molecular mechanisms. The authors manage to keep the diabetic condition at the center of their study and therefore extend on previous knowledge of glucose-induced CTSL activation and their consequences for COVID-19 infections. By doing so, they create a novel connection between CTSL involvement in SARS-CoV2 infections and diabetes.

Weaknesses:<br /> The authors suggest that hyperglycemia as a symptom of diabetes leads to an increased infection rate in those patients. Throughout their study, the team focuses on two select symptoms of a diabetic condition, hyperglycemia and hyperinsulinemia. The team acknowledges in the discussion that there could be various other reasons. Hyperglycemia can lead to metabolic acidosis and a shift in blood pH. As CTSL activity is highly dependent on pH, it would have been crucial to include this parameter in the study.

The study rarely differentiates between cellular and extracellular CTSL activity. A more detailed explanation for the connection between the intracellular CTSL and serum CTSL in diabetic individuals, presumably via lysosomal exocytosis, could be helpful with regard to the final model to give a more complete picture.

In the early result section, an effect of hyperglycemia on total CTSL concentrations is described, but the data is not very convincing. Over the course of the manuscript, the hypothesis shifts increasingly towards an increase in protease trans-localization and processing to the active form rather than a change in total protease amounts. The overall importance of CTSL concentrations remains questionable.

Reviewer #2 (Public Review):

Summary:<br /> In this study, the authors hypothesized that individuals with diabetes have elevated blood CTSL levels, which facilitates SARS-CoV-2 infection. The authors conducted in vitro experiments, revealing that elevated glucose levels promote SARS-CoV-2 infection in wild-type cells. In contrast, CTSL knockout cells show reduced susceptibility to high glucose-promoted effects. Additionally, the authors utilized lung tissue samples obtained from both diabetic and non-diabetic patients, along with db/db diabetic and control mice. Their findings indicate that diabetic conditions lead to an elevation in CTSL activity in both humans and mice.

Strengths:<br /> The authors have effectively met their research objectives, and their conclusions are supported by the data presented. Their findings suggest that high glucose levels promote CTSL maturation and translocation from the endoplasmic reticulum to the lysosome, potentially contributing to diabetic comorbidities and complications.

Weaknesses:<br /> 1. In Figure 1e, the authors measured plasma levels of COVID-19 related proteins, including ACE2, CTSL, and CTSB, in both diabetic and non-diabetic COVID-19 patients. Notably, only CTSL levels exhibited a significant increase in diabetic patients compared to non-diabetic patients, and these levels varied throughout the course of COVID-19. Given that the diabetes groups encompass both male and female patients, it is essential to ascertain whether the authors considered the potential impact of gender on CTSL levels. The diabetes groups comprised a higher percentage of male patients (61.3%) compared to the non-diabetes group, where males constituted only 38.7%.

2. Lines 145-149: "The results showed that WT Huh7 cell cultured in high glucose medium exhibited a much higher infective rate than those in low glucose medium. However, CTSL KO Huh7 cells maintained a low infective rate of SARS-CoV-2 regardless of glucose or insulin levels (Fig. 3f-h). Therefore, hyperglycemia enhanced SARS-CoV-2 infection dependent on CTSL." However, this evidence may be insufficient to support the claim that hyperglycemia enhances SARS-CoV-2 infection dependent on CTSL. The human hepatoma cell line Huh7 might not be an ideal model to validate the authors' hypothesis regarding high blood glucose promoting SARS-CoV-2 infection through CTSL.

3. The Abstract and Introduction sections lack effective organization.

eLife assessment

The authors present evidence suggesting that MDA5 can substitute as a sensor for triphosphate RNA in a species that naturally lacks RIG-I. The key findings are potentially important for our understanding of the evolution of innate immune responses, but the evidence is incomplete, as additional biochemical and functional experiments are needed to unambiguously assign MDA5 as a bona fide sensor of triphosphate RNA in this model. This also leaves the title as overstating its case.

Reviewer #1 (Public Review):

This study offers valuable insights into host-virus interactions, emphasizing the adaptability of the immune system. Readers should recognize the significance of MDA5 in potentially replacing RIG-I and the adversarial strategy employed by 5'ppp-RNA SCRV in degrading MDA5 mediated by m6A modification in different species, further indicating that m6A is a conservational process in the antiviral immune response.

However, caution is warranted in extrapolating these findings universally, given the dynamic nature of host-virus dynamics. The study provides a snapshot into the complexity of these interactions, but further research is needed to validate and extend these insights, considering potential variations across viral species and environmental contexts.

Reviewer #2 (Public Review):

This manuscript by Geng et al. aims to demonstrate that MDA5 compensates for the loss of RIG-I in certain species, such as teleofish miiuy croacker. The authors use siniperca cheats rhabdovirus (SCRV) and poly(I:C) to demonstrate that these RNA ligands induce an IFN response in an MDA5-dependent manner in m.miiuy derived cells. Furthermore, they show that MDA5 requires its RD domain to directly bind to SCRV RNA and to induce an IFN response. They use in vitro synthesized RNA with a 5'triphosphate (or lacking a 5'triphosphate as a control) to demonstrate that MDA5 can directly bind to 5'-triphosphorylated RNA. The second part of the paper is devoted to m6A modification of MDA5 transcripts by SCRV as an immune evasion strategy. The authors demonstrate that the modification of MDA5 with m6A is increased upon infection and that this causes increased decay of MDA5 and consequently a decreased IFN response.

The key message of this paper, i.e. MDA5 can sense 5'-triphosphorylated RNA and thereby compensate for the loss of RIG-I, is novel and interesting, yet there is insufficient evidence provided to prove this hypothesis. Most importantly, it is crucial to test the capacity of in vitro synthesized 5'-triphosphorylated RNA to induce an IFN response in MDA5-sufficient and -deficient cells. In addition, a number of important controls are missing, as detailed below. The authors describe an interaction between MDA5 and STING which, if true, is very interesting. However, the functional implications of this interaction are not further investigated in the manuscript. Is STING required to relay signalling downstream of MDA5? The second part of the paper is quite distinct from the first part. The fact that MDA5 is an interferon-stimulated gene is not mentioned and complicates the analyses (i.e. is there truly more m6A modification of MDA5 on a per molecule basis, or is there simply more total MDA5 and therefore more total m6A modification of MDA5).

Finally, it should be pointed out that several figures require additional labels, markings, or information in the figure itself or in the accompanying legend to increase the overall clarity of the manuscript. There are frequently details missing from figures that make them difficult to interpret and not self-explanatory. These details are sometimes not even found in the legend, only in the materials and methods section. The manuscript also requires extensive language editing by the editorial team or the authors.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, the authors investigated the interaction between the pattern recognition receptor MDA5 and 5'ppp-RNA in a teleost fish called Miiuy croaker. They claimed that MDA5 can replace RIG-I in sensing 5'ppp-RNA of Siniperca cheats rhabdovirus (SCRV) in the absence of RIG-I in Miiuy croaker. The recognition of MDA5 to 5'ppp-RNA was also observed in the chicken (Gallus gallus), a bird species that lacks RIG-I. Additionally, they reported that the function of MDA5 can be impaired through m6A-mediated methylation and degradation of MDA5 mRNA by the METTL3/14-YTHDF2/3 regulatory network in Miiuy croaker under SCRV infection. This impairment weakens the innate antiviral immunity of fish and promotes the immune evasion of SCRV.

Strengths:<br /> These findings provide insights into the adaptation and functional diversity of innate antiviral activity in vertebrates.

Weaknesses:<br /> However, there are some major and minor concerns that need to be further addressed. Addressing these concerns will help the authors improve the quality of their manuscript.

One significant issue with the manuscript is that the authors claim to be investigating the role of MDA5 as a substitute for RIG-I in recognizing 5'ppp-RNA, but their study extends beyond this specific scenario. Based on my understanding, it appears that sections 2.2, 2.3, 2.5, 2.6, and 2.7 do not strictly adhere to this particular scenario. Instead, these sections tend to investigate the functional involvement of Miiuy croaker MDA5 in the innate immune response to viral infection. Furthermore, the majority of the data is focused on Miiuy croaker MDA5, with only a limited and insufficient study on chicken MDA5. Consequently, the authors cannot make broad claims that their research represents events in all RIG-I deficient species, considering the limited scope of the species studied.

The current title of the article does not align well with its actual content. It is recommended that the focus of the research be redirected to the recognition function and molecular mechanism of MDA5 in the absence of RIG-I concerning 5'ppp-RNA. This can be achieved through bolstering experimental analysis in the fields of biochemistry and molecular biology, as well as enhancing theoretical research on the molecular evolution of MDA5. It is advisable to decrease or eliminate content related to m6A modification.

Additionally, the main body of the writing contains several aspects that lack rigor and tend to exaggerate, necessitating significant improvement.

eLife assessment

This important study addresses the mechanisms by which mutations in the PURA protein, a regulator of gene transcription and mRNA transport and translation, cause the neurodevelopmental PURA syndrome. Based on convincing evidence from structural biology, molecular dynamics simulation, biochemical, and cell biological analyses, the authors show that the PURA structure is very dynamic, rendering it generally sensitive to structure-altering mutations that affect its folding, DNA-unwinding activity, RNA binding, dimerization, and partitioning into processing bodies. These findings are of substantial importance to cell biology, neurogenetics, and neurology alike, because they provide first insights into how very diverse PURA mutations can cause similar and penetrant molecular, cellular, and clinical defects.

Joint Public Review

The present study focuses on the structure and function of human PURA, a regulator of gene transcription and mRNA transport and translation whose mutation causes the neurodevelopmental PURA syndrome, characterized by developmental delay, intellectual disability, hypotonia, epileptic seizures, a.o. deficits. The authors combined structural biology, molecular dynamics simulation, and various cell biological assays to study the effects of disease-causing PURA mutations on protein structure and function. The corresponding data reveal a highly dynamic PURA structure and show that disease-related mutations in PURA cause complex defects in folding, DNA-unwinding activity, RNA binding, dimerization, and partitioning into processing bodies. These findings provide first insights into how very diverse PURA mutations can cause penetrant molecular, cellular, and clinical defects. This will be of substantial interest to cell biologists, neurogeneticists, and neurologists alike.

A particular strength of the present study is the structural characterization of human PURA, which is a challenging target for structural biology approaches. The molecular dynamics simulations are state-of-the-art, allowing a statistically meaningful assessment of the differences between wild-type and mutant proteins. The functional consequences of PURA mutations at the cellular level are fascinating, particularly the differential compartmentalization of wild-type and mutant PURA variants into certain subcellular condensates.

Weaknesses that warrant rectification relate to (i) the interpretation of statistically non-significant effects seen in the molecular dynamics simulations, (ii) the statistical analysis of the differential compartmentalization of PURA variants into processing bodies vs. stress granules, and (iii) the documentation of protein expression levels and knock-down efficiencies.

eLife assessment

This important study investigates the molecular mechanisms underpinning how the tumor necrosis factor alpha-induced protein, TIPE, regulates aerobic glycolysis to promote tumor growth in melanoma. Data using multiple independent approaches provide new insights into the molecular mechanisms underpinning aerobic glycolysis, also known as the Warburg Effect, in melanoma cells. The claims of the authors are solid, although more in-depth metabolic assays as well as the inclusion of melanoma patient survival analysis in TIPE high and low tumors would strengthen the study. The work will be of interest to biomedical researchers working in cancer and metabolism.

Reviewer #1 (Public Review):

Summary:<br /> Tian et al. describe how TIPE regulates melanoma progression, stemness, and glycolysis. The authors link high TIPE expression to increased melanoma cell proliferation and tumor growth. TIPE causes dimerization of PKM2, as well as translocation of PKM2 to the nucleus, thereby activating HIF-1alpha. TIPE promotes the phosphorylation of S37 on PKM2 in an ERK-dependent manner. TIPE is shown to increase stem-like phenotype markers. The expression of TIPE is positively correlated with the levels of PKM2 Ser37 phosphorylation in murine and clinical tissue samples. Taken together, the authors demonstrate how TIPE impacts melanoma progression, stemness, and glycolysis through dimeric PKM2 and HIF-1alpha crosstalk.

Strengths:<br /> The authors manipulated TIPE expression using both shRNA and overexpression approaches throughout the manuscript. Using these models, they provide strong evidence of the involvement of TIPE in mediating PKM2 Ser37 phosphorylation and dimerization. The authors also used mutants of PKM2 at S37A to block its interaction with TIPE and HIF-1alpha. In addition, an ERK inhibitor (U0126) was used to block the phosphorylation of Ser37 on PKM2. The authors show how dimerization of PKM2 by TIPE causes nuclear import of PKM2 and activation of HIF-1alpha and target genes. Pyridoxine was used to induce PKM2 dimer formation, while TEPP-46 was used to suppress PKM2 dimer formation. TIPE maintains stem cell phenotypes by increasing the expression of stem-like markers. Furthermore, the relationship between TIPE and Ser37 PKM2 was demonstrated in murine and clinical tissue samples.

Weaknesses:<br /> The evaluation of how TIPE causes metabolic reprogramming can be better assessed using isotope tracing experiments and improved bioenergetic analysis.

Reviewer #2 (Public Review):

In this article, Tian et al present a convincing analysis of the molecular mechanisms underpinning TIPE-mediated regulation of glycolysis and tumor growth in melanoma. The authors begin by confirming TIPE expression in melanoma cell lines and identify "high" and "low" expressing models for functional analysis. They show that TIPE depletion slows tumour growth in vivo, and using both knockdown and over-expression approaches, show that this is associated with changes in glycolysis in vitro. Compelling data using multiple independent approaches is presented to support an interaction between TIPE and the glycolysis regulator PKM2, and the over-expression of TIPE-promoted nuclear translocation of PKM2 dimers. Mechanistically, the authors also demonstrate that PKM2 is required for TIPE-mediated activation of HIF1a transcriptional activity, as assessed using an HRE-promoter reporter assay, and that TIPE-mediated PKM2 dimerization is p-ERK dependent. Finally, the dependence of TIPE activity on PKM2 dimerization was demonstrated on tumor growth in vivo and in the regulation of glycolysis in vitro, and ectopic expression of HIF1a could rescue the inhibition of PKM2 dimerization in TIPE overexpressing cells and reduced induction of general cancer stem cell markers, showing a clear role for HIF1a in this pathway. The main conclusions of this paper are well supported by data, but some aspects of the experiments need clarification and some data panels are difficult to read and interpret as currently presented.

The detailed mechanistic analysis of TIPE-mediated regulation of PKM2 to control aerobic glycolysis and tumor growth is a major strength of the study and provides new insights into the molecular mechanisms that underpin the Warburg effect in cancer cells. However, despite these strengths, some weaknesses were noted, which if addressed will further strengthen the study.

1. The analysis of patient samples should be expanded to more directly measure the relationship between TIPE levels and melanoma patient outcome and progression (primary vs metastasis), to build on the association between TIPE levels and proliferation (Ki67) and hypoxia gene sets that are currently shown.

2. The duration of the in vivo experiments was not clearly defined in the figures, however, it was clear from the tumor volume measurements that they ended well before standard ethical endpoints in some of the experiments. A rationale for this should be provided because longer-duration experiments might significantly change the interpretation of the data. For example, does TIPE depletion transiently reduce or lead to sustained reductions in tumor growth?

3. The analysis of general cancer stem cell markers is solid and interesting, however inclusion of neural crest stem cell markers that are more relevant to melanoma biology would greatly strengthen this aspect of the study.

4. The authors should take care that all data panels are clearly readable in the figures to facilitate appropriate interpretation by the reader.

eLife assessment

This study presents a cellular automaton model to study the dynamics of virus-induced signalling and innate host defense against viruses such as SARS-CoV-2 in epithelial tissue. The data presented are convincing and represent a valuable contribution that would be of interest to researchers studying the dynamics of viral propogation. The significance of the study might be further elevated with more details on the reduction of expression data to the model rules discussed.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript ``Self-inhibiting percolation and viral spreading in epithelial tissue' describes a model based on 5-state cellular automata of development of an infection. The model is motivated and qualitatively justified by time-resolved measurements of expression levels of viral, interferon-producing, and antiviral genes. The model is set up in such a way that the crucial difference in outcomes (infection spreading vs. confinement) depends on the initial fraction of special virus-sensing cells. Those cells (denoted as 'type a') cannot be infected and do not support the propagation of infection, but rather inhibit it in a somewhat autocatalytic way. Presumably, such feedback makes the transition between two outcomes very sharp: a minor variation in concentration of ``a' cells results in qualitative change from one outcome to another. As in any percolation-like system, the transition between propagation and inhibition of infection goes through a critical state with all its attributes. A power-law distribution of the cluster size (corresponding to the fraction of infected cells) with a fairly universal exponent and a cutoff at the upper limit of this distribution.

Strengths:<br /> The proposed model suggests an explanation for the apparent diversity of outcomes of viral infections such as COVID.

Weaknesses:<br /> Those are not real points of weakness, though I think addressing them would substantially improve the manuscript.

The key point in the manuscript is the reduction of actual biochemical processes to the NOVAa rules. I think more could be said about it, be it referring to a set of well-known connections between expression states of cells and their reaction to infection or justifying it as an educated guess.

Another aspect where the manuscript could be improved would be to look a little beyond the strange and 'not-so-relevant for a biomedical audience' focus on the percolation critical state. While the presented calculation of the precise percolation threshold and the critical exponent confirm the numerical skills of the authors, the probability that an actual infected tissue is right at the threshold is negligible. So in addition to the critical properties, it would be interesting to learn about the system not exactly at the threshold: For example, how the speed of propagation of infection depends on subcritical p_a and what is the cluster size distribution for supercritical p_a.

Reviewer #2 (Public Review):

Xu et al. introduce a cellular automaton model to investigate the spatiotemporal spreading of viral infection. In this study, the author first analyzes the single-cell RNA sequencing data from experiments and identifies four clusters of cells at 48 hours post-viral infection, including susceptible cells (O), infected cells (V), IFN-secreting cells (N), and antiviral cells (A). Next, a cellular automaton model (NOVAa model) is introduced by assuming the existence of a transient pre-antiviral state (a). The model consists of an LxL lattice; each site represents one cell. The cells change their state following the rules depending on the interaction of neighboring cells. The model introduces a key parameter, p_a, representing the fraction of pre-antiviral state cells. Cell apoptosis is omitted in the model. Model simulations show a threshold-like behavior of the final attack rate of the virus when p_a changes continuously. There is a critical value p_c, so that when p_a < p_c, infections typically spread to the entire system, while at a higher p_a > p_c, the propagation of the infected state is inhibited. Moreover, the radius R that quantifies the diffusion range of N cells may affect the critical value p_c; a larger R yields a smaller value of the critical value p_c. The structure of clusters is different for different values of R; greater R leads to a different microscopic structure with fewer A and N cells in the final state. Compared with the single-cell RNA seq data, which implies a low fraction of IFN-positive cells - around 1.7% - the model simulation suggests R=5. The authors also explored a simplified version of the model, the OVA model, with only three states. The OVA model also has an outbreak size. The OVA model shows dynamics similar to the NOVAa model. However, the change in microstructure as a function of the IFN range R observed in the NOVAa model is not observed in the OVA model.

Data and model simulation mainly support the conclusions of this paper, but some weaknesses should be considered or clarified.

1) In the automaton model, the authors introduce a parameter p_a, representing the fraction of pre-antiviral state cells. The authors wrote: ``The parameter p_a can also be understood as the probability that an O cell will switch to the N or A state when exposed to the virus of IFNs, respectively.' Nevertheless, biologically, the fraction of pre-antiviral state cells does not mean the same value as the probability that an O cell switches to the N or A state. Moreover, in the numerical scheme, the cell state changes according to the deterministic role N(O)=a and N(a)=A. Hence, the probability p_a did not apply to the model simulation. It may need to clarify the exact meaning of the parameter p_a.

2) The current model is deterministic. However, biologically, considering the probabilistic model may be more realistic. Are the results valid when the probability update strategy is considered? By the probability model, the cells change their state randomly to the state of the neighbor cells. The probability of cell state changes may be relevant for the threshold of p_a. It is interesting to know how the random response of cells may affect the main results and the critical value of p_a.

3) Figure 2 shows a critical value p_c = 27.8% following a simulation on a lattice with dimension L = 1000. However, it is unclear if dimension changes may affect the critical value.

Reviewer #3 (Public Review):

Summary:<br /> This study considers how to model distinct host cell states that correspond to different stages of a viral infection: from naïve and susceptible cells to infected cells and a minority of important interferon-secreting cells that are the first line of defense against viral spread. The study first considers the distinct host cell states by analyzing previously published single-cell RNAseq data. Then an agent-based model on a square lattice is used to probe the dependence of the system on various parameters. Finally, a simplified version of the model is explored, and shown to have some similarity with the more complex model, yet lacks the dependence on the interferon range. By exploring these models one gains an intuitive understanding of the system, and the model may be used to generate hypotheses that could be tested experimentally, telling us "when to be surprised" if the biological system deviates from the model predictions.

Strengths:<br /> - Clear presentation of the experimental findings and a clear logical progression from these experimental findings to the modeling.<br /> - The modeling results are easy to understand, revealing interesting behavior and percolation-like features.<br /> - The scaling results presented span several decades and are therefore compelling.<br /> - The results presented suggest several interesting directions for theoretical follow-up work, as well as possible experiments to probe the system (e.g. by stimulating or blocking IFN secretion).

Weaknesses:<br /> - Since the "range" of IFN is an important parameter, it makes sense to consider lattice geometries other than the square lattice, which is somewhat pathological. Perhaps a hexagonal lattice would generalize better.

- Tissues are typically three-dimensional, not two-dimensional. (Epithelium is an exception). It would be interesting to see how the modeling translates to the three-dimensional case. Percolation transitions are known to be very sensitive to the dimensionality of the system.

- The fixed time-step of the agent-based modeling may introduce biases. I would consider simulating the system with Gillespie dynamics where the reaction rates depend on the ambient system parameters.

- Single-cell RNAseq data typically involves data imputation due to the high sparsity of the measured gene expression. More information could be provided on this crucial data processing step since it may significantly alter the experimental findings.

Justification of claims and conclusions:<br /> The claims and conclusions are well justified.

eLife assessment

This study presents a valuable finding that a testis-enriched gene is essential for normal formation and function of the sperm flagellum, motility, and male fertility in mice. The data on phenotypic characterization are solid, but the evidence supporting the direct role of this protein in preventing RNP granule formation in the sperm flagellum appears insufficient. This work will be of interest to biomedical researchers who work on testicular biology and male fertility.

Reviewer #1 (Public Review):

Summary:

The main goal of the authors was to study the testis-specific role of the protein FBXO24 in the formation and function of the ribonucleoprotein granules (membraneless electron-dense structures rich in RNAs and proteins).

Strengths:

The wide variety of methods used to support their conclusions (including transgenic models)

Weaknesses:

The lack of specific antibodies against FBXO24. Some of the experiments showing a specific phenotype are descriptive and lack of logical explanation about the possible mechanism (i.e. AR or the tail structure).

Questions:

The paper is excellent and employs a wide variety of methods to substantiate the conclusions. I have very few questions to ask:

1) KO mice cannot undergo acrosome reaction (AR) even spontaneously. How do you account for this, given that no visible defects were observed in the acrosome?

2) KO sperm are unable to migrate in the female tract, and, more intriguingly, they do not pass through the utero-tubal junction (UTJ). The levels of ADAM3 are normal, suggesting that the phenotype is influenced by other factors. The authors should investigate the levels of Ly6K since mice also exhibit the same phenotype but with normal levels of ADAM3.

3) In Figure 4A, the authors assert that "RBGS Tg mice revealed that mitochondria were abnormally segmented in Fbxo24 KO spermatozoa." I am unable to discern this from the picture shown in that panel. Could you please provide a more detailed explanation or display the information more explicitly?

Reviewer #2 (Public Review):

Summary:

The manuscript by Kaneda et al "FBXO24 ensures male fertility by preventing abnormal accumulation 2 of membraneless granules in sperm flagella" is a significant paper on the role of FBXO24 in murine male germ cell development and sperm ultrastructure and function. The body of experimental evidence that the authors present is extraordinarily strong in both breadth and depth. The authors investigate the protein's functions in male germ cells and sperm using a wide variety of approaches but focusing predominantly on their novel mouse model featuring deletion of the Fbxo24 gene and its product. Using this mouse, and a cross of it with another model that expresses reporters in the head and midpiece, they logically build from one experiment to the next. Together, their data show that this protein is involved in the regulation of membraneless electron-dense structures; loss of FBXO24 led to an accumulation of these materials and defects in the sperm flagellum and fertilizing ability. Interestingly, the authors found that several of the best-known components of electron-dense ribonucleoprotein granules that are found in the intermitochondrial cement and chromatoid body were not disrupted in the Fbxo24 knockout, suggesting that the electron-dense material and these structures are not all the same, and the biology is more complicated than some might have thought. They found evidence for the most changes in IPO5 and KPNB1, and biochemical evidence that FBXO24 and IPO5 could interact.

Strengths:

The authors are to be commended for the thoroughness of their experimental approaches and the extent to which they investigated impacts on sperm function and potential biochemical mechanisms. Very briefly, they start by showing that the Fbxo24 message is present in spermatids and that the protein can interact with SKP1, in a way that is dependent on its F-box domain. This points toward a potential function in protein degradation. To test this, they next made the knockout mouse, validated it, and found the males to be sterile, although capable of plugging a female. Looking at the sperm, they identified a number of ultrastructural and morphological abnormalities, which they looked at in high resolution using TEM. They also cross their model with RBGS mice so that they have reporters in both the acrosome and mitochondria. The authors test a variety of sperm functions, including motility parameters, ability to fertilize by IVF, cumulus-free IVF, zona-free-IVF, and ICSI. They found that ICSI could rescue the knockout but not other assisted reproductive technologies. Defects in male fertility likely resulted from motility disruption and failure to get through the utero-tubal junction but defects in acrosome exocytosis also were noted. The authors performed thorough investigations including both targeted and unbiased approaches such as mass spectrometry. These enabled them to show that although the loss of the FBXO24 protein led to more RNA and elevated levels of some proteins, it did not change others that were previously identified in the electron-dense RNP material.

The manuscript will be highly significant in the field because the exact functions of the electron-dense RNP materials have remained somewhat elusive for decades. Much progress has been made in the past 15 years but this work shows that the situation is more complex than previously recognized. The results show critical impacts of protein degradation in the differentiation process that enables sperm to change from non-descript round cells into highly polarized and compartmentalized mature sperm, with an equally highly compartmentalized flagellum. This manuscript also sets a high bar for the field in terms of how thorough it is, which reveals wide-ranging impacts on processes such as mitochondrial compaction and arrangement in the midpiece, the correct building of the major cytoskeletal elements in the flagellum, etc.

Weaknesses:

There are no real weaknesses in the manuscript that result from anything in the control of the authors. They attempted to rescue the knockout by expressing a FLAG-tagged Fbxo24 transgene, but that did not rescue the phenotype, either because of inappropriate levels/timing/location of expression, or because of interference by the tag. They also could not make anti-FBXO24 that worked for co-immunoprecipitation experiments, so relied on the FLAG epitope, an approach that successfully showed co-IP with IPO5 and SKP1.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors found that FBXO24, a testis-enriched F-box protein, is indispensable for male fertility. Fbxo24 KO mice exhibited malformed sperm flagellar and compromised sperm motility.

Strengths:

The phenotype of Fbxo24 KO spermatozoa was well analyzed.

Weaknesses:

The authors observed numerous membraneless electron-dense granules in the Fbxo24 KO spermatozoa. They also showed abnormal accumulation of two importins, IPO5 and KPNB1, in the Fbxo24 KO spermatozoa. However, the data presented in the manuscript do not support the conclusion that FBXO24 ensures male fertility by preventing the abnormal accumulation of membraneless granules in sperm flagella, as indicated in the manuscript title.

eLife assessment

This study provides a detailed evaluation of how HIV evades nascent immune pressure from people living with HIV followed nearly immediately after infection. There is convincing evidence that H173 mutations in the V2 loop was a key determinant of selection pressure and escape. These data are congruent with protection in the RV144 clinical trial, the only trial that showed protection from infection. Overall, this study is valuable to the field.

Reviewer #1 (Public Review):

Summary:<br /> This study used a unique acute HIV-1 infection cohort, RV217, to study the evolution of transmitted founder viral Envelope sequences under nascent immune pressure. The striking feature of the RV217 cohort is the ability to detect viremia in the first week of infection, which can be followed at discrete Fiebig stages over long time intervals. This study evaluated Env sequences at 1 week, 4 weeks, and 24 weeks to provide a picture of viral and immunological co-evolution from Fiebig Stage I (1 week), Fiebig Stages IV (4 weeks), and Fiebig Stage VI (>24 weeks). This study design enabled lineage tracing of viral variants from a single transmitted founder (T/F) over the Fiebig Stages I, IV, and VI under nascent immune pressure generated in response to the T/F virus and its subsequent mutants.

Strengths:<br /> As expected, there were temporal differences in the appearance of virus quasispecies among the individuals, which were located predominantly in solvent-exposed residues of Env, which is consistent with prior literature. Interestingly, two waves of antibody reactivity were observed for variants with mutations in the V2 region that harbors V2i and V2p epitopes correlated with protection in the RV144 clinical trial. Two waves of antibody response, detected by SPR, were observed, with the first wave being predominated by antibodies specific for the T/F07 V2 epitope associated with H173 located on the C β-strand in the V2 region. The second wave was dominated by antibodies specific for an H to Y mutation at 173 that emerged due to antibody-mediated pressure to the original H173 virus. This is a remarkable finding in three ways.

First, the mutation is in the C β-strand, an unlikely paratope contact residue, as this region of the V2 loop is shielded by glycans in Env trimer structures with full glycan representation (see PDB:5t3x). The structure used for modeling in the current study was an earlier structure, PDB:4TVP, that had many truncated glycans. This does not detract from the finding that the H173Y mutation likely causes a conformational shift from a more rigid helical/coil conformation to a more dynamic conformation with a β-stranded and β-sheet core preference as indicated by the literature and by the MD simulations carried out by the authors. This observation suggests that using V2 scaffolds with both the H173 and H173Y variants will increase the breadth of potentially protective antibody responses to HIV-1, as indicated in reference 42, cited by the authors. Interestingly, the H173Y mutation abrogates reactivity to mAb CH58 and attenuates reactivity to mAb CH59 isolated from RV144 volunteers. These mAbs recognize conformationally distinct V2 epitopes, adding further credence to the conclusion that the H173Y mutation results in a conformational switch of the V2 region.

Second, the H173Y mutation affects the conformation of V2 epitopes recognized by mAbs that do not neutralize T/F HIV-1 while mediating potent ADCC. The ADCC data in the manuscript provides strong support for this hypothesis and augers for further exploration of the V2 epitopes as HIV-1 vaccine targets.<br /> Third, it is striking that immunogens based on the H173Y mutation successfully recapitulated the observed human antibody responses in wild-type Balb/c mice. The investigators used their extensive knowledge of V2 structures and scaffold-immunogens to create the library of constructs used for this study. In this case, the ΔDSV mutation increased the breadth and magnitude of the murine antibody responses.

Weaknesses:<br /> 1. V2 epitopes exhibit properties of CD4i epitopes in that they are largely absent from the native Env surface, probably by glycan-occlusion, but become more exposed upon CD4 binding. Although the V2-scaffolds were produced in GnTi- cells to produce high-mannose proteins, it appears that no systematic analysis of glycan content or structure was carried out save for enzymatic deglycosylation of the constructs to sharpen bands on SDS-PAGE gels. It would be helpful if the authors could comment on how the lack of this information might impact their conclusions.

2. Similarly, the MD simulations appear to be performed without taking glycan structure/occupancy.

Reviewer #2 (Public Review):

Summary:<br /> In this study, researchers aimed to understand how a transmitted/founder (T/F) HIV virus escapes host immune pressure during early infection. They focused on the V1V2 domain of the HIV-1 envelope protein, a key determinant of virus escape. The study involved four participants from the RV217 Early Capture HIV Cohort (ECHO) project, which allowed tracking HIV infection from just days after infection.

The study identified a significant H173Y escape mutation in the V2 domain of a T/F virus from one participant. This mutation, located in the relatively conserved "C" β-strand, was linked to viral escape against host immune pressure. The study further investigated the epitope specificity of antibodies in the participant's plasma, revealing that the H173Y mutation played a crucial role in epitope switching during virus escape. Monoclonal antibodies from the RV144 vaccine trial, CH58, and CH59, showed reduced binding to the V1V2-Y173 escape variant. Additionally, the study examined antibody-dependent cellular cytotoxicity (ADCC) responses and found resistance to killing in the Y173 mutants. The H173Y mutation was identified as the key variant selected against the host's immune pressure directed at the V2 domain.

The researchers hypothesized that the H173Y mutation caused a structural/conformational change in the C β-strand epitope, leading to viral escape. This was supported by molecular dynamics simulations and structural modeling analyses. They then designed combinatorial V2 immunogen libraries based on natural HIV-1 sequence diversity, aiming to broaden antibody responses. Mouse immunizations with these libraries demonstrated enhanced recognition of diverse Env antigens, suggesting a potential strategy for developing a more effective HIV vaccine.

In summary, the study provides insights into the early evolution of HIV-1 during infection, highlighting the importance of the V1V2 domain and identifying key escape mutations. The findings suggest a novel approach for designing HIV vaccine candidates that consider the diversity of escape mutations to induce broader and more effective immune responses.

Strengths:<br /> The article presents several strengths:

1. The experimental design is well-structured, involving multiple stages from phylogenetic analyses to mouse model testing, providing a comprehensive approach to studying virus escape mutations.

2. The study utilizes a unique dataset from the RV217 Early Capture HIV Cohort (ECHO) project, allowing for the tracking of HIV infection from the very early stages in the absence of antiretroviral therapy. This provides valuable insights into the evolution of the virus.

3. The use of advanced techniques such as phylogenetic analyses, nanoscaffold technology, controlled mutagenesis, and monoclonal antibody evaluations demonstrates the application of cutting-edge methodologies in the study.

4. The research goes beyond genetic analysis and provides an in-depth characterization of the escape mutation's impact, including structural analyses through Molecular Dynamics simulations, antibody responses, and functional implications for virus survival.

5. The study provides insights into the immune responses triggered by the escape mutation, including the specificity of antibodies and their ability to recognize diverse HIV-1 Env antigens.

7. The exploration of combinatorial immunogen libraries is a strength, as it offers a novel approach to broaden antibody responses, providing a potential avenue for future vaccine design.

8. The research is highly relevant to vaccine development, as it sheds light on the dynamics of HIV escape mutations and their interaction with the host immune system. This information is crucial for designing effective vaccines that can preemptively interfere with viral acquisition.

9. The study integrates findings from virology, immunology, structural biology, and bioinformatics, showcasing an interdisciplinary approach that enhances the depth and breadth of the research.

10. The article is well-written, with a clear presentation of methods, results, and implications, making it accessible to both specialists and a broader scientific audience.

Weaknesses:<br /> While the article presents several strengths, it's important to consider potential weaknesses as well:

1. While the exploration of combinatorial immunogen libraries is innovative, the complexity of this approach may pose challenges in terms of practical implementation, scalability, and cost-effectiveness in large-scale vaccine development.

2. The article will benefit from a more explicit discussion of the limitations and potential drawbacks of the methodologies employed. For example, structural analyses, such as Molecular Dynamics simulations, involve complex computational models. The accuracy and reliability of these simulations may vary, and uncertainties in the interpretation of structural data should be acknowledged.

Author Response

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

Reviewer #1:

1) Can the authors statistically define the egg-laying classes? In some parts of the manuscript, the division between the different classes could be more ambiguous. I understand that the class III strains are divided by the kcnl-1 genotype, but given the different results for diverse traits, it could be more clear to keep them as one class. Also, overall, the authors choose a collection of 15 strains across the different classes to phenotype for many traits and perform genome edits. It is understandable that they cannot test all strains, but given the variation across traits and classes, it might be good to add a few more caveats about how these strains might not be representative of all strains across the species.

Response: The egg-laying classes were defined as in Figure 1A by arbitrarily chosen cut-offs (at 10, 10-25, and 25 eggs in utero) to simplify subsequent analyses. We added this explanation to the first paragraph of the results section. However, the differences in average egg retention are significantly different between the four defined classes using the 15 selected strains (Fig. 2A).

We think that the distinction between Class IIIA and IIIB strains is important and justified because the two Classes significantly differ in mean egg retention (Fig. 2A) and because Class IIIB harbour the large-effect variant KCNL-1 V530L whereas Class IIIA do not.

We agree that the 15 selected strains are not necessarily representative of all strains across the species. We have added a note of caution regarding this point to the first paragraph of the section “Temporal progression of egg retention and internal hatching”: “Note that this strain selection, especially concerning the largest Class II, is unlikely to reflect the overall strain diversity observed across the species". In addition, we have reworded the first sentence of this paragraph as follows: “ To better characterize natural variation in C. elegans egg retention, we focused on a subset of 15 strains from divergent phenotypic Classes I-III, with an emphasis on Class III strains exhibiting strong egg retention (at mid-L4 + 30h) (Fig. 2A and 2B).”

2) For the GWAS experiments, the authors should describe if any of the QTL overlap with hyper-divergent regions in the strain set. The QTL could be driven by these less well defined regions.

Response: We have added the following sentence: “The three QTLs do not align with any of the recently identified hyper-divergent regions of the genome (Lee et al., 2021).

3) The authors should look at correlations between the mod-5(n822) edit phenotypes and the exogenous 5-HT and SSRI phenotypes to demonstrate how the traits can differ. Some correlation plots might help that point as well.

Response: We examined all possible correlations as suggested: none are significant and strain effects on trait differences are idiosyncratic, as written in our results section. The correlational analyses remain of limited value due to small samples: N=10 for mean strain values for measured phenotypes. We therefore feel that these analyses do not provide any additional insights beyond our figures (4C, 4D, 5C, 5D, S5A-C ) and our statement on page 15: “As in previous experiments (Fig. 4C and 5C), we find again that strains sharing the same egg retention phenotype may differ strongly in egg-laying behaviour in response to modulation of both exo- and endogenous serotonin levels (Class IIIA: ED3005 and JU2829) (Fig. 5D and S5C).”

4) Figure 6D, was there any censoring of the data? Normally, these types of studies are plagued by an increase in censored animals that can decrease significance. The effects among the classes seem large, but statistical comparisons might help as well.

Response: There was no censoring of animals (censoring of animals in lifespan studies is usually done by removing “bags of worms”, which here was our study phenotype). We now mention this in the corresponding figure legend. We also added a statistical analysis showing that mean survival was significantly different between all Classes.

5) Many of the traits, edits, and deeper analyses are performed on the JU751 genetic background. This choice is sensible, otherwise, the work can increase exponentially. However, the authors should add a caveat about how these results might be limited to JU751 and other strains might respond differently.

Response: For certain experiments, it was not feasible to include multiple strains from all phenotypic classes, so we selected JU751 (Class IIIB) and JU1200 (Class II), for which we had established CRISPR-engineered lines to modulate the egg retention phenotype by a single amino acid change in KCNL-1. To emphasize that these experimental observations cannot be generalized, we added the following statement in the relevant results section: “These experimental results offer preliminary evidence (bearing in mind that our analysis was primarily centered on a single genetic background) that laying of advanced-stage embryos may enhance intraspecific competitive ability, particularly in scenarios where multiple genotypes compete for colonization and exploitation of limited, patchily distributed resources.”

6) The authors argue that evolution could be acting on specific parts of the egg-laying machinery (e.g., muscledirected signaling components). It might be useful to look at levels of standing variation and selection at groups of loci compared to genomic controls to see if this conclusion can be strengthened.

Response: This is a good idea but how to select pertinent candidate loci is unclear (there are over 300 genes with effects on egg laying, www.wormbase.org). In addition, the genetics of muscle-directed signalling components in egg laying is only starting to be explored, with no specific candidate genes having been identified (Medrano & Collins, 2023, Curr Biol). We therefore think that such an analysis is currently not possible.

7) Completely optional: The authors present a compelling and interesting case for transitions and trade-offs between oviparity and viviparity. The C. vivipara species has a different egg-laying mode than other Caenorhabditis species. The authors could add a short section describing their expectations about the neuronal morphology, 5-HT circuits, and muscle function in this species given their results. What genes or circuits should be the focus of future studies to address this question in Caenorhabditis. Also, Loer and Rivard present some similar ideas based on the differences in 5-HT staining neurons across diverse nematodes. Those results can be incorporated and discussed as well.

Response: Our current research focuses on the evolution of egg laying in different Caenorhabditis species. So far, however, it remains difficult to provide specific hypotheses on how the egg-laying circuit has changed in C. vivipara. We rephrased the final paragraph of the discussion to incorporate some of the reviewer’s suggestions: “Nematodes display frequent transitions from oviparity to obligate viviparity in many distinct genera (Sudhaus, 1976; Ostrovsky et al., 2015), including in the genus Caenorhabditis, with at least one viviparous species, C. vivipara (Stevens et al., 2019). Although evidence exists for the evolution of egg-laying circuitry across oviparous Caenorhabditis species (Loer and Rivard, 2007), the specific cellular and genetic changes responsible for the transition to obligate viviparity in C. vivipara have yet to be examined. Resolving the genetic basis of intraspecific variation in C. elegans egg retention, including partial or facultative viviparity, may thus shed light on the molecular changes underlying the initial steps of evolutionary transitions from oviparity to obligate viviparity in invertebrates.”

Specific edits:

1) Perhaps a silly point, but "parity" (to my knowledge) does not have a biological meaning on its own. I suggest "egg-laying mode" or "birth mode".

Response: This term has been used previously in the literature (e.g.https://onlinelibrary.wiley.com/doi/10.1111/jeb.13886 or https://doi.org/10.1101/2023.10.22.563505). However, as the referee rightly points out, this is not a standard term. We therefore replaced “parity mode” with “egg-laying mode”.

2) "Against fluctuating environmental fluctuations" is a bit strange

Response: Corrected.

3) The first publications of Egl mutants were by the Horvitz lab so some citations are not in all of the first descriptions of the trait (early in Results)

Response: We have added the relevant work (Trent 1982, Trent 1983, Desai & Horvitz 1989) to this paragraph in the early results section.

4) "Strong egg retention usually strongly..." is a bit strange

Response: Corrected.

1. Figure 8G font looks smaller than the others.

Response: Corrected.

Reviewer #2:

1) In Figure 1A, I infer that in the graph class I measurements are represented by dark blue dots and class II by purple dots. I am having a really hard time distinguishing between these two colors in the graph. In the pie chart I have no problem, but in the graph the black lines around the colored dots seem to obscure the colors. Not sure how to fix this graphical problem, but it is preventing the graph from communicating the results effectively.

Response: We have changed the colours, spacing and format of this figure to resolve this problem.

2) The behavioral analysis of Figure 3B-3F is problematic. The experimental methods used and the interpretation of the results each have issues. This is cause for concern since this is the most direct analysis of the actual variations in egg-laying behavior across strains presented in this paper.

This experiment is modeled after the work of Waggoner et al. 1998, who recorded egg laying events of individual worms on video over several hours and noted the exact time of individual egg laying events. Waggoner et al. found in the reference C. elegans strain N2 that egg-laying events occurred in ~2 minute clusters ("active phases") separated by ~20 minute silent periods ("inactive phases"). Mignerot et al. did not take continuous videos of animals, but rather examined plates bearing a single worm only every 5 minutes and noted the number of new eggs that appeared on the plate in each 5-minute interval. From these data, the authors claim they have measured the intervals between "egg-laying phases" (the term used in the Figure 3 legend). In the Results, the authors explicitly claim they are measuring the timing and frequency of actual active and inactive egg-laying phases. Apparently, all the eggs laid within one 5-minute interval are considered to have been laid in a single active phase, and the time between 5-minute intervals containing egg laying events is considered an "inactive phase" and is measured only with a resolution of 5 minutes. It is not explained anywhere how the authors handle the situation of seeing eggs laid in two consecutive 5-minute intervals. Is that one active phase that is 10 minutes long, or is that two separate active phases with a 5-minute active phase in between? Because of this ambiguity in how they define active and inactive phases, I find it impossible to understand and judge the data presented in Fig. 3D-3F. The authors in the results state that "Class I and Class IIIB displayed significantly accelerated and reduced egg laying activity respectively (Fig. 3C to 3E)" . I assume they are referring to the statistical analysis described in the figure legend, which is quite difficult to understand. Frankly, just looking at the graphs in Fig. 3D3F, it is hard for the reader to identify specific features shown in the graphs can explain why, for example, Class I strains have fewer retained eggs than Class III strains. So, I found this analysis very unsatisfying.

I also feel the authors are making an unwarranted assumption that their non-N2 strains will have distinguishable active and inactive phases of egg-laying behavior analogous to those seen in the N2 strain. Given the possibly large variations in egg-laying behavior in the various strains examined, that assumption should be questioned. Thus, framing the entire analysis of behavior patterns in terms of the length of active and inactive phases might not be appropriate.

Response: This comment validly highlights important problems and limitations of our scan-sampling method to quantify strain differences in egg-laying behaviour. We acknowledge that we failed to present the data with due diligence, and clarity regarding terminology and interpretation. However, we think that some of these results are still of value after revised presentation. Our biggest mistake was to use the terms “active and inactive phase”, as coined by Waggoner et al. 1998. We are aware that our measures are not equivalent to these previously defined measures but have been sloppy with terminology. We therefore carefully reworded this entire results section, using clear definitions to indicate differences between the Waggoner assay and our assay (including a graphical representation of our assay design in the revised Fig. 3B). In brief, our simplified assay is useful to estimate the frequency and approximate duration of prolonged inactive periods of egg laying because we can unambiguously determine intervals in which eggs were laid or not. In contrast, as pointed out by the reviewer, we cannot determine if multiple active phases occurred within a 5-min interval, nor can we estimate the duration of an active “phase”. We now state this limitation explicitly in the manuscript. What our results do show is that the number of intervals during which egg laying occurred is significantly different between strains and Classes: Class I (low retention) have a higher number of intervals with egg-laying events, whereas Class IIIB showed a reduced number of such events (Fig. 3D). We can therefore also roughly estimate the mean time (per individual) between two egg-laying intervals, giving us a proxy for prolonged periods when egg-laying is inactive (Fig. 3E); we note that our estimate for N2 is very close to what has been previously measured (~20 min). Therefore, we can confidently conclude that there are natural strains which have both shorter (Class I) and longer (Class IIIB) inactive periods of egg laying. These results partly align with observed variation in egg retention. However, we agree with the reviewer – as we had stated both in results and discussion sections – that these behavioural differences act together with differences in the sensing of egg accumulation in utero (as suggested by results shown in Fig. 3G and 3H). We also agree that it seems very plausible that the observed behavioural differences, as revealed by scan-sampling, may only have a secondary role in accounting for natural variation in egg retention. We will be testing these hypotheses specifically in our future research.

Note: The statistical analyses are nested ANOVAs to ask (a) does the value differ between strains within a given class and (b) does the value differ between Classes? Classes labelled with different letters in the figures therefore significantly differ in their mean values, demonstrating that measured behavioural phenotypes consistently differ between some (but not all) phenotypic classes, yet largely in line with their egg retention phenotypes (Fig. 3D and 3E).

3) Figure 4A is a schematic diagram of how the egg-laying circuit works based on previous literature, and the authors cite Collins et al. 2015 and Kopchock et al. 2021 as their sources. One feature of this figure seems unwarranted, namely the part indicating that egg accumulation acts on the UM muscles, and the statement in the legend that "mechanical excitation of uterine muscles (UM) in response to egg accumulation favours exit from the inactive state (Collins et al., 2016)". I believe Collins et al. 2016 showed that egg accumulation favors egg laying and may have speculated that it does so by stretching the um muscles, but this idea remains speculative and has not been established by any experimental data. I point out this issue,in particular, because it may bear on the nice data the authors of this manuscript show in Figure 3G and 3H, which show that some strains accumulate many eggs in the uterus before they initiate egg laying.

Also, in Figure 4A and 4B, the legend does not explain the logic of the green areas labeled "egg-laying active phase" and the yellow area labeled "egg-laying inactive state". I was not sure what sure how to interpret these features of the graphics.

Response: The input from uterine muscles remains indeed hypothetical, and we have corrected the figure accordingly, now simply referring to the feedback of egg accumulation on egg laying activity, as recently characterized in more detail by Medrano & Collins (2023, Curr Biol).

The green/yellow backgrounds shown in figures 4A (and 4B) are not useful and we have removed them.

4) Results, page 11: "We used standard assays, in which animals are reared in liquid M9 buffer without bacterial food." In the standard assays, animals are reared on NGM agar plates with bacterial food, and then at the start of the egg-laying assay, are transferred to liquid M9 buffer without bacterial food. I assume that is what these authors did, and they should correct the language of the text to make it more accurate.

Response: The reviewer is correct. We have incorporated this change to improve accuracy.

5) The authors note that "serotonin induced a much stronger egg-laying responds in the Class IIIA strain ED3005 than in other strains (Fig. 4C)". I would like to point out to the authors that strains such as ED3005 that have a very large number of unlaid eggs in their uterus are prone to lay a very large number of eggs when treated with exogenous serotonin, simply for the trivial reason that they have more eggs to release. This was previously seen in, for example, in Desai and Horvitz (1989) in certain egg-laying defective mutants.

Response: This is an important point and our comparison of ED3005 to ALL other strains is problematic. We changed this result description by stating that ED3005 shows possible serotonin hypersensitivity compared to strains with similar levels of egg retention (Class IIIA): “In addition, serotonin induced a much stronger egg-laying response in the strain ED3005 than in other Class IIIA strains with similar levels of egg retention (Fig. 4B). ED3005 may thus exhibit serotonin hypersensitivity, which has been observed in certain egg-laying mutants where perturbed synaptic transmission impacts serotonin signalling (Schafer and Kenyon, 1995; Schafer et al., 1996).”

6) In Figure 4 the authors show that all strains lay eggs in response to fluoxetine and imipramine, but some strains (Class IIIB) do not lay eggs in response to serotonin. They then cite a series of papers, starting with Trent et al. 1983, that they claim show that this specific phenotype demonstrates that the HSN neurons are functionally releasing serotonin (bottom of page 11). This statement needs to be removed - it is incorrect. It is true that egg laying in response to fluoxetine and/or imipramine AS WELL AS egg laying in response to serotonin has been interpreted as indicating the presence of HSN neurons that functionally release serotonin to stimulate egg laying (these were referred to as Category C by Trent et al., 1983). However, the mutants that Mignerot et al. are talking about (those that don't respond to serotonin but do respond to imipramine/fluoxetine) were called Category D by Trent et al., 1983, and to my knowledge these have never been interpreted as necessarily having functionally intact HSN neurons. Mutants such as these that can lay eggs in some circumstances but cannot lay eggs in response to exogenous serotonin have usually been interpreted as having egg-laying muscles that are defective in responding to serotonin.

How can we interpret strains that respond to imipramine/fluoxetine and not serotonin? Mignerot et al. cite some of the papers (Kullyev et al. 2010; Wenishenker et al., 1999; Yue et al., 2018) showing that imipramine and fluoxetene have off-target effects and can stimulate egg laying by acting through proteins other than the serotonin-reuptake inhibitor. The authors later in their discussion at the top of Page 24 also cite Dempsey et al 2005, a paper that also argues that imipramine and fluoxetene act via off target effects. However, currently in Figure 4B Mignerot et al. emphasize that the serotonin reuptake inhibitor is the target of these drugs. Since the results presented for Class IIIB strains are not in accord with this interpretation, this seems misleading to me. The bottom line for me is that class IIIB strains cannot respond to exogenous serotonin, but can lay eggs in other conditions, so perhaps there is something specifically wrong with their ability to respond to serotonin.

Response: We thank the reviewer for this important comment – we misinterpreted some of these past findings and our statements were either inexact or incorrect. We have revised this section accordingly: “Both drugs also stimulated egg laying in the Class IIIB strains and the Class IIIA strain JU2829 for which exogenous serotonin either inhibited egg laying or had no effect on it (Fig. 4B). In the past, mutants unresponsive to serotonin yet responsive to other drugs, including fluoxetine and imipramine, have been interpreted as being defective in the serotonin response of vulval muscles (Trent et al., 1983; Reiner et al., 1995; Weinshenker et al., 1995). This is indeed the likely case of Class IIIB strains carrying the KCNL-1 V530L variant thought to specifically reduce excitability of vulval muscles (Vigne et al., 2021). Our results therefore suggest that JU2829 (Class IIIA) may exhibit a similar defect in vulval muscle activation via serotonin caused by an alternative genetic change. Overall, these pharmacological assays do not allow us to conclude if and how HSN function has diverged among strains because the mode of action and targets of tested drugs has not been fully resolved. Nevertheless, our results are consistent with previous models proposing that these drugs do not simply block serotonin reuptake but can stimulate egg laying, to some extent, through mechanisms independent of serotonergic signaling (Trent et al., 1983; Desai and Horvitz, 1989; Reiner et al., 1995; Weinshenker et al., 1995, 1999; Dempsey et al., 2005; Kullyev et al., 2010; Branicky et al., 2014; Yue et al., 2018).”

We removed the oversimplified Fig. 4B to avoid any misinterpretation.

8) In Figure 7B and 7C, the authors should add some type of error bars to the graphs to and give the readers an idea of whether the differences between strains that they write about are statistically significant or not.

Response: These are frequency data to describe temporal dynamics of hatching (N=45-72 eggs per strain) (Fig. 7B) and development in single cohorts (N=48-177 eggs per strain) (Fig. 7C), hence, the absence of error bars.

We agree that this representation of the data is not very telling. We therefore changed the data representation in these two figures to show that there are clear, statistically significant, negative correlations between egg retention and time to hatching / egg-to-adult developmental time.

9) When the authors reference a list of papers in a single list, e.g. "(Burton et al., 2021; Fausett et al., 2021; Garsin et al., 2001; Padilla et al., 2002; Van Voorhies and Ward, 2000)" they seem to do so in alphabetical order by the first author's last name. I believe the usual practice is to list references by year of publication, with the earliest first.

Response: We corrected citation style according to eLIFE format.

10) At the top of page 24, the authors write "It seems unlikely, however, that any of these variants strongly alter central function of HSN and HSN-mediated signalling because fluoxetine and imipramine, known to act via HSN (Dempsey et al., 2005; Trent et al., 1983; Weinshenker et al., 1995), triggered a robust stimulatory effect on egg laying in all examined strains (Fig. 4C)." I believe that the Weinshenker paper in fact showed that imipramine does not act via the HSN, and the Dempsey paper suggested that both drugs can act at least in part independently of the HSN. Therefore, the authors should revise their statement.

Response: We have removed the sentence.

Reviewing Editor:

Minor suggestions:

1) p. 2, fifth line from bottom: "lead" instead of "leads";

2) p. 2, last line: "muscle" instead of "muscles";

3) p. 3, first full paragraph, 17th line: "populations" instead of "population";

4) p. 5, fourth line from bottom: Delete first comma;

5) p. 6, Figure 1D: "of" instead of "off";

6) p. 7, fifth line: "KCNL-1";

7) p. 9, third paragraph, second line: please clarify "late mid-L4";

8) p. 16, first line: "exogenous";

9) p 20, first paragraph, beginning of second sentence: "Whether" instead of "If";

10) p. 22, ninth line from bottom: delete "shaped by";

11) p. 23, last paragraph, third and eighth lines from bottom: change "between" to "among"

Response: Thank you. All corrected.

Additional changes:

Figure 5A: We removed figure 5A showing a cartoon of mod-5/SERT and its effects on serotonin signalling. This figure was incorrectly showing that MOD-5 is expressed in HSN (Jafari et al 2011 J. Neuroscience, Hammarlund et al 2018 Neuron).

Abstract: We reworded the abstract to reduce its length.

Reviewer #1 (Public Review):

Mignerot et al. performed a Herculean effort to measure and describe natural variation in C. elegans egg-laying behavior and egg retention. The paper is well written and organized. The authors show wild strains vary in egg retention with some extremes that appear phenotypically similar to species with viviparity (or live birth / internal hatching of offspring). They previously published a rare variant in the gene kcnl-1 that plays a role in egg retention but identify common variants in this study. They classify wild strains based on egg-retention to separate out the extremely different isolates. Egg laying has been extensively studied in the laboratory strain N2, but rarely addressed in natural strains. The authors investigate egg-laying behaviors using standard assays and find that their classified egg-laying groups have differences in sub-behaviors suggesting diverse roles in the ultimate egg-laying output. Then, they turn to the egg-laying circuit using both exogenous serotonin (5-HT), 5-HT modulatory drugs (e.g. SSRIs), and even genome editing to test epistasis with the mod-5 5-HT reuptake. The effects of 5-HT modulation and mutants are not predictive based on the basal behaviors and egg-retention phenotypes with the most extreme egg-retention strains differing in their responses. Interestingly, strains with more egg retention have decreased fitness (in their laboratory) measures but also provide a protective environment for offspring when exposed to common "natural" stressors. Their final conclusion that egg retention could be a trade-off between antagonistic effects of maternal vs. offspring fitness is supported well and sets the stage for future mechanistic studies across Caenorhabditis.

Reviewer #2 (Public Review):

Mignerot et al. study variations in egg retention in a large set of wild C. elegans strains use detailed analysis of a subset of these strains to those that these variations in egg retention appear to arise from variations in egg-laying behavior. The authors then take advantage of the advanced genetic technology available in C. elegans, and the fact that the cellular and molecular mechanisms that drive egg-laying behavior in the N2 laboratory strain of C. elegans have been studied intensely for decades. Thus, they demonstrate that variations multiple genetic loci appear to drive variations in egg laying across species, although they are unable to identify the specific genes that vary other than a potassium channel already identified in a previous study from some of these same authors (Vigne et al., 2021). Mignerot et al. also present evidence that variations in response of the egg-laying system to the neuromodulator serotonin appear to underlie variations in egg-laying behavior across species. Finally, the authors present a series of studies examining how the retention of eggs in utero affects the fertility and survival of mothers versus the survival of their progeny in a variety of adverse conditions, including limiting food, and the presence of acute environmental insults such as alcohol or acid. The results suggest that variations in egg-laying behavior evolved as a response to adverse environmental conditions that impose a trade-off between survival of the mothers versus their progeny.

Strengths:

The analysis of variations in egg laying by a large set of wild species significantly extends the previous work of Vigne et al. (2021), who focused on just one wild variant strain. Mignerot find that variations in egg laying are widespread across C. elegans strains and result from changes in multiple genetic loci.

To determine why various strains vary in their egg-laying behavior, the authors take advantage the genetic tractability of C. elegans and the huge body of previous studies on the cellular and molecular basis of egg-laying behavior in the laboratory N2 strain. Since serotonin is one signal that induces egg laying, the authors subject various strains to serotonin and to drugs thought to alter serotonin signaling, and they also use CRISPR induced gene editing to mutate a serotonin reuptake transporter in some strains. The results are largely consistent with the idea that variations across strains alter how the egg-laying system responds to serotonin.

The final figures in the paper presents a far more detailed analysis than did Vigne et al. (2021) of how variations in egg retention across species can affect fitness under various environmental stresses. Thus, Mignerot et al. look at competition under conditions of limiting food, and response to acute environmental insults, and compare the ability of adults, in utero eggs, and ex vivo eggs to survive. The results lead to an interesting discussion of how variations in behavior result in a trade-off in survival of mothers versus their progeny. The authors in their Discussion do a good job describing the challenges in interpreting the relevance of these laboratory results to the poorly-understood environmental conditions that C. elegans may experience in the wild. The Discussion also had an excellent section about how the ability of a single species to strongly regulate egg-laying behavior in response to its environment, and how this ability could be adaptive. Overall, these were the strongest and most interesting aspects of Mignerot et al.

Weaknesses<br /> The specific potassium channel variation studied by Vigne et al. (2021) has by far the strongest effect on egg laying seen in the Mignerot et al. study and remains the only genetic variation that has been molecularly identified. So, Mignerot et al. were not able to identify any additional specific genes that vary across species to cause changes in egg laying, and this limited their ability to generate new insights into the specific cellular and molecular mechanisms that have changed across species to result in changes in egg laying behavior.

The authors' use of drug treatments and CRISPR to alter serotonin signaling yielded some insights into mechanistic variations in how the egg-laying system functions across strains, but these experiments only allow very indirect inferences into what is going on. The analysis in Figures 4 and 5 generates a complex set of results that are not easy to interpret. The clearest result seems to be that strains carrying the KCNL-1 point mutation lay eggs poorly and exogenous serotonin inhibits rather than stimulates egg laying in these strains. This basic result was to a large extent reported previously in Vigne et al. 2021.

The analysis of how differences between strains mechanistically result in changes in egg-laying behavior and egg retention, while excellent in concept, is only modestly successful. The analysis of the temporal pattern egg-laying behavior in Figure 3B-3F is relatively weak. Whereas the state of the art in analyzing this behavior is to take videos of animals and track exactly when they lay eggs, analyzing 40 or more hours of behavior per strain, the authors used a lower-tech method of just examining how many eggs were laid within 5-minute intervals over a period of just three hours per strain. While this analysis was sufficient to demonstrate some statistically significant differences in the pattern of egg laying in some strains, it is unclear to what extent these differences could be sufficient to explain the differences in accumulation of unlaid eggs between these strains. In contrast, the variations in age of the onset of egg-laying behavior in Fig 3G and 3H between strains were very strong and may be more likely to reflect mechanistic differences in how egg laying is controlled that could result in the differences in retention of unlaid eggs seen among the strains tested. In the Discussion, the authors extensively write about the work of the Collins lab showing that retained eggs stretch the uterus to produce a signal that activates egg-laying muscles. Could it be that really this mechanism is the main one that varies between strains, leading to the observed variations in time to laying the first egg as well as variations in the number of retained eggs throughout adulthood?

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Koumoundourou et al., identify a pathway downstream of Bcl11b that controls synapse morphology and plasticity of hippocampal mossy fiber synapses. Using an elegant combination of in vivo, ex vivo, and in vitro approaches, the authors build on their previous work that indicated C1ql2 as a functional target of Bcl11b (De Bruyckere et al., 2018). Here, they examine the functional implications of C1ql2 at MF synapses in Bcl11b cKO mice and following C1ql2 shRNA. The authors find that Bcl11b KO and shRNA against C1ql2 significantly reduces the recruitment of synaptic vesicles and impairs LTP at MF synapses. Importantly, the authors test a role for the previously identified C1ql2 binding partner, exon 25b-containing Nrxn3 (Matsuda et al., 2016), as relevant at MF synapses to maintain synaptic vesicle recruitment. To test this, the authors developed a K262E C1ql2 mutant that disrupts binding to Nrxn3. Curiously, while Bcl11b KO and C1ql2 KD largely phenocopy (reduced vesicle recruitment and impaired LTP), only vesicle recruitment is dependent on C1ql2-Nrxn3 interactions. These findings provide new insight into the functional role of C1ql2 at MF synapses. While the authors convincingly demonstrate a role for C1ql2-Nrxn3(25b+) interaction for vesicle recruitment and a Nrxn3(25b+)independent role for C1ql2 in LTP, the underlying mechanisms remain inconclusive. Additionally, a discussion of how these findings relate to previous work on C1ql2 at mossy fiber synapses and how the findings contribute to the biology of Nrxn3 would increase the interpretability of this work.

As suggested by reviewer #1, we extended our discussion of previous work on C1ql2 and additionally discussed the biology of Nrxn3 and how our work relates to it. Moreover, we extended our mechanistic analysis of how Bcl11b/C1ql2/Nrxn3 pathway controls synaptic vesicle recruitment as well as LTP (please see also response to reviewer #2 points 5 and 8 and reviewer #3 point 4 of public reviews below for detailed discussion).

Reviewer #2 (Public Review):

This manuscript describes experiments that further investigate the actions of the transcription factor Bcl11b in regulating mossy fiber (MF) synapses in the hippocampus. Prior work from the same group had demonstrated that loss of Bcl11b results in loss of MF synapses as well as a decrease in LTP. Here the authors focus on a target of Bcl11b a secreted synaptic organizer C1ql2 which is almost completely lost in Bcl11b KO. Viral reintroduction of C1ql2 rescues the synaptic phenotypes, whereas direct KD of C1ql2 recapitulates the Bcl1 phenotype. C1ql2 itself interacts directly with Nrxn3 and replacement with a binding deficient mutant C1q was not able to rescue the Bcl11b KO phenotype. Overall there are some interesting observations in the study, however there are also some concerns about the measures and interpretation of data.

The authors state that they used a differential transcriptomic analysis to screen for candidate targets of Bcl11b, yet they do not present any details of this screen. This should be included and at the very least a table of all DE genes included. It is likely that many other genes are also regulated by Bcl11b so it would be important to the reader to see the rationale for focusing attention on C1ql2 in this study.

The transcriptome analysis mentioned in our manuscript was published in detail in our previous study (De Bruyckere et al., 2018), including chromatin-immunoprecipitation that revealed C1ql2 as a direct transcriptional target of Bcl11b. Upon revision of the manuscript, we made sure that this was clearly stated within the main text module to avoid future confusion. In the same publication (De Bruyckere et al., 2018), we discuss in detail several identified candidate genes such as Sema5b, Ptgs2, Pdyn and Penk as putative effectors of Bcl11b in the structural and functional integrity of MFS. C1ql2 has been previously demonstrated to be almost exclusively expressed in DG neurons and localized to the MFS.

There it bridges the pre- and post-synaptic sides through interaction with Nrxn3 and KAR subunits, respectively, and regulates synaptic function (Matsuda et al., 2016). Taken together, C1ql2 was a very good candidate to study as a potential effector downstream of Bcl11b in the maintenance of MFS structure and function. However, as our data reveal, not all Bcl11b mutant phenotypes were rescued by C1ql2 (see supplementary figures 2d-f of revised manuscript). We expect additional candidate genes, identified in our transcriptomic screen, to act downstream of Bcl11b in the control of MFS.

All viral-mediated expression uses AAVs which are known to ablate neurogenesis in the DG (Johnston DOI: 10.7554/eLife.59291) through the ITR regions and leads to hyperexcitability of the dentate. While it is not clear how this would impact the measurements the authors make in MF-CA3 synapses, this should be acknowledged as a potential caveat in this study.

We agree with reviewer #2 and are aware that it has been demonstrated that AAV-mediated gene expression ablates neurogenesis in the DG. To avoid potential interference of the AAVs with the interpretability of our phenotypes, we made sure during the design of the study that all of our control groups were treated in the same way as our groups of interest, and were, thus, injected with control AAVs. Moreover, the observed phenotypes were first described in Bcl11b mutants that were not injected with AVVs (De Bruyckere et al., 2018). Finally, we thoroughly examined the individual components of the proposed mechanism (rescue of C1ql2 expression, over-expression of C1ql3 and introduction of mutant C1ql2 in Bcl11b cKOs, KD of C1ql2 in WT mice, and Nrxn123 cKO) and reached similar conclusions. Together, this strongly supports that the observed phenotypes occur as a result of the physiological function of the proteins involved in the described mechanism and not due to interference of the AAVs with these biological processes. We have now addressed this point in the main text module of the revised ms.

The authors claim that the viral re-introduction "restored C1ql2 protein expression to control levels. This is misleading given that the mean of the data is 2.5x the control (Figure 1d and also see Figure 6c). The low n and large variance are a problem for these data. Moreover, they are marked ns but the authors should report p values for these. At the least, this likely large overexpression and variability should be acknowledged. In addition, the use of clipped bands on Western blots should be avoided. Please show the complete protein gel in primary figures of supplemental information.

We agree with reviewer #2 that C1ql2 expression after its re-introduction in Bcl11b cKO mice was higher compared to controls and that this should be taken into consideration for proper interpretation of the data. To address this, based also on the suggestion of reviewer #3 point 1 below, we overexpressed C1ql2 in DG neurons of control animals. We found no changes in synaptic vesicle organization upon C1ql2 over-expression compared to controls. This further supports that the observed effect upon rescue of C1ql2 expression in Bcl11b cKOs is due to the physiological function of C1ql2 and not as result of the overexpression. These data are included in supplementary figure 2g-j and are described in detail in the results part of the revised manuscript.

Additionally, we looked at the effects of C1ql2 overexpression in Bcl11b cKO DGN on basal synaptic transmission. We plotted fEPSP slopes versus fiber volley amplitudes, measured in slices from rescue animals, as we had previously done for the control and Bcl11b cKO (Author response image 1a). Although regression analysis revealed a trend towards steeper slopes in the rescue mice (Author response image 1a and b), the observation did not prove to be statistically significant, indicating that C1ql2 overexpression in Bcl11b cKO animals does not strongly alter basal synaptic transmission at MFS. Overall, our previous and new findings support that the observed effects of the C1ql2 rescue are not caused by the artificially elevated levels of C1ql2, as compared to controls, but are rather a result of the physiological function of C1ql2.

Following the suggestion of reviewer #2 all western blot clipped bands were exchanged for images of the full blot. This includes figures 1c, 4c, 6b and supplementary figure 2g of the revised manuscript. P-value for Figure 1d has now been included.

Author response image 1.

C1ql2 reintroduction in Bcl11b cKO DGN does not significantly alter basal synaptic transmission at mossy fiber-CA3 synapses. a Input-output curves generated by plotting fEPSP slope against fiber volley amplitude at increasing stimulation intensities. b Quantification of regression line slopes for input-output curves for all three conditions. Control+EGFP, 35 slices from 16 mice; Bcl11b cKO+EGFP, 32 slices from 14 mice; Bcl11b cKO+EGFP-2A-C1ql2, 22 slices from 11 mice. The data are presented as means, error bars represent SEM. Kruskal-Wallis test (non-parametric ANOVA) followed by Dunn’s post hoc pairwise comparisons. p=0.106; ns, not significant.

Measurement of EM micrographs: As prior work suggested that MF synapse structure is disrupted the authors should report active zone length as this may itself affect "synapse score" defined by the number of vesicles docked. More concerning is that the example KO micrographs seem to have lost all the densely clustered synaptic vesicles that are away from the AZ in normal MF synapses e.g. compare control and KO terminals in Fig 2a or 6f or 7f. These terminals look aberrant and suggest that the important measure is not what is docked but what is present in the terminal cytoplasm that normally makes up the reserve pool. This needs to be addressed with further analysis and modifications to the manuscript.

As requested by reviewer #2 we analyzed and reported in the revised manuscript the active zone length. We found that the active zone length remained unchanged in all conditions (control/Bcl11b cKO/C1ql2 rescue, WT/C1ql2 KD, control/K262E and control/Nrxn123 cKO), strengthening our results that the described Bcl11b/C1ql2/Nrxn3 mechanism is involved in the recruitment of synaptic vesicles. These data have been included in supplementary figures 2c, 4h, 5f and 6g and are described in the results part of the revised manuscript.

We want to clarify that the synapse score is not defined by the number of docked vesicles to the plasma membrane. The synapse score, which is described in great detail in our materials and methods part and has been previously published (De Bruyckere et al., 2018), rates MFS based on the number of synaptic vesicles and their distance from the active zone and was designed according to previously described properties of the vesicle pools at the MFS. The EM micrographs refer to the general misdistribution of SV in the proximity of MFS. Upon revision of the manuscript, we made sure that this was clearly stated in the main text module to avoid further confusion.

The study also presents correlated changes in MF LTP in Bcl11b KO which are rescued by C1ql2 expression. It is not clear whether the structural and functional deficits are causally linked and this should be made clearer in the manuscript. It is also not apparent why this functional measure was chosen as it is unlikely that C1ql2 plays a direct role in presynaptic plasticity mechanisms that are through a cAMP/ PKA pathway and likely disrupted LTP is due to dysfunctional synapses rather than a specific LTP effect.

The inclusion of functional experiments in this and our previous study (de Bruyckere et al., 2018) was first and foremost intended to determine whether the structural alterations observed at MFB disrupt MFS signaling. From the signaling properties we tested, basal synaptic transmission (this study) and short-term potentiation (de Bruyckere et al., 2018) were unaltered by Bcl11b KO, whereas MF LTP was found to be abolished (de Bruyckere et al., 2018). Indeed, because MF LTP largely depends on presynaptic mechanisms, including the redistribution of the readily releasable pool and recruitment of new active zones (Orlando et al., 2021; Vandael et al., 2020), it appears to be particularly sensitive to the specific structural changes we observed. We therefore believe that it is valuable information that MF LTP is affected in Bcl11b cKO animals - it conveys a direct proof for the functional importance of the observed morphological alterations, while basic transmission remains largely normal. Furthermore, it subsequently provided a functional marker for testing whether the reintroduction of C1ql2 in Bcl11b cKO animals or the KD of C1ql2 in WT animals can functionally recapitulate the control or the Bcl11b KO phenotype, respectively.

We fully agree with the reviewer that C1ql2 is unlikely to directly participate in the cAMP/PKA pathway and that the ablation of C1ql2 likely disrupts MF LTP through an alternative mode of action. Our original wording in the paragraph describing the results of the forskolin-induced LTP experiment might have overstressed the importance of the cAMP pathway. We have now rephrased that paragraph to better describe the main idea behind the forskolin experiment, namely to circumvent the initial Ca2+ influx in order to test whether deficient presynaptic Ca2+ channel/KAR signaling might be responsible for the loss of LTP in Bcl11b cKO. The results are strongly indicative of a downstream mechanism and further investigation is needed to determine the specific mechanisms by which C1ql2 regulates MFLTP, especially in light of the result that C1ql2.K262E rescued LTP, while it was unable to rescue the SV recruitment at the MF presynapse. This raises the possibility that C1ql2 can influence MF-LTP through additional, yet uncharacterized mechanisms, independent of SV recruitment. As such, a causal link between the structural and functional deficits remains tentative and we have now emphasized that point by adding a respective sentence to the discussion of our revised manuscript. Nevertheless, we again want to stress that the main rationale behind the LTP experiments was to assess the functional significance of structural changes at MFS and not to elucidate the mechanisms by which MF LTP is established.

The authors should consider measures that might support the role of Bcl11b targets in SV recruitment during the depletion of synapses or measurements of the readily releasable pool size that would complement their findings in structural studies.

We fully agree that functional measurements of the readily releasable pool (RRP) size would be a valuable addition to the reported redistribution of SV in structural studies. We have, in fact, attempted to use high-frequency stimulus trains in both field and single-cell recordings (details on single-cell experiments are described in the response to point 8) to evaluate potential differences in RRP size between the control and Bcl11b KO (Figure for reviewers 2a and b). Under both recording conditions we see a trend towards lower values of the intersection between a regression line of late responses and the y-axis. This could be taken as an indication of slightly smaller RRP size in Bcl11b mutant animals compared to controls. However, due to several technical reasons we are extremely cautious about drawing such far-reaching conclusions based on these data. At most, they suffice to conclude that the availability of release-ready vesicles in the KO is likely not dramatically smaller than in the control.

The primary issue with using high-frequency stimulus trains for RRP measurements at MFS is the particularly low initial release probability (Pr) at these synapses. This means that a large number of stimulations is required to deplete the RRP. As the RRP is constantly replenished, it remains unclear when steady state responses are reached (reviewed by Kaeser and Regehr, 2017). This is clearly visible in our single-cell recordings (Author response image 2b), which were additionally complicated by prominent asynchronous release at later stages of the stimulus train and by a large variability in the shapes of cumulative amplitude curves between cells. In contrast, while the cumulative amplitude curves for field potential recordings do reach a steady state (Author response image 2a), field potential recordings in this context are not a reliable substitute for single cell or, in the case of MFB, singlebouton recordings. Postsynaptic cells in field potential recordings are not clamped, meaning that the massive release of glutamate due to continuous stimulation depolarizes the postsynaptic cells and reduces the driving force for Na+, irrespective of depletion of the RRP. This is supported by the fact that we consistently observed a recovery of fEPSP amplitudes later in the trains where RRP had presumably been maximally depleted. In summary, high-frequency stimulus trains at the field potential level are not a valid and established technique for estimating RRP size at MFS.

Specialized laboratories have used highly advanced techniques, such as paired recordings between individual MFB and postsynaptic CA3 pyramidal cells, to estimate the RRP size of MFB (Vandael et al., 2020). These approaches are outside the scope of our present study which, while elucidating functional changes following Bcl11b depletion and C1ql2 rescue, does not aim to provide a high-end biophysical analysis of the presynaptic mechanisms involved.

Author response image 2.

Estimation of RRP size using high-frequency stimulus trains at mossy fiber-CA3 synapses. a Results from field potential recordings. Cumulative fEPSP amplitude in response to a train of 40 stimuli at 100 Hz. All subsequent peak amplitudes were normalized to the amplitude of the first peak. Data points corresponding to putative steady state responses were fit with linear regression (RRP size is indirectly reflected by the intersection of the regression line with the yaxis). Control+EGFP, 6 slices from 5 mice; Bcl11b cKO+EGFP, 6 slices from 3 mice. b Results from single-cell recordings. Cumulative EPSC amplitude in response to a train of 15 stimuli at 50 Hz. The last four stimuli were fit with linear regression. Control, 5 cells from 4 mice; Bcl11b cKO, 3 cells from 3 mice. Note the shallow onset of response amplitudes and the subsequent frequency potentiation. Due to the resulting increase in slope at higher stimulus numbers, intersection with the y-axis occurs at negative values. The differences shown were not found to be statistically significant; unpaired t-test or Mann-Whitney U-test.

Bcl11b KO reduces the number of synapses, yet the I-O curve reported in Supp Fig 2 is not changed. How is that possible? This should be explained.

We agree with reviewer #2– this apparent discrepancy has indeed struck us as a counterintuitive result. It might be that synapses that are preferentially eliminated in Bcl11b cKO are predominantly silent or have weak coupling strength, such that their loss has only a minimal effect on basal synaptic transmission. Although perplexing, the result is fully supported by our single-cell data which shows no significant differences in MF EPSC amplitudes recorded from CA3 pyramidal cells between controls and Bcl11b mutants (Author response image 3; please see the response below for details and also our response to Reviewer #1 question 2).

Matsuda et al DOI: 10.1016/j.neuron.2016.04.001 previously reported that C1ql2 organizes MF synapses by aligning postsynaptic kainate receptors with presynaptic elements. As this may have consequences for the functional properties of MF synapses including their plasticity, the authors should report whether they see deficient postsynaptic glutamate receptor signaling in the Bcl11b KO and rescue in the C1ql2 re-expression.

We agree that the study by Matsuda et al. is of key importance for our present work. Although MF LTP is governed by presynaptic mechanisms and we previously did not see differences in short-term plasticity between the control and Bcl11b cKO (De Bruyckere et al., 2018), the clustering of postsynaptic kainate receptors by C1ql2 is indeed an important detail that could potentially alter synaptic signaling at MFS in Bcl11b KO. We, therefore, re-analyzed previously recorded single-cell data by performing a kinetic analysis on MF EPSCs recorded from CA3 pyramidal cells in control and Bcl11b cKO mice (Figure for reviewers 3a) to evaluate postsynaptic AMPA and kainate receptor responses in both conditions. We took advantage of the fact that AMPA receptors deactivate roughly 10 times faster than kainate receptors, allowing the contributions of the two receptors to mossy fiber EPSCs to be separated (Castillo et al., 1997 and reviewed by Lerma, 2003). We fit the decay phase of the second (larger) EPSC evoked by paired-pulse stimulation with a double exponential function, yielding a fast and a slow component, which roughly correspond to the fractional currents evoked by AMPA and kainate receptors, respectively. Analysis of both fast and slow time constants and the corresponding fractional amplitudes revealed no significant differences between controls and Bcl11b mutants (Figure for reviewers 3e-h), indicating that both AMPA and kainate receptor signaling is unaffected by the ablation of C1ql2 following Bcl11b KO.

Importantly, MF EPSC amplitudes evoked by the first and the second pulse (Author response image 3b), paired-pulse facilitation (Author response image 3c) and failure rates (Author response image 3d) were all comparable between controls and Bcl11b mutants. These results further corroborate our observations from field recordings that basal synaptic transmission at MFS is unaltered by Bcl11b KO.

We note that the results from single cell recordings regarding basal synaptic transmission merely confirm the observations from field potential recordings, and that the attempted measurement of RRP size at the single cell level was not successful. Thus, our single-cell data do not add new information about the mechanisms underlying the effects of Bcl11b-deficiency and we therefore decided not to report these data in the manuscript.

Author response image 3.

Basal synaptic transmission at mossy fiber-CA3 synapses is unaltered in Bcl11b cKO mice. a Representative average trace (20 sweeps) recorded from CA3 pyramidal cells in control and Bcl11b cKO mice at minimal stimulation conditions, showing EPSCs in response to paired-pulse stimulation (PPS) at an interstimulus interval of 40 ms. The signal is almost entirely blocked by the application of 2 μM DCG-IV (red). b Quantification of MF EPSC amplitudes in response to PPS for both the first and the second pulse. c Ratio between the amplitude of the second over the first EPSC. d Percentage of stimulation events resulting in no detectable EPSCs for the first pulse. Events <5 pA were considered as noise. e Fast decay time constant obtained by fitting the average second EPSC with the following double exponential function: I(t)=Afaste−t/τfast+Aslowe−t/τslow+C, where I is the recorded current amplitude after time t, Afast and Aslow represent fractional current amplitudes decaying with the fast (τfast) and slow (τslow) time constant, respectively, and C is the offset. Starting from the peak of the EPSC, the first 200 ms of the decaying trace were used for fitting. f Fractional current amplitude decaying with the fast time constant. g-h Slow decay time constant and fractional current amplitude decaying with the slow time constant. For all figures: Control, 8 cells from 4 mice; Bcl11b cKO, 8 cells from 6 mice. All data are presented as means, error bars indicate SEM. None of the differences shown were found to be statistically significant; Mann-Whitney U-test for nonnormally and unpaired t-test for normally distributed data.

Reviewer #3 (Public Review):

Overall, this is a strong manuscript that uses multiple current techniques to provide specific mechanistic insight into prior discoveries of the contributions of the Bcl11b transcription factor to mossy fiber synapses of dentate gyrus granule cells. The authors employ an adult deletion of Bcl11b via Tamoxifen-inducible Cre and use immunohistochemical, electron microscopy, and electrophysiological studies of synaptic plasticity, together with viral rescue of C1ql2, a direct transcriptional target of Bcl11b or Nrxn3, to construct a molecular cascade downstream of Bcl11b for DG mossy fiber synapse development. They find that C1ql2 re-expression in Bcl11b cKOs can rescue the synaptic vesicle docking phenotype and the impairments in MF-LTP of these mutants. They also show that C1ql2 knockdown in DG neurons can phenocopy the vesicle docking and plasticity phenotypes of the Bcl11b cKO. They also use artificial synapse formation assays to suggest that C1ql2 functions together with a specific Nrxn3 splice isoform in mediating MF axon development, extending these data with a C1ql2-K262E mutant that purports to specifically disrupt interactions with Nrxn3. All of the molecules involved in this cascade are disease-associated and this study provides an excellent blueprint for uncovering downstream mediators of transcription factor disruption. Together this makes this work of great interest to the field. Strengths are the sophisticated use of viral replacement and multi-level phenotypic analysis while weaknesses include the linkage of C1ql2 with a specific Nrxn3 splice variant in mediating these effects.

Here is an appraisal of the main claims and conclusions:

1) C1ql2 is a downstream target of Bcl11b which mediates the synaptic vesicle recruitment and synaptic plasticity phenotypes seen in these cKOs. This is supported by the clear rescue phenotypes of synapse anatomy (Fig.2) and MF synaptic plasticity (Fig.3). One weakness here is the absence of a control assessing over-expression phenotypes of C1ql2. It's clear from Fig.1D that viral rescue is often greater than WT expression (totally expected). In the case where you are trying to suppress a LoF phenotype, it is important to make sure that enhanced expression of C1ql2 in a WT background does not cause your rescue phenotype. A strong overexpression phenotype in WT would weaken the claim that C1ql2 is the main mediator of the Bcl11b phenotype for MF synapse phenotypes.

As suggested by reviewer #3, we carried out C1ql2 over-expression experiments in control animals. We show that the over-expression of C1ql2 in the DG of control animals had no effect on the synaptic vesicle organization in the proximity of MFS. This further supports that the observed effect upon rescue of C1ql2 expression in Bcl11b cKOs is due to the physiological function of C1ql2 and not a result of the artificial overexpression. These data are now included in supplementary figure 2g-j and are described in detail in the results part of the revised manuscript. Please also see response to point 3 of reviewer #2.

2) Knockdown of C1ql2 via 4 shRNAs is sufficient to produce the synaptic vesicle recruitment and MFLTP phenotypes. This is supported by clear effects in the shRNA-C1ql2 groups as compared to nonsense-EGFP controls. One concern (particularly given the use of 4 distinct shRNAs) is the potential for off-target effects, which is best controlled for by a rescue experiment with RNA insensitive C1ql2 cDNA as opposed to nonsense sequences, which may not elicit the same off-target effects.

We agree with reviewer #3 that the usage of shRNAs could potentially create unexpected off-target effects and that the introduction of a shRNA-insensitive C1ql2 in parallel to the expression on the shRNA cassette would be a very effective control experiment. However, the suggested experiment would require an additional 6 months (2 months for AAV production, 2-3 months from animal injection to sacrifice and 1-2 months for EM imaging/analysis and LTP measurements) and a high number of additional animals (minimum 8 for EM and 8 for LTP measurements). We note here, that before the production of the shRNA-C1ql2 and the shRNA-NS, the individual sequences were systematically checked for off-target bindings on the murine exome with up to two mismatches and presented with no other target except the proposed (C1ql2 for shRNA-C1ql2 and no target for shRNA-NS). Taking into consideration our in-silico analysis, we feel that the interpretation of our findings is valid without this (very reasonable) additional control experiment.

3) C1ql2 interacts with Nrxn3(25b+) to facilitate MF terminal SV clustering. This claim is theoretically supported by the HEK cell artificial synapse formation assay (Fig.5), the inability of the K262-C1ql2 mutation to rescue the Bcl11b phenotype (Fig.6), and the altered localization of C1ql2 in the Nrxn1-3 deletion mice (Fig.7). Each of these lines of experimental evidence has caveats that should be acknowledged and addressed. Given the hypothesis that C1ql2 and Nrxn3b(25b) are expressed in DG neurons and work together, the heterologous co-culture experiment seems strange. Up till now, the authors are looking at pre-synaptic function of C1ql2 since they are re-expressing it in DGNs. The phenotypes they are seeing are also pre-synaptic and/or consistent with pre-synaptic dysfunction. In Fig.5, they are testing whether C1ql2 can induce pre-synaptic differentiation in trans, i.e. theoretically being released from the 293 cells "post-synaptically". But the post-synaptic ligands (Nlgn1 and and GluKs) are not present in the 293 cells, so a heterologous synapse assay doesn't really make sense here. The effect that the authors are seeing likely reflects the fact that C1ql2 and Nrxn3 do bind to each other, so C1ql2 is acting as an artificial post-synaptic ligand, in that it can cluster Nrxn3 which in turn clusters synaptic vesicles. But this does not test the model that the authors propose (i.e. C1ql2 and Nrxn3 are both expressed in MF terminals). Perhaps a heterologous assay where GluK2 is put into HEK cells and the C1ql2 and Nrxn3 are simultaneously or individually manipulated in DG neurons?

C1ql2 is expressed by DG neurons and is then secreted in the MFS synaptic cleft, while Nrxn3, that is also expressed by DG neurons, is anchored at the presynaptic side. In our work we used the well established co-culture system assay and cultured HEK293 cells secreting C1ql2 (an IgK secretion sequence was inserted at the N-terminus of C1ql2) together with hippocampal neurons expressing Nrxn3(25b+). We used the HEK293 cells as a delivery system of secreted C1ql2 to the neurons to create regions of high concentration of C1ql2. By interfering with the C1ql2-Nrxn3 interaction in this system either by expression of the non-binding mutant C1ql2 variant in the HEK cells or by manipulating Nrxn expression in the neurons, we could show that C1ql2 binding to Nrxn3(25b+) is necessary for the accumulation of vGlut1. However, we did not examine and do not claim within our manuscript that the interaction between C1ql2 and Nrxn3(25b+) induces presynaptic differentiation. Our experiment only aimed to analyze the ability of C1ql2 to cluster SV through interaction with Nrxn3. Moreover, by not expressing potential postsynaptic interaction partners of C1ql2 in our system, we could show that C1ql2 controls SV recruitment through a purely presynaptic mechanism. Co-culturing GluK2-expressing HEK cells with simultaneous manipulation of C1ql2 and/or Nrxn3 in neurons would not allow us to appropriately answer our scientific question, but rather focus on the potential synaptogenic function of the Nrxn3/C1ql2/GluK2 complex and the role of the postsynaptic ligand in it. Thus, we feel that the proposed experiment, while very interesting in characterization of additional putative functions of C1ql2, may not provide additional information for the point we were addressing. In the revised manuscript we tried to make the aim and methodological approach of this set of experiments more clear.

4) K262-C1ql2 mutation blocks the normal rescue through a Nrxn3(25b) mechanism (Fig.6). The strength of this experiment rests upon the specificity of this mutation for disrupting Nrxn3b binding (presynaptic) as opposed to any of the known postsynaptic C1ql2 ligands such as GluK2. While this is not relevant for interpreting the heterologous assay (Fig.5), it is relevant for the in vivo phenotypes in Fig.6. Similar approaches as employed in this paper can test whether binding to other known postsynaptic targets is altered by this point mutation.

It has been previously shown that C1ql2 together with C1ql3 recruit postsynaptic GluK2 at the MFS. However, loss of just C1ql2 did not affect the recruitment of GluK2, which was disrupted only upon loss of both C1ql2 and C1ql3 (Matsuda et al., 2018). In our study we demonstrate a purely presynaptic function of C1ql2 through Nrxn3 in the synaptic vesicle recruitment. This function is independent of C1ql3, as C1ql3 expression is unchanged in all of our models and its over-expression did not compensate for C1ql2 functions (Fig. 2, 3a-c). Our in vitro experiments also reveal that C1ql2 can recruit both Nrxn3 and vGlut1 in the absence of any known postsynaptic C1ql2 partner (KARs and BAI3; Fig.5; please also see response above). Furthermore, we have now performed a kinetic analysis on single-cell data which we had previously collected to evaluate postsynaptic AMPA and kainate receptor responses in both the control and Bcl11b KO. Our analysis reveals no significant differences in postsynaptic current kinetics, making it unlikely that AMPA and kainate receptor signaling is altered upon the loss of C1ql2 following Bcl11b cKO (Author response image 3e-h; please also see our response to reviewer #2 point 8). Thus, we have no experimental evidence supporting the idea that a loss of interaction between C1ql2.K262E and GluK2 would interfere with the examined phenotype. However, to exclude that the K262E mutation disrupts interaction between C1ql2 and GluK2, we performed co-immunoprecipitation from protein lysate of HEK293 cells expressing GluK2myc-flag and GFP-C1ql2 or GluK2-myc-flag and GFP-K262E and could show that both C1ql2 and K262E had GluK2 bound when precipitated. These data are included in supplementary figure 5k of the revised manuscript.

5) Altered localization of C1ql2 in Nrxn1-3 cKOs. These data are presented to suggest that Nrx3(25b) is important for localizing C1ql2 to the SL of CA3. Weaknesses of this data include both the lack of Nrxn specificity in the triple a/b KOs as well as the profound effects of Nrxn LoF on the total levels of C1ql2 protein. Some measure that isn't biased by this large difference in C1ql2 levels should be attempted (something like in Fig.1F).

We acknowledge that the lack of specificity in the Nrxn123 model makes it difficult to interpret our data. We have now examined the mRNA levels of Nrxn1 and Nrxn2 upon stereotaxic injection of Cre in the DG of Nrxn123flox/flox animals and found that Nrxn1 was only mildly reduced. At the same time Nrxn2 showed a tendency for reduction that was not significant (data included in supplementary figure 6a of revised manuscript). Only Nrxn3 expression was strongly suppressed. Of course, this does not exclude that the mild reduction of Nrxn1 and Nrxn2 interferes with the C1ql2 localization at the MFS. We further examined the mRNA levels of C1ql2 in control and Nrxn123 mutants to ensure that the observed changes in C1ql2 protein levels at the MFS are not due to reduced mRNA expression and found no changes (data are included in supplementary figure 6b of the revised manuscript), suggesting that overall protein C1ql2 expression is normal.

The reduced C1ql2 fluorescence intensity at the MFS was first observed when non-binding C1ql2 variant K262E was introduced to Bcl11b cKO mice that lack endogenous C1ql2 (Fig.6). In these experiments, we found that despite the overall high protein levels of C1ql2.K262E in the hippocampus (Fig. 6c), its fluorescence intensity at the SL was significantly reduced compared to WT C1ql2 (Fig. 6d-e). The remaining signal of the C1ql2.K262E at the SL was equally distributed and in a punctate form, similar to WT C1ql2. Together, this suggests that loss of C1ql2-Nrxn3 interaction interferes with the localization of C1ql2 at the MFS, but not with the expression of C1ql2. Of course, this does not exclude that other mechanisms are involved in the synaptic localization of C1ql2, beyond the interaction with Nrxn3, as both the mutant C1ql2 in Bcl11b cKO and the endogenous C1ql2 in Nrxn123 cKOs show residual immunofluorescence at the SL. Further studies are required to determine how C1ql2-Nrxn3 interaction regulates C1ql2 localization at the MFS.

Reviewer #1 (Recommendations For The Authors):

In addition to addressing the comments below, this study would benefit significantly from providing insight and discussion into the relevant potential postsynaptic signaling components controlled exclusively by C1ql2 (postsynaptic kainate receptors and the BAI family of proteins).

We have now performed a kinetic analysis on single-cell data that we had previously collected to evaluate postsynaptic AMPA and kainate receptor responses in both the control and Bcl11b cKO. Our analysis reveals no significant differences in postsynaptic current kinetics, making it unlikely that AMPA and kainate receptor signaling differ between controls and upon the loss of C1ql2 following Bcl11b cKO (Author response image 3e-h; please also see our response to Reviewer #2 point 8). This agrees with previous findings that C1ql2 regulates postsynaptic GluK2 recruitment together with C1ql3 and only loss of both C1ql2 and C1ql3 results in a disruption of KAR signaling (Matsuda et al., 2018). In our study we demonstrate a purely presynaptic function of C1ql2 through Nrxn3 in the synaptic vesicle recruitment. This function is independent of C1ql3, as C1ql3 expression is unchanged in all of our models and its over-expression did not compensate for C1ql2 functions (Fig. 2, 3a-c). Our in vitro experiments also reveal that C1ql2 can recruit both Nrxn3 and vGlut1 in the absence of any known postsynaptic C1ql2 partner (KARs and BAI3; Fig.5; please also see our response to reviewer #3 point 4 above). We believe that further studies are needed to fully understand both the pre- and the postsynaptic functions of C1ql2. Because the focus of this manuscript was on the role of the C1ql2-Nrxn3 interaction and our investigation on postsynaptic functions of C1ql2 was incomplete, we did not include our findings on postsynaptic current kinetics in our revised manuscript. However, we increased the discussion on the known postsynaptic partners of C1ql2 in the revised manuscript to increase the interpretability of our results.

Major Comments:

The authors demonstrate that the ultrastructural properties of presynaptic boutons are altered after Bcl11b KO and C1ql2 KD. However, whether C1ql2 functions as part of a tripartite complex and the identity of the postsynaptic receptor (BAI, KAR) should be examined.

Matsuda and colleagues have nicely demonstrated in their 2016 (Neuron) study that C1ql2 is part of a tripartite complex with presynaptic Nrxn3 and postsynaptic KARs. Moreover, they demonstrated that C1ql2, together with C1ql3, recruit postsynaptic KARs at the MFS, while the KO of just C1ql2 did not affect the KAR localization. In our study we demonstrate a purely presynaptic function of C1ql2 through Nrxn3 in the synaptic vesicle recruitment. This function is independent of C1ql3, as C1ql3 expression is unchanged in all of our models and its over-expression did not compensate for C1ql2 functions (Fig. 2, 3a-c). Our in vitro experiments also reveal that C1ql2 is able to recruit both Nrxn3 and vGlut1 in the absence of any known postsynaptic C1ql2 partner (Fig. 5; please also see our response to reviewer #3 point 4 above). Moreover, we were able to show that the SV recruitment depends on C1ql2 interaction with Nrxn3 through the expression of a non-binding C1ql2 (Fig. 6) that retains the ability to interact with GluK2 (supplementary figure 5k of revised manuscript) or by KO of Nrxns (Fig. 7). Furthermore, we have now performed a kinetic analysis on single-cell data which we had previously collected to evaluate postsynaptic AMPA and kainate receptor responses in both the control and Bcl11b cKO. Our analysis reveals no significant differences in postsynaptic current kinetics, making it unlikely that AMPA and kainate receptor signaling differ between controls and Bcl11b mutants (Author response image 3e-h; please also see our response to Reviewer #2 question 8). Together, we have no experimental evidence so far that would support that the postsynaptic partners of C1ql2 are involved in the observed phenotype. While it would be very interesting to characterize the postsynaptic partners of C1ql2 in depth, we feel this would be beyond the scope of the present study.

Figure 1f: For a more comprehensive understanding of the Bcl11b KO phenotype and the potential role for C1ql2 on MF synapse number, a complete quantification of vGlut1 and Homer1 for all conditions (Supplement Figure 2e) should be included in the main text.

In our study we focused on the role of C1ql2 in the structural and functional integrity of the MFS downstream of Bcl11b. Bcl11b ablation leads to several phenotypes in the MFS that have been thoroughly described in our previous study (De Bruyckere et al., 2018). As expected, re-expression of C1ql2 only partially rescued these phenotypes, with full recovery of the SV recruitment (Fig. 2) and of the LTP (Fig. 3), but had no effect on the reduced numbers of MFS nor the structural complexity of the MFB created by the Bcl11b KO (supplementary figure 2d-f of revised manuscript). We understand that including the quantification of vGlut1 and Homer1 co-localization in the main figures would help with a better understanding of the Bcl11b mutant phenotype. However, in our manuscript we investigate C1ql2 as an effector of Bcl11b and thus we focus on its functions in SV recruitment and LTP. As we did not find a link between C1ql2 and the number of MFS/MFB upon re-expression of C1ql2 in Bcl11b cKO or now also in C1ql2 KD (see response to comment #4 below), we believe it is more suitable to present these data in the supplement.

Figure 3/4: Given the striking reduction in the numbers of synapses (Supplement Figure 2e) and docked vesicles (Figure 2d) in the Bcl11b KO and C1ql2 KD (Figure 4e-f), it is extremely surprising that basal synaptic transmission is unaffected (Supplement Figure 2g). The authors should determine the EPSP input-output relationship following C1ql2 KD and measure EPSPs following trains of stimuli at various high frequencies.

We fully acknowledge that this is an unexpected result. It is, however, well feasible that the modest displacement of SV fails to noticeably influence basal synaptic transmission. This would be the case, for example, if only a low number of vesicles are released by single stimuli, in line with the very low initial Pr at MFS. In contrast, the reduction in synapse numbers in the Bcl11b mutant might indeed be expected to reflect in the input-output relationship. It is possible, however, that synapses that are preferentially eliminated in Bcl11b cKO are predominantly silent or have weak coupling strength, such that their loss has only a minimal effect on basal synaptic transmission. Finally, we cannot exclude compensatory mechanisms (homeostatic plasticity) at the remaining synapses. A detailed analysis of these potential mechanisms would be a whole project in its own right.

As additional information, we can say that the largely unchanged input-output-relation in Bcl11b cKO is also present in the single-cell level data (Author response image 3; details on single-cell experiments are described in the response to Reviewer #2 point 8).

As suggested by the reviewer, we have now additionally analyzed the input-output relationship following C1ql2 KD and again did not observe any significant difference between control and KD animals. We have incorporated the respective input-output curves into the revised manuscript under Supplementary figure 3c-d.

Figure 4: Does C1ql2 shRNA also reduce the number of MFBs? This should be tested to further identify C1ql2-dependent and independent functions.

As requested by reviewer #1 we quantified the number of MFBs upon C1ql2 KD. We show that C1ql2 KD in WT animals does not alter the number of MFBs. The data are presented in supplementary figure 4d of the revised manuscript. Re-expression of C1ql2 in Bcl11b cKO did not rescue the loss of MFS created by the Bcl11b mutation. Moreover, C1ql2 re-expression did not rescue the complexity of the MFB ultrastructure perturbed by the Bcl11b ablation. Together, this suggests that Bcl11b regulates MFs maintenance through additional C1ql2-independent pathways. In our previously published work (De Bruyckere et al., 2018) we identified and discussed in detail several candidate genes such as Sema5b, Ptgs2, Pdyn and Penk as putative effectors of Bcl11b in the structural and functional integrity of MFS (please also see response to reviewer #2- point 1 of public reviews).

Figure 5: Clarification is required regarding the experimental design of the HEK/Neuron co-culture: 1. C1ql2 is a secreted soluble protein - how is the protein anchored to the HEK cell membrane to recruit Nrxn3(25b+) binding and, subsequently, vGlut1?

C1ql2 was secreted by the HEK293 cells through an IgK signaling peptide at the N-terminus of C1ql2. The high concentration of C1ql2 close to the secretion site together with the sparse coculturing of the HEK293 cells on the neurons allows for the quantification of accumulation of neuronal proteins. We have now described the experimental conditions in greater detail in the main text module of the revised manuscript

2) Why are the neurons transfected and not infected? Transfection efficiency of neurons with lipofectamine is usually poor (1-5%; Karra et al., 2010), while infection of neurons with lentiviruses or AAVs encoding cDNAs routinely are >90% efficient. Thus, interpretation of the recruitment assays may be influenced by the density of neurons transfected near a HEK cell.

We agree with reviewer #1 that viral infection of the neurons would have been a more effective way of expressing our constructs. However, due to safety allowances in the used facility and time limitation at the time of conception of this set of experiments, a lipofectamine transfection was chosen.

However, as all of our examined groups were handled in the same way and multiple cells from three independent experiments were examined for each experimental set, we believe that possible biases introduced by the transfection efficiency have been eliminated and thus have trust in our interpretation of these results.

3) Surface labeling of HEK cells for wild-type C1ql2 and K262 C1ql2 would be helpful to assess the trafficking of the mutant.

We recognize that potential changes to the trafficking of C1ql2 caused by the K262E mutation would be important to characterize, in light of the reduced localization of the mutant protein at the SL in the in vivo experiments (Fig. 6e). In our culture system, C1ql2 and K262E were secreted by the HEK cells through insertion of an IgK signaling peptide at the N-terminus of the myc-tagged C1ql2/K262E. Thus, trafficking analysis on this system would not be informative, as the system is highly artificial compared to the in vivo model. Further studies are needed to characterize C1ql2 trafficking in neurons to understand how C1ql2-Nrxn3 interaction regulates the localization of C1ql2. However, labeling of the myc-tag in C1ql2 or K262E expressing HEK cells of the co-culture model reveals a similar signal for the two proteins (Fig. 5a,c). Nrxn-null mutation in neurons co-cultured with C1ql2-expressing HEK cells disrupted C1ql2 mediated vGlut1 accumulation in the neurons. Selective expression of Nrxn3(25b) in the Nrxn-null neurons restored vGlut1 clustering was (Fig. 5e-f). Together, these data suggest that it is the interaction between C1ql2 and Nrxn3 that drives the accumulation of vGlut1.

Figure 6: Bcl11b KO should also be included in 6f-h.

As suggested by reviewer #1, we included the Bcl11b cKO in figures 6f-h and in corresponding supplementary figures 5c-j.

Figure 7b: What is the abundance of mRNA for Nrxn1 and Nrxn2 as well as the abundance of Nrxns after EGFP-Cre injection into DG?

We addressed this point raised by reviewer #1 by quantifying the relative mRNA levels of Nrxn1 and Nrxn2 via qPCR upon Nrxn123 mutation induction with EGFP-Cre injection. We have now examined the mRNA levels of Nrxn1 and Nrxn2 upon stereotaxic injection of Cre in the DG of Nrxn123flox/flox animals and found that Nrxn1 was only mildly reduced. At the same time Nrxn2 showed a tendency for reduction that was not significant. The data are presented in supplementary figure 6a of the revised maunscript.

Minor Comments for readability:

Synapse score is referred to frequently in the text and should be defined within the text for clarification.

'n' numbers should be better defined in the figure legends. For example, for protein expression analysis in 1c, n=3. Is this a biological or technical triplicate? For electrophysiology (e.g. 3c), does "n=7" reflect the number of animals or the number of slices? n/N (slices/animals) should be presented.

Figure 7a: Should the diagrams of the cre viruses be EGFP-Inactive or active Cre and not CRE-EGFP as shown in the diagram?

Figure 7b: the region used for the inset should be identified in the larger image.

All minor points have been fixed in the revised manuscript according to the suggestions.

Reviewer #3 (Recommendations For The Authors):

-Please describe the 'synapse score' somewhere in the text - it is too prominently featured to not have a clear description of what it is.

The description of the synapse score has been included in the main text module of the revised manuscript.

-The claim that Bcl11b controls SV recruitment "specifically" through C1ql2 is a bit stronger than is warranted by the data. Particularly given that C1ql2 is expressed at 2.5X control levels in their rescue experiments. See pt.2

Please see response to reviewer #3 point 1 of public reviews. To address this, we over-expressed C1ql2 in control animals and found no changes in the synaptic vesicle distribution (supplementary figure 2g-j of revised manuscript). This supports that the observed rescue of synaptic vesicle recruitment by re-expression of C1ql2 is due to its physiological function and not due to the artificially elevated protein levels. Of course, we cannot exclude the possibility that other, C1ql2-independent, mechanisms also contribute to the SV recruitment downstream of Bcl11b. Our data from the C1ql2 rescue, C1ql2 KD, the in vitro experiments and the interruption of C1ql2-Nrxn3 in vivo, strongly suggest C1ql2 to be an important regulator of SV recruitment.

-Does Bcl11b regulate Nrxn3 expression? Considering the apparent loss of C1ql2 expression in the Nrxn KO mice, this is an important detail.

We agree with reviewer #3 that this is an important point. We have previously done differential transcriptomics from DG neurons of Bcl11b cKOs compared to controls and did not find Nrxn3 among the differentially expressed genes. To further validate this, we now quantified the Nrxn3 mRNA levels via qPCR in Bcl11b cKOs compared to controls and found no differences. These data are included in supplementary figure 5a of the revised manuscript.

-It appears that C1ql2 expression is much lower in the Nrxn123 KO mice. Since the authors are trying to test whether Nrxn3 is required for the correct targeting of C1ql2, this is a confounding factor. We can't really tell if what we are seeing is a "mistargeting" of C1ql2, loss of expression, or both. If the authors did a similar analysis to what they did in Figure 1 where they looked at the synaptic localization of C1ql2 (and quantified it) that could provide more evidence to support or refute the "mistargeting" claim.

Please also see response to reviewer #3 point 5 of public reviews. To exclude that reduction of fluorescence intensity of C1ql2 at the SL in Nrxn123 KO mice is due to loss of C1ql2 expression, we examined the mRNA levels of C1ql2 in control and Nrxn123 mutants and found no changes (data are included in supplementary figure 6b of the revised manuscript), suggesting that C1ql2 gene expression is normal. The reduced C1ql2 fluorescence intensity at the MFS was first observed when non-binding C1ql2 variant K262E was introduced to Bcl11b cKO mice that lack endogenous C1ql2 (Fig.6). In these experiments, we found that despite the overall high protein levels of C1ql2.K262E in the hippocampus (Fig. 6c), its fluorescence intensity at the SL was significantly reduced compared to WT C1ql2 (Fig. 6d-e). The remaining C1ql2.K262E signal in the SL was equally distributed and in a punctate form, similar to WT C1ql2. Together, this indicates that the loss of C1ql2-Nrxn3 interaction interferes with the localization of C1ql2 along the MFS, but not with expression of C1ql2. Of course, this does not exclude that additional mechanisms regulate C1ql2 localization at the synapse, as both the mutant C1ql2 in Bcl11b cKO and the endogenous C1ql2 in Nrxn123 cKO show residual immunofluorescence at the SL.

We note here that we have not previously quantified the co-localization of C1ql2 with individual synapses. C1ql2 is a secreted molecule that localizes at the MFS synaptic cleft. However, not much is known about the number of MFS that are positive for C1ql2 nor about the mechanisms regulating C1ql2 targeting, transport, and secretion to the MFS. Whether C1ql2 interaction with Nrxn3 is necessary for the protection of C1ql2 from degradation, its surface presentation and transport or stabilization to the synapse is currently unclear. Upon revision of our manuscript, we realized that we might have overstated this particular finding and have now rephrased the specific parts within the results to appropriately describe the observation and have also included a sentence in the discussion referring to the lack of understanding of the mechanism behind this observation.

-Title of Figure S5 is "Nrxn KO perturbs C1ql2 localization and SV recruitment at the MFS", but there is no data on C1ql2 localization.

This issue has been fixed in the revised manusript.

-S5 should be labeled more clearly than just Cre+/-

This issue has been fixed in the revised manuscript.

References

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De Bruyckere, E., Simon, R., Nestel, S., Heimrich, B., Kätzel, D., Egorov, A.V., Liu, P., Jenkins, N.A., Copeland, N.G., Schwegler, H., Draguhn, A., Britsch, S., 2018. Stability and Function of Hippocampal Mossy Fiber Synapses Depend on Bcl11b/Ctip2. Front. Mol. Neurosci. 11. https://doi.org/10.3389/fnmol.2018.00103

Kaeser, P.S., Regehr, W.G., 2017. The readily releasable pool of synaptic vesicles. Curr. Opin. Neurobiol. 43, 63–70. https://doi.org/10.1016/j.conb.2016.12.012

Lerma, J., 2003. Roles and rules of kainate receptors in synaptic transmission. Nat. Rev. Neurosci. 4, 481–495. https://doi.org/10.1038/nrn1118

Orlando, M., Dvorzhak, A., Bruentgens, F., Maglione, M., Rost, B.R., Sigrist, S.J., Breustedt, J., Schmitz, D., 2021. Recruitment of release sites underlies chemical presynaptic potentiation at hippocampal mossy fiber boutons. PLoS Biol. 19, e3001149. https://doi.org/10.1371/journal.pbio.3001149

Vandael, D., Borges-Merjane, C., Zhang, X., Jonas, P., 2020. Short-Term Plasticity at Hippocampal Mossy Fiber Synapses Is Induced by Natural Activity Patterns and Associated with Vesicle Pool Engram Formation. Neuron 107, 509-521.e7. https://doi.org/10.1016/j.neuron.2020.05.013

Reviewer #1 (Public Review):

Koumoundourou et al., identify a pathway downstream of Bcl11b that controls synapse morphology and plasticity of hippocampal mossy fiber synapses. Using an elegant combination of in vivo, ex vivo, and in vitro approaches, the authors build on their previous work that indicated C1ql2 as a functional target of Bcl11b (De Bruyckere et al., 2018). Here, they examine the functional implications of C1ql2 at MF synapses in Bcl11b cKO mice and following C1ql2 shRNA. The authors find that Bcl11b KO and shRNA against C1ql2 significantly reduces the recruitment of synaptic vesicles and impairs LTP at MF synapses. Importantly, the authors test a role for the previously identified C1ql2 binding partner, exon 25b-containing Nrxn3 (Matsuda et al., 2016), as relevant at MF synapses to maintain synaptic vesicle recruitment. To test this, the authors developed a K262E C1ql2 mutant that disrupts binding to Nrxn3. Curiously, while Bcl11b KO and C1ql2 KD largely phenocopy (reduced vesicle recruitment and impaired LTP), only vesicle recruitment is dependent on C1ql2-Nrxn3 interactions. These findings provide new insight into the functional role of C1ql2 at MF synapses. The authors utilize a multidisciplinary approach to convincingly demonstrate a role for C1ql2-Nrxn3(25b+) interactions for vesicle recruitment and a Nrxn3(25b+)-independent role for C1ql2 in LTP, The authors establish an important signaling pathway that offers insight into how disruptions of Bcl11b contribute to synapse dysfunction and provide a much needed advance toward understanding the functional consequences of neurexin alternative splicing.

Reviewer #2 (Public Review):

This manuscript describes experiments that further investigate the actions of the transcription factor Bcl11b in regulating mossy fiber (MF) synapses in the hippocampus. Prior work from the same group had demonstrated that loss of Bcl11b results in loss of MF synapses as well as a decrease in LTP. Here the authors focus on a target of Bcl11b a secreted synaptic organizer C1ql2 which is almost completed lost in Bcl11b KO. Viral reintroduction of C1ql2 rescues the synaptic phenotypes, whereas direct KD of C1ql2 recapitulates the Bcl1 phenotype. C1ql2 itself interacts directly with Nrxn3 and replacement with a binding deficient mutant C1q was not able to rescue the Bcl11b KO phenotype. Overall there are some interesting observations in the study, however there are also some concerns about the measures and interpretation of data.

The authors state they used a differential transcriptomic analysis to screen for candidate targets of Bcl11b, yet they do not present any details of this screen. This should be included and at the very least a table of all DE genes included. It is likely that many other genes are also regulated by Bcl11b so it would be important to the reader to see the rationale for focusing attention on C1ql2 in this study.

All viral mediated expression uses AAVs which are known to ablate neurogenesis in the DG (Johnston DOI: 10.7554/eLife.59291) through the ITR regions and leads to hyperexcitability of the dentate. While it is not clear how this would impact the measurements the authors make in MF-CA3 synapses, this should be acknowledged as a potential caveat in this study.

The authors claim that the viral re-introduction "restored C1ql2 protein expression to control levels. This is misleading given that the mean of the data is 2.5x the control (Figure 1d and also see Figure 6c). The low n and large variance are a problem for these data. Moreover, they are marked ns but the authors should report p values for these. At the least this likely large overexpression and variability should be acknowledged. In addition, the use of clipped bands on Western blots should be avoided. Please show the complete protein gel in primary figures of supplemental information.

Measurement of EM micrographs: As prior work suggested that MF synapse structure is disrupted the authors should report active zone length as this may itself affect "synapse score" defined by the number of vesicles docked. More concerning is that the example KO micrographs seem to have lost all the densely clustered synaptic vesicles that are away from the AZ in normal MF synapses e.g. compare control and KO terminals in Fig 2a or 6f or 7f. These terminals look aberrant and suggest that the important measure is not what is docked but what is present in the terminal cytoplasm that normally makes up the reserve pool. This needs to be addressed with further analysis and modifications to the manuscript.

The study also presents correlated changes in MF LTP in Bcl11b KO which are rescued by C1ql2 expression. It is not clear whether the structural and functional deficits are causally linked and this should be made clearer in the manuscript. It is also not apparent why this functional measure was chosen as it is unlikely that C1ql2 plays a direct role in presynaptic plasticity mechanisms that are through a cAMP/ PKA pathway and likely disrupted LTP is due to dysfunctional synapses rather than a specific LTP effect. The authors should consider measures that might support the role of Bcl11b targets in SV recruitment during depletion of synapses or measurements of the readily releasable pool size that would complement their finding in structural studies.

Bcl11b KO reduces the number of synapses, yet the I-O curve reported in Supp Fig 2 is not changed. How is that possible? This should be explained.

Matsuda et al DOI: 10.1016/j.neuron.2016.04.001 previously reported that C1ql2 organizes MF synapses by aligning postsynaptic kainate receptors with presynaptic elements. As this may have consequences for the functional properties of MF synapses including their plasticity, the authors should report whether they see deficient postsynaptic glutamate receptor signaling in the Bcl11b KO and rescue in the C1ql2 re-expression.

These are all addressed in the revised version.

Reviewer #3 (Public Review):

Overall, this is a strong manuscript that uses multiple current techniques to provide specific mechanistic insight into prior discoveries of the contributions of the Bcl11b transcription factor to mossy fiber synapses of dentate gyrus granule cells. The authors employ an adult deletion of Bcl11b via Tamoxifen-inducible Cre and use immunohistochemical, electron microscopy, and electrophysiological studies of synaptic plasticity, together with viral rescue of C1ql2, a direct transcriptional target of Bcl11b or Nrxn3, to construct a molecular cascade downstream of Bcl11b for DG mossy fiber synapse development. They find that C1ql2 re-expression in Bcl11b cKOs can rescue the synaptic vesicle docking phenotype and the impairments in MF-LTP of these mutants. They also show that C1ql2 knockdown in DG neurons can phenocopy the vesicle docking and plasticity phenotypes of the Bcl11b cKO. They also use artificial synapse formation assays to suggest that C1ql2 functions together with a specific Nrxn3 splice isoform in mediating MF axon development, extending these data with a C1ql2-K262E mutant that purports to specifically disrupt interactions with Nrxn3. All of the molecules involved in this cascade are disease-associated and this study provides an excellent blueprint for uncovering downstream mediators of transcription factor disruption. Together this makes this work of great interest to the field. Strengths are the sophisticated use of viral replacement and multi-level phenotypic analysis while weaknesses include the linkage of C1ql2 with a specific Nrxn3 splice variant in mediating these effects.

Here is an appraisal of the main claims and conclusions:

1. C1ql2 is a downstream target of Bcl11b which mediates the synaptic vesicle recruitment and synaptic plasticity phenotypes seen in these cKOs. This is supported by the clear rescue phenotypes of synapse anatomy (Fig.2) and MF synaptic plasticity (Fig.3). One weakness here is the absence of a control assessing over-expression phenotypes of C1ql2. It's clear from Fig.1D that viral rescue is often greater than WT expression (totally expected). In the case where you are trying to suppress a LoF phenotype, it is important to make sure that enhanced expression of C1ql2 in a WT background does not cause your rescue phenotype. A strong overexpression phenotype in WT would weaken the claim that C1ql2 is the main mediator of the Bcl11b phenotype for MF synapse phenotypes.

2. Knockdown of C1ql2 via 4 shRNAs is sufficient to produce the synaptic vesicle recruitment and MF-LTP phenotypes. This is supported by clear effects in the shRNA-C1ql2 groups as compared to nonsense-EGFP controls. One concern (particularly given the use of 4 distinct shRNAs) is the potential for off-target effects, which is best controlled for by a rescue experiment with RNA-insensitive C1ql2 cDNA as opposed to nonsense sequences, which may not elicit the same off-target effects.

3. C1ql2 interacts with Nrxn3(25b+) to facilitate MF terminal SV clustering. This claim is theoretically supported by the HEK cell artificial synapse formation assay (Fig.5), the inability of the K262-C1ql2 mutation to rescue the Bcl11b phenotype (Fig.6) and the altered localization of C1ql2 in the Nrxn1-3 deletion mice (Fig.7). Each of these lines of experimental evidence has caveats that should be acknowledged and addressed. Given the hypothesis that C1ql2 and Nrxn3b(25b) are expressed in DG neurons and work together, the heterologous co-culture experiment seems weird. Up till now, the authors are looking at pre-synaptic function of C1ql2 since they are re-expressing it in DGNs. The phenotypes they are seeing are also pre-synaptic and/or consistent with pre-synaptic dysfunction. In Fig.5, they are testing whether C1ql2 can induce pre-synaptic differentiation in trans, i.e. theoretically being released from the 293 cells "post-synaptically". But the post-synaptic ligands (Nlgn1 and and GluKs) are not present in the 293 cells, so a heterologous synapse assay doesn't really make sense here. The effect that the authors are seeing likely reflects the fact that C1ql2 and Nrxn3 do bind to each other, so C1ql2 is acting as an artificial post-synaptic ligand, in that it can cluster Nrxn3 which in turn clusters synaptic vesicles. But this does not test the model that the authors propose (i.e. C1ql2 and Nrxn3 are both expressed in MF terminals). Perhaps a heterologous assay where GluK2 is put into HEK cells and the C1ql2 and Nrxn3 are simultaneously or individually manipulated in DG neurons?

4. K262-C1ql2 mutation blocks the normal rescue through a Nrxn3(25b) mechanism (Fig.6). The strength of this experiment rests upon the specificity of this mutation for disrupting Nrxn3b binding (presynaptic) as opposed to any of the known postsynaptic C1ql2 ligands such as GluK2. While this is not relevant for interpreting the heterologous assay (Fig.5), it is relevant for the in vivo phenotypes in Fig.6. Similar approaches as employed in this paper can test whether binding to other known postsynaptic targets is altered by this point mutation.

5. Altered localization of C1ql2 in Nrxn1-3 cKOs. These data are presented to suggest that Nrx3(25b) is important for localizing C1ql2 to the SL of CA3. Weaknesses of this data include both the lack of Nrxn specificity in the triple a/b KOs as well as the profound effects of Nrxn LoF on the total levels of C1ql2 protein. Some measure that isn't biased by this large difference in C1ql2 levels should be attempted (something like in Fig.1F).

Author Response

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

eLife assessment

This work describes new validated conditional double KO (cDKO) mice for LRRK1 and LRRK2 that will be useful for the field, given that LRRK2 is widely expressed in the brain and periphery, and many divergent phenotypes have been attributed previously to LRRK2 expression. The manuscript presents solid data demonstrating that it is the loss of LRRK1 and LRRK2 expression within the SNpc DA cells that is not well tolerated, as it was previously unclear from past work whether neurodegeneration in the LRRK double Knock Out (DKO) was cell autonomous or the result of loss of LRRK1/LRRK2 expression in other types of cells. Future studies may pursue the biochemical mechanisms underlying the reason for the apoptotic cells noted in this study, as here, the LRRK1/LRRK2 KO mice did not replicate the dramatic increase in the number of autophagic vacuoles previously noted in germline global LRRK1/LRRK2 KO mice.

We thank the editors for handling our manuscript and for the succinct summary that recognizes the significance of our findings and points out interesting directions for future studies. We also thank the reviewers for their helpful comments and positive evaluation of our work. Below, we have provided point-by-point responses to the reviewers’ comments.

Reviewer #1 (Public Review):

Summary:

This is an important work showing that loss of LRRK function causes late-onset dopaminergic neurodegeneration in a cell-autonomous manner. One of the LRRK members, LRRK2, is of significant translational importance as mutations in LRRK2 cause late-onset autosomal dominant Parkinson's disease (PD). While many in the field assume that LRRK2 mutant causes PD via increased LRRK2 activity (i.e., kinase activity), it is not a settled issue as not all disease-causing mutant LRRK2 exhibit increased activity. Further, while LRRK2 inhibitors are under clinical trials for PD, the consequence of chronic, long-term LRRK2 inhibition is unknown. Thus, studies evaluating the long-term impact of LRRK deficit have important translational implications. Moreover, because LRRK proteins, particularly LRRK2, are known to modulate immune response and intracellular membrane trafficking, the study's results and the reagents will be valuable for others interested in LRRK function.

Strengths:

This report describes a mouse model where the LRRK1 and LRRK2 gene is conditionally deleted in dopaminergic neurons. Previously, this group showed that while loss of LRRK2 expression does not cause brain phenotype, loss of both LRRK1 and LRRK2 causes a later onset, progressive degeneration of catecholaminergic neurons and dopaminergic (DAergic) neurons in the substantia nigra (SN), and noradrenergic neurons in the locus coeruleus (LC). However, because LRRK genes are widely expressed with some peripheral phenotypes, it was unknown if the neurodegeneration in the LRRK double knockout (DKO) was cell autonomous. To rigorously test this question, the authors have generated a double conditional (cDKO) allele where both LRRK1 and LRRK2 genes were targeted to contain loxP sites. In my view, this was beyond what is usually required, as most investigators might might combine one KO allele with another floxed allele. The authors provide a rigorous validation showing that the Driver (DAT-Cre) is expressed in most DAergic neurons in the SN and that LRRK levers are decreased selectively in the ventral midbrain. Using these mice, the authors show that the number of DAergic neurons is normal at 15 but significantly decreased at 20 months of age. Moreover, the authors show that the number of apoptotic neurons is increased by ~2X in aged SN, demonstrating increased ongoing cell death, as well as an increase in activated microglia. The degeneration is limited to DAergic neurons as LC neurons are not lost as this population does not express DAT. Overall, the mouse genetics and experimental analysis were performed rigorously, and the results were statistically sound and compelling.

Weaknesses:

I only have a few minor comments. First is that in PD and other degenerative conditions, loss of axons and terminals occurs prior to cell bodies. It might be beneficial to show the status of DAergic markers in the striatum. Second, previous studies indicate that very little, if any, LRRK1 is expressed in SN DAergic neurons. This also the case with the Allen Brain Atlas profile. Thus, authors should discuss the discrepancy as authors seem to imply significant LRRK1 expression in DA neurons.

We appreciate the reviewer’s recognition of the importance of the study as well as our rigorous experimental approaches and compelling results. Our responses to the reviewer's two minor comments are below.

1) DAergic markers in the striatum: We performed TH immunostaining in the striatum and quantified TH+ DA terminals in the striatum of DA neuron-specific LRRK cDKO and littermate control mice at the ages of 15 and 24 months. We found similar levels of TH immunoreactivity in the striatum of LRRK cDKO and littermate control mice at the age of 15 months (p = 0.6565, unpaired Student’s t-test) and significantly reduced levels of TH immunoreactivity in the striatum of LRRK cDKO, compared to control mice at the age of 24 months (~19%, p = 0.0215), suggesting an age-dependent loss of dopaminergic terminals in the striatum of DA neuron-specific LRRK cDKO mice. These results are now included as Figure 5 of the revised manuscript.

2) LRRK1 expression in the SNpc: It is shown in the Mouse brain RNA-seq dataset and the Allen Mouse brain ISH dataset (https://www.proteinatlas.org/ENSG00000154237-LRRK1/brain) that LRRK1 is broadly expressed in the mouse brain and is expressed at modest levels in the midbrain, comparable to the cerebral cortex. Indeed, our Western analysis also showed that levels of LRRK1 detected in the dissected ventral midbrain and the cerebral cortex of control mice are similar (40µg total protein loaded per lane; Figure 2E). Furthermore, we previously demonstrated that deletion of LRRK2 (or LRRK1) alone does not cause age-dependent loss of DA neurons in the SNpc, but deletions of both LRRK1 and LRRK2 result in age-dependent loss of DA neurons in LRRK DKO mice, indicating the functional importance of LRRK1 in the protection of DA neuron survival in the aging mouse brain (Tong et al., PNAS 2010, 107: 9879-9884, Giaime et al., Neuron 2017, 96: 796-807).

Reviewer #2 (Public Review):

Summary:

In this manuscript, Shen and collaborators described the generation of cDKO mice lacking LRRK1 and LRRK2 selectively in DAT-positive DAergic neurons. The Authors asked whether selective deletion of both LRRK isoforms could lead to a Parkinsonian phenotype, as previously reported by the same group in germline double LRRK1 and LRRK2 knockout mice (PMID: 29056298). Indeed, cDKO mice developed a late reduction of TH+ neurons in SNpc that partially correlated with the reduction of NeuN+ cells. This was associated with increased apoptotic cell and microglial cell numbers in SNpc.

Unlike the constitutive DKO mice described earlier, however, cDKO mice did not replicate the dramatic increase in the number of autophagic vacuoles. The study supports the authors' hypothesis that loss of function rather than gain of function of LRRK2 leads to PD.

Strengths:

The study described for the first time a model where both the PD-associated gene LRRK2 and its homolog LRRK1 are deleted selectively in DAergic neurons, offering a new tool to understand the physiopathological role of LRRK2 and the compensating role of LRRK1 in modulating DAergic cell function.

Weaknesses:

The model has no construct validity since loss of function mutations of LRRK2 are well-tolerated in humans and do not lead to PD. The evidence of a Parkinsonian phenotype in these cDKO mice is limited and should be considered preliminary.

We thank the reviewer for commenting on the usefulness of this new PD mouse model.

The reviewer did not include a reference citation for the statement "loss of function mutations of LRRK2 are well-tolerated in humans and do not lead to PD." It is possible that the reviewer was referring to a human population study (Whiffin et al., Nat Med 2020, 26: 869-877), entitled "The effect of LRRK2 lossof-function variants in humans." In this study, the authors analyzed 141,456 individuals sequenced in the Genome Aggregation Database, 49,960 exome-sequenced individuals from the UK Biobank, and more than 4 million participants in the 23andMe genotyped dataset, and they looked for human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants). The reported findings were interesting, and the authors were careful in stating their conclusions. However, this is not a linkage study of large pedigrees carrying a single, clear-cut loss-of-function mutation (e.g. large deletions of most exons and coding sequences). Therefore, the experimental evidence is not compelling enough to conclude whether loss-of-function mutations in LRRK2 cause PD or do not cause PD.

The current report is an unbiased genetic study in an effort to reveal the normal physiological role of LRRK in dopaminergic neurons. It was not intended to produce Parkinsonian phenotypes in LRRK cDKO mice, which would be a biased effort. However, the unequivocal discovery of the cell intrinsic role of LRRK in the protection of DA neurons from age-dependent degeneration and apoptotic cell death should be considered seriously, while we contemplate the disease mechanism and how LRRK2 mutations may cause DA neuron loss and PD.

Reviewer #3 (Public Review):

Kang, Huang, and colleagues investigated the impact of LRRK1 and LRRK2 deletion, specifically in dopaminergic neurons, using a novel cDKO mouse model. They observed a significant reduction in DAergic neurons in the substantia nigra in their conditional LRRK1 and LRRK2 KO mice and a corresponding increase in markers of apoptosis and gliosis. This work set out to address a longstanding question within the field around the role and importance of LRRK1 and LRRK2 in DAergic neurons and suggests that the loss of both proteins triggers some neurodegeneration and glial activation.

The studies included in this work are carefully performed and clearly communicated, but additional studies are needed to strengthen further the authors' claims around the consequences of LRRK2 deletion in DAergic neurons.

1) In Figures 2E and F, the authors assess the protein levels of LRRK1 and LRRK2 in their cDKO mouse model to confirm the deletion of both proteins. They observe a mild loss of LRRK1 and LRRK2 signals in the ventral midbrain compared to wild-type animals. While this is not surprising given other cell types that still express LRRK1 and LRRK2 would be present in their dissected ventral midbrain samples, it does not sufficiently confirm that LRRK1 and LRRK2 are not expressed in DAergic neurons. Additional data is needed to more directly demonstrate that LRRK1 and LRRK2 protein levels are reduced in DAergic neurons, including analysis of LRRK1 and LRRK2 protein levels via immunohistochemistry or FACS-based analysis of TH+ neurons.

We thank the reviewer for highlighting this incredibly important but often overlooked issue. We agree that the data in Figure 2E, F alone would be inadequate to validate DA neuron-specific LRRK cDKO mice.

Cell type-specific conditional knockouts are a mosaic with KO cells mixed with other cell types expressing the gene normally. DA neuron-specific cDKO is particularly challenging, as DA neurons are a subset of cells embedded in the ventral midbrain. Rather than using immunostaining, which relies upon specific, good LRRK1 and LRRK2 antibodies for IHC, or FACS sorting of TH+ neurons followed by Western blotting (few cells, mixed cell populations, etc.), we chose a clean genetic approach by generating germline mutant mice carrying the deleted LRRK1 and LRRK2 alleles in all cells from the floxed LRRK1 and LRRK2 alleles. This approach permits characterization of these deletion mutations in germline mutant mice using molecular approaches that yield unambiguous results.

We crossed CMV-Cre deleter mice with floxed LRRK1 and LRRK2 mice to generate respective germline LRRK1 KO and LRRK2 KO mice, in which all cells carry the LRRK1 or LRRK2 deleted alleles that are identical to those in DA neurons of cDKO mice. We then performed Northern, extensive RTPCR followed by sequencing, and Western analyses to show the absence of the full length LRRK1 and LRRK2 mRNA (Figure 1G, H, Figure 1-figure supplement 8 and 10), and the expected truncation of LRRK1 and LRRK2 mRNA (Figure 1-figure supplement 9 and 11), and the absence of LRRK1 and LRRK2 proteins (Figure 1I). These analyses together demonstrate that in the presence of Cre, either CMV-Cre expressed in all cells or DAT-Cre expressed selectively in DA neurons, the floxed LRRK1 and LRRK2 exons are deleted, resulting in null alleles. We further demonstrated the specificity of DAT-Cremediated recombination (deletion) by crossing DAT-Cre mice with a GFP reporter, showing that 99% TH+ DA neurons in the SNpc are also GFP+ (Figure 2A, B), indicating that DAT-Cre-mediated recombination of the floxed alleles occurs in essentially all TH+ DA neurons in the SNpc.

2) The authors observed a significant but modest effect of LRRK1 and LRRK2 deletion on the number of TH+ neurons in the substantia nigra (12-15% loss at 20-24 months of age). It is unclear whether this extent of neuron loss is functionally relevant. To strengthen the impact of these data, additional studies are warranted to determine whether this translates into any PD-relevant deficits in the mice, including motor deficits or alterations in alpha-synuclein accumulation/aggregation.

Yes, the reduction of DA neurons in the SNpc of cDKO mice at the age of 20-24 months is modest. At 15 months of age, the number of TH+ DA neurons in the SNpc is similar between LRRK cDKO mice (10,000 ± 141) and littermate controls (10,077 ± 310, p > 0.9999). At 20 months of age, the number of DA neurons in the SNpc of LRRK cDKO mice (8,948 ± 273) is significantly reduced (-12.7%), compared to control mice (10,244 ± 220, F1,46 = 16.59, p = 0.0002, two-way ANOVA with Bonferroni’s post hoc multiple comparisons, p = 0.0041). By 24 months of age, the number of DA neurons in the SNpc of LRRK cDKO mice (8,188 ± 452) relative to controls (9,675 ± 232, p = 0.0010) is further reduced (15.4%).

Similar results were obtained by an independent quantification by another investigator, also conducted in a genotype blind manner, using the fractionator and optical dissector method, by which TH+ cells were quantified in 25% areas. These results are included as Figure 3-figure supplement 1 in the revised manuscript. Because of the more limited sampling, the quantification data are more variable, compared to quantification of TH+ cells in all areas of the SNpc, shown in Figure 3. With both methods, we quantified TH+ cells in every 10th sections encompassing the entire SNpc (3D structure), as sampling using every 5th or every 10th sections yielded similar results.

We also performed behavioral analysis of LRRK cDKO mice and littermate controls at the ages of 10 and 25 months using the beam walk test (10 mm and 20 mm beam) and the pole test, which are sensitive to impairment of motor coordination. We found that LRRK cDKO mice at 10 months of age showed significantly more hindlimb errors (p = 0.0005, unpaired two-tailed Student’s t-test) and longer traversal time (p = 0.0075) in the 10mm beam walk test, compared to control mice, though their performance is similar in the 20 mm beam walk (hindlimb slips: p = 0.0733, traversal time: p = 0.9796) and in the pole test. At 22 months of age, the performance of LRRK cDKO mice and littermate controls is more variable and worse, compared to the younger mice, and is not significantly different between the genotypic groups. These results are now included as Figure 9 of the revised manuscript.

3) The authors demonstrate that, unlike in the germline LRRK DKO mice, they do not observe any alterations in electron-dense vacuoles via EM. Given their data showing increased apoptosis and gliosis, it remains unclear how the loss of LRRK proteins leads to DAergic neuronal cell loss. Mechanistic studies would be insightful to understand better potential explanations for how the loss of LRRK1 and LRRK2 may impair cellular survival, and additional text should be added to the discussion to discuss potential hypotheses for how this might occur.

We agree that this phenotypic difference between germline DKO and DA neuron-specific cDKO mice is intriguing, suggesting a non-cell autonomous contribution of LRRK in age-dependent accumulation of autophagic and lysosomal vacuoles in SNpc neurons of germline LRRK DKO mice. We will discuss the phenotypic difference further in the revised manuscript. We are generating microglial specific LRRK cDKO mice to investigate the role of LRRK in microglia and whether microglia contribute in a cell extrinsic manner to the regulation of the autophagy-lysosomal pathway in DA neurons.

4) The authors discuss the potential implications of the neuronal cell loss observed in cDKO mice for LRRK1 and LRRK2 for therapeutic approaches targeting LRRK2 and suggest this argues that LRRK2 variants may exert their effects through a loss-of-protein function. However, all of the data generated in this work focus on a mouse in which both LRRK1 and LRRK2 have been deleted, and it is therefore difficult to make any definitive conclusions about the consequences of specifically targeting LRRK2. The authors note potential redundancy between the two LRRK proteins, and they should soften some of their conclusions in the discussion section around implications for the effects of LRRK2 variants. Human subjects that carry LRRK2 loss-of-function alleles do not have an increased risk for developing PD, which argues against the author's conclusions that LRRK2 variants associated with PD are loss-o-ffunction. Additional text should be included in their discussion to better address these nuances and caution should be used in terms of extrapolating their data to effects observed with PD-linked variants in LRRK2.

We will modify the discussion accordingly in the revised manuscript.

eLife assessment

This current revision builds on observations in validated conditional double KO (cDKO) mice for LRRK1 and LRRK2 that will be useful for the field, given that LRRK2 is widely expressed in the brain and periphery, and many divergent phenotypes have been attributed previously to LRRK2 expression. The manuscript presents solid data demonstrating that it is the loss of LRRK1 and LRRK2 expression within the SNpc DA cells that is not well tolerated, as it was previously unclear from past work whether neurodegeneration in the LRRK double Knock Out (DKO) was cell autonomous or the result of loss of LRRK1/LRRK2 expression in other types of cells. Future studies may pursue the biochemical mechanisms underlying the reason for the apoptotic cells noted in this study, as here, the LRRK1/LRRK2 KO mice did not replicate the dramatic increase in the number of autophagic vacuoles previously noted in germline global LRRK1/LRRK2 KO mice.

Reviewer #1 (Public Review):

Summary:<br /> This is an important work showing that loss of LRRK function causes late-onset dopaminergic neurodegeneration in a cell-autonomous manner. One of the LRRK members, LRRK2, is of significant translational importance as mutations in LRRK2 cause late-onset autosomal dominant Parkinson's disease (PD). While many in the field assume that LRRK2 mutant causes PD via increased LRRK2 activity (i.e., kinase activity), it is not a settled issue as not all disease-causing mutant LRRK2 exhibits increased activity. Further, while LRRK2 inhibitors are under clinical trials for PD, the consequence of chronic, long-term LRRK2 inhibition is unknown. Thus, studies evaluating the long-term impact of LRRK deficit have important translational implications. Moreover, because LRRK proteins, particularly LRRK2, are known to modulate immune response and intracellular membrane trafficking, the study's results and the reagents will be valuable for others interested in LRRK function.

Strengths:<br /> This report describes a mouse model where LRRK1 and LRRK2 genes are conditionally deleted in dopaminergic neurons. Previously, this group showed that while loss of LRRK2 expression does not cause brain phenotype, loss of both LRRK1 and LRRK2 causes a later onset, progressive degeneration of catecholaminergic neurons, Dopaminergic (DAergic) neurons in substantia niga (SN) and Noradrenergic neurons in Locus Coeruleus (LC). However, because LRRK genes are widely expressed with some peripheral phenotypes, it was unknown if the neurodegeneration in LRRK double Knock Out (DKO) was cell autonomous. To rigorously test this question, the authors have generated a double conditional KO allele where both LRRK1 and LRRK2 genes were targeted to contain loxP sites. In my view, this was beyond what is normally required as most investigators might just combine one KO allele with another floxed allele. The authors provide a rigorous validation showing that the Driver (DAT-Cre) is expressed in the majority of DAergic neurons in SN and that LRRK levels are decreased selectively in the ventral midbrain. Using these mice, the authors show that the number of DA neurons is average at 15 but significantly decreased at 20 months of age. Moreover, the authors show that the number of apoptotic neurons is increased by ~2X in aged SN, demonstrating increased ongoing cell death, as well as an increase in activated microglia. The degeneration is limited to DA neurons as LC neurons are not lost as this population does not express DAT. Overall, the mouse genetics and experimental analysis were performed in a rigorous manner and the results were statistically sound and compelling.

Weakness: I only have a few minor comments. First, in PD and other degenerative conditions, axons and terminals loss occurs prior to cell bodies. It might be beneficial to show the status of DAergic markers in the striatum. Second, previous studies indicate that very little, if any, LRRK1 is expressed in SN DAergic neurons. This also seems to be the case with the Allen Brain Atlas profile. Thus, it is preferable that authors discuss the discrepancy as authors seem to imply significant LRRK1 expression in DA neurons.

Revision: I appreciate the authors revising the manuscript with additional data that have clarified my prior comments. They now show that TH levels in the striatum decrease with SNpc neurons. Further, while there is some disagreement regarding the expression LRRK1 in SNpc, the authors provide a convincing case that LRRK1 function is important in SNpc DA neurons.

Reviewer #2 (Public Review):

Summary: In this manuscript, Shen and collaborators described the generation of conditional double knockout (cDKO) mice lacking LRRK1 and LRRK2 selectively in DAT positive dopaminergic neurons. The Authors asked whether selective deletion of both LRRK isoforms could lead to a Parkinsonian phenotype, as previously reported by the same group in germline double LRRK1 and LRRK2 knockout mice (PMID: 29056298). Indeed, cDKO mice developed a late reduction of TH+ neurons in SNpc that partially correlated with the reduction of NeuN+ cells. This was associated with increased apoptotic cell and microglial cell numbers in SNpc. Unlike the constitutive DKO mice described earlier, however, cDKO mice did not replicate the dramatic increase in the number of autophagic vacuoles. The study supports the authors' hypothesis that loss of function rather than gain of function of LRRK2 leads to Parkinson's Disease.

Strengths: The study described for the first time a model where both the Parkinson's disease-associated gene LRRK2 and its homolog LRRK1 are deleted selectively in dopaminergic neurons, offering a new tool to understand the physiopathological role of LRRK2 and the compensating role of LRRK1 in modulating dopaminergic cell function.

Weaknesses: The model has no construct validity since loss of function mutations of LRRK2 are well tolerated in humans and do not lead to Parkinson's disease. The evidence of a Parkinsonian phenotype in these conditional knockout mice is limited and should be considered preliminary.

Reviewer #3 (Public Review):

Kang, Huang, and colleagues have provided new data to address concerns regarding confirmation of LRRK1 and LRRK2 deletion in their mouse model and the functional impact of the modest loss of TH+ neurons observed in the substantia nigra of their double KO mice. In the revised manuscript, the new data around the characterization of the germline-deleted LRRK1 and LRRK2 mice add confidence that LRRK1 and LRRK2 can be deleted using the genetic approach. They have also added new text to the discussion to try and address some of the comments and questions raised regarding how LRRK1/2 loss may impact cell survival and the implications of this work for PD-linked variants in LRRK2 and therapeutic approaches targeting LRRK2.

The new data provides additional support for the author's claims. I have provided below some suggestions for clarification/additions to the text that can be addressed without additional experiments.

1) The authors added additional text highlighting that more studies are warranted in mice where LRRK1/2 are deleted in other CNS cell types (microglia/astrocytes) to understand cell extrinsic drivers of the autophagy deficits observed in their previous work. It still remains unclear how loss of LRRK1/2 leads to increased apoptosis and gliosis in dopaminergic neurons in a cell-intrinsic manner, and, as suggested in the original review, it would be helpful to add some text to the discussion speculating on potential mechanisms by which this might occur.

2) Revisions have been made to the discussion to clarify their rationale around how variants in LRRK2 associated with PD may be loss-of-function to support the relevance of this mouse model to phenotypes observed in PD. However, as written, the argument that PD-linked variants are loss-of-function is based on the fact that the double KO mice have a mild loss of TH+ neurons while the transgenic mice overexpressing PD-linked LRRK2 variants often do not and that early characterization of kinase activity was done in vitro are relatively weak. Given that the majority of evidence generated by many labs in the field supports a gain-of-function mechanism, the discussion should be further tempered to better highlight the uncertainty around this (rather than strongly arguing for a loss-of-function effect). This could include the mention of increased Rab phosphorylation observed in cellular and animal models and opposing consequences on lysosomal function observed in cellular studies in KO and pathogenic variant expressing cells. Further, a reference to the Whiffen et al. 2020 paper mentioned by another reviewer should be included in the discussion for completeness.