eLife assessment

This valuable study reports novel active learning batch selection methods that have been applied to optimization tasks related to ADMET and affinity properties relevant within the drug discovery field. While the evidence is solid, the paper could have benefited from a clearer and deeper description of methods as well as interpretation of the obtained models, and a wider comparison to existing methods. The article will be of general interest to scientist working in the field of drug discovery and, in general, to researchers within the fields of machine learning and data analysis.

Reviewer #1 (Public Review):

The authors present a study focused on addressing the key challenge in drug discovery, which is the optimization of absorption and affinity properties of small molecules through in silico methods. They propose active learning as a strategy for optimizing these properties and describe the development of two novel active learning batch selection methods. The methods are tested on various public datasets with different optimization goals and sizes, and new affinity datasets are curated to provide up-to-date experimental information. The authors claim that their active learning methods outperform existing batch selection methods, potentially reducing the number of experiments required to achieve the same model performance. They also emphasize the general applicability of their methods, including compatibility with popular packages like DeepChem.

Strengths:

Relevance and Importance: The study addresses a significant challenge in the field of drug discovery, highlighting the importance of optimizing absorption and affinity properties of small molecules through in silico methods. This topic is of great interest to researchers and pharmaceutical industries.

Novelty: The development of two novel active learning batch selection methods is a commendable contribution. The study also adds value by curating new affinity datasets that provide chronological information on state-of-the-art experimental strategies.<br /> Comprehensive Evaluation: Testing the proposed methods on multiple public datasets with varying optimization goals and sizes enhances the credibility and generalizability of the findings. The focus on comparing the performance of the new methods against existing batch selection methods further strengthens the evaluation.

Weaknesses:

Lack of Technical Details: The feedback lacks specific technical details regarding the developed active learning batch selection methods. Information such as the underlying algorithms, implementation specifics, and key design choices should be provided to enable readers to understand and evaluate the methods thoroughly.

Evaluation Metrics: The feedback does not mention the specific evaluation metrics used to assess the performance of the proposed methods. The authors should clarify the criteria employed to compare their methods against existing batch selection methods and demonstrate the statistical significance of the observed improvements.

Reproducibility: While the authors claim that their methods can be used with any package, including DeepChem, no mention is made of providing the necessary code or resources to reproduce the experiments. Including code repositories or detailed instructions would enhance the reproducibility and practical utility of the study.

Suggestions for Improvement:

Elaborate on the Methodology: Provide an in-depth explanation of the two active learning batch selection methods, including algorithmic details, implementation considerations, and any specific assumptions made. This will enable readers to better comprehend and evaluate the proposed techniques.

Clarify Evaluation Metrics: Clearly specify the evaluation metrics employed in the study to measure the performance of the active learning methods. Additionally, conduct statistical tests to establish the significance of the improvements observed over existing batch selection methods.

Enhance Reproducibility: To facilitate the reproducibility of the study, consider sharing the code, data, and resources necessary for readers to replicate the experiments. This will allow researchers in the field to validate and build upon your work more effectively.

Conclusion:<br /> The authors' study on active learning methods for optimizing drug discovery presents an important and relevant contribution to the field. The proposed batch selection methods and curated affinity datasets hold promise for improving the efficiency of drug discovery processes. However, to strengthen the study, it is crucial to provide more technical details, clarify evaluation metrics, and enhance reproducibility by sharing code and resources. Addressing these limitations will further enhance the value and impact of the research.

Reviewer #2 (Public Review):

The authors presented a well-written manuscript describing the comparison of active-learning methods with state-of-art methods for several datasets of pharmaceutical interest. This is a very important topic since active learning is similar to a cyclic drug design campaign such as testing compounds followed by designing new ones which could be used to further tests and a new design cycle and so on. The experimental design is comprehensive and adequate for proposed comparisons.

1) Text in figures still very small and difficult to read. Please redraw the figures increasing the font size: 10-12pt is ideal in comparison with the main text. In my opinion, it seems like the authors drew the Figure properly but there is an automatic resizing by inserting it in the document. Please consider ensuring that the font size will remain legible in the final document.

2) I think this work will benefit from a comparison of obtained models to other models reported in the literature and the interpretability of models (e.g. contribution of molecule groups to the modeled activity) as it is required by OECD guide for QSAR purposes.

eLife assessment

The authors provide an important series of metabolic measurements characterizing group dynamics in fish, rationalizing that schooling behavior presents several benefits. The strength of evidence supporting this conclusion is solid, but the specific methodological and analytical approaches taken should be considered for further interpretation.

Reviewer #2 (Public Review):

Summary:

This paper tests the idea that schooling can provide an energetic advantage over solitary swimming. The present study measures oxygen consumption over a wide range of speeds, to determine the differences in aerobic and anaerobic cost of swimming, providing a potentially valuable addition to the literature related to the advantages of group living.

Strengths:<br /> The strength of this paper is related to providing direct measurements of the energetics (oxygen consumption) of fish while swimming in a group vs solitary. The energetic advantages of schooling has been claimed to be one of the major advantages of schooling and therefore a direct energetic assessment is a useful result.

Weaknesses:

1) Regarding the fish to water volume ratio, the arguments raised by the authors are valid. However, the ratio used is still quite high (as high as >2000 in solitary fish), much higher than that recommended by Svendsen et al (2006). Hence this point needs to be discussed in the ms (summarising the points raised in the authors' response)

2) Wall effects: Fish in a school may have been swimming closer to the wall. The fact that the convex hull volume of the fish school did not change as speed increased is not a demonstration that fish were not closer to the wall, nor is it a demonstration that wall effect were not present. Therefore the issue of potential wall effects is a weakness of this paper.

3) The authors stated "Because we took high-speed videos simultaneously with the respirometry measurements, we can state unequivocally that individual fish within the school did not swim closer to the walls than solitary fish over the testing period". This is however not quantified.

4) Statistical analysis. The authors have dealt satisfactorily with most of the comments.<br /> However :<br /> (a) the following comment has not been dealt with directly in the ms "One can see from the graphs that schooling MO2 tends to have a smaller SD than solitary data. This may well be due to the fact that schooling data are based on 5 points (five schools) and each point is the result of the MO2 of five fish, thereby reducing the variability compared to solitary fish."<br /> (b) Different sizes were used for solitary and schooling fishes. The authors justify using larger fish as solitary to provide a better ratio of respirometer volume to fish volume in the tests on individual fish. However, mass scaling for tail beat frequency was not provided. Although (1) this is because of lack of data for this species and (2) using scaling exponent of distant species would introduce errors of unknown magnitude, this is still a weakness of the paper that needs to be acknowledged here and in the ms.

Reviewer #3 (Public Review):

Zhang and Lauder characterized both aerobic and anaerobic metabolic energy contributions in schools and solitary fishes in the Giant danio (Devario aequipinnatus) over a wide range of water velocities. By using a highly sophisticated respirometer system, the authors measure the aerobic metabolisms by oxygen uptake rate and the non-aerobic oxygen cost as excess post-exercise oxygen consumption (EPOC). With these data, the authors model the bioenergetic cost of schools and solitary fishes. The authors found that fish schools have a J-shaped metabolism-speed curve, with reduced total energy expenditure per tail beat compared to solitary fish. Fish in schools also recovered from exercise faster than solitary fish. Finally, the authors conclude that these energetic savings may underlie the prevalence of coordinated group locomotion in fish.

The conclusions of this paper are mostly well supported by data.

eLife assessment

This solid manuscript describes a preclinical model to assess different methods of infusion of organoids for clinical applications. This is an important and timely study with practical implications beyond a single subfield. The methods described, including the analysis, broadly support the claims although there are some areas for improvement.

Reviewer #1 (Public Review):

Summary:<br /> The authors describe their work on finding optimal ways of infusing organoids into mice. They describe five delivery methods and compare organoid survival two weeks after delivery. This work is concluded with the use of a vascularized chamber being the most optimal for organoid viability.

Strengths:<br /> The aim is to have a preclinical, translational model to test methods of organoid infusion. This is important and timely to the field.

Weaknesses:<br /> - A clear aim seems to be missing, although I can extract this from the manuscript. The approach is described a bit cryptically. The manuscript could use a bit more explanation here and there.<br /> - Although the authors themselves argue in the introduction that the use of mice is not optimal, they show a mouse study in which human-derived iPSC organoids are infused in mice.<br /> - As far as I can extract from the Methods section, only one iPSC line was used. Given the huge donor variance, it is essential to repeat the work with multiple iPSC lines.<br /> - I am missing the right control groups, especially for the surgical groups. And the group size is very variable (3 to 7 mice per group). Three per group is then somewhat small in size.

Reviewer #2 (Public Review):

Summary:<br /> In this study, human induced pluripotent stem cell (hiPSC)-derived liver progenitor cell organoids were transplanted into four different transplantation sites in a mouse model of liver disease, using five organoid delivery methods. Organoids were transplanted into the vascularised chamber device established in the groin, or into the liver, spleen, and subcutaneous fat. Results show that the vascularised chamber had the highest organoid survival, 5.1x higher than the site with the second highest survival (p=0.0002), being the intra-hepatic scaffold approach. Animals with the vascularised chamber also had the highest human albumin levels (0.33 {plus minus} 0.09 ng/mL). No organoid survival was observed when delivered into the liver without a scaffold, or when injected into the spleen. Meager survival occurred in transplantations into subcutaneous fat.

Strengths:<br /> A systematic study with a clear line of experiments and well-presented results. The manuscript is well-written and easy to follow. The results and conclusions drawn are convincing.

Weaknesses:<br /> Although the number of organoids and albumin secretion is visibly higher in the vascularised chamber device, the organoids possess relatively higher Sox9+ cells compared to HNFa4a+ cells suggesting higher biliary differentiation than hepatic differentiation. On the other hand, although the intrahepatic scaffold approach, with a relatively smaller number of organoids and less albumin secretion, showed higher hepatic differentiation (although non-significant) suggesting that better scaffolds could be researched further to assess the clinical application of intrahepatic scaffold-based organoid transplantation.

eLife assessment

This important study reports a new mutant mouse line with compromised function of a DNA damage response protein. The evidence supporting the conclusion that the mutants display defective maintenance of meiotic sex chromosome inactivation is solid. This work is of interest to biomedical researchers working on meiosis and meiotic sex chromosome inactivation.

Reviewer #1 (Public Review):

This is a very well-written and performed study describing a TOPBP1 separation of function mutation, resulting in defective MSCI maintenance but normal sex body formation. The phenotype differs from that of a previous TOPBP1 null allele, in which both MSCI and sex body formation were defective. Additional defects in CHK phosphorylation and SETX localization are also described.

Strengths:

The study is very rigorous, with a remarkably large number spectrum of techniques deployed to support the conclusions

Weaknesses

The study claims that MSCI is initiated but not maintained in the mutant. I think alternative hypotheses are possible.

Reviewer #2 (Public Review):

Summary:<br /> This paper described the role of BRCT repeat 5 in TOPBP1, a DNA damage response protein, in the maintenance of meiotic sex chromosome inactivation (MSCI). By analyzing a Topbp1 mutant mouse with amino acid substitutions in BRCT repeat 5, the authors found reduced phosphorylation of a DNA/RNA helicase, Sentaxin, and decreased localization of the protein to the X-Y sex body in pachynema. Moreover, the authors also found decreased repression of several genes on the sex chromosomes in the male mice.

Strengths:<br /> The works including phospho-proteomics and single-cell RNA sequencing with lots of data have been done with great care and most of the results are convincing.

Weaknesses:<br /> No weakness.

Reviewer #3 (Public Review):

The work presented by Ascencao and coworkers aims to deepen into the process of sex chromosome inactivation during meiosis (MSCI) as a critical factor in the regulation of meiosis progression in male mammals. For this purpose, they have generated a transgenic mouse model in which a specific domain of TOPBP1 protein has been mutated, hampering the binding of a number of protein partners and interfering with the regulatory cascade initiated by ATR. Through the use of immunolocalization of an impressive number of markers of MSCI, phosphoproteomics and single cell RNA sequencing (scRNAseq), the authors are able to show that despite a proper morphological formation of the sex body and the incorporation of most canonical MSCI makers, sex chromosome-liked genes are reactivated at some point during pachytene and this triggers meiosis progression breakdown, likely due to a defective phosphorylation of the helicase SETX.<br /> The manuscript presents a clear advance in the understanding of MSCI and meiosis progression with two main strengths. First, the generation of a mouse model with a very uncommon phenotype. Second, the use of a vast methodological approach. The results are well presented and illustrated. Nevertheless, the discussion could be still a bit tuned by the inclusion of some ideas, and perhaps speculations, that have not been considered.

eLife assessment

In this important study, a mathematical model to predict biological age by leveraging physiological traits across multiple organ systems is developed. The results presented are convincing, utilizing comprehensive data-driven approaches, although additional external validation would further strengthen its generalizability. The model provides a way to identify environmental and genetic factors impacting aging and lifespan, revealing new factors potentially affecting aging and it also shows promise for evaluating therapeutics aimed at prolonging a healthy lifespan.

Reviewer #1 (Public Review):

Summary:

In this study, the authors developed a mathematical model to predict human biological ages using physiological traits. This model provides a way to identify environmental and genetic factors that impact aging and lifespan.

Strengths:

1. The topic addressed by the authors - human age predication using physiological traits - is an extremely interesting, important, and challenging question in the aging field. One of the biggest challenges is the lack of well-controlled data from a large number of humans. However, the authors took this challenge and tried their best to extract useful information from available data.

2. Some of the findings can provide valuable guidelines for future experimental design for human and animal studies. For example, it was found that this mathematical model can best predict age when all different organ and physiological systems are sampled. This finding makes sense in general but can be, and has been, neglected when people use molecular markers to predict age. Most of those studies have used only one molecular trait or different traits from one tissue.

Weaknesses:

1. As I mentioned above, the Biobank data used here are not designed for this current study, so there are many limitations for model development using these data, e.g., missing data points and irrelevant measurements for aging. This is a common caveat for human studies and has been discussed by the authors.

2. There is no validation dataset to verify the proposed model. The authors suggested that human biological age can be predicted with high accuracy using 12 simple physiological measurements. It will be super useful and convincing if another biobank dataset containing those 12 traits can be applied to the current model.

Reviewer #2 (Public Review):

In this manuscript, Libert et al. develop a model to predict an individual's age using physiological traits from multiple organ systems. The difference between the predicted biological age and the chronological age -- ∆Age, has an effect equivalent to that of a chronological year on Gompertz mortality risk. By conducting GWAS on ∆Age, the authors identify genetic factors that affect aging and distinguish those associated with age-related diseases. The study also uncovers environmental factors and employs dropout analysis to identify potential biomarkers and drivers for ∆Age. This research not only reveals new factors potentially affecting aging but also shows promise for evaluating therapeutics aimed at prolonging a healthy lifespan. This work represents a significant advancement in data-driven understanding of aging and provides new insights into human aging. Addressing the points raised would enhance its scientific validity and broaden its implications.

Major points:

1. Enhance the description and clarity of model evaluation.

The manuscript requires additional details regarding the model's evaluation. The authors have stated "To develop a model that predicts age, we experimented with several algorithms, including simple linear regression, Gradient Boosting Machine (GBM) and Partial Least Squares regression (PLS). The outcomes of these approaches were almost identical". It is currently unclear whether the 'almost identical outcomes' mentioned refer to the similarity in top contribution phenotypes, the accuracy of age prediction, or both. To resolve this ambiguity, it would be beneficial to include specific results and comparisons from each of these models.

Furthermore, the authors mention "to test for overfitting, a PLS model had been generated on randomly selected 90% of individuals and tested on the remaining 10% with similar results". To comprehensively assess the model's performance, it is crucial to provide detailed results for both the test and validation datasets. This should at least include metrics such as correlation coefficients and mean squared error for both training and test datasets.

2. External validation and generalization of results

To enhance the robustness and generalizability of the study's findings, it is crucial to perform external validation using an independent population. Specifically, conducting validation with the participants of the 'All of Us' research program offers a unique opportunity. This diverse and extensive cohort, distinct from the initial study group, will serve as an independent validation set, providing insights into the applicability of the study's conclusions across varied demographics.

eLife assessment

This study documents important findings on three variants in SNAP25 that are associated with developmental and epileptic encephalopathy. The thorough characterization of synaptic release and in vitro vesicle fusion phenotypes provides interesting information about the nature of the SNAP25 variants. The evidence supporting the claims is compelling, and this work will be of interest to neuroscientists working on SNAP25, SNAP25-associated encephalopathy, and synaptic vesicle exocytosis.

Reviewer #1 (Public Review):

The manuscript by Kadkova et al. describes an electrophysiological analysis of 3 neurodevelopmental disease-causing SNAP-25 mutations in hippocampal neuron autaptic cultures. The work expands on a prior study of these 3 mutations, along with several others in SNAP-25, that was performed in acutely dissociated hippocampal cultures by another group (Alten et al, 2021). Most of the physiology defects found are pretty similar for the 3 mutations the two research groups characterized, with differences largely found in the effects on the size of the readily releasable pool (RRP) of SVs. These differences could be due to technical differences in the approach but are also likely to reflect in part differences in autapses as a model that have been previously described. In addition to the physiological analysis in cultured neurons, the current work extends the analysis beyond the prior study by analyzing the effects of these SNAP-25 mutations in in vitro liposome fusion assays with purified proteins, and some modeling of the effects on energy landscapes during priming and fusion.

The authors use lentiviral expression of wildtype or one of the 3 mutants in SNAP-25 autaptic neurons and assay neuronal survival and synaptic output. The authors also combine wildtype with each of the 3 mutants as well, given these diseases manifest as spontaneous mutations in only 1 of the SNAP-25 alleles, suggesting a dominant effect. The authors observe that the V48F and D166Y alleles (that are suggested to disrupt the Syt1-SNAP-25/SNARE interface) result in a very large increase in spontaneous release that exceeds the Syt1 null mutant alone, suggesting an effect on spontaneous SV release beyond a lack of Syt1 regulation of SNARE-mediated fusion. In contrast, Syt1 nulls have a much more severe loss of evoked release, through both V48F and D166Y also have modest decreases in release. They find both mutants also cause a decrease in the RRP. Applying some modeling for these results, the authors suggest V48F and D166Y lowers the energy barrier for fusion, creating the enhanced spontaneous release rates and causing a decrease of the RRP. They also find evidence for reduced SV priming. In contrast, a SNAP-25 I167N disease mutation in the SNARE assembly interaction layer causes dramatic decreases in both evoked and spontaneous release, consistent with a disruption to SNARE assembly/stability. In vitro fusion assays with these mutant SNAP-25 alleles was also done and provided supportive evidence for these interpretations for all 3 alleles. The ability to control calcium, Syt1, PIP2 and Complexin levels in the in vitro assays provided additional information on defining the precise steps of the fusion process these mutations disrupt. Together, the study indicates the I167N mutation acts as a dominant-negative allele to block fusion, while the other two alleles have both loss- and gain-of function properties that cause more complex disruptions that decrease evoked release while dramatically enhancing spontaneous fusion.

Overall, these results build on prior work and shed light on how disruptions to the SNAP-25 t-SNARE alter the process of SV priming and fusion.

Reviewer #2 (Public Review):

Kádková, Murach, Pedersen, and colleagues studied how three disease-causing missense mutations in SNAP25 affect synaptic vesicle exocytosis. These mutations have previously been studied by Alten et al., 2021. The authors observed similar impairments in spontaneous and evoked release as Alten et al., 2021, but the measurement of readily releasable pool (RRP) size differed between the two studies. The authors found that the V48F and D166Y mutations affect the interaction with the Ca2+ sensor synaptotagmin-1 (Syt1), but do not entirely phenocopy Syt1 loss-of-function because they also exhibit a gain-of-function. Thus, these mutations affect multiple aspects of the energy landscape for vesicle priming and fusion. The I67N mutation specifically increases the fusion energy barrier without affecting upstream vesicle priming.

The strength of the study includes careful and technically excellent dissection of the synaptic release process and a combination of electrophysiology, biophysics, and modeling approaches. This study gained a deeper mechanistic understanding of these mutations in vesicle exocytosis than the previous study but did not result in a paradigm shift in our understanding of SNAP25-associated encephalopathy because the same spontaneous and evoked release phenotypes were previously identified.

Comments on revised version:

The authors fully addressed the two previous technical concerns and improved the introduction of the paper.

eLife assessment

The authors use human intracranial recordings to investigate the relationship between the power of brain oscillations and the latency and strength of cortico-cortical couplings. In the current version, the authors provide a valuable finding that the delay between nearby electrodes in ECoG data is correlated with the amplitude of power, differently so for high and low frequencies. The findings of this study will interest investigators in the wider field of systems neurophysiology; however, editors and reviewers perceived headroom for improving clarity in the presentation of analyses and results, and the strength of evidence for some of the claims as currently presented was viewed as incomplete.

Reviewer #1 (Public Review):

Summary:<br /> Moon et al analyse ECoG data obtained during speech listening and focus on the relationship of two aspects: 1) delays between voltage signals at individual electrodes to other electrodes in the vicinity and 2) the power of those signals in a range of spectral bands. They find that high power in frequencies below 30 Hz is correlated with longer delays. They further look for this pattern of results in an oscillator model.

Strengths:<br /> The manuscript examines whether a finding made in cats in the late 90s generalises to intracranial recordings from humans. Specifically, the amplitude of low-frequency oscillations should be related to the delay of cross-correlation between areas. The authors find evidence for such a relationship and show this in individual participants. After inspecting this phenomenon from many different angles, they also added an oscillator model and claimed that they found a similar pattern there. As such, the manuscript reports an extensive body of work carried out on high-quality data.

Weaknesses:<br /> The manuscript's readability and flow could be optimised: terms are used that aren't explained, and the structure seems somewhat convoluted. Showing single-subject results is laudable, however, the authors could consider adding group results that integrate across participants, and perhaps relaying single-participant plots to the supplemental material. The manuscript would benefit if analyses were motivated more clearly. Sometimes, I am unsure why a given analysis was carried out, why it was carried out in a specific way, and what question it was intended to answer.

Reviewer #2 (Public Review):

Summary:<br /> In the paper "Inter-regional Delays Fluctuate in the Human Cerebral Cortex," the authors aim to investigate how global changes in the power of brain oscillations affect the latency and strength of cortico-cortical couplings. They measured changes in brain oscillations and inter-regional couplings using human intracranial recordings. Additionally, the authors employed oscillator models to elucidate their empirical findings.

Strengths:<br /> The authors tested their hypotheses using human intracranial data, which provides a direct measurement of brain activity with high spatial and temporal resolution. This offers a unique insight into the interplay between oscillatory power and inter-regional coupling in the human brain.

Weaknesses:<br /> The authors had access to only a subset of brain regions. Although this limitation is common in many intracranial studies, their discussion of global changes in brain oscillations is impacted by the lack of whole-brain coverage, and thus the global nature of these oscillations should be interpreted with caution.

The description of the analysis procedure is not always clear.

Summary of main concerns:<br /> My primary concerns relate to possible circularity in the analysis and the incomplete reporting of statistical results. For instance, correlation values are often provided without associated p-values, making it difficult to assess their significance. Furthermore, in some sections of the text, it is unclear whether specific results are supported by any statistical tests.

Crucial information is buried in the supplemental materials (e.g., the figure showing results for broad-band high-frequency power). Some details about the specific paradigm are missing in the methods section, making it challenging to determine if additional controls are necessary in the analyses. I encourage the authors to clarify certain aspects of the analysis and results to ensure their conclusions are substantiated by the data. Should the results be robust, I believe the study will be significant for researchers interested in brain oscillations and beyond.

Reviewer #3 (Public Review):

Summary:<br /> This is my assessment of the manuscript entitled "Inter-regional delays fluctuate in the human cerebral cortex" submitted by Moon et al. to eleventh article deals with an interesting question, namely: how do different areas in the brain synchronize with each other. As the title indicates, the article shows that interregional activity can be more or less out of sync, and that the degree of synchronicity depends on the global power of low and high-frequency oscillations.

Overall, I found the paper interesting, although, as written, it is sometimes not clear why studying these inter-regional delays is important. For a broader audience, it is necessary to better emphasize the relevance of inter-regional delays, and what we learn from studies like this beyond the mechanistic aspect of how waves spread in the human brain. Also, it is important to explain why the task (listening to audio) was chosen, and what this task offers in comparison to, for example, studying spontaneous activity. I understand that intra-cranial data from humans is precious and difficult to obtain, so I am not asking for more data, just for a clear honest explanation of why this task was chosen.

Beyond these minor formatting issues, I have two main concerns on the data analysis and interpretation. In a nutshell, they deal with:

- Cross-correlating alpha power with inter-electrode lags computed from raw signals where alpha itself is included. IMO this could lead to obvious high correlation values simply because low-frequency signals spread passively (with some delays) across electrodes. High-frequency signals spread less and are thus less correlated in neighboring electrodes.

- Possible influence of the referencing scheme on the data. I could not find any information about where reference and ground electrodes were located but I fear that epochs of zero-lag coherence could be simply due to common referencing. Non-zero lag synchrony could be explained by generators becoming more or less active close to the recording electrodes. This is probably the most parsimonious explanation of the activity observed and explaining it does not require any coupled oscillators.

Strengths:<br /> The paper relies on a strong dataset from intracranial recordings in humans. Conceptually the paper has strong value as it seeks to explore global and local activity dynamics within the human brain.

Weaknesses:<br /> There are a number of methodological issues that need to be clarified, which could potentially influence the results obtained and their interpretation (i.e. corr-correlating alpha with itself, the influence of the referencing scheme on inter-electrode lags).

eLife assessment

This important study examines the relationship between expiratory airflow and vocal pitch in adult mice during the production of ultrasonic vocalizations and also identifies a molecularly defined population of brainstem neurons that regulates mouse vocal production across development. The evidence supporting the study's conclusions that expiratory airflow shapes vocal pitch and that these brainstem neurons preferentially regulate expiratory airflow is incomplete and would benefit from the inclusion of additional analyses and discussion. This work will be of interest to neuroscientists working on mechanisms and brainstem circuits that regulate vocal production and vocal-respiratory coordination.

Reviewer #1 (Public Review):

Summary:<br /> In this important work, the authors propose and test a model for the control of murine ultrasonic vocalizations (USV) in which two independent mechanisms, involving changes in laryngeal opening or airflow, control vocal tone. They present compelling experimental evidence for this dual control model by demonstrating the ability of freely behaving adult mice to generate vocalizations with various intonations by modulating both the breathing pattern and the laryngeal muscles. They also present novel evidence that these mechanisms are encoded in the brainstem vocalization central neural pattern generator, particularly in the component in the medulla called the intermediate reticular oscillator (iRO). The results presented clearly advance understanding of the developmental nature of the iRO, its ability to intrinsically generate and control many of the dynamic features of USV including those related to intonation, and its coordination with/control of expiratory airflow patterns. This work will interest neuroscientists investigating the neural generation and control of vocalization, breathing, and more generally, neuromotor control mechanisms.

Strengths:<br /> Important features and novelty of this work include:

1) The study employs an effective combination of anatomical, molecular, and functional/ behavioral approaches to examine the hypothesis and provide novel data indicating that variations in expiratory airflow can change the pitch patterns of adult murine USV.

2) The results significantly extend the authors' previous work that identified the iRO in neonatal mice by now presenting data that functionally demonstrates the existence of the critical Penk+Vglut2+ iRO neurons in adult mice, indicating that the iRO neurons maintain their function in generating vocalization throughout development.

3) The results convincingly demonstrate that the iRO neurons encode and can generate vocalizations by modulating both breathing and the laryngeal muscles.

4) The anatomical mapping and tracing results establish an important set of input and output circuit connections to the iRO, including input from the vocalization-promoting subregions of the midbrain periaqueductal gray (PAG), as well as output axonal projections to laryngeal motoneurons, and to the respiratory rhythm generator in the preBötzinger complex.

5) These studies advance the important concept that the brainstem vocalization pattern generator integrates with the medullary respiratory pattern generator to control expiratory airflow as a key mechanism to produce various USV types characterized by different pitch patterns.

Weaknesses:<br /> A limitation is that the cellular and circuit mechanisms by which the vocalization pattern generator integrates with the respiratory pattern generator to control expiratory airflow have not been fully worked out, requiring future studies.

Reviewer #2 (Public Review):

Summary:<br /> Both human and non-human animals modulate the frequency of their vocalizations to communicate important information about context and internal state. While regulation of the size of the laryngeal opening is a well-established mechanism to regulate vocal pitch, the contribution of expiratory airflow to vocal pitch is less clear. To consider this question, this study first characterizes the relationship between the dominant frequency contours of adult mouse ultrasonic vocalizations (USVs) and expiratory airflow using whole-body plethysmography. Next, the authors build off of their previous work characterizing intermediate reticular oscillator (iRO) neurons in mouse pups to establish the existence of a genetically similar population of neurons in adults and show that artificial activation of iRO neurons elicits USV production in adults. Third, the authors examine the acoustic features of USV elicited by optogenetic activation of iRO and find that a majority of natural USV types (as defined by pitch contour) are elicited by iRO activation.

Strengths:<br /> Strengths of the study include the novel consideration of expiratory airflow as a mechanism to regulate vocal pitch and the use of intersectional methods to identify and activate the iRO in adult mice. The establishment of iRO neurons as a brainstem population that regulates vocal production across development is an important finding.

Weaknesses:<br /> The study does not include statistical analyses to compare the observed relationships between expiratory airflow and USV pitch to a null model in which expiratory airflow and USV pitch are unrelated. The findings of the study also do not provide clear evidence to support the authors' model in which distinct brainstem populations (iRO and RAm) independently regulate expiratory airflow and laryngeal adduction. Although this study establishes iRO as an important population that regulates USV production in adult mice, the question of whether and how different brainstem populations contribute differentially to vocal production remains an important open question. Lastly, the addition of statistical analyses would help to strengthen the study's conclusion that iRO activation positively biases the relationship between expiratory airflow and USV pitch across multiple USV types.

eLife assessment

This study presents a valuable exploration of the complex relationship between structure and function in the developing human brain using a large-scale imaging dataset from the Human Connectome Project in Development and gene expression profiles from the Allen Brain Atlas. The evidence supporting the claims of the authors is convincing, although the inclusion of more systematic analyses of structural and functional connectivity with respect to myelin measures and oligodendrocyte-related genes, and also more details regarding the imaging analyses, cognitive scores, and design and validation strategies, would have strengthened the paper. The work will be of interest to developmental biologists and neuroscientists seeking to elucidate structure-function relationships in the human brain.

Reviewer #1 (Public Review):

Summary:<br /> This work studies spatiotemporal patterns of structure-function coupling in developing brains, using a large set of imaging data acquired from children and young adults aged 5-22. Magnetic resonance imaging data of brain structure and function were obtained from a publicly available database, from which structural and functional features and measures were derived. The authors examined the spatial patterns of structure-function coupling and how they evolve with brain development. This work further examined correlations between brain structure-function coupling and behaviour, and explored evolutionary, microarchitectural and genetic bases that could potentially account for the observed patterns.

Strengths:<br /> The strength of this work is the use of currently available state-of-the-art analysis methods, along with a large set of high-quality imaging data, and comprehensive examination of structure-function coupling in developing brains. The results are comprehensive and illuminative.

Weakness:<br /> As in most other studies, transcriptomic and cellular architectures of structure-function coupling were characterized only on the basis of a common atlas in this work.

The authors have achieved their aims in this study, and the findings provide mechanistic insights into brain development, which could inspire further basic and clinical studies along this line.

Reviewer #2 (Public Review):

Summary:<br /> Feng et al. investigated dynamic changes in functional and structural connectivity relationships across a broad age range from childhood to early adulthood (6-22 years) using the large open-source HCP-Development database of multimodal magnetic resonance imaging (MRI). Employing a multilinear model, the study integrates three white-matter structural descriptors derived from diffusion tractography with 'microstructure profile covariance' (MPC) descriptors of relationships between cortical regions in terms of regional T1w/T2w ratio, and evaluates the coupling between these structural connectome (SC) descriptors and functional connectivity (FC) as adjusted coefficients of determination, i.e. how well the structural descriptors correspond to the functional connectivity derived from resting-state functional MRI.

The findings reveal a global increase in SC-FC coupling over development. At a regional level, coupling exhibited distinct profiles of age-related increases and decreases within and between functional networks. Individual variability captured by the presented measures of SC-FC coupling was implicated as a potential marker for the prediction of general intelligence scores. Additionally, the investigation extended to associating changes in SC-FC coupling with age to regional gene expression profiles (derived from Allen Human Brain Atlas that analysed six neurotypical adult brains), suggesting positive associations with oligodendrocyte-related pathways and negative associations with astrocyte-related genes.

Strengths:<br /> Overall, the paper offers an interesting and valuable contribution to our understanding of structure-function relationships in the context of brain development. The commendable efforts to assess robustness across various methodologies, including brain parcellation and tractography, and reproducibility analyses on different data subsets enhance the paper's credibility. Combining cortical MPC with more usual white-matter descriptors of structural connectivity is interesting and provides (potentially) complementary information for the study of structure-function relationships with age. Analysing the changes in SC-FC coupling in relation to profiles of evolutionary expansion and functional principal gradients shows a good effort to position the present observations on SC-FC coupling within the previously described work.

Weaknesses:<br /> Although the paper has many strengths, some aspects of the analysis need to be clarified to further support the proposed conclusions. In particular:

* The authors propose that combining cortical and white-matter connectivity measures yields a more comprehensive descriptor of SC-FC coupling. While this is likely true, the claim is not directly tested by assessing different descriptors separately and then in combination to compare the benefits of incorporating additional information for the description of SC-FC coupling.

* The authors report changes in SC-FC coupling with myelin content (reporting a positive association of coupling with regional myelin) and report positive associations between SC-FC correlation with age and expression of oligodendrocyte-related genes. Given that the computation of SC-FC coupling involves the T1w/T2w ratios within cortical regions (recognised as a myelin marker), it's plausible that these findings may be influenced by potential bias introduced by myelin-related measures in the coupling computation process.

* The authors investigate the predictive power of SC-FC coupling, suggesting non-random (but weak) prediction of individual variability in general intelligence (after age correction). However, again the benefit of using SC-FC coupling measures over using each modality alone is not evaluated. Such comparison might indicate whether the coupling is an informative measure in itself or whether it might be informative only to the extent to which it is a proxy measure of SC and FC (in case the predictive power of each separate modality is much higher).

* Generally, more information on quality assessment of tractography and parcellations (including potential age effects on processing given the wide age range of the participants), additional details on the distribution of cognitive scores used in the predictive section, and further clarifications regarding the design choices and validation strategy would provide the reader with a more detailed understanding of the cohort and proposed analytical pipeline (these minor comments are included in the private recommendations to authors).

eLife assessment

The authors investigated the requirement and function of Blimp1/Prdm1 in murine natural killer (NK) cells and the ILC1 lineage of innate lymphoid cells, using a conditional knockout model. The single-cell mRNA-seq data provided here represent a valuable resource for the community, but the lack of mechanistic investigations leaves the study incomplete. The work will be of interest to the fields of innate lymphoid cell biology and tissue immunology.

Reviewer #1 (Public Review):

He et al. investigate the requirement and function of Blimp1 (encoded by Prdm1) in murine NK cells and ILC1. Employing a conditional knockout mouse model (Prdm1flox x Ncr1cre), the authors describe impaired abundance and maturation of Prdm1-deficient NK cells and ILC1 in different tissues. Blimp1-deficient NK cells have reduced expression of cytotoxic molecules (Gzmb, Prf1) and, in some instances, Ifng production, and Prdm1flox x Ncr1cre mice show impaired tumor control in experimental metastasis models. Using single-cell RNA sequencing analysis, the authors propose that Prdm1 regulates JunB expression and NK cell maturation. Based on in silico analyses, the authors suggest manifold intercellular communication between NK/ILC1 and macrophages. Without following up on any of these potentially interesting suggestions, the authors conclude their study reiterating that Prdm1 regulates IFNg-production of tumor-infiltrating NK cells and ILC1.

Many of the reported functions of Blimp1 in NK cells have previously been identified using a mixed-chimera strategy comparing Prdm1 WT and KO NK cells (Kallies et al., Blood 2011). Here, the authors expand on these findings using a conditional model to delete Prdm1 in NK/ILC1 and single-cell sequencing and provide a more refined analysis of the functions of Blimp1 in these cells. Cell-chat analysis suggests close interactions of Blimp-dependent NK/ILC1 subsets with hepatic macrophages, but these suggestions are not followed up by experiments. Potentially interesting differences in the macrophage compartment of Ncr1-Cre x Prdm1-fl/fl mice are suggested by the scc-RNA-Seq data but are not validated e.g. by FACS. The study falls short in providing new mechanistic insights. Nevertheless, it is an interesting confirmation of "old" suggestions in a more refined setting, and the provided single-cell mRNA-Seq data represents a potentially valuable resource for the community. There are some control analyses that are required to support the conclusions of the authors, and I have a few suggestions that would help to improve the manuscript.

Major comments:

- The authors do not control for the potential effects of Cre expression. Expression of Cre from within the Ncr1 locus (using the mouse model established by Narni-Mancinelli et al.) has significant effects on NK cells and especially ILC1s (reducing their frequency and absolute numbers and altering their functionality. The authors should characterize the Ncr1cre mice used here (developed by Shanghai Model Organism Center) in this regard and should use proper controls (Ncr1Cre+ Prdm1wt/wt as control for Ncr1Cre+ Prdm1fl/fl, instead of WT littermates) for all of their key data, e.g. those depicted in Fig 1FG, 2ADFH, 7D, S2,3,4.

- Several of the phenotypic findings on NK cells have been described before by Kallies et al. in 2011 (Ref 29), although using a different genetic Prdm1-ablation model (Prdm1-GFP/GFP knockin/knockout model). This study reported impaired NK cell maturation, reduced Gzmb expression, impaired in vivo cytotoxicity against subcutaneous RMA-S cells, impaired in vitro proliferation, comparable in vitro killing, increase in BM NK cell numbers. The authors should discuss/mention this more prominently in their manuscript, and highlight where they confirm or refine these previous findings, and where they actually provide new information.

- What is the reason to refer to the enriched cluster in Blimp1-deficient NK cells as "Junbhi"? There is no follow-up for a function of Junb, and there are many other genes upregulated in these cells. Most critically, these cells seem to represent exactly the c-Kithi cells that Kallies et al. already showed and discussed in their paper. The authors should stain for Kit, and also refer to this. Also, MacKay et al. performed Blimp1-Chip-Seq (in T cells), maybe it would be interesting to check to which of the identified DEGs Blimp1 can bind.

- cNK cells are considered circulating cells, that transiently pass through the liver. Previous studies have suggested almost identical gene expression patterns in hepatic and splenic NK cells. In functional tests, they often "perform" identically. I am therefore a bit surprised that the authors find a differential dependency of Blimp1 for the IFNg production of splenic (no role of Blimp1) versus hepatic (Blimp1 regulating IFNg production) NK cells (Fig S3). Do the authors have any suggestions on that? The analyses are performed by 12+4h stimulations with IL12/18, which could involve the effects of altered bystander cells (as suggested by Figure 6). Therefore, these analyses should be provided upon standard 4h stimulations with IL12/18 and also with PMA/I under BFA. Note: liver and splenic cNK cells look quite different in the chosen histograms in Figures 7 A, B, C, yet there is massive variability in these analyses - is there any systematic/technical problem?

- Figure 4 H/I - In contrast to NK cells in Fig 4E, F, the KO and WT ILC1s seem to co-cluster largely. Authors should validate differentially expressed genes. How strong is the effect of Blimp1 in ILC1s? Or is Blimp1 a critical TF driving effector differentiation in NK cells, while it has only subtle effects in ILC1 (these may be regulated by Hobit?)? This seems an interesting finding that should at least be discussed. For these types of small differences in ILC1, FACS confirmation analyses should be performed and findings be reevaluated using Cre-expressing controls (see above).

The authors describe and discuss some of Figure 1 and 2 data as if Blimp1 would be involved in alternative NK versus ILC1 fates, but there is no evidence for this.

- There are several recent studies suggesting a role for Hobit, homologue of Blimp1, in NK cells and in ILC1, and in the control of liver metastases. The authors should discuss similar and unique functions of Hobit and Blimp1, also in the regulation of gene expression patterns, and should refer to these studies.

- Figure 4: The authors should discuss (and cross-validate) their liver gene expression analyses in the context of published datasets of NK and ILC1, such as the ones by Lopez et al, Friedrich et al, Ducimetiere et al and Yomogida et al.

Reviewer #2 (Public Review):

Summary:<br /> This study offers a significant advancement in understanding liver innate lymphoid cell (ILC) biology by elucidating the role of the transcription factor Prdm1. It shows that Prdm1 is crucial in maintaining the balance between conventional natural killer (cNK) cells and ILC1s in the liver, with knockout models revealing a vital role in cancer defense mechanisms. Despite not affecting direct cytotoxicity, Prdm1 deficiency leads to increased cancer metastasis and reduced secretion of key molecules like IFN-γ, pointing to its importance in immune regulation. The use of single-cell RNA sequencing further underscores Prdm1's role in cellular communication within the liver's immune milieu. This study is a robust contribution to the field, providing insights that could inform new immunotherapy approaches for liver cancer.

Strengths:<br /> The study's strength lies in its comprehensive approach, combining the specificity of Prdm1 conditional deletion in Ncr1-cre mice with integrative omics analyses and cutting-edge cytometry to delineate Prdm1's role in liver Type 1 ILC biology and its functional implications in tumor immunity. This multifaceted strategy not only clarifies Prdm1's influence on ILC composition and maturation but also conveys potential therapeutic insights for liver cancer immunotherapy.

Weaknesses:<br /> A notable weakness of the study is the limited scope of in vivo disease models, primarily relying on the B16F10 melanoma model, which may not fully capture the complex behavior of Type 1 ILCs across diverse cancer types. Furthermore, the absence of direct human data, such as the effects of PRDM1 deletion in human NK cells or stem cells during their differentiation into NK and ILC1, leaves a gap in translating these findings to clinical settings.

eLife assessment

This fundamental study evaluates the evolutionary significance of variations in the accuracy of the intron-splicing process across vertebrates and insects. Using a powerful combination of comparative and population genomics approaches, the authors present convincing evidence that species with lower effective population size tend to exhibit higher rates of alternative splicing, a key prediction of the drift-barrier hypothesis. The analysis is carefully conducted and all observations fit with this hypothesis, but focusing on a greater diversity of metazoan lineages would make these results even more broadly relevant. This study will strongly appeal to anyone interested in the evolution of genome architecture and the optimisation of genetic systems.

Reviewer #1 (Public Review):

Summary:<br /> Functionally important alternative isoforms are gold nuggets found in a swamp of errors produced by the splicing machinery.

The architecture of eukaryotic genomes, when compared with prokaryotes, is characterised by a preponderance of introns. These elements, which are still present within transcripts, are rapidly removed during the splicing of messenger RNA (mRNA), thus not contributing to the final protein. The extreme rarity of introns in prokaryotes, and the elimination of these introns from mRNAs before translation into protein, raises questions about the function of introns in genomes. One explanation comes from functional biology: introns are thought to be involved in post-transcriptional regulation and in the production of translational variants. The latter function is possible when the positions of the edges of the spliced intron vary. While some light has been shed on specific examples of the functional role of alternative splicing, to what extent are they representative of all introns in metazoans?

In this study, the hypothesis of a functional role for alternative splicing, and therefore to a certain extent for introns, is evaluated against another explanation coming from evolutionary biology: isoforms are above all errors of imprecision by the molecular machinery at work during splicing. This hypothesis is based on a principle established by Motoo Mikura, which has become central to population genetics, explaining that the evolutionary trajectory of a mutation with a given effect is intimately linked to the effective population size (Ne) where this mutation emerges. Thus, the probability of fixation of a weakly deleterious mutation increases when Ne decreases, and the probability of fixation of a weakly advantageous mutation increases when Ne increases. The genomes of populations with low Ne are therefore expected to accumulate more weakly deleterious mutations and fewer weakly advantageous mutations than populations with high Ne. In this framework, if splicing errors have only small effects on the fitness of individuals, then natural selection cannot increase the precision of the splicing machinery, allowing tolerance for the production of alternative isoforms.

In the past, the debate opposed one-off observations of effectively functional isoforms on the one hand, to global genomic quantities describing patterns without the possibility of interpreting them in detail. The authors here propose an elegant quantitative approach in line with the expected continuous variation in the effectiveness of selection, both between species and within genomes. The result describing the inter-specific pattern on a large scale confirms what was already known (there is a negative relationship between effective size and average alternative splicing rate). The essential novelty of this study lies in 1) the quantification, for each intron studied, of the relative abundance of each isoform, and 2) the analysis of a relationship between this abundance and the evolutionary constraints acting on these isoforms.

What is striking is the light shed on the general very low abundance of alternative isoforms. Depending on the species, 60% to 96% of cases of alternatively spliced introns lead to an isoform whose abundance is less than 5% of the total variants for a given intron.

In addition to the fact that 60%-96% of the total isoforms are more than 20 times less abundant than their majority form, this large proportion of alternative isoforms exhibit coding-phase shift at rates similar to what would be expected by chance, i.e. for a third of them, which reinforces the idea that there is no particular constraint on these isoforms.

The remaining 4%-40% of isoforms see their coding-phase shift rate decrease as their relative abundance increases. This result represents a major step forward in our understanding of alternative splicing and makes it possible to establish a quantitative model directly linking the relative abundance of an isoform with a putative functional role concerning only those isoforms produced in abundance. Only the (rare) isoforms which are abundantly produced are thought to be involved in a biological function.

Within the same genome, the authors show that only highly expressed genes, i.e. those that tend to be more constrained on average, are also the genes with the lowest alternative splicing rates on average.

The comparison between species in this study reveals that the smaller the effective size of a species, the more its genome produces isoforms that are low in abundance and low in constraint. Conversely, species with a large effective size relatively reduce rare isoforms, and increase stress on abundant isoforms.

To sum up:<br /> • the higher the effective size of a species, the fewer introns are spliced.<br /> • highly expressed genes are spliced less.<br /> • when splicing occurs, it is mainly to produce low-abundance isoforms.<br /> • low-abundance isoforms are also less constrained.

Taken together, these results reinforce a quantitative view of the evolution of alternative splicing as being mainly the product of imprecision in the splicing machinery, generating a great deal of molecular noise. Then, out of all this noise, a few functional gold nuggets can sometimes emerge. From the point of view of the reviewer, the evolutionary dynamics of genomes are depressing. The small effective population sizes are responsible for the accumulation of multiple slightly deleterious introns. Admittedly, metazoan genomes try to get rid of these introns during RNA maturation, but this mechanism is itself rendered imprecise by population sizes.

Strengths:<br /> • The authors simultaneously study the effects of effective population size, isoform abundance, and gene expression levels on the evolutionary constraints acting on isoforms. Within this framework, they clearly show that an isoform becomes functionally important only under certain rare conditions.<br /> • The authors rule out an effect putatively linked to variations in expression between different organs which could have biased comparisons between different species.

Weaknesses:<br /> • While the longevity of organisms as a measure of effective size seems to work overall, it may not be relevant for discriminating within a clade. For example, within Hymenoptera, we might expect them to have the same overall longevity, but that effective size would be influenced more by the degree of sociality: solitary bees/ants/wasps versus eusocial. I am therefore certain that the relationship shown in Figure 4D is currently not significant because the measure of effective size is not relevant for Hymenoptera. The article would have been even more convincing by contrasting the rates of alternative splicing between solitary versus social hymenopterans.<br /> • When functionalist biologists emphasise the role of the complexity of living things, I'm not sure they're thinking of the comparison between "drosophila" and "homo sapiens", but rather of a broader evolutionary scale. Which gives the impression of an exaggeration of the debate in the introduction.

Reviewer #2 (Public Review):

Summary:<br /> Two hypotheses could explain the observation that genes of more complex organisms tend to undergo more alternative splicing. On one hand, alternative splicing could be adaptive since it provides the functional diversity required for complexity. On the other hand, increased rates of alternative splicing could result through nonadaptive processes since more complex organisms tend to have smaller effective population sizes and are thus more prone to deleterious mutations resulting in more spurious splicing events (drift-barrier hypothesis). To evaluate the latter, B́enitiere et al. analyzed transcriptome sequencing data across 53 metazoan species. They show that proxies for effective population size and alternative splicing rates are negatively correlated. Furthermore, the authors find that rare, nonfunctional (and likely erroneous) isoforms occur more frequently in more complex species. Additionally, they show evidence that the strength of selection on splice sites increases with increasing effective population size and that the abundance of rare splice variants decreases with increased gene expression. All of these findings are consistent with the drift-barrier hypothesis.

This study conducts a comprehensive set of separate analyses that all converge on the same overall result and the manuscript is well organized. Furthermore, this study is useful in that it provides a modified null hypothesis that can be used for future tests of adaptive explanations for variation in alternative splicing.

Strengths:<br /> The major strength of this study lies in its complementary approach combining comparative and population genomics. Comparing evolutionary trends across phylogenetic diversity is a powerful way to test hypotheses about the origins of genome complexity. This approach alone reveals several convincing lines of evidence in support of the drift-barrier hypothesis. However, the authors also provide evidence from a population genetics perspective (using resequencing data for humans and fruit flies), making results even more convincing.

The authors are forward about the study's limitations and explain them in detail. They elaborate on possible confounding factors as well as the issues with data quality (e.g. proxies for Ne, inadequacies of short reads, heterogeneity in RNA-sequencing data).

Weaknesses:<br /> The authors primarily consider insects and mammals in their study. This only represents a small fraction of metazoan diversity. Sampling from a greater diversity of metazoan lineages would make these results and their relevance to broader metazoans substantially more convincing. Although the authors are careful about their tone, it is challenging to reconcile these results with trends across greater metazoans when the underlying dataset exhibits ascertainment bias and represents samples from only a few phylogenetic groups. Relatedly, some trends (such as Figure 1B-C) seem to be driven primarily by non-insect species, raising the question of whether some results may be primarily explained by specific phylogenetic groups (although the authors do correct for phylogeny in their statistics). How might results look if insects and mammals (or vertebrates) are considered independently?

Throughout the manuscript, the authors refer to infrequently spliced (mode <5%) introns as "minor introns" and frequently spliced (mode >95%) as "major introns". This is extremely confusing since "minor introns" typically represent introns spliced by the U12 spliceosome, whereas "major introns" are those spliced by the U2 spliceosome. Furthermore, it remains unclear whether the study only considers major introns or both major and minor introns. Minor introns typically have AT-AC splice sites whereas major introns usually have GT/GC-AG splice sites, although in rare cases the U2 can recognize AT-AC (see Wu and Krainer 1997 for example). The authors also note that some introns show noncanonical AT-AC splice sites while these are actually canonical splice sites for minor introns.

eLife assessment

This important study advances our understanding of sperm motility regulation during fertilization by uncovering the midpiece/mitochondria contraction associated with motility cessation and structural changes in the midpiece actin network as its mode of action by using various advanced microscopic techniques. The evidence supporting the association is solid, but the evidence to support the causality of contraction and motility cessation is incomplete and would benefit from time-resolved imaging monitoring contraction, motility, and cell viability simultaneously. With the causality part strengthened, the work will be significant and of broad interest to cell biologists working on the cytoskeleton, mitochondria, cell fusion, and fertilization.

Reviewer #1 (Public Review):

Summary:

This important work advances our understanding of sperm motility regulation during fertilization by uncovering the midpiece/mitochondria contraction associated with motility cessation and structural changes in the midpiece actin network as its mode of action. The evidence supporting the conclusion is solid, with rigorous live cell imaging using state-of-the-art microscopy, although more functional analysis of the midpiece/mitochondria contraction would have further strengthened the study. The work will be of broad interest to cell biologists working on the cytoskeleton, mitochondria, cell fusion, and fertilization.

Strengths:

The authors demonstrate that structural changes in the flagellar midpiece F-actin network are concomitant to midpiece/mitochondrial contraction and motility arrest during sperm-egg fusion by rigorous live cell imaging using state-of-art microscopy.

Weaknesses:

Many interesting observations are listed as correlated or in time series but do not necessarily demonstrate the causality and it remains to be further tested whether the sperm undergoing midpiece contraction are those that fertilize or those that are not selected. Further elaboration of the function of the midpiece contraction associated with motility cessation (a major key discovery of the manuscript) would benefit from a more mechanistic study.

Reviewer #2 (Public Review):

The authors used various microscopy techniques, including super-resolution microscopy, to observe the changes that occur in the midpiece of mouse sperm flagella. Previously, it was shown that actin filaments form a double helix in the midpiece. This study reveals that the structure of these actin filaments changes after the acrosome reaction and before sperm-egg fusion, resulting in a thinner midpiece. Furthermore, by combining midpiece structure observation with calcium imaging, the authors show that changes in intracellular calcium concentrations precede structural changes in the midpiece. The cessation of sperm motility by these changes may be important for fusion with the egg. Elucidation of the structural changes in the midpiece could lead to a better understanding of fertilization and the etiology of male infertility. The conclusions of this manuscript are largely supported by the data, but there are several areas for improvement in data analysis and interpretation. Please see the major points below.

1. It is unclear whether an increased FM4-64 signal in the midpiece precedes the arrest of sperm motility. This needs to be clarified in order to argue that structural changes in the midpiece cause sperm motility arrest. The authors should analyze changes in both motility and FM4-64 signal over time for individual sperm.

2. It is possible that sperm stop moving because they die. Figure 1G shows that the FM4-64 signal is increased in the midpiece of immotile sperm, but it is necessary to show that the FM4-64 signal is increased in sperm that are not dead and retain plasma membrane integrity by checking sperm viability with propidium iodide or other means.

3. It is unclear how the structural change in the midpiece causes the entire sperm flagellum, including the principal piece, to stop moving. It will be easier for readers to understand if the authors discuss possible mechanisms.

4. The mitochondrial sheath and cell membrane are very close together when observed by transmission electron microscopy. The image in Figure 9A with the large space between the plasma membrane and mitochondria is misleading and should be corrected. The authors state that the distance between the plasma membrane and mitochondria approaches about 100 nm after the acrosome reaction (Line 330 - Line 333), but this is a very long distance and large structural changes may occur in the midpiece. Was there any change in the mitochondria themselves when they were observed with the DsRed2 signal?

5. In the TG sperm used, the green fluorescence of the acrosome disappears when sperm die. Figure 1C should be analyzed only with live sperm by checking viability with propidium iodide or other means.

Reviewer #3 (Public Review):

While progressive and also hyperactivated motility are required for sperm to reach the site of fertilization and to penetrate the oocyte's outer vestments, during fusion with the oocyte's plasma membrane it has been observed that sperm motility ceases. Identifying the underlying molecular mechanisms would provide novel insights into a crucial but mostly overlooked physiological change during the sperm's life cycle. In this publication, the authors aim to provide evidence that the helical actin structure surrounding the sperm mitochondria in the midpiece plays a role in regulating sperm motility, specifically the motility arrest during sperm fusion but also during earlier cessation of motility in a subpopulation of sperm post acrosomal exocytosis.

The main observation the authors make is that in a subpopulation of sperm undergoing acrosomal exocytosis and sperm that fuse with the plasma membrane of the oocyte display a decrease in midpiece parameter due to a 200 nm shift of the plasma membrane towards the actin helix. The authors show the decrease in midpiece diameter via various microscopy techniques all based on membrane dyes, bright-field images and other orthogonal approaches like electron microscopy would confirm those observations if true but are missing. The lack of additional experimental evidence and the fact that the authors simultaneously observe an increase in membrane dye fluorescence suggests that the membrane dyes instead might be internalized and are now staining intracellular membranes, creating a false-positive result. The authors also propose that the midpiece diameter decrease is driven by changes in sperm intracellular Ca2+ and structural changes of the actin helix network. Important controls and additional experiments are needed to prove that the events observed by the authors are causally dependent and not simply a result of sperm cells dying.

eLife assessment

This important study provides solid evidence for a non-genomic action of progesterone in Xenopus oocyte activation. The findings demonstrate that two non-genomic progesterone receptors, ABHD2 and mPRb, function as a novel progesterone-stimulated phospholipase A2. However, the findings are reliant on high concentrations of inhibitor drugs, and mechanistic details about the molecular interaction and respective functions of ABHD2 and mPRb are incomplete. The findings will be of broad interest to reproductive endocrinologists and physiologists.

Reviewer #1 (Public Review):

Summary:

Numerous pathways have been proposed to elucidate the nongenomic actions of progesterone within both male and female reproductive tissues. The authors employed the Xenopus oocyte system to investigate the PLA2 activity of ABHD2 and the downstream lipid mediators in conjunction with mPRb and P4, on their significance in meiosis. The research has been conducted extensively and is presented clearly.

Strengths:

While the interaction between membranous PR and ABHD2 is not a novel concept, this present study exhibits several strengths:

1. mPRbeta, a member of the PAQR family, has been elusive in terms of detailed signal transduction. Through mutation studies involving the Zn binding domain, the authors discovered that the hydrolase activity of mPRbeta is not essential for meiosis and oocyte maturation. Instead, they suggest that ABHD2, acting as a coreceptor of mPRbeta, demonstrates phospholipase activity, indicating that downstream lipid mediators may play a dominant role when stimulated by progesterone.

2. Extensive exploration of downstream signaling pathways and the identification of several potential meiotic activity-related lipid mediators make this aspect of the study novel and potentially significant.

Weaknesses:

However, there are some weaknesses and areas that need further clarification:

1. The mechanism governing the molecular assembly of mPRbeta and ABHD2 remains unclear. Are they constitutively associated or is their association ligand-dependent? Does P4 bind not only to mPRbeta but also to ABHD2, as indicated in Figure 6J? In the latter case, the reviewer suggests that the authors conduct a binding experiment using labeled P4 with ABHD2 to confirm this interaction and assess any potential positive or negative cooperativity with a partner receptor.

2. The authors have diligently determined the metabolite profile using numerous egg cells. However, the interpretation of the results appears incomplete, and inconsistencies were noted between Figure 2B and Supplementary Figure 2C. Furthermore, PGE2 and D2 serve distinct roles and have different elution patterns by LC-MS/MS, thus requiring separate measurements. In addition, the extremely short half-life of PGI2 necessitates the measurement of its stable metabolite, 6-keto-PGF1a, instead. The authors also need to clarify why they measured PGF1a but not PGF2a.

3. Although they propose PGs, LPA, and S1P are important downstream mediators, the exact roles of the identified lipid mediators have not been clearly demonstrated, as receptor expression and activation were not demonstrated. While the authors showed S1PR3 expression and its importance by genetic manipulation, there was no observed change in S1P levels following P4 treatment (Supplementary Figure 2D). It is essential to identify which receptors (subtypes) are expressed and how downstream signaling pathways (PKA, Ca, MAPK, etc.) relate to oocyte phenotypes.

These clarifications and further experiments would enhance the overall impact and comprehensiveness of the study.

Reviewer #2 (Public Review):

Summary:

This interesting paper examines the earliest steps in progesterone-induced frog oocyte maturation, an example of non-genomic steroid hormone signaling that has been studied for decades but is still very incompletely understood. In fish and frog oocytes it seems clear that mPR proteins are involved, but exactly how they relay signals is less clear. In human sperm, the lipid hydrolase ABHD2 has been identified as a receptor for progesterone, and so the authors here examine whether ABHD2 might contribute to progesterone-induced oocyte maturation as well. The main results are:

1. Knocking down ABHD2 makes oocytes less responsive to progesterone, and ectopically expressing ABHD2.S (but not the shorter ABHD2.L gene product) partially rescues responsiveness. The rescue depends upon the presence of critical residues in the protein's conserved lipid hydrolase domain, but not upon the presence of critical residues in its acyltransferase domain.

2. Treatment of oocytes with progesterone causes a decrease in sphingolipid and glycerophospholipid content within 5 min. This is accompanied by an increase in LPA content and arachidonic acid metabolites. These species may contribute to signaling through GPCRs. Perhaps surprisingly, there was no detectable increase in sphingosine-1-phosphate, which might have been expected given the apparent substantial hydrolysis of sphingolipids. The authors speculate that S1P is formed and contributes to signaling but diffuses away.

3. Pharmacological inhibitors of lipid-metabolizing enzymes support, for the most part, the inferences from the lipidomics studies, although there are some puzzling findings. The puzzling findings may be due to uncertainty about whether the inhibitors are working as advertised.

4. Pharmacological inhibitors of G-protein signaling support a role for G-proteins and GPCRs in this signaling, although again there are some puzzling findings.

5. Reticulocyte expression supports the idea that mPR and ABHD2 function together to generate a progesterone-regulated PLA2 activity.

6. Knocking down or inhibiting ABHD2 inhibited progesterone-induced mPRinternalization, and knocking down ABHD2 inhibited SNAP2520-induced maturation.

Strengths:

All in all, this could be a very interesting paper and a nice contribution. The data add a lot to our understanding of the process, and, given how ubiquitous mPR and AdipoQ receptor signaling appear to be, something like this may be happening in many other physiological contexts.

Weaknesses:

I have several suggestions for how to make the main points more convincing.

Main criticisms:

1. The ABHD2 knockdown and rescue, presented in Fig 1, is one of the most important findings. It can and should be presented in more detail to allow the reader to understand the experiments better. E.g.: the antisense oligos hybridize to both ABHD2.S and ABHD2.L, and they knock down both (ectopically expressed) proteins. Do they hybridize to either or both of the rescue constructs? If so, wouldn't you expect that both rescue constructs would rescue the phenotype since they both should sequester the AS oligo? Maybe I'm missing something here.

In addition, it is critical to know whether the partial rescue (Fig 1E, I, and K) is accomplished by expressing reasonable levels of the ABHD2 protein, or only by greatly overexpressing the protein. The author's antibodies do not appear to be sensitive enough to detect the endogenous levels of ABHD2.S or .L, but they do detect the overexpressed proteins (Fig 1D). The authors could thus start by microinjecting enough of the rescue mRNAs to get detectable protein levels, and then titer down, assessing how low one can go and still get rescue. And/or compare the mRNA levels achieved with the rescue construct to the endogenous mRNAs.

Finally, please make it clear what is meant by n = 7 or n = 3 for these experiments. Does n = 7 mean 7 independently lysed oocytes from the same frog? Or 7 groups of, say, 10 oocytes from the same frog? Or different frogs on different days? I could not tell from the figure legends, the methods, or the supplementary methods. Ideally one wants to be sure that the knockdown and rescue can be demonstrated in different batches of oocytes, and that the experimental variability is substantially smaller than the effect size.

2. The lipidomics results should be presented more clearly. First, please drop the heat map presentations (Fig 2A-C) and instead show individual time course results, like those shown in Fig 2E, which make it easy to see the magnitude of the change and the experiment-to-experiment variability. As it stands, the lipidomics data really cannot be critically assessed.

[Even as heat map data go, panels A-C are hard to understand. The labels are too small, especially on the heat map on the right side of panel B. The 25 rows in panel C are not defined (the legend makes me think the panel is data from 10 individual oocytes, so are the 25 rows 25 metabolites? If so, are the individual oocyte data being collapsed into an average? Doesn't that defeat the purpose of assessing individual oocytes?) And those readers with red-green colorblindness (8% of men) will not be able to tell an increase from a decrease. But please don't bother improving the heat maps; they should just be replaced with more informative bar graphs or scatter plots.]

3. The reticulocyte lysate co-expression data are quite important and are both intriguing and puzzling. My impression had been that to express functional membrane proteins, one needed to add some membrane source, like microsomes, to the standard kits. Yet it seems like co-expression of mPR and ABHD2 proteins in a standard kit is sufficient to yield progesterone-regulated PLA2 activity. I could be wrong here - I'm not a protein expression expert - but I was surprised by this result, and I think it is critical that the authors make absolutely certain that it is correct. Do you get much greater activities if microsomes are added? Are the specific activities of the putative mPR-ABHD2 complexes reasonable?

Reviewer #3 (Public Review):

Summary:

The authors report two P4 receptors, ABHD2 and mPRβ that function as co-receptors to induce PLA2 activity and thus drive meiosis. In their experimental studies, the authors knock down ABHD2 and demonstrated inhibition of oocyte maturation and inactivation of Plk1, MAPK, and MPF, which indicated that ABHD2 is required for P4-induced oocyte maturation. Next, they showed three residues (S207, D345, H376) in the lipase domain that are crucial for ABHD2 P4-mediated oocyte maturation in functional assays. They performed global lipidomics analysis on mPRβ or ABHD2 knockdown oocytes, among which the downregulation of GPL and sphingolipid species were observed, and enrichment in LPA was also detected using their metabolomics method. Furthermore, they investigated pharmacological profiles of enzymes predicted to be important for maturation based on their metabolomic analyses and ascertained the central role of PLA2 in inducing oocyte maturation downstream of P4. They showed the modulation of S1P/S1PR3 pathway on oocyte maturation and the potential role for Gαs signaling and potentially Gβγ downstream of P4.

Strengths:

The authors make a very interesting finding that ABHD2 has PLA2 catalytic activity but only in the presence of mPRβ and P4. Finally, they provided supporting data for a relationship between ABHD2/PLA2 activity and mPRβ endocytosis and further downstream signaling. Collectively, this research report defines early steps in nongenomic P4 signaling, which has broad physiological implications.

Weaknesses:

There were concerns with the pharmacological studies presented. Many of these inhibitors are used at high (double-digit micromolar) concentrations that could result in non-specific pharmacological effects and the authors have provided very little data in support of target engagement and selectivity under the multiple experimental paradigms. In addition, the use of an available ABHD2 small molecule inhibitor was lacking in these studies.

eLife assessment

The authors present a valuable observation that challenges existing knowledge about DNA methylation dynamics in pre-implantation mammalian development. Their findings suggest a novel role for a well-studied epigenetic mark, with potential implications for gene expression regulation in early embryonic stages. However, the evidence provided is incomplete and only partially supports the main claims.

Reviewer #1 (Public Review):

Summary:

In this study, Yue et al. re-processed publicly available DNA methylation data (published in 2012 and 2017 from the Meissner lab) from pre- and post-implantation mouse embryos. Against the global wave of genome-wide reduction of DNA methylation occurring during pre-implantation development, they detected a slight increase (~1% on average) of DNA methylation at gene promoter regions during the transition from 8-cell to blastocyst stage. They claim that many such promoters are located in the X chromosome. Subsequently, they knocked down Dnmt3b (presumably because of its upregulation during the transition from the 8-cell to blastocyst stage) and detected the aberrant patterning of H3K27me3 in the mutant female embryos. Based on this observation, they claim that imprinted X-chromosome inactivation is impaired in the Dnmt3b-Kd pre-implantation embryos. Finally, they propose a model where such an increase of DNA methylation together with H3K27me3 regulates imprinted X-chromosome inactivation in the pre-implantation embryos. While their observation is of potential interest, the current version of the work fails to provide enough evidence to support their conclusions. Below are suggestions and comments on the manuscript.

Major issues:

1. Sex of the embryos of the genome-wide bisulfite-sequencing data<br /> The authors re-analyzed publicly available genome-wide DNA methylation data from the Meissner lab published in 2012 and 2017. The former used reduced representation bisulfite sequencing (RRBS) and the latter used whole-genome bisulfite sequencing (WGBS). Based mainly on the RRBS data, Yue et al. detected de novo DNA methylated promoters during the transition from 8-cell to blastocyst against the global wave of genome-wide DNA demethylation. They claim that such promoter regions are enriched at the "inactive" X chromosome. However, it would be difficult to discuss DNA methylation at inactive X-chromosomes as the RRBS data were derived from a mixture of male and female embryos. It would also be notable that the increase of DNA methylation at these promoter regions is ~1% on average. Such a slight increase in DNA methylation during pre-implantation development could also be due to the developmental variations between the embryos or between the sexes of embryos.

2. Imprinted X-chromosome inactivation and evaluation of H3K27me3 (related to Figures 2C, D; 3F; Figure2-supplement 2 F, G; Figure3-supplement 3G)<br /> Based on the slight change in the H3K27me3 signals in the Dnmt3b-Kd blastocysts, the authors claim that imprinted X-chromosome inactivation is impaired in the mutant embryo. It would be not easy to reach this conclusion from such a rough analysis of H3K27me3 presented in Figure 2C, D. Rigorous quantification/evaluation of the H3K27me3 signals in the Dnmt3b-Kd embryos should be considered. Additional evidence for the impairment of H3K27me3 in the mutant embryos should also be provided (expression of a subset of X-linked genes by RNA-FISH or RT-PCR etc.). Though technically challenging, high-resolution genome-wide approach such as ChIP-seq of H3K27me3 in the Dnmt3b-kd female embryos (with traceable SNPs between maternal and paternal X chromosome to distinguish inactive and active X-chromosome) could more precisely evaluate regions that lose H3K27me3 in the X-chromosome (de novo DNA methylated promoters from 8-cell to blastocyst, for example).

3. Analysis of the developmental potential of Dnmt3b-kd embryos<br /> While the authors claim that Dnmt3b-mediated de novo DNA methylation plays an important role in imprinted X-chromosome inactivation, it remains unclear whether the analysis presented in Figure 4 is derived from "female" embryos. This analysis seemed confusing as the authors claim that de novo DNA methylation in the promoter regions during the transition from 8-cell to blastocyst regulates imprinted X-chromosome inactivation, but this should not happen in the male embryos. Was the impairment of embryonic proliferation and differentiation observed in both male and female embryos? Or is this specific to the female embryos? We think that the sex of the embryos would be critical for the analysis presented in Figure 4.

Reviewer #2 (Public Review):

Summary:

Here, Yue et al. set out to determine if the low DNMT3B expression that is observed prior to de novo DNA methylation (before the blastocyst stage) has a function. Re-analyzing existing DNA methylation data from Smith et al. (2012) they find a small DNA methylation gain over a subset of promoters and gene bodies, occurring between the 8-cell and blastocyst stages, and refer to this as "minor de novo DNA methylation". They attempt to assess the relevance/functionality of this minor DNA methylation gain, and report reduced H3K27me3 in Dnmt3b knockdown (KD) trophoblast cells that normally undergo imprinted X-chromosome inactivation (iXCI) before the blastocyst stage. In addition, they assess the proliferation, differentiation, metabolic function, implantation rate, and live birth rate of Dnmt3b KD blastocysts.

Strengths:

Working with early embryos is technically demanding, making the well-designed experiments from this manuscript useful to the epigenetics community. Particularly, the DNMT3B expression and 5-mC staining at different embryonic stages.

Weaknesses:

- Throughout the manuscript, please represent DNA methylation changes as delta DNA methylation instead of fold change.

- Detailed methods on the re-analysis of the DNA methylation data from Smith et al. 2012 are missing from the materials and methods section. Was a minimum coverage threshold used?

- Detailed methods on the establishment and validation of Dnmt3b KO blastocysts and 5-aza-dC treated blastocysts are missing (related to Figure 2).

- Detailed methods on the re-analysis of the ChIPseq data from Liu et al. 2016 are missing from the materials and methods section.

- Some of the data represented in bar graphs does not look convincing/significant. Maybe this data can be better represented differently, such as in box plots or violin plots, which would better represent the data.

- The relevance and rationale for experiments using 5-aza-dC treatment is unclear.

eLife assessment

The manuscript describes important findings regarding the significance of CHD2 in ovarian folliculogenesis. Overall, the results lead to convincing conclusions, with minimal concerns raised by the reviewers. Both the results and conclusions are well discussed. This work will be of interest to ovarian biologists and physicians working on female fertility.

Reviewer #1 (Public Review):

Summary:

This manuscript reports experiments designed to dissect the function of N-cadherin during mammalian folliculogenesis, using the mouse as a model system. Prior studies have shown that this is the principal cadherin expressed by the follicular granulosa cells. Two main strategies are used - small-molecule inhibitors that target N-cadherin and a conditional knockout where the gene encoding N-cad is deleted in granulosa cells. The authors also take advantage of the ability to reproduce key events of folliculogenesis, such as oocyte meiotic maturation, in vitro. Four main conclusions are drawn from the studies: (i) cadherin-based cell contact is required to maintain cadherin (N-cad in the granulosa cells; E-cad in the oocyte) at the plasma membrane; (ii) N-cad is required for cumulus layer expansion; (iii) N-cad is required for meiotic maturation of the oocyte; (iv) N-cad is required for ovulation.

Strengths:

The experiments are logically conceived, clearly described and presented, and carefully interpreted. A key strength of the paper is that multiple approaches have been used (drugs, knockouts, immunofluorescence, PLA, in vitro and in vivo studies). Taken together, they clearly establish essential roles for N-cadherin during folliculogenesis.

It is intriguing that, when cadherin activity is impaired, the cadherins are lost from the plasma membrane. This suggests that, in a multicellular context, interactions with other cadherins, either in cis within the same cell or in trans with a neighboring cell, are required to maintain cadherins at the membrane. Hence, beyond their significance for understanding female reproductive biology, these experiments have broader implications for cell biology.

Weaknesses:

A few points could be considered or clarified by the authors:

The YAP experiments were confusing to the reviewer. CRS-066 increased YAP activity, as indicated by increased expression of target genes. Since CRS-066 prevents expansion, this result suggests that YAP antagonizes expansion. Therefore, blocking YAP should favor expansion. Yet, the YAP inhibitor impaired expansion. In the reviewer's eyes, these results seem to be contradictory.

It is intriguing that the inhibitors were able to efficiently block oocyte maturation. Oocytes from which the cumulus granulosa cells have been removed (denuded) will mature in vitro in the absence of LH or EGF. Since the effect of the inhibitors is to break the contact between the cumulus cells and oocyte, one might have predicted that this would not impair the ability of the oocytes to mature. Perhaps the authors could comment on this.

Regarding the experiments where the inhibitors were administered intra-peritoneally, the authors might comment on the rationale for choosing the doses that were used. An additional point to consider is that, since N-cadherin is expressed in a variety of tissues, an effect of interfering with N-cadherin at these non-ovarian sites could indirectly influence ovarian function.

Reviewer #2 (Public Review):

Summary:

The manuscript entitled "N-cadherin mechanosensing in ovarian follicles controls oocyte maturation and ovulation" aimed to investigate the role of N-cadherin in different ovarian physiological processes, including cumulus oocyte expansion, oocyte maturation, and ovulation. The authors performed several in vitro and in vivo mice experiments, using diverse techniques to reinforce their results.

First, they identified two compounds (N-cadherin antagonists) that block the adhesion of periovulatory COCs to fibronectin through screening a small molecule library, using the xCELLigenceTM system, performing proper and complementary controls. Second, the authors showed the presence of N-cadherin adherens junctions between granulosa cells and cumulus cells and at the interface of cumulus cell transzonal projections and the oocyte throughout folliculogenesis. And that these adherens complexes between cumulus cells and oocytes were disrupted when inhibited N-cadherin, as observed by nice representative confocal images. Then, the authors assessed COC expansion and oocyte meiotic maturation to determine whether the loss of oocyte membrane β-catenin and E-cadherin upon N-cadherin inhibitor treatment disrupts the bi-directional communication between cumulus cells and the oocyte. Indeed, N-cadherin antagonists disrupted both processes (cumulus expansion and oocyte meiotic). However, the expression of known mediators of COC expansion (E.g., Areg and Ptgs2) were either increased or unaffected. Nevertheless, RNA-Seq showed consistent effects on cell signaling mRNA genes by the antagonist CRS-066.

In vivo studies using mice were also achieved using stimulated protocols (together with one of the antagonists or vehicle) or granulosa-specific Cdh2 Knockouts to further analyze the role of N-cadherin. N-cadherin antagonist CRS-066 (but not LCRF-0006) significantly reduced mouse ovulation compared to controls. RNA-sequencing data analysis identified distinct gene expression profiles in CRS-066 treated compared to control ovaries. Ovulation in CdhFl/FL; Amhr2Cre mice after stimulation were also significantly reduced; multiple large unruptured follicles were observed in these granulosa-specific Cdh2 mutant ovaries, and the mRNA expression of Areg and Ptgs2 were reduced.

The authors conclude that their study identified N-cadherin as a mechanosensory regulator important in ovarian granulosa cell differentiation able to respond to hormone stimuli both in vivo and in vitro, demonstrating a critical role for N-cadherin in ovarian follicular development and ovulation. They highlighted the potential to inhibit ovulation by targeting this signaling mechanism.

Strengths:<br /> This remarkable manuscript is very well designed, performed, and discussed. The authors analyzed different aspects, and their data supports their conclusions.

Weaknesses:<br /> This study was performed using the mouse as a research model; further studies in larger animals and humans would be interesting and warranted.

Minor comments:

Some results are intriguing. While the AREG y PTGS2 mRNA increased within the COC in vitro by the N-cadherin antagonists, in vivo, the treatment induced a significant increase in both genes when analyzing the whole ovary. What are the authors' ideas that could explain these discrepancies in outcomes?

The authors stated that the ovaries from mice treated in the same manner and collected either before hCG treatment (eCG 44 h) or 11 h after hCG showed equivalent numbers of follicles at each stage of development from primary to antral. However, in Panel l from Figure 5, there is a significant increase in the number of antral follicles in the CRS-066 group (hCG 11 h) compared to the vehicle. Could the author discuss it in the manuscript?

eLife assessment

Using state-of-the-art fate-mapping models and genetic and pharmacological targeting approaches, this study provides valuable findings on the distinct functions exerted by resident and recruited macrophages during cardiac healing after myocardial ischemia. The evidence supporting the conclusions is solid with the use of the FIRE mouse model in combination with fate-mapping to target fetal-derived macrophages. This study will be of interest for the macrophage biologists working in the heart but also in other tissues in the context of inflammation.

Reviewer #1 (Public Review):

Weinberger et al. use different fate-mapping models, the FIRE model and PLX-diet to follow and target different macrophage populations and combine them with single-cell data to understand their contribution to heart regeneration after I/R injury. This question has already been addressed by other groups in the field using different models. However, the major strength of this manuscript is the usage of the FIRE mouse model that, for the first time, allows specific targeting of only fetal-derived macrophages.

The data show that the absence of resident macrophages is not influencing infarct size but instead is altering the immune cell crosstalk in response to injury, which is in line with the current idea in the field that macrophages of different origins have distinct functions in tissues, especially after an injury.

To fully support the claims of the study, specific targeting of monocyte-derived macrophages or the inhibition of their influx at different stages after injury would be of high interest.

In summary, the study is well done and important for the field of cardiac injury. But it also provides a novel model (FIRE mice + RANK-Cre fate-mapping) for other tissues to study the function of fetal-derived macrophages while monocyte-derived macrophages remain intact.

Reviewer #2 (Public Review):

In this study Weinberger et al. investigated cardiac macrophage subsets after ischemia/reperfusion (I/R) injury in mice. The authors studied a ∆FIRE mouse model (deletion of a regulatory element in the Csf1r locus), in which only tissue resident macrophages might be ablated. The authors showed a reduction of resident macrophages in ∆FIRE mice and characterized its macrophages populations via scRNAseq at baseline conditions and after I/R injury. 2 days after I/R protocol ∆FIRE mice showed an enhanced pro inflammatory phenotype in the RNAseq data and differential effects on echocardiographic function 6 and 30 days after I/R injury. Via flow cytometry and histology the authors confirmed existing evidence of increased bone marrow-derived macrophage infiltration to the heart, specifically to the ischemic myocardium. Macrophage population in ∆FIRE mice after I/R injury were only changed in the remote zone. Further RNAseq data on resident or recruited macrophages showed transcriptional differences between both cell types in terms of homeostasis-related genes and inflammation. Depleting all macrophage using a Csf1r inhibitor resulted in a reduced cardiac function and increased fibrosis.

eLife assessment

Toll like receptor 2 (TLR2) signaling has traditionally been viewed a surface protein that induces innate immune responses and improves acquired immunity. Here, the authors suggest a different role for TLR2 in the hair cycle. By using a Cre reporter that is largely, but not solely active in hair follicle stem cells, the authors conditionally delete Tlr2 in mice and report that BMP signaling is sustained and hair cycle entry is delayed. Delving further, the authors identify CEP (2-ω-carboxyethyl pyrrole) as an endogenous ligand of TLR2 in hair follicle stem cell regulation. Although a role for TLR2 signaling in hair follicle stem cells is potentially novel and important, the reviewers remain in consensus that evidence presented in two significant areas continues to be incomplete: 1) where TLR2 and CEP are expressed and how specific is their expression to the hair follicle stem cells; 2) whether as the authors suggest, TLR2 functions by regulating BMP signaling in the stem cell niche of the hair follicle.

Reviewer #1 (Public Review):

Summary:

In this manuscript by Xiong L et al., the authors have uncovered an important link between innate immune signaling and hair regeneration. The authors provide convincing evidence supporting the critical roles of TLR2 in sensing CEP levels in hair follicles, counteracting the action of BMP signaling, and facilitating the activation of HFSCs during the hair cycle and wound repair. Importantly, the authors also propose that decreased CEP production and TLR2 expression might be factors contributing to the decreased hair regeneration associated with aging.

Strengths:

The experiments in this manuscript are well-designed and presented. The authors provided extensive evidence supporting the roles of TLR2 signaling in regulating hair follicle stem cell functions. Importantly, the findings from this paper could have sustained impacts on our understanding of the roles of innate immunity in regulating tissue regeneration in the absence of inflammation.

Weaknesses:

1. The central conclusion of this study is that the activation of TLR2 can suppress BMP signaling. However, the molecular link between TLR2 and BMP signaling is still missing. Given the importance of this finding, it would be intriguing to further investigate how TLR2 activation suppresses BMP signaling. A better characterization of the molecular-level interaction between TLR2 and BMP signaling can further enhance the impact of this study.

2. The authors imply that the decreased CEP level in aged mice could lead to deficient TLR2 signaling, which could further cause aging-associated hair regeneration defects. But this has not been demonstrated. What are the BMPs and pSmad1/5 levels in aged skin? Another important experiment to confirm the importance of this link during aging would be to inject CEP into the aged skin and examine whether this could restore hair regeneration in aged mice.

3. The impacts of CEP/TLR2 on proliferation of keratinocytes is still weak. How much of this effect is a result of NFkB activation, and how much is simply due to inhibiting BMP signaling?

Updated comments on the revised manuscript:<br /> The authors have addressed my previous questions.

Reviewer #3 (Public Review):

Summary:

In the manuscript by Xiong and colleagues, the roles of TLR2 in hair follicle cycle regulation were investigated. By analyzing published dataset and using immunostaining and transgenic TLR2-GFP reporter mice, the authors showed that TLR2 expression is increased in the late telogen compared to the early telogen, implying that it is important for the transition between telogen to anagen hair cycle. They found that the genetic deletion of Tlr2 in hair follicle stem cells delays hair cycle entry in both homeostatic and wound-induced hair follicle regeneration. In addition, they found that CEP is an endogenous TLR2 activating ligand and triggers the progression of hair cycle in a TLR2-dependent manner. Mechanistically, the activation of TLR2 signaling antagonizes BMP signaling which is critical for the maintenance of hair follicle stem cell quiescence. Clinically, they showed that TLR2 expression is decreased in aging and high-fat diet condition, suggesting that the dysfunctional regulation of TLR2 pathway is responsible for age-related and obesity-related hair thinning and hair loss phenotypes.

Strengths:

Overall, this study presents the role and mechanism of TLR2 in regulating hair follicle regeneration. The functional interrogation parts using HFSC-specific TLR2 genetic deletion is solid, and an endogenous regulator, CEP, is identified.

Weaknesses:<br /> 1)<br /> - In SFig1A, the IF staining of TLR2 and Tlr2-GFP expression seem almost 100% co-localized, which is not usual experimentally.<br /> - In Fig 2J, the relative expression levels of Tlr2 in anagen, telogen, catagen HFSCs were tested. But it is just relative comparison and does not mean whether the expression level is meaningful or not. To make this convincing, adding other cell types such as dermal fibroblasts and immunes to the comparison as negative and positive controls would be a good idea.<br /> - In Fig 2K, the expression of Tlr2 is comparable or a bit lesser in epidermal cells and HFSCs, but the expressions of TLR2 (IF) and Tlr2-GFP in epidermal cells have not been presented at all in the manuscript. As the authors used K15-CrePR1 mice to delete Tlr2 in HFSCs specifically, showing TLR2 IF staining in TLR2-HFSC-KO mice would be nice evidence of significant expression of TLR2 in HFSCs. (still TLR2 expression in epidermis, but no TLR2 expression in HFSCs).<br /> - In Fig 1B, it is still unclear whether TLR2 staining is in epithelial cell or in dermal cells. TLR2 staining patterns in Fig 1B, SFig 1A, and rebuttal seem different. In Fig S1B and rebuttal, TLR2 expression in HFSCs, HG, DP cells, but in Fig 1B, most of HG and DP cells are not TLR2+.<br /> - Together, this reviewer still does not think that there is a clear and solid evidence of Tlr2 expression in HFSCs. Searching the Tlr2 expression in published bulk and single cell RNA-seq dataset would be helpful.

2)<br /> - In SFig 4B, C, the activation of BMP signaling was hindered by TLR2 signaling activation by PAM3CSK4. But it is in vitro data, and cultured HFSCs are different from in vivo HFSCs, and particularly the changes of HFSCs from quiescence to activation can hardly be recapitulated in vitro.<br /> - In Fig 4H, it is curious that in TLR2-HFSC-KO mice, P21 HFSCs showed no pSMAD1/5/9, but it is increased in P24.<br /> - Also, it is wondered that if ID1 and ID2, key target genes, are increased in TLR2-HFCS-KO.<br /> - The author suggested that BMP7 is a key connection between TLR2 signaling and BMP signaling. It is curious whether BMP7 is a direct target of TLR2 pathway? Are there Nfkb (putative) binding sites in cis-regulatory regions of BMP7?

3)<br /> - In Fig 6C, CEP expression is close to hair follicle in both anagen and telogen. Also, in Telogen, CEP expression is strong and very close to HFSCs. But In rebuttal Fig 2, CEP is localized to sebaceous gland, where MPO, a CEP producing enzyme, is expressed. Which one is correct? Also, if CEP is strongly expressed in Telogen (Fig 6C), how can HFSCs stay in quiescence with decreased BMP signaling?

eLife assessment:

The authors report a novel hepatic lncRNA FincoR regulating FXR with therapeutic implications in the treatment of MASH. The findings are important and use an appropriate methodology in line with the current state-of-the-art, with convincing support for the claims.

Reviewer #1 (Public Review):

Summary:

In the article titled "Hammerhead-type FXR agonists induce an eRNA FincoR that ameliorates nonalcoholic steatohepatitis in mice," the authors explore the role of the Farnesoid X Receptor (FXR) in treating metabolic disorders like NASH. They identify a new liver-specific long non-coding RNA (lncRNA), FincoR, regulated by FXR, notably induced by agonists such as tropifexor. The study shows that FincoR plays a significant role in enhancing the efficacy of tropifexor in mitigating liver fibrosis and inflammation associated with NASH, suggesting its potential as a novel therapeutic target. The study makes a promising contribution to understanding the role of FincoR in alleviating liver fibrosis in NASH, providing initial insights into the mechanisms involved. While it offers a valuable starting point, there is potential for further exploration into the functional roles of FincoR and their specific actions in human NASH cases. Building upon the current findings to elucidate more detailed mechanistic pathways through which FincoR exerts its therapeutic effects in liver disease would elevate the research's significance and potential impact in the field.

Strengths:

This study stands out for its comprehensive and unbiased approach to investigating the role of FincoR, a liver-specific lncRNA, in the treatment of NASH. Key strengths include: 1) The application of advanced sequencing methods like GRO-seq and RNA-seq offered a comprehensive and unbiased view of the transcriptional changes induced by tropifexor, particularly highlighting the role of FincoR. 2) Utilizing a genetic mouse model of FXR KO and a FincoR liver-specific knockdown (FincoR-LKD) mouse model provided a controlled and relevant environment for studying NASH, allowing for precise assessment of tropifexor's therapeutic effects. 3) The inclusion of tropifexor, an FDA-approved FXR agonist, adds significant clinical relevance to the study. It bridges the gap between experimental research and potential therapeutic application, providing a direct pathway for translating these findings into real-world clinical benefits for NASH patients. 4) The study's rigorous experimental design, incorporating both negative and positive controls, ensured that the results were specifically attributable to the action of FincoR and tropifexor.

Weaknesses:

The study presents several notable weaknesses that could be addressed to strengthen its findings and conclusions: 1) The authors focus on FincoR, but do not extensively test other lncRNAs identified in Figure 1A. A more comprehensive approach, such as rescue experiments with these lncRNAs, would provide a better understanding of whether similar roles are played by other lncRNAs in mitigating NASH. 2) FincoR was chosen for further study primarily because it is the most upregulated lncRNA induced by GW4064. Including another GW4064-induced lncRNA as a control in functional studies would strengthen the argument for FincoR's unique role in NASH. 3) The study does not conclusively demonstrate whether FincoR is specifically expressed in hepatocytes or other liver cell types. Conducting FincoR RNA-FISH with immunofluorescent experiments or RT-PCR, using markers for different liver cell types, would clarify its expression profile. 4) Understanding the absolute copy number of FincoR is crucial. Determining whether there are sufficient copies of FincoR to function as proposed would lend more credibility to its suggested role. 5) The manuscript, although technically proficient, does not thoroughly address the relevance of these findings to human NASH. Questions like the conservation of FincoR in humans and its potential role in human NASH should be discussed.

Reviewer #2 (Public Review):

Summary:

Nonalcoholic fatty liver disease (NASH), recently renamed as metabolic dysfunction-associated steatohepatitis (MASH) is a leading cause of liver-related death. Farnesoid X receptor (FXR) is a promising drug target for treating NASH and several drugs targeting FXR are under clinical investigation for their efficacy in treating NASH. The authors intended to address whether FXR mediates its hepatic protective effects through the regulation of lncRNAs, which would provide novel insights into the pharmacological targeting of FXR for NASH treatment. The authors went from an unbiased transcriptomics profiling to identify a novel enhancer-derived lncRNA FincoR enriched in the liver and showed that the knockdown of FincoR in a murine NASH model attenuated part of the effect of tropifexor, an FXR agonist, namely inflammation and fibrosis, but not steatosis. This study provides a framework for how one can investigate the role of noncoding genes in pharmacological intervention targeting known protein-coding genes. Given that many disease-associated genetic variants are located in the non-coding regions, this study, together with others, may provide useful information for improved and individualized treatment for metabolic disorders.

Strengths:

The study leverages both transcriptional profile and epigenetic signatures to identify the top candidate eRNA for further study. The subsequent biochemical characterization of FincoR using FXR-KO mice combined with Gro-seq and Luciferase reporter assays convincingly demonstrates this eRNA as a FXR transcriptional target sensitive to FXR agonists. The use of in vitro culture cells and the in vivo mouse model of NASH provide multi-level evaluation of the context-dependent importance of the FincoR downstream of FXR in the regulation of functions related to liver dysfunction.

Weaknesses:

As discussed, future work to dissect the mechanisms by which FincoR facilitates the action of FXR and its agonists is warranted. It would be helpful if the authors could base this on the current understanding of eRNA modes of action and the observed biochemical features of FincoR to speculate potential molecular mechanisms explaining the observed functional phenotype. It is unclear if this eRNA is conserved in humans in any way, which will provide relevance to human disease. Additionally, the eRNA knockdown was achieved by deletion of an upstream region of the eRNA transcription. A more direct approach to alter eRNA levels, e.g., overexpression of FincoR in the liver would provide important data to interpret its functional regulation.

eLife assessment

This study presents a valuable finding that serum androstenedione levels may provide a new biomarker for early detection and progression of glaucoma, although a single biomarker is unlikely to be singularly predictive due to the etiological heterogeneity of the disease. The strength of the evidence presented is solid, supported by multiple lines of evidence.

Reviewer #1 (Public Review):

Glaucoma is the leading cause of irreversible blindness worldwide, affecting more than 80 million people. Primary open angle glaucoma (POAG) is the prevalent form of glaucoma, while prevalence of primary angle closure glaucoma (PACG) is highest in Asia compared to over the world. Early detection of glaucoma and severity prediction is mandatory, and therefore the main aim of this study focused on characterizing the metabolite profile associated with PACG, identify potential blood diagnostic markers, assess their specificity for PACG and verify their applicability to predict progression of visual field loss. To this end, Li et al. implemented a 5-phases multicenter prospective study to identify novel candidate biomarkers of PACG. A total of 616 individuals were recruited, identifying 1464 distinct metabolites in the serum by metabolomics and chemiluminescence immunoassays. By applying different machine learning algorithms the metabolite androstenedione showed good discrimination between PACG and control subjects, both the discovery and validation phases. This metabolite also showed alterations in the aqueous humor and higher levels of androstenedione seemed to be associated with faster loss of visual field. Overall, the authors claimed that serum androstenedione levels may provide a new biomarker for early detection and monitoring/predicting PACG severity/progression.

Strengths:

• Omics research on glaucoma is constrained by inadequate sample sizes, a dearth of validation sets to corroborate findings and absence of specificity analyses. The 5-phases study designed try overcoming these limitations. The proposed study design is very robust, with well described discovery set (1 and 2), validation phase (1 and 2), supplemental phase and cohort phase. Large and well-characterized patients with adequate control subjects contributed to the robustness of the results.<br /> • Combining untargeted and targeted metabolomics using mass spectrometry instruments (high resolution and low resolution) with an additional chemiluminiscence immunoassay determining androstenedione levels<br /> • Androstenedione achieved better diagnostic accuracy across the discovery and validation sets, with AUC varying between 0.85 and 1.0. Interestingly, baseline androstenedione levels can predict glaucoma progression via visual field loss results.<br /> • Positive correlation was observed between levels of androstenedione in serum and aqueous humor of PACG patients.<br /> • A level higher of 1.66 ng/mL of the metabolite androstenedione seems to imply high risk of visual field loss. Androstenedione may serve as predictor of glaucomatous visual field progression.

Weaknesses:

• A single biomarker seems very unlikely to be of much help in the detection of glaucoma due to the etiological heterogeneity of the disease, the existence of different subtypes, and the genetic variability among patients. Rather, a panel of biomarkers may provide more useful information for clinical prediction, including better sensitivity and specificity. The inclusion of additional metabolites already identifying in the study, in combination, may provide more reliable and correct assignment results.<br /> • The number of samples in the supplementary phase is low, larger samples sizes are mandatory to confirm the diagnostic accuracy.<br /> • Cohorts from different populations are needed to verify the applicability of this candidate biomarker.<br /> • Sex hormones seem to be associated also with other types of glaucoma, such as primary open-angle glaucoma (POAG), although the molecular mechanisms are unclear (see doi:10.1167/iovs.17-22708). The inclusion of patients diagnosed with other subtypes of glaucoma, like POAG, may contribute to determine the sensitivity and specificity of the proposed biomarker. Androstenedione levels should be determined in POAG, NTG or PEXG patients.<br /> • In addition, the levels of androstenedione were found significantly altered during other diseases as described by the authors or by conditions like polycystic ovary syndrome, limiting the utility of the proposed biomarker.<br /> • Uncertainty of the androstenedione levels compromises its usefulness in clinical practice.

Reviewer #2 (Public Review):

Summary:

The objective of authors using metabolomics analysis of primary angle closure glaucoma (PACG) is to demonstrate that serum androstenedione is a novel biomarker that can be used to diagnose PACG and predict visual field progression.

Strengths:

Use of widely targeted and untargeted metabolite detection conditions. Use of liquid chromatography-tandem mass spectrometry and a chemiluminescence method for confirmation of androstenedione.

The authors have incorporated the relevant changes in their manuscript and improved the presentation.

eLife assessment

This fundamental study examines the effects of herbivory-induced maize volatiles on neighbouring plants and their responses over time. Measurements of volatile compound classes and gene expression in receiver plants exposed to these volatiles led to the conclusion that the delayed emission of certain terpenes in receiver plants after the onset of light may be a result of stress memory, highlighting the role of priming and induction in plant defences triggered by herbivore-induced plant volatiles. The evidence supporting the conclusions is compelling, with rigorous chemical assays of and state-of-the-art high throughput real time mass spectrometry. The work will be of broad interest to plant biologists and chemical ecologists.

Reviewer #1 (Public Review):

The authors of the manuscript "High-resolution kinetics of herbivore-induced plant volatile transfer reveal tightly clocked responses in neighboring plants" assessed the effects of herbivory induced maize volatiles on receiver plants over a period of time in order to assess the dynamics of the responses of receiver plants. Different volatile compound classes were measured over a period of time using PTR-ToF-MS and GC-MS, under both natural light:dark conditions, and continuous light. They also measured gene expression of related genes as well as defense related phytohormones. The effects of a secondary exposure to GLVs on primed receiver plants was also measured.

The paper addresses some interesting points, however some questions arise regarding some of the methods employed. Firstly, I am wondering why VOCs (as measured by GC-MS) were not quantified. While I understand that quantification is time consuming and requires more work, it allows for comparisons to be made between lines of the same species, as well as across other literature on the subject. Simply relying on the area under the curve and presenting results using arbitrary units is not enough for analyses like these. AU values do not allow for conclusions regarding total quantities, and while I understand that this is not the main focus of this paper, it raises a lot of uncertainty for readers (for example, the references cited show that TMTT has been found to accumulate at similar levels of caryophyllene, however the AU values reported are an order of magnitude higher for TMTT. Again, without actual quantification this is meaningless, but for readers it is confusing).

With regards to the correlation analyses shown in figure 6, the results presented in many of the correlation plots are not actually informative. While there is a trend, I do not think that this is an appropriate way to show the data, as there are clearly other relationships at play. The comparison between plants under continuous light and normal light:dark conditions is interesting.

This paper addresses a very interesting idea and I look forward to seeing further work that builds on these ideas.

Reviewer #2 (Public Review):

The exact dynamics of responses to volatiles from herbivore-attacked neighbouring plants have been little studied so far. Also, we still lack evidence whether herbivore-induced plant volatiles (HIPVs) induce or prime plant defences of neighbours. The authors investigated the volatile emission patterns of receiver plants that respond to the volatile emission of neighbouring sender plants which are fed upon by herbivorous caterpillars. They applied a very elegant approach (more rigorous than the current state-of-the-art) to monitor temporal response patterns of neighbouring plants to HIPVs by measuring volatile emissions of senders and receivers, senders only and receivers only. Different terpenoids were produced within 2 h of such exposure in receiver plants, but not during the dark phase. Once the light turned on again, large amounts of terpenoids were released from the receiver plants. This may indicate a delayed terpene burst, but terpenoids may also be induced by the sudden change in light. As one contrasting control, the authors also studied the time-delay in volatile emission when plants were just kept under continuous light. Here they also found a delayed terpenoid production, but this seemed to be lower compared to the plants exposed to the day-night-cycle. Another helpful control was now performed for the revision in which the herbivory treatment was started in the evening hours and lights were left on. This experiment revealed that the burst of terpenoid emission indeed shifted somewhat. Circadiane and diurnal processes must thus interact.

Interestingly, internal terpene pools of one of the leaves tested here remained more comparable between night and day, indicating that their pools stay higher in plants exposed to HIPVs. In contrast, terpene synthases were only induced during the light-phase, not in the dark-phase. Moreover, jasmonates were only significantly induced 22 h after onset of the volatile exposure and thus parallel with the burst of terpene release.

An additional experiment exposing plants to the green leaf volatile (glv) (Z)-3-hexenyl acetate revealed that plants can be primed by this glv, leading to a stronger terpene burst. The results are discussed with nice logic and considering potential ecological consequences. All data are now well discussed.

Overall, this study provides intriguing insights in the potential interplay between priming and induction, which may co-occur, enhancing (indirect and direct) plant defence. Follow-up studies are suggested that may provide additional evidence.

eLife assessment

This useful study asks how the architecture of gene expression differences relates to the development of two alternative morphs in a marine annelid species. The dataset will be of value to the field and the work has the potential to substantially advance our understanding of life history evolution. However, in its current form, the lack of details for some methods and analyses makes the strength of the evidence incomplete. If suitably improved, the work would be of interest to anyone studying the evolution of development and life histories.

Reviewer #1 (Public Review):

Summary:<br /> Overall, this study provides a meticulous comparison of developmental transcriptomes between two sub-species of the annelid Streblospio benedicti. Different lineages of S. benedicti maintain one of two genetically programmed alternative life histories, the ancestral planktotrophic or derived lecithotrophic forms of development. This contrast is also seen at the inter-species level in many marine invertebrate taxa, such as echinoderms and molluscs. The authors report relatively (surprisingly?) modest differences in transcriptomes overall but also find some genes whose expression is essentially morph-specific (which they term "exclusive").

Strengths:<br /> The study is based on a dense and appropriately replicated sampling of early development. The tight clustering of each stage/morph combination in PCA space suggests the specimens were accurately categorized. The similar overall trajectories of the two morphs were surprising to me for two stages: 1) the earliest stage (16-cell), at which we might expect maternal differences due to the several-fold difference in zygote size, and 2) the latest stage (1-week), where there appears to be the most obvious morphological difference. This is why we need to do experiments!

The examination of F1 hybrids was another major strength of the study. It also produced one of the most surprising results: though intermediate in phenotype, F1 embryos have the most distinct transcriptomes, and reveal a range of fixed, compensatory differences in the parental lines.

Weaknesses:<br /> Overall I really enjoyed this paper, but I see a few places where it can be tightened and made more insightful. These relate to better defining the basis for "exclusive" expression (regulation or gene presence/absence?), providing more examples of how specific genes related to trophic mode behave, and placing the study in the context of similar work in other phyla.

Reviewer #2 (Public Review):

The manuscript by Harry and Zakas determined the extent to which gene expression differences contribute to developmental divergence by using a model that has two distinct developmental morphs within a single species. Although the authors did collect a valuable dataset and trends in differential expression between the two morphs of S. benedicti were presented, we found limitations about the methods, system, and resources that the authors should address.

We have two major points:

1. Background information about the biological system needs to be clarified in the introduction of this manuscript. The authors stated that F1 offspring can have intermediate larval traits compared to the parents (Line 81). However, the authors collected F1 offspring at the same time as the mother in the cross. If offspring have intermediate larval traits, their developmental timeline might be different than both parents and necessitate the collection of offspring at different times to obtain the same stages as the parents. Could the authors (1) explain why they collected offspring at the same time as parents given that other literature and Line 81 state these F1 offspring develop at intermediate rates, and (2) add the F1 offspring to Figure 1 to show morphological and timeline differences in development?

Additionally, the authors state (Lines 83-85) that they detail the full-time course of embryogenesis for both the parents and the F1 crosses. However, we do not see where the authors have reported the full-time course for embryogenesis of the F1 offspring. Providing this information would shape the remaining results of the manuscript.

2. We have several concerns about the S. benedicti genome and steps regarding the read mapping for RNA-seq:

The S. benedicti genome used (Zakas et al. 2022) was generated using the PP morph. The largest scaffolds of this assembly correspond to linkage groups, showing the quality of this genome. The authors should point out in the Methods and/or Results sections that the quality of this genome means that PP-specific gene expression can be quantified well. However, the challenges and limitations of mapping LL-specific expression data to the PP genome should be discussed.

It is possible that the authors did not find exclusive gene expression in the LL morph because they require at least one gene to be turned on in one morph as part of the data-cleaning criteria. Because the authors are comparing all genes to the PP morph, they could be missing true exclusive genes responsible for the biological differences between the two morphs. Did they make the decision to only count genes expressed in one stage of the other morph because the gene models and mapping quality led to too much noise?

The authors state that the mapping rates between the two morphs are comparable (Supplementary Figure 1). However, there is a lot of variation in mapping the LL individuals (~20% to 43%) compared to the PP individuals. What is the level of differentiation within the two morphs in the species (pi and theta)? The statistical tests for this comparison should be added and the associated p-value should be reported. The statistical test used to compare mapping rates between the two morphs may be inappropriate. The authors used Salmon for their RNA alignment and differential expression analysis, but it is possible that a different method would be more appropriate. For example, Salmon has some limitations as compared to Kallisto as others have noted. The chosen statistical test should be explained, as well as how RNA-seq data are processed and interpreted.

What about the read mapping rate and details for the F1 LP and PL individuals? How did the offspring map to the P genome? These details should be included in Supplementary Figure 1. Could the authors also provide information about the number of genes expressed at each stage in the F1 LP and PL samples in S Figure 2? How many genes went into the PCA? Many of these details are necessary to evaluate the F1 RNA-seq analyses.

Generally, the authors need to report the statistics used in data processing more thoroughly. The authors need to report the statistics used to (1) process and evaluate the RNA-seq data and (2) determine the significance between the two morphs (Supplementary Figures 1 and 2).

eLife assessment

This is an important paper as it is the first to use a reconstituted translation system to study competition among mRNAs for the initiation machinery. Understanding the principles of the biochemistry of mRNA competition for initiation factors cannot be achieved without such a system. The authors provide compelling evidence that Ded1 is required for efficient initiation in highly structured RNAs. A highly significant finding that validates the in vitro reconstituted system indeed recapitulates the effects of in-vivo perturbations of translation initiation.

Reviewer #2 (Public Review):

Summary:

Zhou et al report development of a new method, Rec-Seq, that allows rigorous quantitation of the efficiency of 48S ribosomal pre-initiation complex (PIC) formation on messenger RNAs at transcriptome scale in vitro. With a next-generation deep-sequencing approach, Rec-Seq allows precisely targeted dissection of the roles of translation initiation factors in PIC assembly. This level of molecular precision is important to understanding mechanisms of translational control, making Rec-Seq a significant methodological advance. The authors leverage Rec-Seq to investigate the relative roles of two key helicase enzymes, Ded1p and eIF4A. While past work has pointed to differing roles for Ded1p and eIF4A helicase activity in PIC assembly, unambiguous interpretation of prior in-vivo data has been hindered by technical requirements for performing the experiments in cells. Rec-Seq circumvents these challenges, providing robust mechanistic insights. The authors find that Ded1p stimulates PIC formation selectively on mRNAs with long, structured leaders in the Rec-Seq system, while eIF4A provides much more general stimulation across mRNAs. The findings substantiate the past in-vivo results, along with adding new insights. They contrast with evidence that Ded1p promotes translation by suppressing inhibitory upstream initiation through structural remodeling, or through formation of intracellular, phase-separated granules. The conclusions of the study are generally well-supported by the data.

Strengths:

The quantitative nature of Rec-Seq, which uses an internal standard to measure absolute recruitment efficiencies, is an important strength.

The methodology decisively overcomes past experimental limitations, allowing the authors to make clear conclusions with regard to the relative roles of Ded1p and eIF4A in PIC formation. An important and useful addition to the toolbox for studying translation and translational control mechanisms, Rec-Seq substantially expands the throughput and scope of mechanistic analyses for translation initiation.

One significant finding to emerge is that the in-vitro reconstituted system used here recapitulates effects of in-vivo perturbations of translation initiation. Despite the lack of a cellular environment and its components, PIC formation appears to operate much as it does in the cell. Importantly, this highlights an inherent "modularity" to the system that is especially of interest in the context of how regulatory machinery beyond the PIC may control translation.

Weaknesses:

Several findings in this report are quite surprising and may require additional work to fully interpret. Primary among these is the finding that Ded1p stimulates accumulation of PICs at internal site in mRNA coding sequences at an incidence of up to ~50%. The physiological relevance of this is unclear.

A limitation of the methodology is that, as an endpoint assay, Rec-Seq does not readily decouple effects of Ded1p on PIC-mRNA loading from those on the subsequent scanning step where the PIC locates the start codon. Considering that Ded1p activity may influence each of these initiation steps through distinct mechanisms - i.e., binding to the mRNA cap-recognition factor eIF4F, or direct mRNA interaction outside eIF4F - additional studies may be needed to gain deeper mechanistic insights.

As the authors note, the achievable Ded1p concentrations in Rec-Seq may mask potential effects of Ded1p-based granule formation on translation initiation. Additional factors present in the cell could potentially also promote this mechanism. Consequently, the results do not fully rule out granule formation as a potential parallel Ded1p-mediated translation-inhibitory mechanism in cells.

Reviewer #3 (Public Review):

Summary:

The manuscript of Zhou et al. reports a genome wide study of in vitro translation initiation using a novel version of ribosome profiling. Here they probe the role of the key RNA helicase, Ded1 in yeast translation initiation using a reconstituted biochemical system and all polyA+ mRNAs in the cell. The authors use ribosome profiling to identify mRNAs that assemble a preinitiation complex at the AUG start codon (48S PIC). They confirm that Ded1 is required for efficient initiation in highly structured RNAs, leading to an increase in PIC formation at the start codon, and nicely correlate their results with prior in vivo investigations using mutant Ded1s.

Strengths:

Rigorous in vitro biochemistry, careful correlation with in vivo results, genome wide analysis. Novel sequencing-based assay.

Weaknesses:

The slow nature of the biochemical experiments could bias results.

Reviewer #1 (Public Review):

Summary:

The authors have developed and optimized a footprinting assay to monitor the recruitment of mRNAs to a reconstituted translation initiation system. This assay is named Recruitment-Sequencing (Rec-Seq) and enables the analysis of many purified mRNAs in the reconstituted system.

This system possesses the ability to determine how competition occurs between mRNAs for the initiation machinery. This is the first approach using a reconstituted system that enables this important feature, and this is an important advance for the field.

Strengths:

Using purified mRNAs in a fully reconstituted system and being able to monitor start site selection is an important advance. The method enables one to observe changes in mRNA recruitment and start site selection in response to the absence or presence of different initiation components or accessory proteins.

Weaknesses:

Start site fidelity in purified reconstituted systems can be dramatically altered in different buffer conditions. Interpretation of the observed changes to start site selection in mRNAs in the absence or presence of Ded1 using only the one buffer condition used is therefore limited.

Author Response

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

Reviewer #1 (Public Review):

Issue 1: The relevance is somewhat unclear. High cysteine levels can be achieved in the laboratory, but, is this relevant in the life of C. elegans? Or is there physiological relevance in humans, e.g. a disease? The authors state "cells and animals fed excess cysteine and methionine", but is this more than a laboratory excess condition? SUOX nonfunctional conditions in humans don't appear to tie into this, since, in that context, the goal is to inactivate CDO or CTH to prevent sulfite production. The authors also mention cancer, but the link to cysteine levels is unclear. In that sense, then, the conditions studied here may not carry much physiological relevance.

Response 1: We set out to answer a fundamental question: what pathways regulate the function of cysteine dioxygenase, a highly conserved enzyme in sulfur amino acid metabolism? In an unbiased genetic screen that sampled millions of EMS generated mutations across all ~20,000 C. elegans genes, we discovered loss of function/null mutations in egl-9 and rhy-1, two negative regulators of the hypoxia inducible transcription factor (hif-1). Genetic ablation of the egl-9 or rhy-1 loci are likely not relevant to the life of a C. elegans animal, i.e. this is not representative of a natural state. Yet, this extreme genetic intervention has taught us a new fundamental truth about the interaction between EGL-9/RHY-1, HIF-1, and the transcriptional activation of cdo1. Similarly, the high cysteine levels used in our assays may or may not be representative of a state in nature, we do not know (nor do we make any claims about the environmental relevance of our choice of cysteine concentrations). It seems very plausible that pathological states exist where cysteine concentrations may rise to comparable levels in our experimental system. More importantly, we have started with excess to physiology to elicit a clear response that we can study in the lab. Similar strategies established the cysteine-induction phenotype of CDO1 in mammalian systems. For instance, in Kwon and Stipanuk 2001, hepatocytes are cultured in media supplemented with 2mmol/L cysteine to promote a ~4-fold increase in CDO1 mRNA.

Issue 2: The pathway is described as important for cysteine detoxification, which is described to act via H2S (Figure 6). Much of that pathway has already been previously established by the Roth, Miller, and Horvitz labs as critical for the H2S response. While the present manuscript adds some additional insight such as the additional role of RHY-1 downstream on HIF-1 in promoting toxicity, this study therefore mainly confirms the importance of a previously described signalling pathway, essentially adding a new downstream target rhy-1 -> cysl-1 -> egl-9 -> hif-1 -> sqrd-1/cdo-1. The impact of this finding is reduced by the fact that cdo-1 itself isn't actually required for survival in high cysteine, suggesting it is merely a maker of the activity of this previously described pathway.

Response 2: We agree that the primary impact of our manuscript is the establishment of a novel intersection between the H2S-sensing pathway (largely worked out by Roth, Miller, and Horvitz) and our gene of interest, cysteine dioxygenase. We believe that the connection between these two pathways is exciting as it suggests a logical homeostatic circuit. High cysteine yields enzymatically produced H2S. This H2S may then act as a signal promoting HIF-1 activity (via RHY-1/CYSL-1/EGL-9). High HIF-1 activity increases cdo-1 transcription and activity promoting the degradation of the high-cysteine trigger. As pointed out by the reviewer, cdo-1(-) loss of function alone does not cause cysteine sensitivity at the concentrations tested. Given that cysl-1(-) and hif-1(-) mutants are exquisitely sensitive to high levels of cysteine, we propose that HIF-1 activates the transcription of additional genes that are required for high cysteine tolerance. However, our genetic data show that cdo-1 is more than simply a marker of HIF-1 transcription. Our genetic data in Table 1 demonstrate that HIF-1 activation (caused by egl-9(-)) is sufficient to cause severe sickness in a suox-1 hypomorphic mutant which cannot detoxify sulfites, a critical product of cysteine catabolism. This severe sickness can be reversed by inactivating hif-1, cth-2, or cdo-1. These data demonstrate a functional intersection between the established H2S-sensing pathway and cysteine catabolism governed by cdo-1.

Reviewer #2 (Public Review):

Issue 3: First, the authors show that the supplementation of exogenous cysteine activates cdo-1p::GFP. Rather than showing data for one dose, the author may consider presenting dose-dependency results and whether cysteine activation of cdo-1 also requires HIF-1 or CYSL-1, which would be important data given the focus and major novelty of the paper in cysteine homeostasis, not the cdo-1 regulatory gene pathway.

Response 3: We agree with the reviewer and have performed the suggested dose-response curve for expression of Pcdo-1::GFP in wild-type C. elegans. We observe substantial activation of the Pcdo-1::GFP transcriptional reporter beginning at 100µM supplemental cysteine (Figure 3C). Higher doses of cysteine do not elicit a substantially stronger induction of the Pcdo-1::GFP reporter. Thus, we find that 100µM supplemental cysteine strikes the right balance between strongly inducing the Pcdo-1::GFP reporter while not inducing any toxicity or lethality in wild-type animals (Figure 3E).

We further agree that testing for induction of the Pcdo-1::GFP reporter in a hif-1(-) or cysl-1(-) mutant background is a critical experiment. However, we have not been able to identify a cysteine concentration that induces Pcdo-1::GFP and is not 100% lethal for hif-1(-) or cysl-1(-) mutant C. elegans. The remarkable sensitivity of hif-1(-) or cysl-1(-) mutant C. elegans to supplemental cysteine demonstrates the critical role of these genes in promoting cysteine homeostasis. But because of this lethality, we could not assay the Pcdo1::GFP reporter in the hif-1(-) or cysl-1(-) mutant animals. But the lethality to excess cysteine demonstrates that this cysteine response is salient. To get at how cysteine might be interacting with the HIF-1-signaling pathway, we performed new additivity experiments by supplementing 100µM cysteine to wild type, egl-9(-), and rhy-1(-) mutant C. elegans expressing the Pcdo-1::GFP reporter. Surprisingly, we found that cysteine had no significant impact on Pcdo-1::GFP expression in an egl-9(-) mutant background but significantly increased the Pcdo-1::GFP expression in a rhy-1(-) background (Figure 3A,B). These data suggest that cysteine acts in a pathway with egl-9 and in parallel to rhy-1. These data have been incorporated into Figure 3A,B and are included in the Results section of the manuscript.

Issue 4: While the genetic manipulation of cdo-1 regulators yields much more striking results, the effect size of exogenous cysteine is rather small. Does this reflect a lack of extensive condition optimization or robust buffering of exogenous/dietary cysteine? Would genetic manipulation to alter intracellular cysteine or its precursors yield similar or stronger effect sizes?

Response 4: We agree that the induction of the Pcdo-1::GFP reporter by supplemental cysteine is not as dramatic as the induction caused by the egl-9 or rhy-1 null alleles. We believe our Response 3 and new Figure 3C demonstrate that this phenomenon is not due to lack of condition optimization, but likely reflects some biology. As pointed out by the reviewer, C. elegans likely buffers exogenous cysteine and this (perhaps) prevents the impressive Pcdo-1::GFP induction observed in the egl-9(-) and rhy-1(-) mutant animals. We have now mentioned this possible interpretation in the Results section. Furthermore, we like the idea of using genetic tricks to promote cysteine accumulation within C. elegans cells and tissues and will consider these approaches in future studies.

Issue 5: Second, there remain several major questions regarding the interpretation of the cysteine homeostasis pathway. How much specificity is involved for the RHY-1/CYSL-1/EGL-9/HIF-1 pathway to control cysteine homeostasis? Is the pathway able to sense cysteine directly or indirectly through its metabolites or redox status in general? Given the very low and high physiological concentrations of intracellular cysteine and glutathione (GSH, a major reserve for cysteine), respectively, there is a surprising lack of mention and testing of GSH metabolism.

Response 5: Future studies are required to determine the specificity of the RHY-1/CYSL-1/EGL-9/HIF-1 pathway for the control of cysteine homeostasis. Our proposed mechanism, that H2S activates the HIF-1 pathway is based largely on the work of the Horvitz lab (Ma et al. 2012). They demonstrate that H2S promotes a direct inhibitory interaction between CYSL-1 and EGL-9, leading to activation of HIF-1. These findings align nicely with our genetic and pharmacological data. However, our work does not provide direct evidence as to the cysteine-derived metabolite that activates HIF-1. We propose H2S as a likely candidate.

We have added a note to the introduction regarding the role of GSH as a reservoir of excess cysteine and agree that future studies might find interesting links between CDO-1, GSH metabolism, and HIF-1.

Issue 6: In addition, what are the major similarities and differences of cysteine homeostasis pathways between C. elegans and other systems (HIF dependency, transcription vs post-transcriptional control)? These questions could be better discussed and noted with novel findings of the current study that are likely C. elegans specific or broadly conserved.

Response 6: We have included a new section in the Discussion highlighting the nature of mammalian CDO1 regulation. We propose the hypothesis that a homologous pathway to the C. elegans RHY-1/CYSL-1/EGL9/HIF-1 pathway might operate in mammalian cells to sense high cysteine and induce CDO1 transcription. Importantly, all proteins in the C. elegans pathway have homologous counterparts in mammals. However, this hypothesis remains to be tested in mammalian systems.

Reviewer #3 (Public Review):

Major weaknesses of the paper include:

Issue 7: the over-reliance on genetic approaches.

Response 7: This is a fair critique. Our expertise is genetics. Our philosophy, which the reviewers may not share, is that there is no such thing as too much genetics!

Issue 8: the lack of novelty regarding prolyl hydroxylase-independent activities of EGL-9.

Response 8: We believe the primary novelty of our work is establishing the intersection between the H2Ssensing HIF-1 pathway and cysteine catabolism governed by cysteine dioxygenase. Our demonstration that cdo-1 regulation operates largely independent of VHL-1 and EGL-9 prolyl hydroxylation is a mechanistic detail of this regulation and not the critical new finding. Although, we believe it does suggest where pathway analyses should be directed in the future. We also believe that our homeostatic feedback model for the regulation of HIF-1 (and cdo-1) by cysteine-derived H2S is new and exciting and provides insight into the logic of why HIF-1 might respond to H2S and promote the activity of cdo-1. Our work suggests that one reason for this intersection of hif-1 and cdo-1 is to sense and maintain cysteine homeostasis when cysteine is in excess.

Issue 9: the lack of biochemical approaches to probe the underlying mechanism of the prolyl hydroxylaseindependent activity of EGL-9.

Response 9: While not the primary focus of our current manuscript, we agree that this is an exciting area of future research. To uncover the prolyl hydroxylase-independent activity of EGL-9, we agree that a combination of approaches will be required including, biochemical, structure-function, and genetic.

Major Issues We Feel the Authors Should Address:

Issue 10: One particularly glaring concern is that the authors really do not know the extent to which the prolyl hydroxylase activity is (or is not) impacted by the H487A mutation in egl-9(rae276). If there is a fair amount of enzymatic activity left in this mutant, then it complicates interpretation. The paper would be strengthened if the authors could show that the egl-9(rae276) eliminates most if not all prolyl hydroxylase activity. In addition, the authors may want to consider doing RNAi for egl-9 in the egl-9(rae276) mutant as a control, as this would support the claim that whatever non-hydroxylase activity EGL-9 may have is indeed the causative agent for the elevation of CDO-1::GFP. Without such experiments, readers are left with the nagging concern that this allele is simply a hypomorph for the single biochemical activity of EGL-9 (i.e., the prolyl hydroxylase activity) rather than the more interesting, hypothesized scenario that EGL-9 has multiple biochemical activities, only one of which is the prolyl hydroxylase activity.

Response 10: We have two lines of evidence that suggest the egl-9(rae276)-encoded H487A variant eliminates prolyl hydroxylase activity. First, Pan et al. 2007 (reference 57) demonstrate that when the equivalent histidine (H313) is mutated in human protein, that protein lacks detectible prolyl hydroxylase activity. Second, the phenotypic similarities caused by egl-9(rae276) and the vhl-1 null allele, ok161. Both alleles cause nearly identical activation of the Pcdo-1::GFP reporter transgene (Fig. 5C,D), and similarly impact the growth of the suox-1(gk738847) hypomorphic mutant (Table 1). This phenotypic overlap is highly relevant as the established role of VHL-1 is to recognize the hydroxyl mark conferred by the EGL-9 prolyl hydroxylase domain and promote the degradation of HIF-1. If EGL-9[H487A] had residual prolyl hydroxylase activity, we would expect the vhl-1(-) null mutant C. elegans to display more dramatic phenotypes than their egl-9(rae276) counterparts. This is not the case.

Issue 11: The authors observed that EGL-9 can inhibit HIF-1 and the expression of the HIF-1 target cdo-1 through a combination of activities that are (1) dependent on its prolyl hydroxylase activity (and subsequent VHL-1 activity that acts on the resulting hydroxylated prolines on HIF-1), and (2) independent of that activity. This is not a novel finding, as the authors themselves carefully note in their Discussion section, as this odd phenomenon has been observed for many HIF-1 target genes in multiple publications. While this manuscript adds to the description of this phenomenon, it does not really probe the underlying mechanism or shed light on how EGL-9 has these dual activities. This limits the overall impact and novelty of the paper.

Response 11: See response to Issues #8.

Issue 12: Cysteine dioxygenases like CDO-1 operate in an oxygen-dependent manner to generate sulfites from cysteine. CDO-1 activity is dependent upon availability of molecular oxygen; this is an unexpected characteristic of a HIF-1 target, as its very activation is dependent on low molecular oxygen. Authors neither address this in the text nor experimentally, and it seems a glaring omission.

Response 12: We agree this is an important point to raise within our manuscript. Although, despite its induction by HIF-1, there is no evidence that cdo-1 transcription is induced by hypoxia. In fact, in a genome wide transcriptomic study, cdo-1 was not found to be induced by hypoxia in C. elegans (Shen et al. 2005, reference 71).

We have newly commented on the use of molecular oxygen as a substrate by both EGL-9 and CDO-1 in our Discussion section. The mammalian oxygen-sensing prolyl hydroxylase (EGLN1) has been demonstrated to have high a Km value for O2 (high µM range). This likely allows EGLN1 to be poised to respond to small decreases in cellular oxygen from normal oxygen tensions. Clearly, CDO-1 also requires oxygen as a substrate, however the Km of CDO-1 for O2 is likely to be much lower, preventing sensitivity of the cysteine catabolism to physiological decreases in O2 availability. Although, to our knowledge, the CDO1 Km value for O2 has not been experimentally determined. We have added a new Discussion section where we address the conundrum about low oxygen inducing HIF-1 but oxygen being needed by CDO-1/CDO1.

Issue 13: The authors determined that the hypodermis is the site of the most prominent CDO-1::GFP expression, relevant to Figure 4. This claim would be strengthened if a negative control tissue, in the animal with the knockin allele, were shown. The hypodermal specific expression is a highlight of this paper, so it would make this article even stronger if they could further substantiate this claim.

Response 13: Our claim that the hypodermis is the critical site of cdo-1 function is based on; i) our hands on experience looking at Pcdo-1::GFP, Pcdo-1::CDO-1::GFP, CDO-1::GFP (encoded by cdo-1(rae273)) and our reporting of these expression patterns in multiple figures throughout the manuscript and ii) the functional rescue of cdo-1(-) phenotypes by a cdo-1 rescue construct expressed by a hypodermal-specific promoter (col10). We agree that providing negative control tissues would modestly improve the manuscript. However, we do not think that adding these controls will substantially alter the conclusions of the paper. Importantly, we acknowledge this limitation of our work with the sentence, “However, we cannot exclude the possibility that CDO-1 also acts in other cells and tissues as well.”

Minor issues to note:

Issue 14: Mutants for hif-1 and cysl-1 are sensitive to exogenous cysteine levels, yet loss of CDO-1 expression is not sufficient to explain this phenomenon, suggesting other targets of HIF-1 are involved. Given the findings the authors (and others) have had showing a role for RHY-1 in sulfur amino acid metabolism, shouldn't the authors consider testing rhy-1 mutants for sensitivity to exogenous cysteine?

Response 14: To test the hypothesis that rhy-1(-) C. elegans might be sensitive to supplemental cysteine, we cultured wild type and rhy-1(-) animals on 0, 100, and 1000µM supplemental cysteine. At 0 and 100µM supplemental cysteine, neither wild-type nor rhy-1(-) animals display any lethality suggesting rhy-1 is not required for survival in the face of excess cysteine (Fig. 3D,E). We also cultured these same strains on 1000µM supplemental cysteine, a concentration that is highly toxic to wild-type animals (100% lethality). rhy1(-) animals were resistant to 1000µM supplemental cysteine with a substantial fraction of the population surviving overnight exposure to this lethal dose of cysteine. Similarly, egl-9(-) mutant C. elegans were also resistant to 1000µM supplemental cysteine. We propose that loss of egl-9 or rhy-1 activates HIF-1-mediated transcription which is priming these mutants to cope with the lethal dose of cysteine. These data are now presented in Figure 3D-F and presented in the Results section.

Issue 15: The cysteine exposure assay was performed by incubating nematodes overnight in liquid M9 media containing OP50 culture. The liquid culture approach adds two complications: (1) the worms are arguably starving or at least undernourished compared to animals grown on NGM plates, and (2) the worms are probably mildly hypoxic in the liquid cultures, which complicates the interpretation.

Response 15: We agree that it is possible that animals growing overnight in liquid culture are undernourished and mildly hypoxic. However, we are confident in our data interpretation as all our experiments are appropriately controlled. Meaning, control and experimental groups were all grown under the same liquid culture conditions. Thus, these animals would all experience the same stressors that come with liquid culture. Importantly, we never make comparisons between groups that were grown under different culture conditions (i.e. solid media vs. liquid culture).

Issue 16: An easily addressable concern is the wording of one of the main conclusions: that cdo-1 transcription is independent of the canonical prolyl hydroxylase function of EGL-9 and is instead dependent on one of EGL-9's non-canonical, non-characterized functions. There are several points in which the wording suggests that CDO-1 toxicity is independent of EGL-9. In their defense, the authors try to avoid this by saying, "EGL-9 PHD," to indicate that it is the prolyl hydroxylase function of EGL-9 that is not required for CDO-1 toxicity. However, this becomes confusing because much of the field uses PHD and EGL-9/EGLN as interchangeable protein names. The authors need to be clear about when they are describing the prolyl hydroxylase activity of EGL-9 rather than other (hypothesized) activities of EGL-9 that are independent of the prolyl hydroxylase activity.

Response 16: We appreciate the reviewer alerting us to this practice within the field. To avoid confusion, we have removed the “PHD” abbreviation from our manuscript and explicitly referred to the “prolyl hydroxylase domain” where relevant.

Issue 17: The authors state in the text, "the egl-9; suox-1 double mutants are extremely sick and slow growing." We appreciate that their "health" assay, based on the exhaustion of food from the plate, is qualitative. We also appreciate that it is a functional measure of many factors that contribute to how fast a population of worms can grow, reproduce, and consume that lawn of food. However, unless they do a lifespan assay and/or measure developmental timing and specifically determine that the double mutant animals themselves are developing and/or growing more slowly, we do not think it is appropriate to use the words "slow growing" to describe the population. As they point out, the rate of consumption of food on the plate in their health assay is determined by a multitude and indeed a confluence of factors; the growth rate is one specific one that is commonly measured and has an established meaning.

Response 17: We see how the phrase ‘slow growing’ might imply a phenotype that we have not actually assessed with this assay. Therefore, we have removed all claims about “slow growth” of the strains presented in Table 1 and have highlighted the assay more overtly in the results section. For example; “While egl-9(-) and suox-1(gk738847) single mutant animals are healthy under standard culture conditions, the egl-9(-); suox1(gk738847) double mutant animals are extremely sick and require significantly more days to exhaust their E. coli food source under standard culture conditions (Table 1).”

Reviewer #1 (Recommendations For The Authors):

Issue 18: Relevance could be addressed further in the text.

Response 18: We have added additional context for our work in the Discussion section. Please see our response to Issues #5, 6, 12, and 24.

Issue 19: Better appreciation and integration of the manuscript's findings with published studies would be appropriate.

Response 19: We have added additional context for our work in the Discussion section. Please see our response to Issues #5, 6, 12, and 24.

Issue 20: It might be perhaps relevant to test whether cdo-1 is relevant for hypoxia resistance since it appears to be a key target for hif-1.

Response 20: We agree that this is an interesting future direction, however given that cdo-1 mRNA is not induced by hypoxia (Shen et al. 2005) we have not prioritized these experiments for the current manuscript.

Issue 21: "egl-9 inhibits cdo-1 transcription in a prolyl-hydroxylase and VHL-1-independent manner" should be tempered. vhl-1 mutants and egl-9 hydroxylase point mutant still have significant induction of the reporter.

Response 21: Thank you for identifying this oversight. We have modified the Figure 5 legend title to read, “egl9 inhibits cdo-1 transcription in a largely prolyl-hydroxylase and VHL-1-independent manner.”

Issue 22: Please use line numbers in the future for easier tracking of comments.

Response 22: We shall.

Issue 23: Abstract and elsewhere, "high cysteine activates...", should be rephrased to "high levels of cysteine".

Response 23: We have made this change throughout the manuscript.

Reviewer #3 (Recommendations For The Authors):

Issue 24: The authors discuss CDO1 in the context of tumorigenesis, as well as the potential regulation between cysteine and the hypoxia response pathway. Thus, I was surprised that there was no mention of the foundational Bill Kaelin paper (Briggs et al 2016) showing how the accumulation of cysteine is related to tumorigenesis, and that cysteine is a direct activator of EglN1. Puzzling that CDO1 is a tumor suppressor: you lose it, cysteine can accumulate and activate EglN1, causing HIF1 turnover. How do the authors reconcile their results with this paper? I was also surprised that there was no mention in the Discussion of the role of hydrogen sulfide, cysteine metabolism, and CTH and CBS in oxygen sensation in the carotid body given the role they play there. Seems important to discuss this issue.

Response 24: We have added new sections to our Discussion that consider the relationship between our work and Briggs et al. 2016 as well as mentioned the role of CTH and H2S in the mammalian carotid body.

Issue 25: The abstract has a variety of contradictory statements. For example, the authors state that "HIF-1mediated induction of cdo-1 functions largely independent of EGL-9," but then go on to conclude in the final sentence that cysteine stimulates H2S production, which then activates EGL-9 signaling, which then increases HIF-1-mediated transcription of cdo-1. A quick reading of the abstract leaves the reader uncertain whether EGL-9 is or is not involved in this regulation of cdo-1 expression. In addition, the conclusion sentence implies that activation of the EGL-9 pathway increases HIF-1-mediated transcription, yet it is well established that EGL-9 is an inhibitor of HIF-1. The abstract fails to deliver a clear summary of the paper's conclusions. Perhaps consider this alternative (changes in capital letters):

The amino acid cysteine is critical for many aspects of life, yet excess cysteine is toxic. Therefore, animals require pathways to maintain cysteine homeostasis. In mammals, high cysteine activates cysteine dioxygenase, a key enzyme in cysteine catabolism. The mechanism by which cysteine dioxygenase is regulated remains largely unknown. We discovered that C. elegans cysteine dioxygenase (cdo-1) is transcriptionally activated by high cysteine and the hypoxia inducible transcription factor (hif-1). hif-1- dependent activation of cdo-1 occurs downstream of an H2S-sensing pathway that includes rhy-1, cysl-1, and egl-9. cdo-1 transcription is primarily activated in the hypodermis where it is sufficient to drive sulfur amino acid metabolism. EGL-9 and HIF-1 are core members of the cellular hypoxia response. However, we demonstrate that the mechanism of HIF-1-mediated induction of cdo-1 IS largely independent of EGL-9 prolyl hydroxylASE ACTIVITY and the von Hippel-Lindau E3 ubiquitin ligase. We propose that the REGULATION OF cdo-1 BY HIF-1 reveals a negative feedback loop for maintaining cysteine homeostasis. High cysteine stimulates the production of an H2S signal. H2S then ACTS THROUGH the rhy-1/cysl-1/egl-9 signaling pathway DISTINCTLY FROM THEIR ROLE IN HYPOXIA RESPONSE TO INCREASE HIF-1-mediated transcription of cdo-1, promoting degradation of cysteine via CDO-1.

Response 25: We agree that the abstract could be clearer. We believe this concern stems from the fact that we did not discuss our initial screen in the abstract. Thus, we failed to establish a role for egl-9 in the regulation of cdo-1. To remedy this, we have modified the abstract as suggested by the reviewer and added additional context. We believe that these changes improve the clarity of the Abstract substantially.

Issue 26: An easily addressable concern involves the "dark" microscopy controls showing lack of fluorescence from a nematode. In these dark negative control micrographs, the authors should draw dotted outlines around where the worms are or include a brightfield image next to the fluorescence image. On a computer screen, it is in fact possible to make out the worms. Yet, when printed out, the reader must assume there are worms in the dark images. Additionally, we realize that adjusting fluorescence so that wild-type CDO-1 expression can be seen will result in oversaturation of the egl-9 and rhy-1; cdo-1 doubles; however, this would be a useful figure to add into the supplement to both provide a normal reference of CDO-1 low-level expression and a demonstration of just how bright it is in the mutant backgrounds. It would also be useful for you to please report your exposure settings for purposes of reproducibility.

Response 26: As suggested, we have added dotted lines around the location of the C. elegans animals in all images where GFP expression is low or basal. We have also reported the exposure times for each image in the appropriate figure legends.

Issue 27: This title is quite generic and doesn't even mention the main players (CDO-1 and sulfite metabolism).

Response 27: We have updated our title to call attention to cysteine dioxygenase. The improved title is: “Hypoxia-inducible factor induces cysteine dioxygenase and promotes cysteine homeostasis in Caenorhabditis elegans”

Issue 28: The authors mention two disorders in which CDO-1 plays a pathogenic role: MoCD and ISOD. We recommend switching the order in which the authors mention these, as the remainder of the paragraph is about MoCD. Also, they should write out the number "2" in the first sentence of that paragraph.

Response 28: We have made the suggested changes.

Issue 29: The authors state in the main text, "...to ubiquitinate HIF-1, targeting it for degradation by the proteosome." Here, they should refer to the pathway in Figure 5a.

Response 29: We have made the suggested change.

Issue 30: The authors state in the main text, "Elements of the HIF-1 pathway have emerged..." which is vague and confusingly worded. Change to, "Members of the HIF-1 pathway and its targets have emerged from C. elegans genetic studies."

Response 30: We have made the suggested change.

Issue 31: Clarify in the figure legends that supplemental cysteine did not affect the mortality of worms that were imaged.

Response 31: We have added this note to Figure 3A and Figure S3A.

Issue 32: Figure 1b. "the cdo-1 promoter is shown..." Add: "as a straight line" to the end of this phrase.

Response 32: We have made the suggested change.

Issue 33: The authors should consider changing the red text in Figure 1 to magenta, which tends to be more readable for people who have limited color vision.

Response 33: We have adjusted the colors in Figure 1 as suggested.

Issue 34: Figure 2, legend title. Consider changing "hif-1" to "HIF-1," as well as rhy-1, cysl-1, and egl-9. In this case, they are talking about proteins, not mutants or genes. This will make the paper easier to follow for readers who lack a C. elegans background.

Response 34: We have made the suggested change.

Issue 35: Figure 5, caption text. "...indicates weak similarity." Add, "amongst species compared."

Response 35: We have made the suggested change.

Issue 36: It is starting to become a standard for showing the datapoints in bar graphs. Although this is done in many graphs in the paper, it should also be done for Figure S1 and Figure 4C.

Response 36: We have made the suggested change.

Issue 37: An extensive ChIP-seq and RNA-seq analysis of C. elegans HIF-1 was recently published (Vora et al, 2022), which the authors should reference in support of the regulation of CDO-1 transcription by HIF-1 in their description of published expression studies of the pathway (Results section, page 4). Indeed, Vora et al were key generators of the ChIP-seq data cited in Warnhoff et al but not included as authors in the ModERN/ModENCODE publication: their contributions were published separately in Vora et al and should be acknowledged equivalently.

Response 37: We appreciate the reviewer pointing this detail out and we have added the correct citation as indicated.

eLife assessment

The study presents valuable findings on how the hypoxia response pathway senses and responds to changes in the homeostasis of the amino acid cysteine and other sulfur-containing molecules. By providing a compelling, rigorous genetic analysis of the pathway, the study adds to a growing body of literature showing that prolyl hydroxylation is not the only mechanism by which the hypoxia response pathway can act. Although the paper does not reveal new biochemical insight into the mechanism, it opens up new areas of investigation that will be of interest to cell biologists and biomedical researchers studying the many pathologies involving hypoxia and/or cysteine metabolism.

Reviewer #2 (Public Review):

The authors investigate the transcriptional regulation of cysteine dioxygenase (CDO-1) in C. elegans and its role in maintaining cysteine homeostasis. They show that high cysteine levels activate cdo-1 transcription through the hypoxia-inducible transcription factor HIF-1. Using transcriptional and translational reporters for CDO-1, the authors propose that a negative feedback pathway involving RHY-1, CYSL-1, EGL-9 and HIF-1 in regulating cysteine homeostasis.

Genetics is a notable strength of this study. The forward genetic screen, gene interaction and epistasis analyses are beautifully designed and rigorously conducted, yielding solid and unambiguous conclusions on the genetic pathway regulating CDO-1. The writing is clear and accessible, contributing to the overall high quality of the manuscript.<br /> Addressing the specifics of cysteine supplementation and interpretation regarding the cysteine homeostasis pathway would further clarify the paper and strengthen the study's conclusions.

First, the authors show that the supplementation of exogenous cysteine activates cdo-1p::GFP. Rather than showing data for one dose, the author may consider presenting dose-dependency results and whether cysteine activation of cdo-1 also requires HIF-1 or CYSL-1, which would be important data given the focus and major novelty of the paper in cysteine homeostasis, not the cdo-1 regulatory gene pathway. While the genetic manipulation of cdo-1 regulators yields much more striking results, the effect size of exogenous cysteine is rather small. Does this reflect a lack of extensive condition optimization or robust buffering of exogenous/dietary cysteine? Would genetic manipulation to alter intracellular cysteine or its precursors yield similar or stronger effect sizes?

Second, there remain several major questions regarding the interpretation of the cysteine homeostasis pathway. How much specificity is involved for the RHY-1/CYSL-1/EGL-9/HIF-1 pathway to control cysteine homeostasis? Is the pathway able to sense cysteine directly or indirectly through its metabolites or redox status in general? Given the very low and high physiological concentrations of intracellular cysteine and glutathione (GSH, a major reserve for cysteine), respectively, there is a surprising lack of mention and testing of GSH metabolism. In addition, what are the major similarities and differences of cysteine homeostasis pathways between C. elegans and other systems (HIF dependency, transcription vs post-transcriptional control)? These questions could be better discussed and noted with novel findings of the current study that are likely C. elegans specific or broadly conserved.

All of my comments and questions above have been satisfactorily addressed in the revised manuscript.

Reviewer #3 (Public Review):

There has been a long-standing link between the biology of sulfur-containing molecules (e.g., hydrogen sulfide gas, the amino acid cysteine, and its close relative cystine, et cetera) and the biology of hypoxia, yet we have a poor understanding of how and why these two biological processes and are co-regulated. Here, the authors use C. elegans to explore the relationship between sulfur metabolism and hypoxia, examining the regulation of cysteine dioxygenase (CDO1 in humans, CDO-1 in C. elegans), which is critical to cysteine catabolism, by the hypoxia inducible factor (HIF1 alpha in humans, HIF-1 in C. elegans), which is the key terminal effector of the hypoxia response pathway that maintains oxygen homeostasis. The authors are trying to demonstrate that (1) the hypoxia response pathway is a key regulator of cysteine homeostasis, specifically through the regulation of cysteine dioxygenase, and (2) that the pathway responds to changes in cysteine homeostasis in a mechanistically distinct way from how it responds to hypoxic stress.

Briefly summarized here, the authors initiated this study by generating transgenic animals expressing a CDO-1::GFP protein chimera from the cdo-1 promoter so that they could identify regulators of CDO-1 expression through a forward genetic screen. This screen identified mutants with elevated CDO-1::GFP expression in two genes, egl-9 and rhy-1, whose wild-type products are negative regulators of HIF-1, raising the possibility that cdo-1 is a HIF-1 transcriptional target. Indeed, the authors provide data showing that cdo-1 regulation by EGL-9 and RHY-1 is dependent on HIF-1 and that regulation by RHY-1 is dependent on CYSL-1, as expected from other published findings of this pathway. The authors show that exogenous cysteine activates cdo-1 expression, reflective of what is known to occur in other systems. Moreover, they find that exogenous cysteine is toxic to worms lacking CYSL-1 or HIF-1 activity, but not CDO-1 activity, suggesting that HIF-1 mediates a survival response to toxic levels of cysteine and that this response requires more than just the regulation of CDO-1. The authors validate their expression studies using a GFP knockin at the cdo-1 locus, and they demonstrate that a key site of action for CDO-1 is the hypodermis. They present genetic epistasis analysis supporting a role for RHY-1, both as a regulator of HIF-1 and as a transcriptional target of HIF-1, in offsetting toxicity from aberrant sulfur metabolism. The authors use CRISPR/Cas9 editing to mutate a key amino acid in the prolyl hydroxylase domain of EGL-9, arguing that EGL-9 inhibits CDO-1 expression through a mechanism that is largely independent of the prolyl hydroxylase activity.

Overall, the data seem rigorous, and the conclusions drawn from the data seem appropriate. The experiments test the hypothesis using logical and clever molecular genetic tools and design. The sample size is a bit lower than is typical for C. elegans papers; however, the experiments are clearly not underpowered, so this is not an issue. The paper is likely to drive many in the field (including the authors themselves) into deeper experiments on (1) how the pathway senses hypoxia and sulfur/cysteine/H2S using these distinct mechanisms/modalities, (2) how oxygen and sulfur/cysteine/H2S homeostasis influence one another, and (3) how this single pathway evolved to sense and respond to both of these stress modalities.

My previous concerns have been addressed. The authors are commended on an excellent body of research.

Reviewer #4 (Public Review):

Summary:<br /> This is a revised manuscript that describes a role for cdo-1 in regulating cellular cysteine levels. The authors show that expression of cdo-1, predicted to encode a cysteine dioxygenase, is regulated by HIF-1, the conserved hypoxia-induced transcription factor. The expression of cdo-1 is controlled by the RHY-1/CYSL-1/EGL-9/HIF-1 pathway that has been demonstrated to be involved in the response to H2S.

Strengths:<br /> The new finding of this study is that cdo-1, predicted to encode a cysteine dioxygenase, is expressed in the hypodermis and that hypodermal expression rescues at least one phenotype of the cdo-1(mg622) mutant (ability to survive toxic sulfite accumulation in Moco-deficient conditions). Using sulfite toxicity is an interesting reporter for cellular cysteine abundance.

Weaknesses:<br /> The authors claim more than once that the H2S/Cys responsive pathway is RHY-1 - CYSL-1 - EGL-9 - HIF-1. Their data don't seem to support this claim, as they show that Pcdo-1::GFP is induced in rhy-1 mutants incubated with cysteine. It is therefore not appropriate to claim that "HIF-1-induced cysteine catabolism requires the activity of rhy-1" that they include in the description of the model in Fig 6. There is simply no evidence at all that RHY-1 has any role in modulating the activity of CDO-1 other than through transcriptional activation via HIF-1.

I don't find the arguments that this pathway is required for cysteine homeostasis per se (as claimed in the last sentence of the introduction). The authors expose worms to excess cysteine for 48 hours in liquid culture with bacteria. It is well known in these conditions that the bacteria will produce H2S from the cysteine in the culture. All of the cysteine exposure data shown can be explained by the effect of H2S exposure. This would explain why hif-1 and cysl-1 mutants die but cdo-1 mutants do not, for example. The authors don't provide any data to rule out the possibility that bacterial H2S production underlies these results. This explains why the pathway described in this work is the same as has been previously described. Similarly, there is no evidence at all to support their assertion that there are "other pathways" induced by HIF-1 to deal with sulfite produced by cysteine catabolism. However, if the main problem is H2S production (perhaps by bacteria) then cdo-1 would not be relevant and the mutants would be viable as observed.

In a couple of places, the authors seem to argue that H2S-induced expression is limited to the hypodermis and hypoxia-induced gene expression is mostly in the intestine. This is consistent with the expression of cdo-1 (this work) and nhr-57 (Budde and Roth) but it is not appropriate to generalize this. Previous work from the Ruvkun lab (Ma et al) show that the CYSL-1 regulates expression of HIF-1 targets in neurons. Moreover, HIF-1 protein accumulates in the nucleus of nearly all cells, and there is no reason to believe that there are changes in the expression of other genes in different tissues.

eLife assessment

This useful study aimed to examine the relationship of spatial frequency selectivity of single macaque inferotemporal (IT) neurons to category selectivity. There are some interesting findings in this report but some of these findings were difficult to evaluate because several critical details of the analysis are incomplete. The conclusion that single-unit spatial frequency selectivity can predict object coding needs further evidence to confirm.

Reviewer #1 (Public Review):

Summary:<br /> This study reports that IT neurons have biased representations toward low spatial frequency (SF) and faster decoding of low SFs than high SFs. High SF-preferred neurons, and low SF-preferred neurons to a lesser degree, perform better category decoding than neurons with other profiles (U and inverted U shaped). SF coding also shows more sparseness than category coding in the earlier phase of the response and less sparseness in the later phase. The results are also contrasted with predictions of various DNN models.

Strengths:<br /> The study addressed an important issue on the representations of SF information in a high-level visual area. Data are analyzed with LDA which can effectively reduce the dimensionality of neuronal responses and retain category information.

Weaknesses:<br /> The results are likely compromised by improper stimulus timing and unmatched spatial frequency spectrums of stimuli in different categories.

The authors used a very brief stimulus duration (35ms), which would degrade the visual system's contrast sensitivity to medium and high SF information disproportionately (see Nachmias, JOSAA, 1967). Therefore, IT neurons in the study could have received more degraded medium and high SF inputs compared to low SF inputs, which may be at least partially responsible for higher firing rates to low Sf R1 stimuli (Figure 1c) and poorer recall performance with median and high SF R3-R5 stimuli in LDA decoding. The issue may also to some degree explain the delayed onset of recall to higher SF stimuli (Figure 2a), preferred low SF with an earlier T1 onset (Figure 2b), lower firing rate to high SF during T1 (Figure 2c), somewhat increased firing rate to high SF during T2 (because weaker high SF inputs would lead to later onset, Figure 2d).

Figure 3b shows greater face coding than object coding by high SF and to a lesser degree by low SF neurons. Only the inverted-U-shaped neurons displayed slightly better object coding than face coding. Overall the results give an impression that IT neurons are significantly more capable of coding faces than coding objects, which is inconsistent with the general understanding of the functions of IT neurons. The problem may lie with the selection of stimulus images (Figure 1b). To study SF-related category coding, the images in two categories need to have similar SF spectrums in the Fourier domain. Such efforts are not mentioned in the manuscript, and a look at the images in Figure 1b suggests that such efforts are likely not properly made. The ResNet18 decoding results in Figure 6C, in that IT neurons of different profiles show similar face and object coding, might be closer to reality.

Reviewer #2 (Public Review):

Summary:<br /> This paper aimed to examine the spatial frequency selectivity of macaque inferotemporal (IT) neurons and its relation to category selectivity. The authors suggest in the present study that some IT neurons show a sensitivity for the spatial frequency of scrambled images. Their report suggests a shift in preferred spatial frequency during the response, from low to high spatial frequencies. This agrees with a coarse-to-fine processing strategy, which is in line with multiple studies in the early visual cortex. In addition, they report that the selectivity for faces and objects, relative to scrambled stimuli, depends on the spatial frequency tuning of the neurons.

Strengths:<br /> Previous studies using human fMRI and psychophysics studied the contribution of different spatial frequency bands to object recognition, but as pointed out by the authors little is known about the spatial frequency selectivity of single IT neurons. This study addresses this gap and they show that at least some IT neurons show a sensitivity for spatial frequency and interestingly show a tendency for coarse-to-fine processing.

Weaknesses and requested clarifications:<br /> 1. It is unclear whether the effects described in this paper reflect a sensitivity to spatial frequency, i.e. in cycles/ deg (depends on the distance from the observer and changes when rescaling the image), or is a sensitivity to cycles /image, largely independent of image scale. How is it related to the well-documented size tolerance of IT neuron selectivity?

2. The authors' band-pass filtered phase scrambled images of faces and objects. The original images likely differed in their spatial frequency amplitude spectrum and thus it is unclear whether the differing bands contained the same power for the different scrambled images. If not, this could have contributed to the frequency sensitivity of the neurons.

3. How strong were the responses to the phase-scrambled images? Phase-scrambled images are expected to be rather ineffective stimuli for IT neurons. How can one extrapolate the effect of the spatial frequency band observed for ineffective stimuli to that for more effective stimuli, like objects or (for some neurons) faces? A distribution should be provided, of the net responses (in spikes/s) to the scrambled stimuli, and this for the early and late windows.

4. The strength of the spatial frequency selectivity is unclear from the presented data. The authors provide the result of a classification analysis, but this is in normalized units so that the reader does not know the classification score in percent correct. Unnormalized data should be provided. Also, it would be informative to provide a summary plot of the spatial frequency selectivity in spikes/s, e.g. by ranking the spatial frequency bands for each neuron based on half of the trials and then plotting the average responses for the obtained ranks for the other half of the trials. Thus, the reader can appreciate the strength of the spatial frequency selectivity, considering trial-to-trial variability. Also, a plot should be provided of the mean response to the stimuli for the two analysis windows of Figure 2c and 2d in spikes/s so one can appreciate the mean response strengths and effect size (see above).

5. It is unclear why such brief stimulus durations were employed. Will the results be similar, in particular the preference for low spatial frequencies, for longer stimulus durations that are more similar to those encountered during natural vision?

6. The authors report that the spatial frequency band classification accuracy for the population of neurons is not much higher than that of the best neuron (line 151). How does this relate to the SNC analysis, which appears to suggest that many neurons contribute to the spatial frequency selectivity of the population in a non-redundant fashion? Also, the outcome of the analyses should be provided (such as SNC and decoding (e.g. Figure 1D)) in the original units instead of undefined arbitrary units.

7. To me, the results of the analyses of Figure 3c,d, and Figure 4 appear to disagree. The latter figure shows no correlation between category and spatial frequency classification accuracies while Figure 3c,d shows the opposite.

8. If I understand correctly, the "main" test included scrambled versions of each of the "responsive" images selected based on the preceding test. Each stimulus was presented 15 times (once in each of the 15 blocks). The LDA classifier was trained to predict the 5 spatial frequency band labels and they used 70% of the trials to train the classifier. Were the trained and tested trials stratified with respect to the different scrambled images? Also, LDA assumes a normal distribution. Was this the case, especially because of the mixture of repetitions of the same scrambled stimulus and different scrambled stimuli?

9. The LDA classifiers for spatial frequency band (5 labels) and category (2 labels) have different chance and performance levels. Was this taken into account when comparing the SNC between these two classifiers? Details and SNC values should be provided in the original (percent difference) instead of arbitrary units in Figure 5a. Without such details, the results are impossible to evaluate.

10. Recording locations should be described in IT, since the latter is a large region. Did their recordings include the STS? A/P and M/L coordinate ranges of recorded neurons?

11. The authors should show in Supplementary Figures the main data for each of the two animals, to ensure the reader that both monkeys showed similar trends.

12. The authors found that the deep nets encoded better the spatial frequency bands than the IT units. However, IT units have trial-to-trial response variability and CNN units do not. Did they consider this when comparing IT and CNN classification performance? Also, the number of features differs between IT and CNN units. To me, comparing IT and CNN classification performances is like comparing apples and oranges.

13. The authors should define the separability index in their paper. Since it is the main index to show a relationship between category and spatial frequency tuning, it should be described in detail. Also, results should be provided in the original units instead of undefined arbitrary units. The tuning profiles in Figure 3A should be in spikes/s. Also, it was unclear to me whether the classification of the neurons into the different tuning profiles was based on an ANOVA assessing per neuron whether the effect of the spatial frequency band was significant (as should be done).

14. As mentioned above, the separability analysis is the main one suggesting an association between category and spatial frequency tuning. However, they compute the separability of each category with respect to the scrambled images. Since faces are a rather homogeneous category I expect that IT neurons have on average a higher separability index for faces than for the more heterogeneous category of objects, at least for neurons responsive to faces and/or objects. The higher separability for faces of the two low- and high-pass spatial frequency neurons could reflect stronger overall responses for these two classes of neurons. Was this the case? This is a critical analysis since it is essential to assess whether it is category versus responsiveness that is associated with the spatial frequency tuning. Also, I do not believe that one can make a strong claim about category selectivity when only 6 faces and 3 objects (and 6 other, variable stimuli; 15 stimuli in total) are employed to assess the responses for these categories (see next main comment). This and the above control analysis can affect the main conclusion and title of the paper.

15. For the category decoding, the authors employed intact, unscrambled stimuli. Were these from the main test? If yes, then I am concerned that this represents a too small number of stimuli to assess category selectivity. Only 9 fixed + 6 variable stimuli = 15 were in the main test. How many faces/ objects on average? Was the number of stimuli per category equated for the classification? When possible use the data of the preceding selectivity test which has many more stimuli to compute the category selectivity.

eLife assessment

This fundamental study reveals the major role of calcium-binding proteins (CaBP1 and CaBP2) in sustained exocytosis from mouse inner hair cell ribbon synapses. Compelling data and analysis from CaBP1/2 double-knockout mice show enhanced calcium channel (CaV1.3) inactivation, slowed recovery from inactivation, and reduced synaptic vesicle exocytosis as assayed by membrane capacitance measurements, as well as greatly reduced in vivo spontaneous and sound-evoked spikes from the postsynaptic spiral ganglion neurons. Importantly, transgenic expression of CaBP2 led to the rescue of hearing capabilities. The continuous transmission of sound-evoked signals from auditory hair cells thus depends on the expression of both CaBP1 and CaBP2 and their suppression of CaV1.3 inactivation.

Reviewer #1 (Public Review):

Summary:<br /> This manuscript dissects the contribution of the CaBP 1 and 2 on the calcium current in the cochlear inner hair cells. The authors measured the calcium current inactivation from the double knock-out CaBP1 and 2 and showed that both proteins contribute to voltage-dependent and calcium-dependent inactivation. Synaptic release was reduced in the double KO. As a consequence, the authors observed a depressed activity within the auditory nerve. Taken together, this study identifies a new player that regulates the stimulation-secretion coupling in the auditory sensory cells.

Strengths:<br /> In this study, the authors bring compelling evidence that CaBP 1 and 2 are both involved in the inactivation of the calcium current, from cellular up to system level, and by taking care to probe different experimental conditions such as different holding potentials and by rescuing the phenotype with the re-expression of CaBP2. Indeed, while changing the holding potential worsens the secretion, it completely changes the kinetics of the inactivation recovery. It alerts the reader that probing different experimental conditions that may be closer to physiology is better suited to uncovering any deleterious phenotype. This gave pretty solid results.

Weaknesses:<br /> Although this study clearly points out that CaBP1 is involved in the calcium current inactivation, it is not clear how CaBP1 and CaBP2 act together (but this is probably beyond the scope of the study). Another point is that the authors re-express CaBP2 to largely rescue the phenotype in the double KO but no data are available to know whether the re-expression of both CaBP1 and CaBP2 would achieve a full recovery and what would be the effect of the sole re-expression of CaBP1 in the double KO.

Reviewer #2 (Public Review):

Summary:<br /> In the manuscript by Oestreicher et al, the authors use patch-clamp electrophysiology, immunofluorescent imaging of the cochlea, auditory function tests, and single-unit recordings of auditory afferent neurons to probe the unique properties of calcium signaling in cochlear hair cells that allow rapid and sustained neurotransmitter release. The calcium-binding proteins (CaBPs) are thought to modify the inactivation of the Cav1.3 calcium channels in IHCs that initiate vesicle fusion, reducing the calcium-dependent inactivation (CDI) of the channels to allow sustained calcium influx to support neurotransmitter release. The authors use knockout mice of Cabp1 and Cabp2 in a double knockout (Cabp1/2 DKO) to show that these molecules are required for enabling sustained calcium currents by reducing CDI and enabling proper IHC neurotransmitter release. They further support their evidence by re-introducing Cabp2 using an injection of AAV containing the Cabp2 sequence into the cochlea, which restores some of the auditory function and reduces CDI in patch-clamp recordings.

Strengths:<br /> Overall the data is convincing that Cabp1/2 is required for reducing CDI in cochlear hair cells, allowing their sustained neurotransmitter release and sound encoding. Figures are well-prepared, recordings are careful and stats are appropriate, and the manuscript is well-written. The discussion appropriately considers aspects of the data that are not yet explained and await further experimentation.

Weaknesses:<br /> There are some sections of the manuscript that pool data from different experiments with slightly different conditions (wt data from a previous paper, different calcium concentrations, different holding voltages, tones vs clicks, etc). This makes the work harder to follow and more complicated to explain. However, the major conclusion, that cabp1 and 2 work together to reduce calcium-dependent inactivation of L-type calcium channels in cochlear inner hair cells, still holds.

Another weakness is that the authors used injections of AAV-containing sequences for Cabp2, but do not present data from sham surgeries. In most cases, the improvement of hearing function with AAV injection is believable and should be attributed to the cabp2 function. However, in at least one instance (Figure 4B), the results of the AAV injection experiments may be overinterpreted - the authors show that upon AAV injection, the hair cells have a much longer calcium current recovery following a large, long depolarization to inactivate the calcium channels. Without comparison to sham surgery, it is not known if this result could be a subtle result of the surgery or indeed due to the Cabp2 expression.<br /> It would be great to see the auditory nerve recordings in AAV-injected animals that have a recovery of ABRs. However, this is a challenging experiment that requires considerable time and resources, so is not required.

Reviewer #3 (Public Review):

Summary:<br /> The authors attempted to unravel the role of the Ca2+-binding proteins CaBP1 and CaBP2 for the hitherto enigmatic lack of Ca2+-dependent inactivation of Ca2+ currents in sensory inner hair cells (IHCs). As Ca2+ currents through Cav1.3 channels are crucial for exocytosis, the lack of inactivation of those Ca2+ currents is essential for the indefatigable sound encoding by IHCs. Using a deaf mouse model lacking both CaBP1 and CaBP2, the authors convincingly demonstrate that both CaBP1 and CaBP2 together confer a lack of inactivation, with CaBP2 being far more effective. This is surprising given the mild phenotype of the single knockouts, which has been published by the authors before. Re-admission of CaBP2 through viral gene transfer into the inner ear of double-knockout mice largely restored hearing function, normal Ca2+ current properties, and exocytosis.

Strengths:<br /> 1. In vitro electrophysiology: perforated patch-clamp recordings of Ca2+/Ba2+ currents of inner hair cells (IHCs) from 3-4 week-old mice - very difficult recordings - necessary to not interfere with intracellular Ca2+ buffers, including CaBP1 and CaBP2.<br /> 2. Capacitance (exocytosis) recordings from IHCs in perforated patch mode.<br /> 3. The insight that a negative holding potential might underestimate the impact of lack of CaBP1/2 on the inactivation of ICa in IHCs. As the physiological holding potential is much more positive than a preferred holding potential in patch clamp experiments it has a strong impact on inactivation in the pauses between depolarization mimicking receptor potentials.<br /> This truly advances our thinking about the stimulation of IHCs and accumulating inactivation of the Cav1.3 channels.<br /> 4. Insight that the voltage sine method with usual voltage excursions (35 mV) to determine the membrane capacitance (for exocytosis measurements) also favors the inactivated state of Cav1.3 channels<br /> 5. Use of double ko mice (for both CaBP1 and CaBP2, DKO) and use of DKO with virally injected CaBP2-eGFP into the inner ear.<br /> 6. Use of DKO animals/IHCs/SGNs after virus-mediated CaBP2 gene transfer shows a great amount of rescue of the normal ICa inactivation phenotype.<br /> 7. In vivo measurements of SGN AP responses to sound, which is highly demanding.<br /> 8. In vivo measurements of hearing thresholds, DPOAE characteristics, and ABR wave I amplitudes/latencies of DKO mice and DKO+injected mice compared to WT mice.

Very thorough analysis and presentation of the data, excellent statistical analysis.

The authors achieved their aims. Their results fully support their conclusions. The methods used by the authors are state-of-the-art.

The impacts on the field are the following:<br /> Regulation of inactivation of Cav1.3 currents is crucial for the persistent functioning of Cav1.3 channels in sensory transduction.<br /> The findings of the authors better explain the phenotype of the human autosomal recessive DFNB93, which is based on the malfunction of CaBP2.<br /> Future work - by the authors or others - should address the molecular mechanisms of the interaction of CaBP1 and 2 in regulating Cav1.3 inactivation.

Weaknesses:<br /> I do not see weaknesses.<br /> What is not explained (but was not the aim of the authors) is how the CaBPs 1 and 2 interact with the Cav1.3 channels and with each other to reduce CDI. Also, why DFNB93, which is based on mutation of the CaBP2 gene, lead to a severe phenotype in humans in contrast to the phenotype of the CaBP2 ko mouse.

eLife assessment

Given a great need for novel human model systems to study small cell lung cancer (SCLC), the authors describe an important pre-clinical model with broad potential for the study of how genetic perturbations or drug treatments alter SCLC tumor growth, metastasis, and response to therapy. For the major finding, the authors provide convincing evidence that RB/TP53 suppression coupled with MYC overexpression in an ES cell-derived model system results in aggressive and metastatic SCLC. However, comparisons of the RB/TP53-suppressed, MYC overexpressing model with RB/TP53-suppressed cells in supporting the minor conclusion that MYC overexpression increases the neuroendocrine compartment are incomplete, and the impact of the work would have been increased with the inclusion of a broader set of genetic perturbations, such as over-expression of MYCL, to better model major SCLC phenotypes. The new model described will be of significant interest to researchers studying lung cancer.

Reviewer #1 (Public Review):

Summary:<br /> The authors introduced their previous paper with the concise statement that "the relationships between lineage-specific attributes and genotypic differences of tumors are not understood" (Chen et al., JEM 2019, PMID: 30737256). For example, it is not clear why combined loss of RB1 and TP53 is required for tumorigenesis in SCLC or other aggressive neuroendocrine (NE) cancers, or why the oncogenic mutations in KRAS or EGFR that drive NSCLC tumorigenesis are found so infrequently in SCLC. This is the main question addressed by the previous and current papers.

One approach to this question is to identify a discrete set of genetic/biochemical manipulations that are sufficient to transform non-malignant human cells into SCLC-like tumors. One group reported the transformation of primary human bronchial epithelial cells into NE tumors through a complex lentiviral cocktail involving the inactivation of pRB and p53 and activation of AKT, cMYC, and BCL2 (PARCB) (Park et al., Science 2018, PMID: 30287662). The cocktail previously reported by Chen and colleagues to transform human pluripotent stem-cell (hPSC)-derived lung progenitors (LPs) into NE xenografts was more concise: DAPT to inactivate NOTCH signaling combined with shRNAs against RB1 and TP53. However, the resulting RP xenografts lacked important characteristics of SCLC. Unlike SCLC, these tumors proliferated slowly and did not metastasize, and although small subpopulations expressed MYC or MYCL, none expressed NEUROD1.

MYC is frequently amplified or expressed at high levels in SCLC, and here, the authors have tested whether inducible expression of MYC could increase the resemblance of their hPSC-derived NE tumors to SCLC. These RPM cells (or RPM T58A with stabilized cMYC) engrafted more consistently and grew more rapidly than RP cells, and unlike RP cells, formed liver metastases when injected into the renal capsule. Gene expression analyses revealed that RPM tumor subpopulations expressed NEUROD1, ASCL1, and/or YAP1.

The hPSC-derived RPM model is a major advance over the previous RP model. This may become a powerful tool for understanding SCLC tumorigenesis and progression and for discovering gene dependencies and molecular targets for novel therapies. However, the specific role of cMYC in this model needs to be clarified.

cMYC can drive proliferation, tumorigenesis, or apoptosis in a variety of lineages depending on concurrent mutations. For example, in the Park et al., study, normal human prostate cells could be reprogrammed to form adenocarcinoma-like tumors by activation of cMYC and AKT alone, without manipulation of TP53 or RB1. In their previous manuscript, the authors carefully showed the role of each molecular manipulation in NE tumorigenesis. DAPT was required for NE differentiation of LPs to PNECs, shRB1 was required for expansion of the PNECs, and shTP53 was required for xenograft formation. cMYC expression could influence each of these steps, and importantly, could render some steps dispensable. For example, shRB1 was previously necessary to expand the DAPT-induced PNECs, as neither shTP53 nor activation of KRAS or EGFR had no effect on this population, but perhaps cMYC overexpression could expand PNECs even in the presence of pRB, or even induce LPs to become PNECs without DAPT. Similarly, both shRB1 and shTP53 were necessary for xenograft formation, but maybe not if cMYC is overexpressed. If a molecular hallmark of SCLC, such as loss of RB1 or TP53, has become dispensable with the addition of cMYC, this information is critically important in interpreting this as a model of SCLC tumorigenesis.

To interpret the role of cMYC expression in hPSC-derived RPM tumors, we need to know what this manipulation does without manipulation of pRB, p53, or NOTCH, alone or in combination. Seven relevant combinations should be presented in this manuscript: (1) cMYC alone in LPs, (2) cMYC + DAPT, (3) cMYC + shRB1, (4) cMYC + DAPT + shRB1, (5) cMYC + shTP53, (6) cMYC + DAPT + shTP53, and (7) cMYC + shRB1 + shTP53. Wild-type cMYC is sufficient; further exploration with the T58A mutant would not be necessary.

This reviewer considers that there should be a presentation of the effects of these combinations on LP differentiation to PNECs, expansion of PNECs as well as other lung cells, xenograft formation and histology, and xenograft growth rate and capacity for metastasis. If this could be clarified experimentally, and the results discussed in the context of other similar approaches such as the Park et al., paper, this study would be a major addition to the field.

Reviewer #2 (Public Review):

Summary:<br /> Chen et al use human embryonic stem cells (ESCs) to determine the impact of wildtype MYC and a point mutant stable form of MYC (MYC-T58A) in the transformation of induced pulmonary neuroendocrine cells (PNEC) in the context of RB1/P53 (RP) loss (tumor suppressors that are nearly universally lost in small cell lung cancer (SCLC)). Upon transplant into immune-deficient mice, they find that RP-MYC and RP-MYC-T58A cells grow more rapidly, and are more likely to be metastatic when transplanted into the kidney capsule, than RP controls. Through single-cell RNA sequencing and immunostaining approaches, they find that these RPM tumors and their metastases express NEUROD1, which is a transcription factor whose expression marks a distinct molecular state of SCLC. While MYC is already known to promote aggressive NEUROD1+ SCLC in other models, these data demonstrate its capacity in a human setting that provides a rationale for further use of the ESC-based model going forward. Overall, these findings provide a minor advance over the previous characterization of this ESC-based model of SCLC published in Chen et al, J Exp Med, 2019.

The major conclusion of the paper is generally well supported, but some minor conclusions are inadequate and require important controls and more careful analysis.

Strengths:<br /> 1. Both MYC and MYC-T58A yield similar results when RP-MYC and RP-MYCT58A PNEC ESCs are injected subcutaneously, or into the renal capsule, of immune-deficient mice, leading to the conclusion that MYC promotes faster growth and more metastases than RP controls.

2. Consistent with numerous prior studies in mice with a neuroendocrine (NE) cell of origin (Mollaoglu et al, Cancer Cell, 2017; Ireland et al, Cancer Cell, 2020; Olsen et al, Genes Dev, 2021), MYC appears sufficient in the context of RB/P53 loss to induce the NEUROD1 state. Prior studies also show that MYC can convert human ASCL1+ neuroendocrine SCLC cell lines to a NEUROD1 state (Patel et al, Sci Advances, 2021); this study for the first time demonstrates that RB/P53/MYC from a human neuroendocrine cell of origin is sufficient to transform a NE state to aggressive NEUROD1+ SCLC. This finding provides a solid rationale for using the human ESC system to better understand the function of human oncogenes and tumor suppressors from a neuroendocrine origin.

Weaknesses:<br /> 1. There is a major concern about the conclusion that MYC "yields a larger neuroendocrine compartment" related to Figures 4C and 4G, which is inadequately supported and likely inaccurate. There is overwhelming published data that while MYC can promote NEUROD1, it also tends to correlate with reduced ASCL1 and reduced NE fate (Mollaoglu et al, Cancer Cell, 2017; Zhang et al, TLCR, 2018; Ireland et al, Cancer Cell, 2020; Patel et al, Sci Advances, 2021). Most importantly, there is a lack of in vivo RP tumor controls to make the proper comparison to judge MYC's impact on neuroendocrine identity. RPM tumors are largely neuroendocrine compared to in vitro conditions, but since RP control tumors (in vivo) are missing, it is impossible to determine whether MYC promotes more or less neuroendocrine fate than RP controls. It is not appropriate to compare RPM tumors to in vitro RP cells when it comes to cell fate. Upon inspection of the sample identity in S1B, the fibroblast and basal-like cells appear to only grow in vitro and are not well represented in vivo; it is, therefore, unclear whether these are transformed or even lack RB/P53 or express MYC. Indeed, a close inspection of Figure S1B shows that RPM tumor cells have little ASCL1 expression, consistent with lower NE fate than expected in control RP tumors.

In addition, since MYC appears to require Notch signaling to induce NE fate (Ireland et al), the presence of DAPT in culture could enrich for NE fate despite MYC's presence. It's important to clarify in the legend of Fig 4A which samples are used in the scRNA-seq data and whether they were derived from in vitro or in vivo conditions (as such, Supplementary Figure S1B should be provided in the main figure). Given their conclusion is confusing and challenges robustly supported data in other models, it is critical to resolve this issue properly. I suspect when properly resolved, MYC actually consistently does reduce NE fate compared to RP controls, even though tumors are still relatively NE compared to completely distinct cellular identities such as fibroblasts.

2. The rigor of the conclusions in Figure 1 would be strengthened by comparing an equivalent number of RP animals in the renal capsule assay, which is n = 6 compared to n = 11-14 in the MYC conditions.

3. Statistical analysis is not provided for Figures 2A-2B, and while the results are compelling, may be strengthened by additional samples due to the variability observed.

4a. Related to Figure 3, primary tumors and liver metastases from RPM or RPM-T58A-expressing cells express NEUROD1 by immunohistochemistry (IHC) but the putative negative controls (RP) are not shown, and there is no assessment of variability from tumor to tumor, ie, this is not quantified across multiple animals.

4b. Relatedly, MYC has been shown to be able to push cells beyond NEUROD1 to a double-negative or YAP1+ state (Mollaoglu et al, Cancer Cell, 2017; Ireland et al, Cancer Cell, 2020), but the authors do not assess subtype markers by IHC. They do show subtype markers by mRNA levels in Fig 4B, and since there is expression of ASCL1, and potentially expression of YAP1 and POU2F3, it would be valuable to examine the protein levels by IHC in control RP vs. RPM samples.

5. Given that MYC has been shown to function distinctly from MYCL in SCLC models, it would have raised the impact and value of the study if MYC was compared to MYCL or MYCL fusions in this context since generally, SCLC expresses a MYC family member. However, it is quite possible that the control RP cells do express MYCL, and as such, it would be useful to show.

Reviewer #3 (Public Review):

Summary:<br /> The authors continue their study of the experimental model of small cell lung cancer (SCLC) they created from human embryonic stem cells (hESCs) using a protocol for differentiating the hESCs into pulmonary lineages followed by NOTCH signaling inactivation with DAPT, and then knockdown of TP53 and RB1 (RP models) with DOX inducible shRNAs. To this published model, they now add DOX-controlled activation of expression of a MYC or T58A MYC transgenes (RPM and RPMT58A models) and study the impact of this on xenograft tumor growth and metastases. Their major findings are that the addition of MYC increased dramatically subcutaneous tumor growth and also the growth of tumors implanted into the renal capsule. In addition, they only found liver and occasional lung metastases with renal capsule implantation. Molecular studies including scRNAseq showed that tumor lines with MYC or T58A MYC led surprisingly to more neuroendocrine differentiation, and (not surprisingly) that MYC expression was most highly correlated with NEUROD1 expression. Of interest, many of the hESCs with RPM/RPMT58A expressed ASCL1. Of note, even in the renal capsule RPM/RPMT58A models only 6/12 and 4/9 mice developed metastases (mainly liver with one lung metastasis) and a few mice of each type did not even develop a renal sub capsule tumor. The authors start their Discussion by concluding: " In this report, we show that the addition of an efficiently expressed transgene encoding normal or mutant human cMYC can convert weakly tumorigenic human PNEC cells, derived from a human ESC line and depleted of tumor suppressors RB1 and TP53, into highly malignant, metastatic SCLC-like cancers after implantation into the renal capsule of immunodeficient mice.".

Strengths:<br /> The in vivo study of a human preclinical model of SCLC demonstrates the important role of c-Myc in the development of a malignant phenotype and metastases. Also the role of c-Myc in selecting for expression of NEUROD1 lineage oncogene expression.

Weaknesses:<br /> There are no data on results from an orthotopic (pulmonary) implantation on generation of metastases; no comparative study of other myc family members (MYCL, MYCN); no indication of analyses of other common metastatic sites found in SCLC (e.g. brain, adrenal gland, lymph nodes, bone marrow); no studies of response to standard platin-etoposide doublet chemotherapy; no data on the status of NEUROD1 and ASCL1 expression in the individual metastatic lesions they identified.

Reviewer #1 (Public Review):

Summary:<br /> The authors were trying to achieve that Tgif1 expression is regulated by EAK1/2 and PTH in a time-dependent manner, and its roles in suppressing Pak3 for facilitating osteoblast adhesion. The authors further tried to show that the Tgif1-Pak3 signaling plays a significant role in osteoblast migration to the site of bone repair and bone remodeling.

Strengths:<br /> - In a previous study, it was demonstrated that Tgif1 is a target gene of PTH, and the absence of Tgif1 failed to increase bone mass by PTH treatment (Saito et al., Nat Commun., 2019). In this study, the authors found that Tgif1-Pak3 signaling prompts osteoblast migration through osteoblast adhesion to prompt bone regeneration. This novel finding provides a better understanding of how Tgif1 expression in osteoblasts regulates adherence, spreading, and migration during bone healing and bone remodeling.

- The authors demonstrated that ERK1/2 and PTH regulate Tgif1 expression in a time-dependent manner and its role in suppressing Pak3 through various experimental approaches such as luciferase assay, ChIP assay, and gene silencing. These results contribute to the overall strength of the article.

Weaknesses:<br /> -The authors need to further justify why they focused on Pak3 in the introduction by mentioning its known function for cell adhesion.

-Some results indicated statistically significant but small changes. The authors need to explain in the discussion part why they believe this is the major mechanism or why there may be some other possible mechanisms.

-The study does not include enough in vivo data to claim that this mechanism is crucial for bone healing and bone remodeling in vivo.

Reviewer #2 (Public Review):

Summary:<br /> Bolamperti S. et al. 2023 investigate whether the expression of TG-interacting factor (Tgif1) is essential for osteoblastic cellular activity regarding morphology, adherence, migration/recruitment, and repair. Towards this end, germ-line Tgif1 deletion (Tgif1-/-) mice or male mice lacking expression of Tgif1 in mature osteoblastic and osteocytic cells (Dmp1-Cre+; Tgif1fl/fl) and corresponding controls were studied in physiological, bone anabolic, and bone fracture-repair conditions. Both Tgif1-/- and Dmp1-Cre+; Tgif1fl/fl exhibited decreased osteoblasts on cancellous bone surfaces and adherent to collagen I-coated plates. Tgif1-/- mice exhibit impaired healing in the tibial midshaft fracture model, as indicated by decreased bone volume (BV/Cal.V), osteoid (OS/BS), and low osteoblasts (number and surface). Likewise, both Tgif1-/- and Dmp1-Cre+; Tgif1fl/fl show impaired PTH 1-34, (100 µg/kg, 5x/wk for 3 wks) osteoblast activation in vivo, as detected by increases in quiescent bone surfaces. Mechanistic in vitro studies then utilized primary osteoblasts isolated from Tgif1-/- mice and siRNA Tgif1 knockdown OCY454 cells to further investigate and identify the downstream Tgif1 target driving these osteoblastic impairments. In vitro, Tgif1-/- osteoblastic and Tgif1 knockdown OCY454 cells exhibit decreased migration, abnormal morphology, and decreased focal adhesions/cells. Unexpectantly though, localization assays revealed Tgif1 to primarily concentrate in the nucleus and not to co-localize with focal adhesions (paxillin, talin). Also, the expression of major focal adhesion components (paxillin, talin, FAK, Src, etc.) or the Cdc42 family was not altered by loss of Tgif1 expression. In contrast, PAK3 expression is markedly upregulated by loss of Tgif1. In silico analysis followed by mechanistic molecular assays involving ChIP, siRNA (Tgif1, PAK3), and transfection (rat PAK3 promoter) techniques show that Tgif1 physically binds to a specific site in the PAK3 promoter region. Further, the knockdown of PAK3 rescues the Tgif1-deficient abnormal morphology in OCY454 cells. This is the first study to identify the novel transcriptional repression of PAK3 by Tgif1 as well as the specific Tgif1 binding site within the PAK3 promoter.

Strengths:<br /> This work has a plethora of strengths. The co-authors achieved their aim of eliciting the role of Tgif1 expression in osteoblastic cellular functions (morphology, spreading/attachment, migration). Further, this work is the first to depict the novel mechanism of Tgif1 transcriptional repression of PAK3 by a thorough usage of mechanistic molecular assays (in silico analysis, ChIP, siRNA, transfection etc.). The conclusions are well supported and justified by these findings, as the appropriate controls, sample sizes (statistical power), statistics, and assays were fully utilized.

The claims and conclusions are justified by the data.

Weaknesses:<br /> The discussion section could be expanded with a few sentences regarding limitations to the current study and potential future directions.

eLife assessment

This important work substantially advances our understanding of osteoblast migration to the sites of bone formation and regeneration. The evidence supporting the conclusion is convincing, with rigorous in vitro assays for cellular and biochemical aspects and with appropriate in vivo models. The work will be of broad interest to developmental biologists and bone biologists.

eLife assessment

This study presents valuable insights into the potential role of a general transcription factor in MYCN-dependent regulation of transcription. The study presents solid evidence that TFIIIC and MYCN interact to control transcription. The methods, data, and analyses broadly support the claims with minor weaknesses, yet the logic can be improved, and several specific issues should be addressed. The paper would be of interest to molecular biologists working on MYCN-dependent regulation of gene expression.

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript entitled "Association with TFIIIC limits MYCN accumulation in hubs of active promoters and chromatin accumulation of non-phosphorylated RNA polymerase II" the authors examine how the cohesin complex component (and RNA pol III associated factor) TFIIIC interacts with MYCN and controls transcription. They confirm that TFIIIC co-purifies with MYCN, dependent on its amino terminus, as shown in previous work. The authors also find that TFIIIC and MYCN are both found in promoter hubs and suggest that TFIIIC inhibits MYCN's association with these hubs. Finally, the authors indicate that TFIIIC/MYCN alters exosome function, and BRCA1-dependent effects, at MYCN-regulated loci.

Strengths:<br /> The authors utilize multiple experimental approaches to investigate the potential biological and genomic impacts of MYCN association with TFIIIC - the findings are interesting in suggesting that this interaction may limit or otherwise regulate MYC activity.

Weaknesses:<br /> (1) In Figure 1, the authors show that TF3C binds to the amino terminus of MYCN (Myc box I region), as shown previously. The data in Figure 1 B-D support, but do not rigorously confirm a 'direct' interaction because it has not been ruled out that accessory proteins mediating the association may be present in the mixture.

(2) The authors indicate in Figure 2 that TF3C has essentially no effect on MYCN-dependent gene expression and/or transcription elongation. Yet a previous study (PMID: 29262328) associated with several of the same authors concluded that TF3C positively affects transcription elongation. The authors make no attempt to reconcile these disparate results and need to clarify this point.

(3) Figures 2B and C show that unphosphorylated pol2 is TSS-centered, and Ser2-P pol2 occupation is centered beyond the TES. From this data, however, the reader can't tell how much of the phospho-Ser2- pol2 is centered on the TSS. The authors should include overall plots over TSS and TES, and also perhaps the gene-body to allow a better comparison for TSS and TES plotted for both antibodies over the collected gene sets.

(4) The authors see more TF3C at promoters in cells with MYCN (Figure 2F). What are the levels of TF3C in the absence and presence of MYCN?

(5) The finding that TF3C is increased at TSS (Figure 2F) doesn't necessarily indicate that 1) MYCN is recruiting TF3C there, and 2) that this is due to the phosphorylation status of pol2. It could mean many other things. The logic of conflating these 3 points based on the data shown is questionable.

(6) Figure 3A doesn't add much to the paper, as it is overplotted and no relationship is clear, except that Pol2 and MYCN occupy many of the same sites. Perhaps a less complex or different type of plot would allow the interactions to be better visible.

(7) That depletion of TF3C leads to increased promoter hubs may or may not have anything to do with its association with MYCN (Figure 4E). This could be a direct consequence of its known structural function in cohesin complexes, and the MYCN changes as a secondary consequence of this (also see point 4, above).

(8) Depletion of TF3C5 results in a loss of EXOSC5 (exosome) at TSS in the presence and absence of MYCN (Figure 5B). As TF3C5 is a cohesin, could this simply be a consequence of genomic structure changes?

(9) The authors suggest that RNA dynamics are affected by changes in exosome function (RNA degradation, etc). What effect, if any does TF3C depletion have on the overall gene expression profile?

Reviewer #2 (Public Review):

This manuscript reports several interesting observations that invite follow-up. The notion that hubs, and perhaps condensates that may (or may not embrace them) are functionally and physiologically important is an open issue at this time. The authors note that TFIIIC helps to prune extraneous connections from hubs, but do not comment that the connections that are maintained are also reinforced. At the same time only modest changes in gene expression are associated with expanded or decreased connections and changes in bound proteins. One interesting possibility might be that standard methods for assessing expression miss changes in global or background transcription. It seems that the TFIIIC-MYCN-ER connection has features that would help to suppress such background. The results invite a more global consideration of TFIIIC than as primarily RNAPIII/small RNA transcription factor and of MYCN as an E-box dependent transcription factor. The results use state-of-the-art methods to develop interesting new ideas that have the potential to instruct further studies that may reveal new mechanisms of action for TFIIIC and MYCN

Strengths:<br /> Use of a variety of methods to assess the genomic response to increased MYCN in the presence or absence of TFIIIC. Establishes in vitro and in vivo the TFIIIC-MYCN complex.

Weaknesses:<br /> Dynamic inferences are made without kinetic experiments.

Reviewer #3 (Public Review):

Summary:<br /> Vidal et al. investigated how TFIIIC may mediate MYCN effects on transcription. The work builds upon previous reports from the same group where they describe MYCN interactors in neuroblastoma cells (Buchel et al, 2017), which include TFIIIC, and their different roles in MYCN-dependent control of RNA polymerase II function (Herold et al, 2019) (Roeschert et al, 2021) (Papadopoulus et al, 2022). Using baculovirus expression systems, they confirm that MYCN-TFIIIC interaction is direct and likely relevant for neuroblastoma cell proliferation. However, transcriptomics analyses led them to conclude that TFIIC is largely dispensable for MYCN-dependent gene expression. Instead, they propose that TFIIC limits MYCN-mediated promoter-promoter 3D chromatin contacts, which would in turn facilitate the recruitment of the nascent RNA degradation machinery and restrict the accumulation of non-phosphorylated RNA polymerase II at promoters. How this mechanism may impact on MYCN-driven neuroblastoma cell biology remains to be elucidated.

Strengths:<br /> This study presents a nice variety of genomic datasets addressing the specific role of TFIIIC in MYCN-dependent functions. In particular, the technically challenging HiChIP sequencing experiments performed under various conditions provide very useful information about the interplay between MYCN and TFIIIC in the regulation of 3D chromatin contacts. The authors show that MYCN and TFIIIC participate both in unique and overlapping long-range chromatin contacts and that the expression of each of these proteins limits the function of the other. Together, their results suggest a dynamic and interconnected relationship between MYCN and TFIIIC in regulating 3D chromatin contacts.

Weaknesses:<br /> The connection between the three major findings presented in this study regarding the role of TFIIIC in the regulation of MYCN function remains unclear. Specifically, how the TFIIIC-dependent restriction of MYCN localization to promoter hubs enhances the association of factors involved in nascent RNA degradation to prevent the accumulation of inactive RNA polymerase II at promoters is not apparent. As they are currently presented, these findings appear as independent observations. Cross-comparison of the different datasets obtained may provide some insight into addressing this question.

Another concern involves the disparities in RNA polymerase II ChIP-seq results between this study and earlier ones conducted by the same group. In Figure 2, the authors demonstrate that activation of MYCN results in a reduction of non-phosphorylated RNA polymerase II across all expressed genes. This discovery contradicts prior findings obtained using the same methodology, where it was concluded that the expression of MYCN had no significant effect on the chromatin association of hypo-phosphorylated RNA polymerase II (Buchel et al, 2017). In this regard, the choice of the 8WG16 antibody raises concern, as fluctuations in the signal may be attributed to changes in the phosphorylation levels of the C-terminal domain. It remains unclear why the authors decided against using antibodies targeting the N-terminal domain of RNA polymerase II, which are unaffected by phosphorylation and consistently demonstrated a significant signal reduction upon MYCN activation in their previous studies (Buchel et al, 2017) (Herold et al, 2019). Similarly, the authors previously proposed that depletion of TFIIIC5 abrogates the MYCN-dependent increase of Ser2-phosphorylated RNA polymerase II (Buchel et al, 2017), whereas they now show that it has no obvious impact. These aspects need clarification.

Finally, the varied techniques employed to explore the role of TFIIIC in MYCN-dependent recruitment of nascent RNA degradation factors make it challenging to draw definitive conclusions about which factor is affected and which one is not. While conducting ChIPseq experiments for all factors may be beyond the scope of this manuscript, incorporating proximity ligation assays (PLA) or ChIP-qPCR assays with each factor would have enabled a more direct and comprehensive comparison.

Reviewer #1 (Public Review):

Interactions known to be important for melanosome transport include exon F and the globular tail domain (GTD) of MyoVa with Mlph. Motivated by a discrepancy between in vitro and cell culture results regarding necessary interactions for MyoVa to be recruited to the melanosome, the authors used a series of pull-down and pelleting assays experiments to identify an additional interaction that occurs between exon G of MyoVa and Mlph. This interaction is independent of and synergistic with the interaction of Mlph with exon F. However, the interaction of the actin-binding domain of Mlph can occur either with exon G or with the actin filament, but not both simultaneously. These data lead to a modified recruitment model where both exon F and exon G enhance the binding of Mlph to auto-inhibited MyoVa, and then via an unidentified switch (PKA?) the actin-binding domain of Mlph dissociates from MyoVa and interacts with the actin filament to enhance MyoVa processivity.

The only weakness noted is that the authors could have had a more complete story if they pursued whether PKA phosphorylation/dephosphorylation of Mlph is indeed the switch for the actin-binding domain of Mlph to interact with exon G versus the actin filament.

Reviewer #2 (Public Review):

The authors identify a third component in the interaction between myosin Va and melanophilin- an interaction between a 32-residue sequence encoded by exon-g in myosin Va and melanophilin's actin-binding domain. This interaction has implications for how melanosome motility may be regulated.

While this work is largely well done, I believe that additional work would be required to make a more compelling case (e.g. some affinity measurements, necessary controls for the dominant negative experiments).  First, the study provides just one more piece to a well-developed story (the role of exon-F and the GTD in myosin Va: melanophilin (Mlph) interaction), much of which was published 20 years ago by several labs. Second, the study does not demonstrate a physiological significance for their findings other than that exon-G plays an auxiliary role in the binding of myosin Va to Mlph. For example, what dictates the choice between Mlph's actin binding domain (ABD) binding to actin or to exon-G. Is it a PTM or local actin concentration? It is unlikely to be alternative splicing as exon-G is present in all spliced isoforms of myosin Va. And what changes re melanosome dynamics in cells between these two alternatives? Similarly, the paper does not provide any in vitro evidence that binding to exon-G instead of actin effects the processivity of a Rab27a/Myosin Va/Mlph transport complex. For example, if the ABD sticks to exon-G instead of actin, does that block Mlph's ability to promote processivity through its interaction with the actin filament during transport? In summary, given that the authors did not directly test their model either in vitro or in cells, I do not think this story represent a significant conceptual advance.

eLife assessment

To investigate the evolutionary relationship between the RNAi pathway and innate immunity, this study uses biochemistry and structural biology to investigate the trimeric complex of Dicer-1, DRH-1 (a RIGI homologue), and RDE-4, which exists in C. elegans. The results described include rigorous kinetic analysis of the enzymatic activity of the complex and a moderate resolution cryo-EM structure. The results are convincing and valuable to the broader understanding of the evolution of antiviral defense.

Reviewer #1 (Public Review):

Summary:<br /> The authors establish a recombinant insect cell expression and purification scheme for the antiviral Dicer complex of C. elegans. In addition to Dicer-1, the complex harbors two additional proteins, the RIG-I-like helicase DRH-1, and the dsRNA-binding protein RDE-4. The authors show that the complex prefers blunt-end dsRNA over dsRNAs that contain overhangs. Furthermore, whereas ATP-dependent dsRNA cleavage only exacerbates regular dsRNA cleavage activity, the presence of RDE-4 is essential to ATP-dependent and ATP-independent dsRNA cleavage. Single-particle cryo-EM studies of the ternary C. elegans Dicer complex reveal that the N-terminal domain of DRH-1 interacts with the helicase domain of DCR-1, thereby relieving its autoinhibitory state. Lastly, the authors show that the ternary complex is able to processively cleave long dsRNA, an activity primarily relying on the helicase activity of DRH-1.

Strengths:<br /> • First thorough biochemical characterization of the antiviral activity of C. elegans Dicer in complex with the RIG-I-like helicase DRH-1 and the dsRNA-binding protein RDE-4.<br /> • Discovery that RDE-4 is essential to dsRNA processing, whereas ATP hydrolysis is not.<br /> • Discovery of an autoinhibitory role of DRH-1's N-terminal domain (in analogy to the CARD domains of RIG-I).<br /> • First structural insights into the ternary complex DCR-1:DRH-1:RDE-4 by cryo-EM to medium resolution.<br /> • Trap experiments reveal that the ternary DCR-1 complex cleaves blunt-ended dsRNA processively. Likely, the helicase domain of DRH-1 is responsible for this processive cleavage.

Weaknesses:<br /> • Cryo-EM Structure of the ternary Dicer-1:DRH-1:RED-4 complex to only medium resolution.<br /> • High-resolution structure of the C-terminal domain of DRH-1 bound to dsRNA does not reveal the mechanism of how blunt-end dsRNA and overhang-containing one are being discriminated.<br /> • The cryo-EM structure of DCR1:DRH-1:RDE-4 in the presence of ATP only reveals the helicase and CTD domains of DRH-1 bound to dsRNA. No information on dsRNA termini recognition is presented. The paragraph seems detached from the general flow of the manuscript.<br /> • The antiviral DCR-1:DRH-1:RDE-4 complex shows largely homologous activities and regulation than Drosophila Dicer-2.

Reviewer #2 (Public Review):

Summary:<br /> To investigate the evolutionary relationship between the RNAi pathway and innate immunity, this study uses biochemistry and structural biology to investigate the trimeric complex of Dicer-1, DRH-1 (a RIGI homologue), and RDE-4, which exists in C. elegans. The three subunits were co-expressed to promote stable purification of the complex. This complex promoted ATP-dependent cleavage of blunt-ended dsRNAs. A detailed kinetic analysis was also carried out to determine the role of each subunit of the trimeric complex in both the specificity and efficiency of cleavage. These studies indicate that RDE-4 is critical for cleavage while DRC-1 is primarily involved in the specificity of the reaction, and DRH-1 promotes ATP hydrolysis. Finally, a moderate density (6-7 angstrom) cryo-EM structure is presented with attempts to position each of the components.

Strengths:<br /> 1. Newly described methods for studying the C. elegans DICER complex.<br /> 2. New structure, albeit only moderate resolution.<br /> 3. Kinetic study of the complex in the presence and absence of individual subunits and mutations, provides detailed insight into the contribution of each subunit.

Weaknesses:<br /> 1. Limited insight due to limited structural resolution.<br /> 2. No attempts to extend findings to other Dicer or RLR systems.

eLife assessment

This valuable study shows that eliminating a large portion of the principal neurons in the mammalian olfactory bulb does not affect the initial establishment of the circuit but has an impact on its maintenance. The strength of the paper is that the anatomical changes induced by genetic ablation of neurons are clear-cut. There is a solid description of the structural and behavioral effects of ablating the majority of M/T neurons; however, the conclusions are incompletely supported by the findings.

Reviewer #1 (Public Review):

This paper aims to address the establishment and maintenance of neural circuitry in the case of a massive loss of neurons. The authors used genetic manipulations to ablate the principal projection neurons, the mitral/tufted cells, in the mouse olfactory bulb. Using diphtheria toxin (Tbx21-Cre:: loxP-DTA line) the authors ablated progressively large numbers of M/T cells postnatally. By injecting diphtheria toxin (DT) into the Tbx21-Cre:: loxP-iDTR line, the authors were able to control the timing of the ablation in the adult stage. Both methods led to the successful elimination of a majority of M/TCs by 4 months of age. The authors made a few interesting observations. First, they found that the initial pruning of the remaining M/T cell primary dendrite was unaffected. However, in adulthood, a significant portion of these cells extended primary dendrites to innervate multiple glomeruli. Moreover, the incoming olfactory sensory neuron (OSN) axons, as examined for those expressing the M72 receptor, showed a divergent innervation pattern as well. The authors conclude that M/T cell density is required to maintain the dendritic structures and the olfactory map. To address the functional consequences of eliminating a large portion of principal neurons, the authors conducted a series of behavioral assays. They found that learned odor discrimination was largely intact. On the other hand, mating and aggression were reduced. The authors concluded that learned behaviors are more resilient than innate ones.

The study is technically sound, and the results are clear-cut. The most striking result is the contrast between the normal dendritic pruning during early development and the expanded dendritic innervation in adulthood. It is a novel discovery that can lead to further investigation of how the single-glomerulus dendritic innervation is maintained. The authors conducted a few experiments to address potential mechanisms, but it is inconclusive, as detailed below. It is also interesting to see that the massive neuronal loss did not severely impact learned odor discrimination. This result, together with previous studies showing nearly normal odor discrimination in the absence of large portions of the olfactory bulb or scrambled innervation patterns, attests to the redundancy and robustness of the sensory system. The discussion should take into account these other studies in a historical context.

Main comments:

1. In previous studies, it has been concluded that dendritic pruning unfolds independently, regardless of the innervation pattern or activity of the OSNs. The new observation bolsters this conclusion by showing that a loss of neighboring M/T cells does not affect the developmental process. A more nuanced discussion comparing the results of these studies would strengthen the paper.

2. The authors propose that a certain density of M/T is required to prevent the divergent innervation of primary dendrites, but the evidence is not sufficient to support this proposal. The experiment with low-dose DT injection to ablate a smaller portion of M/T cells did not change the percentage of cells innervating two or more glomeruli. The authors suggest that a threshold must be met, but this threshold is not determined. It would be possible to adjust the DT injection dose to find this threshold.

3. The authors suggest that neural activity is not required for this plasticity. The evidence was derived primarily from naris occlusion and neuronal silencing using Kir2.1. While the results are consistent with the notion, it is a rather narrow interpretation of how neural activity affects circuit configuration. Perturbation of neural activity also entails an increase in firing. Inducing the activity of the neurons may alter this plasticity. Silencing per se may induce a homeostatic response that expands the neurite innervation pattern to increase synaptic input to compensate for the loss of activity. Thus, further silencing the cells may not reduce multi-glomerular innervation, but an increased activity may.

4. There is a discrepancy between this study and the one by Fujimoto et al. (Developmental Cell; 2023), which shows that not only glutamatergic inputs to the primary dendrite can facilitate pruning of remaining dendrites but also Kir2.1 overexpression can significantly perturb dendritic pruning. This discrepancy is not discussed by the authors.

5. An alternative interpretation of the discrepancy between the apparent normal pruning by p10 and expanded dendritic innervation in adulthood is that there are more cells before P10, when ~25% of M/T cells are present, but at a later date only 1-3% are present. The relationship between the number of M/T cells and single glomerulus innervation has not been explored during postnatal development. It would be important to test this hypothesis.

6. The authors attribute the change in the olfactory map to the loss of M/T cells. Another obvious possibility is that the diffused projection is a response to the change in the olfactory bulb size. With less space to occupy, the axons may be forced to innervate neighboring glomeruli. It is not known how the total number of glomeruli is affected. This question could be addressed by tracking developmental changes in bulb volume and glomerular numbers.

7. The retained ability to discriminate odors upon reinforced training is not surprising in light of a number of earlier studies. For example, Slotnick and colleagues have shown that rats losing ~90% of the OB can retain odor discrimination. Weiss et al have shown that humans without an olfactory bulb can perform normal olfactory tasks. Gronowitz et al have used theoretical prediction and experimental results to demonstrate that perturbing the olfactory map does not have a major impact on olfactory discrimination.<br /> Fleischmann et al have shown that mice with a monoclonal nose can discriminate odors. The authors should discuss their results in these contexts.

8. It should be noted that odor discrimination resulting from reinforcement training does not mean normal olfactory function. It is a highly artificial situation as the animals are overtrained. It should not be used as a measure of the robustness of the olfactory sense. Natural odor discrimination (without training), detection threshold, and innate appetitive/aversive response to certain odors may be affected. These experiments were not conducted.

9. The social behaviors were conducted using relatively coarse measures (vaginal plug and display of aggression). Moreover, these behaviors are most likely affected by the disruption of the AOB mitral cells and have little to do with the dendritic pruning process described in the paper. It is misleading to lump social behaviors with innate responses to odors.

Reviewer #2 (Public Review):

The authors make the interesting observation that the developmental refinement of apical M/T cell dendrites into individual glomeruli proceeds normally even when the majority of neighboring M/T cells are ablated. At later stages, the remaining neurons develop additional dendrites that invade multiple glomeruli ectopically, and similarly, OSN inputs to glomeruli lose projection specificity as well. The authors conclude that the normal density of M/T neurons is not required for developmental refinement, but rather for maintaining specific connectivity in adults.

The observations are indeed quite striking; however, the authors' conclusions are not entirely supported by the data.

1. It is unclear whether the expression of diphtheria toxin that eventually leads to the ablation of the large majority of M/T neurons compromises the cell biology of the remaining ones.

2. The authors interpret the growth of ectopic dendrites later in life as a lack of maintenance of dendrite structure; however, maybe the observed changes reflect actually adaptations that optimize wiring for extremely low numbers of M/T neurons. The finding that olfactory behavior was less affected than predicted supports this interpretation.

3. The number of remaining M/T neurons is much higher at P10 than later. Can the relatively large number of remaining neurons (or their better health status) be the reason that dendrites refine normally at the early developmental stages rather than a (currently unknown) developmental capacity that preserves refinement?

4. While the effect of reduced M/T neuron density on both M/T dendrites and OSN axons is described well, the relationship between both needs to be characterized better: Is one effect preceding the other or do they occur simultaneously? Can one be the consequence of the other?

5. Page 7: the observation that not all neurons develop additional dendrites is not a sign of differences between cell types, it may be purely stochastic.

6. Page 8: the fact that activity blockade did not affect the formation of ectopic dendrites does not suggest that the process is not activity-dependent: both manipulations have the same effect and may just mask each other.

7. It remains unclear how the observed structural changes can explain the behavioral effects.

eLife assessment

This paper describes valuable results from studies investigating circuits in the brain that underlie behavioral responses in fearful situations. The authors identified a role for a class of neurons that are sufficient to cause these stereotyped behaviors including freezing behaviors. These solid studies increase our understanding of brain pathways regulating these types of behaviors.

eLife assessment

This important work provides a robust yet simple protocol to isolate small extracellular vesicles from small volumes of plasma. The evidence supporting the conclusions is convincing, although a more thorough statistical comparison of the different techniques and technique combinations explored in the study would have been appreciated. The work will be of broad interest to cell biologists and biochemists.

Reviewer #1 (Public Review):

Summary:<br /> In their manuscript, Kong Fang et al describe a robust pipeline for the isolation of small extracellular vesicles through a combination of size exclusion chromatography and miniaturized density gradient separation. Subsequently, they prove that the method is reproducible and suitable for small-volume operations while at the same time not compromising the quality of vesicles.

Strengths:<br /> The paper narrates a robust method for purifying high-quality sEVs from small amounts of blood plasma. They also demonstrate that through this approach, they can derive sEVs without compromising the protein composition, integrity of the vesicles, or contamination with other proteins or lipids.

Weaknesses:<br /> The paper is a nice summary of how to enrich sEVs from blood samples. Although well performed and substantiated with data, the paper primarily deals with method development and optimisation.

Reviewer #2 (Public Review):

Summary:<br /> In this work, the authors manage to optimize a simple and rapid protocol using SEC followed by DGCU to isolate sEVs with adequate purity and yield from small volumes of plasma. Isolated fractions containing sEVs using SEC, DGCU, SEC-DGCU, and DGCU-SEC are compared in terms of their yield, purity surface protein profile, and RNA content. Although the combined use of these methodologies has already been evaluated in previous works, the authors manage to adapt them for the use of small volumes of plasma, which allows working in 1.5 mL tubes and reducing the centrifugation time to 2 hours.

The authors finally find that although both the SEC-DGCU and DGCU-SEC combinations achieve isolates with high purity, the SEC-DGCU combination results in higher yields.

This work provides an interesting tool for the rapid obtention of sEVs with sufficient yield and purity for detailed characterization which could be very useful in research and clinical therapy.

Strengths:<br /> -The work is well-written and organized.<br /> -The authors clearly state the problem they want to address, that is, optimizing a method that allows sEV to be isolated from small volumes of plasma.<br /> -Although these methodologies have been tested in previous works, the authors manage to isolate sEVs of high purity and good performance through a simple and fast methodology.<br /> -The characteristics of all isolated fractions are exhaustively analyzed through various state-of-the-art methodologies.<br /> -They present a good interpretation of the results obtained through the methodologies used.

Weaknesses:<br /> -Lack of references that support some of the results obtained.<br /> -Although this work focuses on comparing different techniques and their combinations to find an optimal option, the authors do not use any statistical method that reliably shows the differences between these techniques, except when repeatability is measured.

eLife assessment

This useful study compares gene expression patterns among different autonomic ganglia and will be of interest to developmental neuroscientists and neurophysiologists. The study expands the database of genes expressed by subpopulations of autonomic neurons in ganglia, a key step in decoding their developmental origins and physiological functions. The evidence supporting the alternative view that the pelvic ganglionic neurons are actually modified sympathetic neurons is incomplete and may cause confusion, given the enrichment of cholinergic neurons, as well as the large number of molecular and functional differences known to be present between cranial and sacral neurons.

Reviewer #1 (Public Review):

In recent years, these investigators have been engaged in a debate regarding the classification of the sacral parasympathetic system as "sympathetic" rather than "parasympathetic," based on shared developmental ontogeny of spinal preganglionic neurons. In this current study, these investigators conducted single-cell RNAseq analyses of four groups of autonomic neurons: paravertebral sympathetic neurons (stellate and lumbar train ganglia), prevertebral sympathetic neurons (coeliac-mesenteric ganglia), rostral parasympathetic ganglia (sphenopalatine ganglia), and the caudal pelvic ganglia (containing traditionally recognized sacral "parasympathetic cholinergic neurons," which the investigators sought to challenge in terms of nomenclature). The authors argued that the pelvic ganglionic neurons shared the expression of more genes with sympathetic ganglia, as opposed to parasympathetic ganglia. Additionally, the pelvic neurons did not express a set of genes observed in the rostral parasympathetic sphenopalatine ganglia. Based on these findings, they claimed that the sacral autonomic system should be considered sympathetic rather than parasympathetic.

However, noradrenergic sympathetic neurons and cholinergic neurons, by the virtue of expressing different neurotransmitters, could have distinct roles. It is true that some cholinergic neurons reside in the sympathetic train ganglia as well, such as those innervating the sweat gland and some vascular systems; in this sense, the pelvic ganglia share some features with sympathetic ganglia, except that the pelvic ganglia contain a much higher percentage of cholinergic neurons compared with sympathetic ganglia. It is much simpler and easier to divide the autonomic nervous system into sympathetic neurons that relieve noradrenaline versus parasympathetic neurons that relieve acetylcholine, and these two systems often act in antagonistic manners, even though in some cases, these two systems can work synergistically. As such, it is not justified to claim that "pelvic organs receive no parasympathetic innervation".

Reviewer #2 (Public Review):

Summary:<br /> Recent advances in single cell profiling of gene expression (RNA) permit the analysis of specialized cell types, an approach that has great value in the nervous system which is characterized by prodigious neuronal diversity. The novel data in this study focus primarily on genetic profiling to compare autonomic neurons from ganglia associated with the cranial parasympathetic outflow (sphenopalatine (also known as pteropalatine), the thoraco-lumbar sympathetic outflow (stellate, coeliac) and the sacral parasympathetic outflow (pelvic). Using statistical methods to reduce the dimensionality of the data and map gene expression, the authors provide interesting evidence that cranial parasympathetic and sacral sympathetic ganglia differ from each other and from sympathetic ganglia (Figures 1, S1 - S4). The authors interpret the mapping analysis as evidence that the cranial and sacral outflows differ so that calling them both parasympathetic is unjustified. Based on anatomical localization of markers (Figure 2 ) (mainly transcription factors) the authors show a similarity between the sympathetic and pelvic ganglion. In Figure 3 they present evidence that some pelvic ganglionic neurons are dually innervated by sympathetic preganglionic neurons and sacral preganglionic neurons. These observations are interpreted to mean that the pelvic ganglion is not parasympathetic, but rather a modified sympathetic ganglion - hence the title of the manuscript.

Strengths:<br /> The extensive use of single cell profiling in this work is both interesting and exciting. Although still in its early stages, it holds promise for a deepened understanding of autonomic development and function. As noted in the introduction, this study extends previous work by Professor Brunet and his associates.

Weaknesses:<br /> This work further documents differences between the cranial and sacral parasympathetic outflows that have been known since the time of Langley - 100 years ago. The approach taken by Brunet et al. has focused on late neonatal and early postnatal development, a time when autonomic function is still maturing. In addition, the sphenopalatine and other cranial ganglia develop from placodes and the neural crest, while sympathetic and sacral ganglia develop from the neural crest alone. How then do genetic programs specifying brainstem and spinal development differ and how can this account for kinship that Brunet documents between spinal and sacral ganglia? One feature that seems to set the pelvic ganglion apart is the mixture of 'sympathetic' and 'parasympthetic' ganglion cells and the convergence of preganglionic sympathetic and parasympathetic synapses on individual ganglion cells (Figure 3). This unusual organization has been reported before using microelectrode recordings (see Crowcroft and Szurszewski, J Physiol (1971) and Janig and McLachlan, Physiol Rev (1987)). Anatomical evidence of convergence in the pelvic ganglion has been reported by Keast, Neuroscience (1995). It should also be noted that the anatomy of the pelvic ganglion in male rodents is unique. Unlike other species where the ganglion forms a distributed plexus of mini-ganglia, in male rodents the ganglion coalesces into one structure that is easier to find and study. Interestingly the image in Figure 3A appears to show a clustering of Chat-positive and Th-positive neurons. Does this result from the developmental fusion of mini ganglia having distinct sympathetic and parasympathetic origins. In addition, Brunet et al dismiss the cholinergic and noradrenergic phenotypes as a basis for defining parasympathetic and parasympathetic neurons. However, see the bottom of Figure S4 and further counterarguments in Horn (Clin Auton Res (2018)). What then about neuropeptides, whose expression pattern is incompatible with the revised nomenclature proposed by Brunet et al.? Figure 1B indicates that VIP is expressed by sacral and cranial ganglion cells, but not thoracolumbar ganglion cells. The authors do not mention neuropeptide Y (NPY). The immunocytochemistry literature indicates that NPY is expressed by a large subpopulation of sympathetic neurons but never by sacral or cranial parasympathetic neurons.

The title of this paper is misleading because it implies a conclusion that is not adequately supported by the data and that is difficult for a general reader to parse. Independent assessments by two referees both agreed on title's problematic message. If one can get beyond the title, then the paper does contain data that is of interest. The authors compared single cell gene expression in neurons from the cranial sphenopalatine ganglion, paravertebral chain ganglia (stellate and lumbar), the prevertebral coeliac ganglion and the bladder ganglion. The cranial and pelvic ganglia are parasympathetic, while the paravertebral and prevertebral ganglia are sympathetic. The gene expression data identified differences between the cranial, sympathetic, and pelvic ganglia. Based primarily on this finding the authors concluded that the sacral bladder ganglion is not parasympathetic. Since some genes suggest a kinship between the pelvic and sympathetic neurons, the authors conclude that the pelvic neurons are pelvo-sympathetic - hence the title. This nomenclature does little to improve understanding of the autonomic motor system and it ignores important anatomical and functional properties that underlie existing definitions of the sympathetic and parasympathetic systems. The idea that the cranial and sacral autonomic outflows have some differences is not new (see for example Nilsson, 1983 and Janig, 2022). Since many of the genes identified in the present study are HOX genes and other transcription factors that specify the rostro-caudal axis during development, it is also not surprising that these genes suggest a kinship between sacral parasympathetic neurons and sympathetic neurons, all of which derive from the neural crest and are supplied by the spinal cord. The different profile of cranial parasympathetic neurons is also not surprising given that they derive from a mixture of placodal and neural crest progenitors and are supplied by the brainstem. (see my previous comments for anatomical and functional criteria that further support the existing nomenclature for the sympathetic and parasympathetic motor systems.

eLife assessment

This useful study compares gene expression patterns among different autonomic ganglia and will be of interest to developmental neuroscientists and neurophysiologists. The study expands the database of genes expressed by subpopulations of autonomic neurons in ganglia, a key step in decoding their developmental origins and physiological functions. The alternative view that the pelvic ganglionic neurons are actually modified sympathetic neurons is incompletely demonstrated given the large number of molecular and functional differences known to be present between cranial and sacral neurons.

eLife assessment

This study explores how Ebola virus evades human immune responses. The study reports a potential new mechanism wherein Ebola virus traps human IRF3, a key transcription factor involved in immune signaling, into virus-produced "inclusion bodies". The topic is important, the paper has many merits, and the biochemical assays are solid. However, the current data do not clearly explain the relationship between the VP35 protein and IRF3.

eLife assessment

This study presents a potentially valuable discovery which indicates that activation of the P2RX7 pathway by the small molecule HEI3090 can reduce lung fibrosis after its establishment by inflammatory damage. If confirmed, the study could clarify the role of specific immune networks in the establishment and progression of lung fibrosis. The presented data and analyses showing the efficacy of HEI3090 small molecule acting via the P2RX7 pathway in reducing lung fibrosis are solid. The studies also show that genetic deletion of P2RX7 itself can reduce the extent of fibrosis. P2RX7 can thus have distinct effects in various phases of the development of lung fibrosis. There is a need for additional definitive studies that specifically identify the discrete phases of when inflammasome activation via P2RX7 signaling can worsen fibrosis versus when the same signaling can be beneficial. It also needs to be established whether distinct immune cell populations mediate the detrimental and beneficial effects of P2RX7 activation in lung fibrosis.

Author Response

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

Point to point response for the editors

We are deeply grateful for the time you have devoted to reviewing this manuscript, and we sincerely thank you. Your insightful feedback has been instrumental in enhancing the quality of our work.

In the revised version of the manuscript, we have carefully addressed each of the concerns you raised. Below, you will find a detailed summary of how your feedback has been incorporated to improve the overall content and clarity of the document.

1. P2RX7 effects: In Figure 2, the vehicle treated P2RX7 knockout (panel M) shows an Ashcroft score of about 1.5 after BLM. Comparing this to the Ashcroft score of 3 after BLM in the wildtype (panel C) suggests that P2RX7 deletion is an effective way to reduce fibrosis by half!.

The argument that HEI3090 also reduces fibrosis by activating P2RX7 is of course very difficult to convey and it seems contradictory that P2RX7 deletion and P2RX7 activation can be both anti-fibrotic. This is an unusual claim and confuses the reviewers as well as the future readers.

This has many important health implications because activating an inflammatory pathway via P2RX7 and IL-18 could be risky in terms of a fibrosis treatment as inflammatory activation can also worsen fibrosis. The authors' own P2RX7 KO data (untreated vehicle groups) indeed confirms that P2RX7 can be pro-fibrotic.

We thank the editors for their comment highlighting the lack of clarity in our message. Indeed, we verified whether the antifibrotic action of HEI3090 depends on the expression of P2RX7 by inducing lung fibrosis in P2RX7 KO mice. In doing so, we initially observed that P2RX7 plays a role in the development of BLM-induced lung fibrosis. This is illustrated by a decrease of 50% in the Ashcroft score, as shown in Figure 2M and Supplemental Figure 2C of the revised manuscript.

To increase the clarity of your message, we added in the text the following paragraph:

"We further verified whether the antifibrotic action of HEI3090 depends on the expression of P2RX7 by inducing lung fibrosis in p2rx7 knockout (KO) mice. In doing so, we initially observed that P2RX7 plays a role in the development of BLM-induced lung fibrosis. This is illustrated by a decrease of 50% in the Ashcroft score, with a mean value of 1.7 in P2RX7 knockout mice compared to 3 in wild-type mice (Figure 2M and Supplemental Figure 2C). It is important to note that p2rx7 -/- mice still exhibit signs of lung fibrosis, such as thickening of the alveolar wall and a reduction in free air space, in comparison to naïve mice that received PBS instead of BLM (see Supplemental Figure 2A). This result confirms a previous report indicating that BLM-induced lung fibrosis partially depends on the activation of the P2RX7/pannexin-1 axis, leading to the production of IL-1β in the lung. Additionally, in contrast to the observations in WT mice, HEI3090 failed to attenuate the remaining lung fibrosis in p2rx7 -/- mice, as measured by the Ashcroft score (Figure 2M), the percentage of lung tissue with fibrotic lesions, or the intensity of collagen fibers (Supplemental Figure 2D). These results show that P2RX7 alone participates in fibrosis and that HEI3090 exerts a specific antifibrotic effect through this receptor (see Supplemental Figure 2C)."

Since we used the HEI3090 compound in this study and to be closer to the results, we have replaced the title of 2 chapters in the results section as followed:

“HEI3090 inhibits the onset of pulmonary fibrosis in the bleomycin mouse model” instead of P2RX7 activation inhibits the onset of pulmonary fibrosis in the bleomycin mouse model and “HEI3090 shapes immune cell infiltration in the lungs" instead of P2RX7 activation shapes immune cell infiltration in the lungs

We concur that the observation of both anti-fibrotic effects following P2RX7 deletion and P2RX7 activation appears contradictory. This specific aspect has been thoroughly addressed and extensively discussed in the revised manuscript.

“A major unmet need in the field of IPF is new treatment to fight this uncurable disease. In this preclinical study, we demonstrate the ability of immune cells to limit lung fibrosis progression. Based on the hypothesis that a local activation of a T cell immune response and upregulation of IFN-γ production has antifibrotic proprieties, we used the HEI3090 positive modulator of the purinergic receptor P2RX7, previously developed in our laboratory (Douguet et al., 2021), to demonstrate that activation of the P2RX7/IL-18 pathway attenuates lung fibrosis in the bleomycin mouse model. We have demonstrated that lung fibrosis progression is inhibited by HEI3090 in the fibrotic phase but also in the acute phase of the BLM fibrosis mouse model, i.e. during the period of inflammation. This lung fibrosis mouse model commonly employed in preclinical investigations, has recently been recognized as the optimal model for studying IPF (Jenkins et al., 2017). In this model, the intrapulmonary administration of BLM induces DNA damage in alveolar epithelial type 1 cells, triggering cellular demise and the release of ATP. The extracellular release of ATP from injured cells activates the P2RX7/pannexin 1 axis, initiating the maturation of IL1β and subsequent induction of inflammation and fibrosis. In line with this, mice lacking P2RX7 exhibited reduced neutrophil counts in their bronchoalveolar fluids and decreased levels of IL1β in their lungs compared to WT mice (Riteau et al., 2010). Based on these findings, Riteau and colleagues postulated that the inhibition of P2RX7 activity may offer a potential strategy for the therapeutic control of fibrosis in lung injury. In the present study we provided strong evidence showing that selective activation of P2RX7 on immune cells, through the use of HEI3090, can dampen inflammation and fibrosis by releasing IL-18. The efficacy of HEI3090 to inhibit lung fibrosis was evaluated histologically on the whole lung’s surface by evaluating the severity of fibrosis using three independent approaches applied to the whole lung, the Ashcroft score, quantification of fibroblasts/myofibroblasts (CD140a) and polarized-light microscopy of Sirius Red staining to quantify collagen fibers. All these methods of fibrosis assessment revealed that HEI3090 exerts an inhibitory effect on lung fibrosis, underscoring the necessity for a thorough pre-clinical assessment of HEI3090's mode of action. Notably, HEI3090 functions as an activator, rather than an inhibitor, of P2RX7, further emphasizing the importance of elucidating its intricate mechanisms.”

We trust that the detailed explanation provided therein will adequately persuade both the reviewers and future readers.

1. The statistical concerns are based on the phrasing of "the experiment was stopped when significantly statistical results were observed". This is different from the power analysis approach that the authors describe in their latest rebuttal. However, it raises the question why the power analysis was performed using "on a one-way ANOVA analysis comparing in each experiment the vehicle and the treated group". The analyses in the manuscript use the Mann-Whitney test for several comparisons which ahs the assumption that the samples do NOT have a normal distribution. An ANOVA and t-tests have the assumption that samples are normally distributed. If the power analysis and "statistical forecasting" assumed a normal distribution and used an ANOVA, then shouldn't all the analyses also use a statistical test appropriate for normally distributed samples such as ANOVA and t-tests?

Several of the data points in the figures seem to be normally distributed and therefore t-test for two group comparisons would be more appropriate. The most rigorous approach would be to check for normal distribution before choosing the correct statistical test and using the t-test/ANOVA in normally distributed data as well as Mann-Whitney for non-normally distributed data.

We described in the Material and Method section of the revised manuscript our approach to determine the size of experimental group.

“The determination of experimental group sizes involved conducting a pilot experiment with four mice in each group. Subsequently, a power analysis, based on the pilot experiment's findings (which revealed a 40% difference with a standard error of 0.9, α risk of 0.05, and power of 0.8), was performed to ascertain the appropriate group size for studying the effects of HEI3090 on BLM-induced lung fibrosis. The results of the pilot experiment and power analysis indicated that a group size of four mice was sufficient to characterize the observed effects. For each full-scale experiment, we initiated the study with 6 to 8 mice per group, ensuring a minimum of 5 mice in each group for robust statistical analysis. Additionally, we systematically employed the ROULT method to identify and subsequently exclude any outliers present in each experiment before conducting statistical analyses”.

We now described in the Material and Method section how we carried out the statistical analyses.

“Quantitative data were described and presented graphically as medians and interquartiles or means and standard deviations. The distribution normality was tested with the Shapiro's test and homoscedasticity with a Bartlett's test. For two categories, statistical comparisons were performed using the Student's t-test or the Mann–Whitney's test. For three and more categories, analysis of variance (ANOVA) or non-parametric data with Kruskal–Wallis was performed to test variables expressed as categories versus continuous variables. If this test was significant, we used the Tukey's test to compare these categories and the Bonferroni’s test to adjust the significant threshold. For the Gene Set Enrichment Analyses (GSEA), bilateral Kolmogorov–Smirnov test, and false discovery rate (FDR) were used. All statistical analyses were performed by biostatistician using Prism8 program from GraphPad software. Tests of significance was two-tailed and considered significant with an alpha level of P < 0.05. (graphically: * for P < 0.05, ** for P < 0.01, *** for P < 0.001).”

We also added in the legend of each figure, the statistical analysis used to determine each p-values.

1. Adoptive transfer: The concerns of the reviewers include an unclear analysis of the effects of adoptive transfer itself and the approaches used to analyze the data independent of the HEI3090 effect. For example, in Figure 4, the adoptive transfer IL18-/- cells (vehicle group) leads to an Ashcroft score of about 1 and among the lowest of the BLM exposed mice. Does that mean that IL18 is pro-fibrotic and that its absence is beneficial? If yes, it would go against the core premise of the study that IL18 is beneficial. Statistical comparisons of the all the vehicle conditions in the adoptive transfer would help clarify whether adoptive transfer of NLRP3-/-, IL18-/- in wild-type and P2RX7-/- mice reduces or increases fibrosis. Such multiple comparisons are necessary to fully understand the adoptive transfer studies and would also require the appropriate statistical test with corrections for multiple comparisons such as Kruskal-Wallis for data without normal distribution and ANOVA with post hoc correction for normal distribution.

We added a new paragraph in the revised version of the manuscript to explain the adoptive transfer approach.

“We wanted to further investigate the mechanism of action of HEI3090 by identifying the cellular compartment and signaling pathway required for its activity. Since the expression of P2RX7 and the P2RX7-dependent release of IL-18 are mostly associated with immune cells (Ferrari et al., 2006), and since HEI3090 shapes the lung immune landscape (Figure 3), we investigated whether immune cells were required for the antifibrotic effect of HEI3090. To do so, we conducted adoptive transfer experiments wherein immune cells from a donor mouse were intravenously injected one day before BLM administration into an acceptor mouse. The intravenous injection route was chosen as it is a standard method for targeting the lungs, as previously documented (Wei and Zhao, 2014). This approach was previously used with success in our laboratory (Douguet et al., 2021). It is noteworthy that this adoptive transfer approach did not influence the response to HEI3090. This was observed consistently in both p2rx7 -/- mice and p2rx7 -/- mice that received splenocytes of the same genetic background. In both cases, HEI3090 failed to mitigate lung fibrosis, as depicted in Figure 2M and Supplemental Figures 2D and 6A and B.”

We added the Supplemental Figure 7 showing that the genetic background does not impact lung fibrosis at steady step levels where p-values were analyzed by one-way ANOVA, with Kruskal-Wallis test for multiple comparisons.

Author response image 1.

Supplemental Figure 7 : The genetic background does not impact lung fibrosis at steady step levels. p2rx7-/- mice were given 3.106 WT, nlrp3-/ , i118-/ or illb -l- splenocytes i_v_ one day prior to BLM delivery (i_n_ 2.5 LJ/kg) p2rx7-/- mice or p2rx7-/- mice adoptively transferred with splenocytes from indicated genetic background were treated daily i.p with mg/kg HE13090 or vehicle for 14 days. Fibrosis score assessed by the Ashcroft method. P-values were analyzed on all treated and non treated groups by one-way ANOVA, with Kruskal-Wallis test for multiple comparisons. The violin plot illustrates the distribution of Ashcroft scores across indicated experimental groups. The width of the violin at each point represents the density of data, and the central line indicates the median expression level. Each point represents one biological replicate. ns, not significant

Reviewer #1 (Public Review):

In this revised preprint the authors investigate whether a presumably allosteric P2RX7 activating compound that they previously discovered reduces fibrosis in a bleomycin mouse model. They chose this particular model as publicly available mRNA data indicate that the P2RX7 pathway is downregulated in idiopathic pulmonary fibrosis patients compared to control individuals. In their revised manuscript, the authors use three proxies of lung damage, Ashcroft score, collagen fibers, and CD140a+ cells, to assess lung damage following the administration of bleomycin. These metrics are significantly reduced on HEI3090 treatment. Additional data implicate specific immune cell infiltrates and cytokines, namely inflammatory macrophages and damped release of IL-17A, as potential mechanistic links between their compound and reduced fibrosis. Finally, the researchers transplant splenocytes from WT, NLRP3-KO, and IL-18-KO mice into animals lacking the P2RX7 receptor to specifically ascertain how the transplanted splenocytes, which are WT for P2RX7 receptor, respond to HEI3090 (a P2RX7 agonist). Based on these results, the authors conclude that HEI3090 enhanced IL-18 production through the P2RX7-NLRP3 inflammasome axis to dampen fibrosis.

These findings could be interesting to the field, as there are conflicting results as to whether NLRP3 activation contributes to fibrosis and if so, at what stage(s) (e.g., acute damage phase versus progression). The revised manuscript is more convincing in that three orthogonal metrics for lung damage were quantified.

However, deletion of the P2RX7 receptor itself reduces the extent of fibrosis, suggesting that P2RX7 signaling can be pro-fibrotic. In the absence of P2RX7, the effects of HEI3900 are also abolished, suggesting that HEI3900 acts in part via P2RX7 signaling. This suggests a paradox that P2RX7 signaling can be both detrimental and beneficial in fibrosis and there is need for a better understanding of when P2RX7 signaling is beneficial and when it is detrimental in lung fibrosis. HEI3900-induced activation of P2RX7 seems to be beneficial but this primarily is shown for when fibrosis is already established. As the P2RX7 genetic deletion mouse model has less fibrosis, P2RX7 signaling and inflammasome activation may be deleterious during the formation of disease but it is also possible that HEI3900 has other beneficial effects that are not directly related to P2RX7.

Molecularly, additional evidence on specificity, such as thermal proteome profiling and direct biophysical binding experiments, would also enhance the authors' argument that the compound indeed binds P2RX7 directly and specifically. Since all small molecules have some degree of promiscuity, the absence of an additional P2RX7 modulator, or direct recombinant IL-18 administration, is needed to orthogonally validate the functional importance of this pathway. Another way the authors could probe pathway specificity would involve co-administering α-IL-18 with HEI3090 in several key experiments (similar to Figure 4L).

eLife assessment

The authors develop a novel genetic strategy for specific and comprehensive labeling of axo-axonic cells, also referred to as Chandelier cells, in the mouse brain. The approach and analysis are rigorous such that the data convincingly support the key conclusions, including the expanded distribution of axo-axonic cells throughout the brain. This study provides valuable new information about the distribution of this neuronal cell type, as well as new tools for future studies. This work will be of broad interest to neuroscientists who work on the anatomical and functional organization of neural circuits.

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, the authors set out to develop genetic tools that can specifically and comprehensively label Axo-Axonic Cells (AACs), also known as Chandelier cells. These AACs possess unique morphological and connectivity features, making them an ideal subject for studying various aspects of cell types across different experimental methods. To achieve both specificity and comprehensiveness in AAC labeling, the authors employ an intersectional strategy that combines lineage origin and molecular markers. This approach successfully targets AACs across the mouse brain and reveals their widespread distribution in various brain structures beyond the previously known regions. Additionally, the authors utilize rabies transneuronal labeling to provide a comprehensive overview of AACs, their variations, and input sources throughout the brain. This experimental approach offers a powerful model system for investigating the role of AACs in circuit development and function across diverse brain regions.

Strengths:<br /> Genetic Tools and Specificity: The authors' genetic tools show qualitative evidence of specificity for AACs, opening new avenues for targeted research on these cells. The use of intersectional strategies enhances the precision of AAC labeling.

Widespread Distribution: The study significantly broadens our understanding of AAC distribution, revealing their presence in brain regions beyond what was previously documented. This expanded knowledge is a valuable contribution to the field.

Transneuronal Labeling: The inclusion of rabies transneuronal labeling provides a comprehensive view of AACs, their variations, and input sources, allowing for a more holistic understanding of their role in neural circuits.

Weaknesses:<br /> Quantitative Analysis: While the claim of specificity appears qualitatively convincing, the manuscript could be improved with more quantitative analysis.

Comprehensiveness Claim: The assertion of comprehensiveness, implying labeling "almost all" AACs in all brain regions, is challenging to substantiate conclusively. Acknowledging the limitations of proving complete comprehensiveness and discussing them in the discussion section would be more appropriate than asserting it in the results section.

Local Inputs: While the manuscript focuses on inter-areal inputs to AACs, it would benefit from exploring local inputs as well. Identifying the local neurons that target AACs and analyzing their patterns could provide valuable insights into AAC function within specific brain regions.

Discussion Focus: The discussion section should delve deeper into the biological implications of the findings, moving beyond technical significance. Exploring similarities and differences in input patterns between AACs and other cell types, and linking them to the locations of starter cells or specific connectivity patterns in the brain, would enrich the discussion. For instance, investigating whether input patterns can be predicted based on the locations of starter cells or connectivity specificity could provide valuable insights.

Reviewer #2 (Public Review):

Summary:<br /> The goals of this study were to develop a genetic approach that would specifically and comprehensively target axo-axonic cells (AACs) throughout the brain and then to describe the patterns and characteristics of the targeted AACs in multiple, selected brain regions. The investigators have been successful in providing the most complete description of the regional distribution of putative (pAACs) throughout the brain to date. The supporting evidence is convincing, even though incomplete in some brain regions. The findings should serve as a guide for more detailed studies of AACs within each brain region and lead to new insights into the connectivity and functional organization of this important group of GABAergic interneurons.

Strengths:<br /> The study has numerous strengths. A major strength is the development of a unique intersectional genetic strategy that uses cell lineage (Nkx2.1) and molecular (Unc5b or Pthlh) markers to identify axo-axonic AACs specifically and, apparently, nearly completely throughout the mouse brain. While AACs have been described previously in the cerebral cortex, hippocampus, and amygdala, there has been no specific genetic marker that selectively identifies all AACs in these regions.

The current genetic strategy has labeled pAACs in a large number of additional brain regions, including the claustrum-insular complex, extended amygdala, and several olfactory centers. In general, the findings provide support for the specificity of the methods for targeting AACs, and include some examples of labeling near markers of axon initial segments. However, the Investigators are careful to refer to labeled neurons as "putative AACs" as they have not been fully characterized and their identity verified.

The descriptions and numerous low-magnification images of the brain provide a roadmap for subsequent, detailed studies of AACs in numerous brain regions. The overview and summaries of the findings in the Abstract, Introduction, and Discussion are particularly clear and helpful in placing the extensive regional descriptions of AACs in context.

Weaknesses:<br /> One weakness of the study is the lack of an illustration of the high-resolution cell labeling that can be achieved with the methods, including labeling of numerous rows of axon terminals in contact with axon initial segments. The initial images of the brain-wide distribution of putative AACs are necessarily presented at low magnification. Although the authors indicate that the cells have "highly characteristic AAC labeling patterns throughout the neocortex, hippocampus and BLA", these morphological details cannot be visualized by the reader at the current magnification, even when the images are enlarged on the computer screen. Some of the details become evident in later Figures, but an initial illustration of single cell labeling with confocal microscopy, or tracing of their characteristic axonal arbors, would support the specificity of the labeling in the low magnification images.

Table 1 indicates that the AAC identity of the cells has been validated in many brain regions but not in all. The methods used for validation have not been described and should be included for completeness. The authors are careful to acknowledge that labeled cells in some regions have not been validated and refer to such cells as pAACs.

The intersectional genetic methods included the use of the lineage marker Nkx2.1 with either Unc5b or Pthlh as the molecular marker. As described, the mice with intersectional targeting of Nkx2.1 and Unc5b appear to show the most specific brain-wide labeling for AACs, and the majority of the descriptions are from these mice. The targeting with Nkx2.1 and Pthlh is less convincing. The title for Figure 1 Supplemental Figure 3 suggests a similar AAC distribution in the Pthlh;Nkx2.1 mouse compared to the Unc5b;Nkx2.1 mouse. However, the descriptions of the individual panels suggest a number of inconsistencies and non-AAC labeling. The heavy labeling in the caudate and cells in layer 4 is particularly problematic. Based on the data presented, it appears that heavy labeling achieved in these mice could not be relied on for specific labeling of all AACs, although specific labeling could be achieved under some conditions, such as following tamoxifen administration at select ages.

The methods described for dense labeling and single-cell labeling are described briefly in the methods. Some discussion of the development of the methods would be useful, including how it was determined that methods for heavy labeling identified AACs specifically and completely.

Reviewer #3 (Public Review):

Summary:<br /> Raudales et al. aimed at providing an insight into the brain-wide distribution and synaptic connectivity of bona fide GABAergic inhibitory interneuron subtypes focusing on the axo-axonic cell (AAC), one of the most distinctive interneuron subtypes, which innervates the axon initial segments of glutamatergic projection neurons. They establish intersectional genetic strategies that enable them to specifically and comprehensively capture AACs based on their lineage (Nkx2.1) and marker expression (Unc5b, Pthlh). They find that AACs are deployed across essentially all the pallium-derived brain structures as well as the anterior olfactory nucleus, taenia tecta, and lateral septum. They show that AACs in distinct areas and layers of the neocortex as well as different subregions of the hippocampal formation display unique soma and synaptic density and morphological variations. Rabies virus-based retrograde monosynaptic input tracing reveals that AACs in the neocortex, the hippocampus, and the basolateral amygdala receive synaptic inputs from common as well as specific brain regions and supports the utility of this novel genetic approach. This study elucidates brain-wide neuroanatomical features and morphological variations of AACs with solid techniques and analysis. Their novel AAC-targeting strategies will facilitate the study of their development and function in different brain regions. The conclusions in this paper are well supported by the data. However, there are a few comments to strengthen this study.

1) The definition of putative AAC (pAAC) is unclear and Table 1 may not be accurate. Although the authors find synaptic cartridges of RFP-labeled cells in the claustro-insular complex and the dorsal endopiriform nuclei, they still consider these cells as pAACs (not validated). The authors claim that without examining the presence of synaptic cartridges, RFP-labeled cells in the hypothalamus and the bed nuclei of the stria terminalis (BNST) are pAACs while those in the L4 of the somatosensory cortex in Pthlh;Nkx2.1;Ai65 mice are non-AACs. In Table 1, the BNST is supposed to contain AACs (validated), but in the text, the authors claim that RFP-labeled cells in the BNST are pAACs. Could the authors clarify how AACs, pAACs, and non-AACs are defined?

2) The intersectional strategies presented in this study could also specifically capture developing AACs. If so, how early are AACs labeled in the brain? It would also be nice if the authors could add a simple schematic like Fig. 1a showing the time course of Pthlh expression.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

Some suggestions:

1) It's obviously concerning that your GWAS results are not at all robust to the approach used (Fig S3). Did you try something non-parametric, like a Kruskal-Wallis test?

We used both GWAS and crosses (F2) to validate the presence of the QTL. So ,evidence is not only brought by GWAS. We did not use non parametric tests as we will have difficulty to account for population structure/relatedness with such approaches. Our GWAS approach is certainly a little underpowered associated with the number of individuals we used and certainly the polygenic nature of the root growth traits. But F2 crosses allow us to put more evidence weight on some region we identified with GWAS.

2) You don't explain what you do with heterozygotes, nor discuss the level of inbreeding in general.

We are dealing with inbred lines, but indeed there are not completely fixed inbred lines. For the remaining heterozygotes, they were randomly fixed in one or the other alleles. The median heterozygosity value was low at 5.6%. We clarified this point in the material and methods.

3) The finding that over 30% of RNA-seq reads don't seem to have an annotated home should give you pause. Do they map anywhere? At least discuss what is going on. Also, note that you likely have enormous errors in SNP-calling due to cryptic structural variation - think about what this might do?

We agree with reviewer #1. We added a few sentences in the result section to clarify this point: “When further analyzed, 15.15% of the unmapped reads (with no correspondence to predicted CDS) were found not to match the reference genome. These might correspond either to unsequenced regions or to genotype-specific genomic regions that are not present in the reference line. The remaining unmapped reads corresponded to either rRNA and tRNA genes (40.28% of the unmapped reads) or to non-annotated genes or non-coding RNAs (44.57% of the unmapped reads).” As we used the same reference genome for mapping the RNAseq reads, some genes might not being present in our analysis for the two lines we studied.

4) Did you consider moving PgGRXC9 into Arabidopsis?

This is a great suggestion. In fact, we plan to explore more how some GRXs regulate root growth and how this is conserved in plants in a follow up project. This is however beyond the scope of this manuscript.

Minor suggestions:

1) Why not calculate H^2 simply as line variance divided by total?

Heritability estimated on single individuals in population, approaches generally used for human and animal breeding led directly to line variance divided by total phenotypic variance.

But in plant breeding (or plant science), we generally work on replicated genotypes in different blocks/experimental repetition. So we estimate the heritability of the mean phenotype of genotypes. There is ample literature (Nyquist, 1991; Holland et al. 2003; for a very nice and smartly written explanation, on the introduction of this PhD: http://opus.uni-hohenheim.de/volltexte/2020/1720/pdf/20200221_PhD_Thesis_Publikationsversion.pdf). Calculation of heritability (of the mean phenotype) should take into account for the calculation of the phenotypic variance (denominator) the number of replicate genotypes (we do not have a single plant, but several clones when using inbred lines: n). The meaning of the formula is that the error in the model is inflated because we have n replicate plants per genotype. And so to estimate the heritability of the average genotype, we have to take into account this inflated variance in the errors.

2) While the paper overall is well-written, the captions need further proof-reading.

We corrected all the captions.

Reviewer #2 (Recommendations For The Authors):

Major suggestions:

1) The experimental support for the mutant phenotype of roxy19 needs to be further substantiated. Current methods available for CRISPR mutagenesis make it relatively easy to generate additional alleles. Alternatively, the authors could complement the mutant with a wild-type copy of the gene. These approaches represent the standard of the field and should be used here as well.

We agree with rev #2. We added some sentences in the discussion to stress out the limitations of our study to link the QTL to PgGRXC9.

As stated above we’d like to explore more how some GRXs regulate root growth and how this is conserved in plants. We plan to generate new single and multiple mutants in ROXY19 and its closest homologues (using CRISPR). This is, however, beyond this manuscript.

2) The authors may want to state more clearly what the hypothesis is for how redox levels might contribute to root length differences and more clearly state what the limits of their current study are.

We modified the discussion to try to clearly indicate the limitations of our study.

3) Differences in root growth can be the consequence of a number of different parameters that contribute to root elongation and the authors need to more clearly define which of these are likely affected in their different genotypes.

We agree with Reviewer #2. However, as stated before, we plan to further explore the molecular and cellular mechanisms responsible for the phenotype we observe in Arabidopsis. This will need extra work and is beyond the scope of this manuscript.

4) Page 13, first paragraph. The authors provide an overly strong statement that suggests they have determined the molecular basis for the difference in PgGRXC9: " Altogether, our results suggest that PgGRXC9 is a positive regulator of root growth and that a polymorphism in the promoter region of PgGRXC9 associated with changes in its expression level appeared responsible for a quantitative difference in root growth between the two lines."

While their results suggest the PgGRXC9 locus is associated with root growth variation, they have not directly tested the effect of the polymorphisms in the promoter on gene expression and this statement needs to be weakened.

We changed the text to: “Altogether, our results suggest that PgGRXC9 is a positive regulator of root growth and that a polymorphism in the promoter region of PgGRXC9 might led to changes in its expression level and ultimately to a quantitative difference in root growth between the two lines. However, the effect of the polymorphisms in the promoter on gene expression need to be tested to validate this hypothesis.”

We also changed the title of the manuscript to better reflect our results.

Minor suggestions:

1) Page 4: "FTSW below 0.3 was considered a stressful condition." It was not specified how this threshold was determined.

This value corresponds to the measured FTSW value at which pearl millet genotypes subjected to a dry down generally start to reduce their transpiration rate (see Fig. 1 of Kholová et al, 2010; https://doi.org/10.1093/jxb/erp314). At FTSW values above 0.3, transpiration is not affected. At FTSW values around 0.3, the water supply from pearl millet roots cannot fully support transpiration. The plant enters a drought stress responsive phase and progressively closes its stomata to reduce water losses and decrease plant productive functions to match water supply. We have clarified this in the manuscript.

2) Page 6: Figure 1; footnote: at the end of the description of panel A, a comma is missing between "red" and "blue."

Thanks for pointing that out. This was corrected.

3) The root growth data determined by X-ray imaging is not significant (Fig S4B), yet the authors describe the result in the main text without qualification. The authors should clarify this in the text.

We added some text to clarify this.

4) Page 9: Figure 2C; It would be better to enlarge these images and annotate them to indicate what specific anatomical features have been measured. Currently, only an expert in the field would be able to interpret these images.

While we understand the point made by Reviewer #2, Fig2C was meant to illustrate differences in the root tip of the two lines.

5) Page 9: Figures 2D and E; the number of biological samples measured is not indicated (what is "n"?).

Thanks again for pointing this out. This was added to the figure legend.

6) Page 14: Figure 4B; scale bar needs to be included.

Scale bars were added to the pictures.

7) Page 14: Figure 4; I recommend adding confocal images or DIC of cleared root apex tissues to easily compare the RAM size and cell lengths in both WT and roxy19 mutant.

Once again, we plan to have a follow up study on the molecular and cellular mechanisms of action of ROXY19 and its closest homologues on root development. We believe a thorough analysis of differences in phenotype could be illustrated in a future manuscript.

8) Page 18: main text; "we propose that redox regulation in the root meristem is responsible for a root growth QTL in pearl millet." This statement is ambiguous in the description of the mechanism. The authors do not clarify if the role they propose for PgGRXC9 is in the meristematic or elongation zone. Likely the authors are not able to know precisely where the gene is acting at this point, and so the presented hypothesis needs to more clearly state what limitations there are in assigning a mode of action for the PgGRXC9 and ROXY19 genes in root growth.

We rewrote this paragraph to clarify the current gap in our understanding of the putative PgGRXC9 function.

eLife assessment

This is an important paper that combines methods ranging from agronomy and plant breeding to Arabidopsis functional genetics, to argue that polymorphism in a single gene affects crop yield in pearl millet by affecting root cell elongation and drought stress resilience in a poorly studied crop. The overall argument is plausible but whether the solid evidence generated with Arabidopsis experiments can be extended to pearl millet itself is unclear.

Reviewer #1 (Public Review):

The authors use a combination of crop modeling and field experiments to argue that drought during seedling establishment likely severely impacts the yield of pearl millet, an important but understudied cereal crop and that rapid seedling root elongation could play a major role in mitigating this. They further argue that this trait has a strong genetic basis and that major polymorphisms in candidate genes can be identified using standard methods from modern genetics and genomics. Finally, they use homology with the model plant Arabidopsis thaliana to argue that the function of one putatively causal gene is to regulate root cell elongation.

The major strength of this paper is that it convincingly demonstrates how modern methods from plant breeding and model organisms can be combined to address questions of great practical importance in important but poorly understood crops. The notion that it is possible to connect single-locus polymorphism and cellular biology to drought tolerance and crop yield in pearl millet is not a trivial one.

The weakness is obvious: while the argument made is convincing, it must be recognized that the strength of the evidence is by no means of the level expected in a model organism. Conclusions could easily be wrong, and there is no direct evidence that regulatory variation in PgGRXC9 leads to higher crop yield via cell elongation and seedling drought tolerance. However, generating such evidence in a poorly studied crop would be a monumental undertaking, and should probably not be the priority of people working on pearl millet!

The utility of this work is that it suggests that it is practicable to gain valuable insight into crop adaptation by clever use of modern methods from a variety of sources.

Reviewer #2 (Public Review):

Carla de la Fuente et al., utilize a diversity of approaches to understand which plant traits contribute to the stress resilience of pearl millet in the Sahelian desert environment. By comparing data resulting from crop modeling of pearl millet growth and meteorological data from a span of 20 years, the authors clearly determined that early season drought resilience is contributed by accelerated growth of the seedling primary root, which confirms a hypothesis generated in a previous study, Passot et al., 2016. To determine the genetic basis for this trait, they performed a combination of GWAS, QTL analysis, and RNA sequencing and identified a previously unannotated coding sequence of a glutaredoxin C9-like protein, PgGRXC9, as the strongest candidate. Phenotypic analysis using a mutant of the closest Arabidopsis homolog AtROXY19 suggests the broad conservation of this pathway. Comparisons between the transcript of PgGRXC9 by in situ hybridization (this work) and AtROXY19 pattern expression (Belin et al., 2014) support the hypothesis that this pathway acts in the elongation zone of the root. Additional analysis of cell production and elongation rates in root apex in both pearl millet and A. thaliana suggests that PgGRXC9 specifically regulates primary root through the promotion of cell elongation. While several studies have established the connection between redox status of cells and root growth, the current study represents an important contribution to the field because of the agricultural importance of the plant studied, and the connection made between this developmental trait and stress resilience in a specific and stressful environmental context of the Sahelian desert.

eLife assessment

The manuscript addresses a fundamental question: are IDRs responsible for subnuclear clustering of transcription factors? A screen of 75 IDRs yielded convincing evidence that IDRs are rarely sufficient for subnuclear clustering, while the experimental design and data analysis provided limited evidence for the authors' claims regarding transcription factor clustering.

Author Response

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

eLife assessment

The study is an important advancement to the consideration of antimalarial drug resistance: the authors make use of both modelling results and supporting empirical evidence to demonstrate the role of malaria strain diversity in explaining biogeographic patterns of drug resistance. The theoretical methods and the corresponding results are convincing, with the novel model presented moving beyond existing models to incorporate malaria strain diversity and antigen-specific immunity. This work is likely to be interesting to malaria researchers and others working with antigenically diverse infectious diseases.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The paper is an attempt to explain a geographic paradox between infection prevalence and antimalarial resistance emergence. The authors developed a compartmental model that importantly contains antigenic strain diversity and in turn antigen-specific immunity. They find a negative correlation between parasite prevalence and the frequency of resistance emergence and validate this result using empirical data on chloroquine-resistance. Overall, the authors conclude that strain diversity is a key player in explaining observed patterns of resistance evolution across different geographic regions.

The authors pose and address the following specific questions:

1. Does strain diversity modulate the equilibrium resistance frequency given different transmission intensities?

2. Does strain diversity modulate the equilibrium resistance frequency and its changes following drug withdrawal?

3. Does the model explain biogeographic patterns of drug resistance evolution?

Strengths:

The model built by the authors is novel. As emphasized in the manuscript, many factors (e.g., drug usage, vectorial capacity, population immunity) have been explored in models attempting to explain resistance emergence, but strain diversity (and strain-specific immunity) has not been explicitly included and thus explored. This is an interesting oversight in previous models, given the vast antigenic diversity of Plasmodium falciparum (the most common human malaria parasite) and its potential to "drive key differences in epidemiological features".

The model also accounts for multiple infections, which is a key feature of malarial infections, with individuals often infected with either multiple Plasmodium species or multiple strains of the same species. Accounting for multiple infections is critical when considering resistance emergence, as with multiple infections there is within-host competition which will mediate the fitness of resistant genotypes. Overall, the model is an interesting combination of a classic epidemiological model (e.g., SIR) and a population genetics model.

In terms of major model innovations, the model also directly links selection pressure via drug administration with local transmission dynamics. This is accomplished by the interaction between strain-specific immunity, generalized immunity, and host immune response.

R: We thank the reviewer for his/her appreciation of the work.

Weaknesses:

In several places, the explanation of the results (i.e., why are we seeing this result?) is underdeveloped. For example, under the section "Response to drug policy change", it is stated that (according to the model) low diversity scenarios show the least decline in resistant genotype frequency after drug withdrawal; however, this result emerges mechanistically. Without an explicit connection to the workings of the model, it can be difficult to gauge whether the result(s) seen are specific to the model itself or likely to be more generalizable.

R: We acknowledge that the explanation of certain results needs to be improved. We have now added the explanation of why low diversity scenarios show the least decline in resistance frequency after drug withdrawal: “Two processes are responsible for the observed trend: first, resistant genotypes have a much higher fitness advantage in low diversity regions even with reduced drug usage because infected hosts are still highly symptomatic; second, due to low transmission potential in low diversity scenarios (i.e., longer generation intervals between transmissions), the rate of change in parasite populations is slower.” (L243-247). We also compared the drug withdrawal response to that of the generalized-immunity-only model (L268-271). The medium transmission region has the fastest reduction in resistance frequency, followed by the high and low transmission regions, which differs from the full model that incorporates strain-specific diversity.

In addition, to provide the context of different biogeographic transmission zones, we now include a new figure (now Fig. 3) that presents the parameter space of transmission potential and strain diversity of different continents, which demonstrates that PNG and South America have less strain diversity than expected by transmission potential (L179-184 and L198-202). Therefore, these two regions have low disease prevalence and high resistance frequency.

The authors emphasize several model limitations, including the specification of resistance by a single locus (thus not addressing the importance of recombination should resistance be specified by more than one locus); the assumption that parasites are independently and randomly distributed among hosts (contrary to empirical evidence); and the assumption of a random association between the resistant genotype and antigenic diversity. However, each of these limitations is addressed in the discussion.

R: As pointed out by the referee, our model presents several limitations that have all been addressed in the discussion and considered for future extensions.

Did the authors achieve their goals? Did the results support their conclusion?

Returning to the questions posed by the authors:

1. Does strain diversity modulate the equilibrium resistance frequency given different transmission intensities? Yes. The authors demonstrate a negative relationship between prevalence/strain diversity and resistance frequency (Figure 2).

2. Does strain diversity modulate the equilibrium resistance frequency and its changes following drug withdrawal? Yes. The authors find that, under resistance invasion and some level of drug treatment, resistance frequency decreased with the number of strains (Figure 4). The authors also find that lower strain diversity results in a slower decline in resistant genotypes after drug withdrawal and higher equilibrium resistance frequency (Figure 6).

3. Does the model explain biogeographic patterns of drug resistance evolution? Yes. The authors find that their full model (which includes strain-specific immunity) produces the empirically observed negative relationship between resistance and prevalence/strain diversity, while a model only incorporating generalised immunity does not (Figure 8).

Utility of work to others and relevance within and beyond the field?

This work is important because antimalarial drug resistance has been an ongoing issue of concern for much of the 20th century and now 21st century. Further, this resistance emergence is not equitably distributed across biogeographic regions, with South America and Southeast Asia experiencing much of the burden of this resistance emergence. Not only can widespread resistant strains be traced back to these two relatively low-transmission regions, but these strains remain at high frequency even after drug treatment ceases.

Reviewer #2 (Public Review):

Summary:

The evolution of resistance to antimalarial drugs follows a seemingly counterintuitive pattern, in which resistant strains typically originate in regions where malaria prevalence is relatively low. Previous investigations have suggested that frequent exposures in high-prevalence regions produce high levels of partial immunity in the host population, leading to subclinical infections that go untreated. These subclinical infections serve as refuges for sensitive strains, maintaining them in the population. Prior investigations have supported this hypothesis; however, many of them excluded important dynamics, and the results cannot be generalized. The authors have taken a novel approach using a deterministic model that includes both general and adaptive immunity. They find that high levels of population immunity produce refuges, maintaining the sensitive strains and allowing them to outcompete resistant strains. While general population immunity contributed, adaptive immunity is key to reproducing empirical patterns. These results are robust across a range of fitness costs, treatment rates, and resistance efficacies. They demonstrate that future investigations cannot overlook adaptive immunity and antigenic diversity.

R: We thank the reviewer for his/her appreciation of the work.

Strengths:

Overall, this is a very nice paper that makes a significant contribution to the field. It is well-framed within the body of literature and achieves its goal of providing a generalizable, unifying explanation for otherwise disparate investigations. As such, this work will likely serve as a foundation for future investigations. The approach is elegant and rigorous, with results that are supported across a broad range of parameters.

Weaknesses:

Although the title states that the authors describe resistance invasion, they do not support or even explore this claim. As they state in the discussion (line 351), this work predicts the equilibrium state and doesn't address temporal patterns. While refuges in partially immune hosts may maintain resistance in a population, they do not account for the patterns of resistance spread, such as the rapid spread of chloroquine resistance in Africa once it was introduced from Asia.

R: We do agree that resistance invasion is not the focus of our manuscript. Rather we mainly investigate the maintenance and decline after drug withdrawal. Therefore, we changed the title to “Antigenic strain diversity predicts different biogeographic patterns of maintenance and decline of anti-malarial drug resistance” (L1-4).

We did, however, present a fast initial invasion phase for the introduction of resistant genotypes regardless of transmission scenarios in Fig. 5 (now Fig. 6). Even though the focus of the manuscript is to investigate long term persistence of resistant genotypes, we did emphasize that the initial invasion phase and how that changes the host immunity profile are key to the coexistence of resistant and wild-type genotypes (L228-239).

As the authors state in the discussion, the evolution of compensatory mutations that negate the cost of resistance is possible, and in vitro experiments have found evidence of such. It appears that their results are dependent on there being a cost, but the lower range of the cost parameter space was not explored.

R: It is true that compensatory mutations might mitigate the negative fitness consequences. We didn’t add a no-cost scenario because in general if there is no cost but only benefit (survival through drug usage), then resistant haplotypes will likely be fixed in the population. This is contingent on the assumption that these compensatory mutations are in perfect linkage with resistant alleles, which is unlikely in high-transmission scenarios. Our model does not incorporate recombination, but earlier models (Dye & Williams 1997, Hastings & D’Alessandro 2000) have demonstrated that recombination will delay the fixation of resistant alleles in high-transmission.

As suggested, we ran our model with costs equal 0 and 0.01 (Fig. 2C and L189-191). We found that resistant alleles almost always fix except for when diversity is extremely high, treatment/resistance efficacy is low. In these cases, additional benefits brought by more transmission from resistant alleles do not bring many benefits (as lower GI classes have a very small number of hosts). This finding does not contradict a wider range of coexistence between wild-type and resistant alleles when the cost is higher. We therefore added these scenarios to our updated results.

Author response image 1.

The use of a deterministic, compartmental model may be a structural weakness. This means that selection alone guides the fixation of new mutations on a semi-homogenous adaptive landscape. In reality, there are two severe bottlenecks in the transmission cycle of Plasmodium spp., introducing a substantial force of stochasticity via genetic drift. The well-mixed nature of this type of model is also likely to have affected the results. In reality, within-host selection is highly heterogeneous, strains are not found with equal frequency either in the population or within hosts, and there will be some linkage between the strain and a resistance mutation, at least at first. Of course, there is no recourse for that at this stage, but it is something that should be considered in future investigations.

R: We thank the reviewer for their insightful comments on the constraints of the deterministic modeling approach. We’ve added these points to discussion in the paragraph discussing the second limitation of the model (L359-364).

The authors mention the observation that patterns of resistance in high-prevalence Papua New Guinea seem to be more similar to Southeast Asia, perhaps because of the low strain diversity in Papua New Guinea. However, they do not investigate that parameter space here. If they did and were able to replicate that observation, not only would that strengthen this work, it could profoundly shape research to come.

R: We appreciate the suggestion to investigate the parameter space of Papua New Guinea. We now include a new figure (now Fig. 3) that presents the parameter space of transmission potential and strain diversity of different continents, which demonstrates that PNG and South America have less strain diversity than expected by transmission potential (L179-184 and L198-202). This translates to low infectivity for most mosquito bites, and most infections only occur in hosts with lower generalized immunity. Therefore resistant genotypes will help ensure disease transmission in these symptomatic hosts and be strongly selected to be maintained.

Reviewer #1 (Recommendations For The Authors):

1. I found lines 41-49 difficult to follow. Please rephrase (particularly punctuation) for clarity.

R: We have edited the lines to improve the writing (L41-50)):

“Various relationships between transmission intensity and stable frequencies of resistance were discovered, each of which has some empirical support: 1) transmission intensity does not influence the fate of resistant genotypes [Models: Koella and Antia (2003); Masserey et al. (2022); Empirical: Diallo et al. (2007); Shah et al. (2011, 2015)]; 2) resistance first increases in frequency and slowly decreases with increasing transmission rates [Models: Klein et al. (2008, 2012)]; and 3) Valley phenomenon: resistance can be fixed at both high and low end of transmission intensity [Model: Artzy-Randrup et al. (2010); Empirical: Talisuna et al. (2002)]. Other stochastic models predict that it is harder for resistance to spread in high transmission regions, but patterns are not systematically inspected across the parameter ranges [Model: Whitlock et al. (2021); Model and examples in Ariey and Robert (2003)].”

1. Line 65: There should be a space after "recombination" and before the citation.

R: Thank you for catching the error. We’ve added the space (L64).

1. I'm interested in the dependency of the results on the assumption that there is a cost to resistance via lowered transmissibility (lines 142-145). I appreciate that variation in the cost(s) of resistance in single and mixed infections is explored; however, from what I can tell the case of zero cost is not explored.

R: As suggested, we have now added the no-cost scenario. Please see the response to the Reviewer2 weaknesses paragraph 2.

1. I felt the commentary/explanation of the response to drug policy change was a bit underdeveloped. I would have liked a walk-through of why in your model low diversity scenarios show the slowest decline in resistant genotypes after switching to different drugs.

R: We acknowledge that the explanation of the response to drug policy change needs to be improved. We have now added the explanation of why we observe low diversity scenarios show the least decline in resistance frequency after drug withdrawal: “Two processes are responsible for the seen trend: first, resistant genotypes have a much higher fitness advantage in low diversity regions even with reduced drug usage because infected hosts are still highly symptomatic; second, due to low transmission potential in low diversity scenarios (i.e., longer generation intervals between transmissions), the rate of change in parasite populations is slower.” (L243-247). We also compared the drug withdrawal response to that of the generalized-immunity-only model. The medium transmission region has the fastest reduction in resistance frequency, followed by the high and low transmission regions, which differs from the full model that incorporates strain-specific diversity.

1. Line 352: persistent drug usage?

R: Yes, we meant persistent drug usage. We’ve clarified the writing (L389-391).

1. The organisation of the manuscript would benefit from structuring around the focal questions so that the reader can easily find the answers to the focal questions within the results and discussion sections.

R: This is a great suggestion. We modified the subheadings of results to provide answers to focal questions (L151, L179, L203-204, and L240).

1. Line 353: Please remove either "shown" or "demonstrated".

R: Thank you for catching the grammatical error, we’ve retained “shown” only for the sentence (L391-392).

Reviewer #2 (Recommendations For The Authors):

Overall, this was very nice work and a pleasure to read.

Major:

1. Please provide a much more thorough explanation of how resistance invasions are modeled. It is not clear from the text and could not be replicated.

R: We have now added a section “drug treatment and resistance invasion” in Methods and Materials to explain how resistance invasions are modeled (L488-496):

“Given each parameter set, we ran the ODE model six times until equilibrium with the following genotypic compositions: 1) wild-type only scenario with no drug treatment; 2) wild-type only scenario with 63.2% drug treatment (0.05 daily treatment rate); 3) wild-type only scenario with 98.2% drug treatment (0.2 daily treatment rate); 4) resistant-only scenario with no drug treatment; 5) resistance invasion with 63.2% drug treatment; 6) resistance invasion with 98.2% drug treatment. Runs 1-4 start with all hosts in G0,U compartment and ten parasites. Runs 5 and 6 (resistance invasion) start from the equilibrium state of 2 and 3, with ten resistant parasites introduced. We then followed the ODE dynamics till the next equilibrium.”

1. Please make your raw data, code, and replicable examples that produce the figures in the manuscript available.

R: We have added the data availability session, which provides the GitHub site with all the code for the model, data processing, and figures: All the ODE codes, numerically-simulated data, empirical data, and analyzing scripts are publicly available at https://github.itap.purdue.edu/HeLab/MalariaResistance.

1. Regarding the limitations described in the paragraph about the model in the public response, these results would be strengthened if there were separate compartments for strains which could be further divided into sensitive and resistant. Could you explore this for at least a subset of the parameter space?

R: In our model, sensitive and resistant pathogens are always modeled as separate compartments (Fig. S1B and Appendix 1). In Results/Model structure, L135-136, we stated the setup:

“The population sizes of resistant (PR) or sensitive (wild-type; PW) parasites are tracked separately in host compartments of different G and drug status.”

1. To what extent do these results rely on a cost to resistance? Were lower costs explored? This would be worth demonstrating. If this cannot be maintained without cost, do you think this is because there is no linkage between strain and resistance?

R: As suggested, we have now added the no-cost scenario (Fig. 2C and L189-191). Please see the response to the Reviewer1 weaknesses paragraph 2. In sum, under a no-cost scenario, if treatment rate is low, then wild-type alleles will still be maintained in high transmission scenarios; when treatment rate is high, resistant alleles will always be fixed.

Minor:

1. "Plasmodium" should be italicized throughout. Ironically, italics aren't permitted in this form.

R: We did italicize “Plasmodium” or “P. falciparum” throughout the text. If the reviewer is referring to “falciparum malaria”, the convention is not to italicize falciparum in this case.

1. Fig 1A: the image is reversed for the non-infected host with prior exposure to strain A. Additionally, the difference between colors for WT and resistant is not visible in monochrome.

R: Thank you for pointing out the problem of color choice in monochrome. We have modified the figure. The image in Fig 1A is not reversed for non-infected hosts with prior exposure to strain A. We now spell out “S” to be “specific immunity”, and explain it better in the figure legend.

1. Fig 2B: add "compare to the pattern of prevalence shown in Fig 2A" or something similar to make the comparison immediately clear.

R: We thank the reviewer’s suggestion. We’ve added a sentence to contrast Fig 2A and B in the Figure legend: “A comparison between the prevalence pattern in (A) and resistance frequency in (B) reveals that high prevalence regions usually correspond to low resistance frequency at the end of resistance invasion dynamics.”

1. Figs 2B & C: Please thoroughly explain how you produced this data in the methods section and briefly describe it in the results sections.

R: We agree that the modeling strategies need to be explained better. Since we explained the rationale for the parameter ranges and the prevalence patterns we observe in the results section “Appropriate pairing of strain diversity and vectorial capacity” (now “Impact of strain diversity and transmission potential on disease prevalence”), we added sentences in this section to explain how we run models until equilibrium for wild-only infections with or without drug treatment (L152-178). Then in the following section “Drug-resistance and disease prevalence” section, we explain how we obtained the resistance invasion data:

“To investigate resistance invasion, we introduce ten resistant infections to the equilibrium states of drug treatment with wild-type only infections, and follow the ODE dynamics till the next equilibrium” (L180-181).

1. Fig 3: The axis labels are not particularly clear. For the Y axis, please state in the label what it is the frequency of (either the mutation or the phenotype). In the X axis, it is better to spell that out in words, like "P. falciparum prevalence in children".

R: Thank you for pointing this out. We’ve modified the axes labels of Fig. 3 (now Fig. 4): X-axis: “P. falciparum prevalence in children aged 2-10”; Y-axis: “Frequency of resistant genotypes (pfcrt 76T)”.

1. Fig 4 and the rest of the figures of this nature: Showing an equilibrium-state timestep before treatment was introduced would improve the readers' understanding of the dynamics.

R: We agree that the equilibrium state before treatment is important. In fact, we have those states in our figure 4 (now figure 5): the left panel- “Daily treatment rate 0” indicates the equilibrium-state timestep before treatment. We clarified this point in the caption.

1. Fig 5 is very compelling, but the relationships in Fig 5 would be clearer if the Y axes were not all different. Consider using the same scale for the hosts, and the same scale for resistant parasites (both conditions) and WT parasites, 113 strains. It may be clearer to reference them if they are given as A-F instead of three figures each for A and B.

R: We agree with the suggested changes and have modified figure 5 (now Fig. 6): we used one Y-axis scale for the hosts, and one Y-axis scale for the parasites. The wild-type one is very low for the low diversity scenario, thus we included one inset plot for that case.

1. Fig 5 caption: High immune protection doesn't select against resistance. The higher relative fitness of the sensitive strain selects against resistance in a high-immunity environment.

R: Thank you for pointing this out. Here we meant that a reduction in resistant population after the initial overshoot occurs in both diversity levels. We are not comparing resistant strains to sensitive ones. We’ve modified the sentence to: “The higher specific immunity reduces the infectivity of new strains, leading to a reduction of the resistant parasite population regardless of the diversity level”.

1. Line 242: "keep" should be plural.

R: We’ve corrected “keep” to “keeps” (L267).

1. Line 360 and elsewhere: The strength of the results is somewhat overstated at times. This absolutely supports the importance of strain-specific immunity, but these results do not explain patterns of the origin of resistance and there are a number of factors that are not incorporated (a necessary evil of modeling to be sure).

R: Thank you for pointing this out. We’ve modified discussion to remove the overstated strength of results:

1) Original: “The inclusion of strain diversity in the model provides a new mechanistic explanation as to why Southeast Asia has been the original source of resistance to certain antimalarial drugs, including chloroquine.”

Modified: “The inclusion of strain diversity in the model provides a new mechanistic explanation as to why Southeast Asia has persisting resistance to certain antimalarial drugs, including chloroquine, despite a lower transmission intensity than Africa. “ (L328-330)

2) In sum, we show that strain diversity and associated strain-specific host immunity, dynamically tracked through the macroparasitic structure, can explainpredict the complex relationship between transmission intensity and drug-resistance frequencies.

1. The color palettes are not discernible in grayscale, especially the orange/blue/gray in Fig 2. The heatmaps appear to be in turbo, the only viridis palette that isn't grayscale-friendly. Just something to keep in mind for the accessibility of individuals with achromatopsia and most people who print out papers.

R: Thank you for the visualization suggestions. We updated all the figures with the “viridis:magma” palette. As for the orange/blue/gray scale used in Fig 2C, it is difficult to pick nine colors that are discernable in brightness in grayscale. Currently, the four colors correspond to clonal genotype cost (i.e. green, red, grey, and blue), and the three-level brightness maps to mixed genotype cost.

eLife assessment

The study is an important advancement to the consideration of antimalarial drug resistance: the authors make use of both modelling results and supporting empirical evidence to demonstrate the role of malaria strain diversity in explaining biogeographic patterns of drug resistance. The theoretical methods and the corresponding results are compelling, with the novel model presented moving beyond existing models to incorporate malaria strain diversity and antigen-specific immunity. This work is likely to be interesting to malaria researchers and others working with antigenically diverse infectious diseases.

Reviewer #1 (Public Review):

Summary:<br /> The paper is an attempt to explain a geographic paradox between infection prevalence and antimalarial resistance emergence. The authors developed a compartmental model that importantly contains antigenic strain diversity and in turn antigen-specific immunity. They find a negative correlation between parasite prevalence and the frequency of resistance emergence and validate this result using empirical data of chloroquine-resistance. Overall, the authors conclude that strain diversity is a key player in explaining observed patterns of resistance evolution across different geographic regions.

The authors pose and address the following specific questions:<br /> 1. Does strain diversity modulate the equilibrium resistance frequency given different transmission intensities?<br /> 2. Does strain diversity modulate the equilibrium resistance frequency and its changes following drug withdrawal?<br /> 3. Does the model explain biogeographic patterns of drug resistance evolution?

Strengths:<br /> The model built by the authors is novel. As emphasized in the manuscript, many factors (e.g., drug usage, vectorial capacity, population immunity) have been explored in models attempting to explain resistance emergence, but strain diversity (and strain specific immunity) has not been explicitly included and thus explored. This is an interesting oversight in previous models, given the vast antigenic diversity of Plasmodium falciparum (the most common human malaria parasite) and its potential to "drive key differences in epidemiological features".

The model also accounts for multiple infections, which is a key feature of malarial infections, with individuals often infected with either multiple Plasmodium species or multiple strains of the same species. Accounting for multiple infections is critical when considering resistance emergence, as with multiple infections there is within-host competition which will mediate the fitness of resistant genotypes. Overall, the model is an interesting combination of a classic epidemiological model (e.g., SIR) and a population genetics model.

In terms of major model innovations, the model also directly links selection pressure via drug administration with local transmission dynamics. This is accomplished by the interaction between strain-specific immunity, generalized immunity and host immune response.

Weaknesses:<br /> The authors emphasize several model limitations, including the specification of resistance by a single locus (thus not addressing the importance of recombination should resistance be specified by more than one locus); the assumption that parasites are independently and randomly distributed among hosts (contrary to empirical evidence); and the assumption of a random association between the resistant genotype and antigenic diversity. However, each of these limitations are addressed in the discussion.

Did the authors achieve their goals? Did the results support their conclusion?<br /> Returning to the questions posed by the authors:<br /> 1. Does strain diversity modulate the equilibrium resistance frequency given different transmission intensities? Yes. The authors demonstrate a negative relationship between prevalence/strain diversity and resistance frequency (Figure 2).

2. Does strain diversity modulate the equilibrium resistance frequency and its changes following drug withdrawal? Yes. The authors find that, under resistance invasion and some level of drug treatment, resistance frequency decreased with the number of strains (Figure 4). The authors also find that lower strain diversity results in a slower decline in resistant genotypes after drug withdrawal and higher equilibrium resistance frequency (Figure 6).

3. Does the model explain biogeographic patterns of drug resistance evolution? Yes. The authors find that their full model (which includes strain-specific immunity) produces the empirically observed negative relationship between resistance and prevalence/strain diversity, while a model only incorporating generalised immunity does not (Figure 8).

Utility of work to others and relevance within and beyond the field?<br /> This work is important because antimalarial drug resistance has been an ongoing issue of concern for much of the 20th century and now 21st century. Further, this resistance emergence is not equitably distributed across biogeographic regions, with South America and Southeast Asia experiencing much of the burden of this resistance emergence. Not only can widespread resistant strains be traced back to these two relatively low-transmission regions, but these strains remain at high frequency even after drug treatment ceases.

Reviewer #2 (Public Review):

Summary:<br /> The evolution of resistance to antimalarial drugs follows a seemingly counterintuitive pattern, in which resistant strains typically originate in regions where malaria prevalence is relatively low. Previous investigations have suggested that frequent exposures in high-prevalence regions produce high levels of partial immunity in the host population, leading to subclinical infections that go untreated. These subclinical infections serve as refuges for sensitive strains, maintaining them in the population. Prior investigations have supported this hypothesis; however, many of them excluded important dynamics, and the results cannot be generalized. The authors have taken a novel approach using a deterministic model that includes both general and adaptive immunity. They find that high levels of population immunity produce refuges, maintaining the sensitive strains and allowing them to outcompete resistant strains. While general population immunity contributed, adaptive immunity is key to reproducing empirical patterns. These results are robust across a range of fitness costs, treatment rates, and resistance efficacies. Given sufficient antigenic diversity and high transmission, sensitive parasites remain in circulation even when there is no cost to resistance. This work demonstrates that future investigations cannot overlook adaptive immunity and antigenic diversity.

Strengths:<br /> Overall, this is a very nice paper that makes a significant contribution to the field. It is well-framed within the body of literature and achieves its goal of providing a generalizable, unifying explanation for otherwise disparate investigations. The model is innovative. The approach is elegant and rigorous, with results that are supported across a broad range of parameters when considered within an equilibrium setting. Their exploration of geographical patterns of resistance makes the results of their simulations even more compelling. As such, this work will likely serve as a foundation for many future investigations.

Weaknesses:

Although the authors model resistance invasion, it does not align with empirical observations of the spread of resistance. For example, Plasmodium's mutation rate and population size mean that mutations providing chloroquine resistance should arise repeatedly even within a single infection. Nevertheless, Africa remained free of chloroquine resistant strains until a lineage was introduced from Asia. Upon introduction, it spread across the continent within ten years. The difference between the fate of chloroquine resistance originating in Africa versus chloroquine resistance originating in Asia cannot be attributed to changes in population immunity and treatment.

The source of this disparity may be in part attributable to the use of a deterministic, compartmental model, as the authors mention in the discussion. Strains are not explicitly modeled. This means that in terms of the distribution of strain diversity, the resistant and the sensitive compartments are identical, and the locus determining resistance is equally distributed across all strain backgrounds. However, substantial rates of linkage disequilibrium and clonal reproduction are found even in high transmission settings. The model assumptions may be met at equilibrium, but are not appropriate for most scenarios involving the invasion of a rare mutation.

Author Response

eLife assessment

This study presents a valuable method to visualize the location of the cell types discovered through single-cell RNA sequencing. The evidence supporting the claims is solid, but the inclusion of a larger number of samples would strengthen the study. It would also be helpful to have the methods explained in more detail. The work will be of interest to those seeking to identify new cell types from scRNA-seq and snRNA-seq data.

Response: We are surprised about the editor’s assessment of our paper as a “valuable” method. This is the first Drosophila adult spatial transcriptomics paper. Hence, we would at least consider this being an “important” method. Spatial transcriptomics has thus far only been done in embryos, which are easy to process for FISH for many decades. Integration with single-cell data is also new. We are further surprised that this assessment does not mention the identification of subcellular mRNA patterns in adult muscles as an “important” biological finding of this paper. We are not aware that any localized mRNAs in Drosophila muscles were known prior to our study. This shows the advantage of spatial transcriptomics over single-cell techniques.

The work indeed does not represent a full spatial fly adult atlas – however, a proof of principle study covering both the head and body that we consider at least “important”.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Janssens et al. addressed the challenge of mapping the location of transcriptionally unique cell types identified by single nuclei sequencing (snRNA-seq) data available through the Fly Cell Atlas. They identified 100 transcripts for head samples and 50 transcripts for fly body samples allowing the identification of every unique cell type discovered through the Fly Cell Atlas. To map all of these cell types, the authors divided the fly body into head and body samples and used the Molecular Cartography (Resolve Biosciences) method to visualize these transcripts. This approach allowed them to build spatial tissue atlases of the fly head and body, to identify the location of previously unknown cell types and the subcellular localization of different transcripts. By combining snRNA-seq data from the Fly Cell Atlas with their spatially resolved transcriptomics (SRT) data, they demonstrated an automated cell type annotation strategy to identify unknown clusters and infer their location in the fly body. This manuscript constitutes a proof-of-principle study to map the location of the cells identified by ever-growing single-cell transcriptomic datasets generated by others.

Strengths:

The authors used the Molecular Cartography (Resolve Biosciences) method to visualize 100 transcripts for head samples and 50 transcripts for fly body samples in high resolution. This method achieves high resolution by multiplexing a large number of transcript visualization steps and allows the authors to map the location of unique cell types identified by the Fly Cell Atlas.

Response: We thank the reviewer for their comment, but are surprised that this assessment does not mention the identification of subcellular mRNA patterns in adult muscles as an important biological finding of this paper. This might be due to the visualization problem that this reviewer was facing with a greyscale version of the PDF as mentioned in the comments below. We do not know what caused the technical problem for this reviewer (the PDF figures are in color on the eLife website and on bioRxiv). We are surprised that the eLife discussion session did not resolve this issue.

Weaknesses:

Combining single-nuclei sequencing (snRNA-seq) data with spatially resolved transcriptomics (SRT) data is challenging, and the methods used by the authors in this study cannot reliably distinguish between cells, especially in brain regions where the processes of different neurons are clustered, such as in neuropils. This means that a grid that the authors mark as a unique cell may actually be composed of processes from multiple cells.

Response: The size of the fly is one of the most challenging aspects of performing spatial transcriptomics. The small size of the samples led to detachment from the slides, which we solved by coating the slides with gelatin. While the resolution of Molecular Cartography is high (<200nm), in the brain challenges remain as noted by the reviewer. Drosophila neuronal nuclei are notoriously small and cannot be easily resolved with current techniques. We agree that for a full atlas either expansion microscopy, 3D techniques or even higher resolution will be required.

Reviewer #2 (Public Review):

Summary:

The landmark publication of the "Fly Atlas" in 2022 provided a single cell/nuclear transcriptomic dataset from 15 individually dissected tissues, the entire head, and the body of male and female flies. These data led to the annotation of more than 250 cell types. While certainly a powerful and data-rich approach, a significant step forward relies on mapping these data back to the organism in time and space. The goal of this manuscript is to map 150 transcripts defined by the Fly Atlas by FISH and in doing so, provide, for the first time, a spatial transcriptomic dataset of the adult fly. Using this approach (Molecular Cartography with Resolve Biosciences), the authors, furthermore, distinguish different RNA localizations within a cell type. In addition, they seek to use this approach to define previously unannotated clusters found in the Fly Atlas. As a resource for the community at large interested in the computational aspects of their pipeline, the authors compare the strengths and weaknesses of their approach to others currently being performed in the field.

Strengths:

1. The authors use Resolve Biosciences and a novel bioinformatics approach to generate a FISH-based spatial transcriptomics map. To achieve this map, they selected 150 genes (50 body; 100 head) that were highly expressed in the single nuclear RNA sequencing dataset and were used in the 2022 paper to annotate specific cell types; moreover, the authors chose several highly expressed genes characteristic of unannotated cell types. Together, the approach and generated data are important next steps in translating the transcriptomic data to spatial data in the organism.

Response: We thank the reviewer for this comment but would like to add that the statement that we selected “150 genes (50 body; 100 head) that were highly expressed in the single nuclear RNA sequencing dataset” is not correct. We have chosen genes with widely differing expression levels (log-scale range of 3.95 in body, 5.76 in head). Many of the chosen genes are also transcription factors. In fact, the here introduced method is more sensitive than the single cell atlas: the tinman positive cells were readily located (even non-heart cells were found to express tinman), whereas in the single cell FCA data tinman expression is often not detected in the cardiomyocytes (Tinman is detected in 273 cells in the entire FCA (mean expression of 1.44 UMI in positive cells), and in 71 cells out of 273 cardial cells (26%)).

Author response image 1.

Density plots for body (left) and head (right) showing levels of gene expression detected in scRNA-seq (body: Fly Cell Atlas, Li et al. 2022, head: Pech et al. (2023)). Blue: all genes, red: genes used in the spatial study.

1. Working with Resolve, the authors developed a relatively high throughput approach to analyze the location of transcripts in Drosophila adults. This approach confirmed the identification of particular cell types suggested by the FlyAtlas as well as revealed interesting subcellular locations of the transcripts within the cell/tissue type. In addition, the authors used co-expression of different RNAs to unbiasedly identify "new cell types". This pipeline and data provide a roadmap for additional analyses of other time points, female flies, specific mutants, etc.

2. The authors show that their approach reveals interesting patterns of mRNA distribution (e.g alpha- and beta-Trypsin in apical and basal regions of gut enterocytes or striped patterns of different sarcomeric proteins in body muscle). These observations are novel and reveal unexpected patterns. Likewise, the authors use their more extensive head database to identify the location of cells in the brain. They report the resolution of 23 clusters suggested by the single-cell sequencing data, given their unsupervised clustering approach. This identification supports the use of spatial cell transcriptomics to characterize cell types (or cell states).

3. Lastly, the authors compare three different approaches --- their own described in this manuscript, Tangram, and SpaGE - which allow integration of single cell/nuclear RNA-seq data with spatial localization FISH. This was a very helpful section as the authors compared the advantages and disadvantages (including practical issues, like computational time).

Weaknesses:

1. Experimental setup. It is not clear how many and, for some of the data, the sex of the flies that were analyzed. It appears that for the body data, only one male was analyzed. For the heads, methods say male and female heads, but nothing is annotated in the figures. As such, it remains unclear how robust these data are, given such a limited sample from one sex. As such, the claims of a spatial atlas of the entire fly body and its head ("a rosetta stone") are overstated. Also, the authors should clearly state in the main text and figure legends the sex, the age, how many flies, and how many replicates contributed to the data presented (not just the methods). What also adds to the confusion is the use of "n" in para 2 of the results. " ... we performed coronal sections at different depths in the head (n=13)..." 13 sections in total from 1 head or sections from 13 heads? Based on the body and what is shown in the figure, one assumes 13 sections from one head. Please clarify.

Response: While we agree that sex differences present indeed an interesting opportunity to study with spatial transcriptomics, our goal was not to define male/female differences but rather to establish the technology to go into this detail if wanted in the future. In the revised version, we will provide a more detailed description of the sections, including their sex/genotype/age. We would like to point out that we verified the specificity of our FISH method on all the body sections (Figure 2A, TpnC4 & Act88F) and not only on one. Furthermore, we also would like to state that the idea of “a rosetta stone” was mentioned as a future prospect. We will rewrite the discussion to make this more clear.

1. Probes selected: Information from the methods section should be put into the main text so that it is clear what and why the gene lists were selected. The current main text is confusing. If the authors want others to use their approach, then some testing or, at the very least, some discussion of lower expressed genes should be added. How useful will this approach be if only highly expressed genes can be resolved? In addition, while it is understood that the company has a propriety design algorithm for the probes, the authors should comment on whether the probes for individual genes detect all isoforms or subsets (exons and introns?), given the high level of splicing in tissues such as muscle.

Response: As stated above, while there is a slight bias to higher expressed genes (as expected for marker genes), we have also used very low expressed genes like tinman (body) or sens (head). This shows that our method is more sensitive than single-cell data, as ALL cardiomyocytes can be identified by tinman expression and not only some are positive, as is the case in the FCA data. In fact, the method can’t resolve too highly expressed genes due to optical crowding of the signal leading to a worse quantification. For this reason, ninaE was removed from the analysis (as mentioned in Spatial transcriptomics allows the localization of cell types in the head and brain and in Methods).

As mentioned in the Methods, the probes are designed on gene level targeting all isoforms, but favoring principal isoforms (weighted by APPRIS level). The high level of splicing is indeed interesting and we expect that in the future spatial transcriptomics can help to generate more insight in this.

1. Imaging: it isn't clear from the text whether the repeated rounds of imaging impacted data collection. In many of what appear to be "stitched" images, there are gradients of signal (eg, figure 2F); please comment. Also, since this a new technique, could a before and after comparison of the original images and the segmented images be shown in the supplemental data so that the reader can better appreciate how the authors assessed/chose/thresholded their data? More discussion of the accuracy of spot detection would be helpful.

Response: Any high-resolution imaging (pixel size = 138 nm) of a large field of view (>1mm) uses a stitching method to combine several individual images to reconstruct a large field of view. This does not generate signal gradients, apart from lower signal at the extreme edges of each of the individual images. The spot detection algorithm was written and used by Resolve Biosciences and benchmarked for human (Hela) and mouse (NIH-3T3) cell lines in Groiss et al. 2021 (Highly resolved spatial transcriptomics for detection of rare events in cells, biorxiv). The specificity of the decoded probes was found to lie between 99.45 and 99.9% here, matching the results we found for TpnC4 and Act88F (99.4 and 99.8%). We will add their analysis to our discussion.

1. The authors comment on how many RNAs they detected (first paragraph of results). How do these numbers compare to the total mRNA present as detected by single-cell or single-nuclear sequencing?

Response: The total number of mRNAs detected per spatial transcriptomics experiment is much higher for the body samples compared to single-cell experiments (FCA data). In the head it is slightly lower, but here it is important to note that not all cell types are present in each slice in the head (while they are all present in the head scRNA experiments). A comparison on the cell-type level would be more meaningful, and we will investigate this for the revision.

Author response image 2.

Barplots showing total number of mRNA molecules detected in Molecular Cartography (Resolve, spatial spots) and in snRNA-seq data from the Fly Cell Atlas (10x Genomics, UMIs). Individual black dots show individual experiments, counts are only shown for the chosen gene panel for each sample. Bar shows the mean, with error bars representing the standard error.

1. Using this higher throughput method of spatial transcriptomics, the authors discern different cell types and different localization patterns within a tissue/cell type.

a. The authors should comment on the resolution provided by this approach, in terms of the detection of populations of mRNAs detected by low throughput methods, for example, in glia, motor neuron axons, and trachea that populate muscle tissue. Are these found in the images? Please show.

Response: We did not add any markers for trachea in our gene panel, but we do detect sparse spots of repo (glia) and elav/VGlut in the muscle tissues (Gad1/VAChT are hardly detected in the muscle tissue). This is consistent with the glutamatergic nature of motor neurons in Drosophila as described previously (Schuster CM (2006) Glutamatergic synapses of Drosophila neuromuscular junctions: a high-resolution model for the analysis of experience-dependent potentiation. Cell Tissue Res 326: 287–299.)

Author response image 3.

Molecular Cartography zoomed in on indirect flight muscle. Segmented nuclei are shown in white (based on DAPI), scalebars represent 100 μm).

b. The authors show interesting localization patterns in muscle tissue for different sarcomere protein-coding mRNAs, including enrichment of sls in muscle nuclei located near the muscle-tendon attachment sites. As this high throughput approach is newly being applied to the adult fly, it would increase confidence in these data, if the authors would confirm these data using a low throughput FISH technique. For example, do the authors detect such alternating "stripes" ( Act 88F, TpnC4, and Mhc) or enriched localization (sls) using FISH that doesn't rely on the repeated colorization, imaging, decolorization of the probes?

Response: We thank the reviewer for their interest in the localization patterns in muscle tissue. We could confirm localized mRNA in all the sections analyzed, in flight muscles as well as in leg muscles. We furthermore show that Act 88F, TpnC4 are not detected outside of flight muscle cells (99.4% and 99.8% of the single molecular signal in flight muscles only). Hence, we already show the specificity test in a much more quantitative way compared to traditional FISH, which often includes amplification.

1. The authors developed an unbiased method to identify "new cell types" which relies on co-expression of different transcripts. Are these new cell types or a cell state? While expression is a helpful first step, without any functional data, the significance of what the authors found is diminished. The authors need to soften their statements.

Response: The term “new cell types” only appears in the title. We agree that with the current spatial map we cannot be sure to have found “new cell types”, instead we have shown where unannotated clusters from scRNA-seq map, based on gene expression. Therefore, we will tone down the title in the revised version and thank the reviewer for this valuable suggestion.

Appraisal:

The authors' goal is to map single cell/nuclear RNAseq data described in the 2022 Fly Atlas paper spatially within an organism to achieve a spatial transcriptomic map of the adult fly; no doubt, this is a critical next step in our use of 'omics approaches. While this manuscript does the hard work of trying to take this next step, including developing and testing a new pipeline for high throughput FISH and its analysis, it falls short, in its present form, in achieving this goal. The authors discuss creating a robust spatial map, based on one male fly. Moreover, they do not reveal principles of mRNA localization, as stated in the abstract; they show us patterns, but nothing about the logic or function of these patterns. This same criticism can be said of the identification of "new cell types, just based on RNA colocalization. In both cases (mRNA subcellular localization or cell type identification), further data in the form of validation with traditional low throughput FISH and genetic manipulations to assess the relation to cell function are required for the authors to make such claims.

Response: We have indeed used one male fly for the adult male body data. This is mainly due to the cost of the sample processing. We used 12 individuals for the head samples (from 1 individual we acquired 2 sections, a total of 13 sections). We show that the body samples show a high correlation with each other, while the head samples cover multiple depths of the head. Still, even in the head, we find that sections at similar depths show a high similarity to each other in terms of gene-gene co-expression and expression patterns. Although obtaining more sections would be valuable, we don’t believe it to be necessary for the current goals. Additional replicates beyond the ones we already provide would require significant amounts of extra time and budget, while they would produce similar results as we already show. We are therefore reluctant to repeat the effort again.

The usage of the term “new cell types” is indeed ambiguous and we will tone this down in the revised version. Instead, we meant that unannotated clusters could be mapped to their location. In the text, we further specify that this means that now we only have inferred the location of the nuclei and that for neurons their function/processes are still unknown. As such, our data provides a starting point to identify new cell types since their marker genes and nuclear location are inferred. The next step to identify “new cell types” would indeed be to acquire genetic access to the cell types and characterize them in more detail. This is currently beyond our goals, and therefore we will tone down the title in the revised version and thank the reviewer for this valuable suggestion.

Discussion of likely impact:

If revised, these data, and importantly the approach, would impact those working on Drosophila adults as well as those working in other model systems where single cell/nuclear sequencing is being translated to the spatial localization within the organism. The subcellular localization data - for example, the size of transcripts and how that relates to localization or the patterns of sarcomeric protein localization in muscle - are intriguing, and would likely impact our thinking on RNA localization, transport, etc if confirmed. Lastly, the authors compare their computational approaches to those available in the field; this is valuable as this is a rapidly evolving field and such considerations are critical for those wishing to use this type of approach.

Response: We believe that our manuscript as it stands now is already an “important” paper that will strongly impact the Drosophila community (and beyond the spatial transcriptomics community). As it stands, it provides the groundwork for a full Drosophila adult spatial atlas, similar to how early scRNA-seq datasets provided a framework for the Fly Cell Atlas. In the manuscript we provide both experimental information on how to successfully perform spatial transcriptomics (treating slides for optimal attachment) and the data serves as a benchmark for future experiments to improve upon (similar to how early Drop-seq datasets were compared to later 10x datasets in single-cell transcriptomics). In addition, it also provides proof of principle methods on how to integrate the FCA data with these spatial data and it identifies localized mRNA species in large adult muscle cells, showing the complementarity of spatial techniques with single-cell RNA-seq. To conclude, this is the first spatial adult Drosophila transcriptomics paper, locating 150 mRNA species with easy data access in our user portal (https://spatialfly.aertslab.org/).

eLife assessment

This study presents a valuable method to visualize the location of the cell types discovered through single-cell RNA sequencing. The evidence supporting the claims is solid, but the inclusion of a larger number of samples would strengthen the study. It would also be helpful to have the methods explained in more detail. The work will be of interest to those seeking to identify new cell types from scRNA-seq and snRNA-seq data.

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, Janssens et al. addressed the challenge of mapping the location of transcriptionally unique cell types identified by single nuclei sequencing (snRNA-seq) data available through the Fly Cell Atlas. They identified 100 transcripts for head samples and 50 transcripts for fly body samples allowing the identification of every unique cell type discovered through the Fly Cell Atlas. To map all of these cell types, the authors divided the fly body into head and body samples and used the Molecular Cartography (Resolve Biosciences) method to visualize these transcripts. This approach allowed them to build spatial tissue atlases of the fly head and body, to identify the location of previously unknown cell types and the subcellular localization of different transcripts. By combining snRNA-seq data from the Fly Cell Atlas with their spatially resolved transcriptomics (SRT) data, they demonstrated an automated cell type annotation strategy to identify unknown clusters and infer their location in the fly body. This manuscript constitutes a proof-of-principle study to map the location of the cells identified by ever-growing single-cell transcriptomic datasets generated by others.

Strengths:<br /> The authors used the Molecular Cartography (Resolve Biosciences) method to visualize 100 transcripts for head samples and 50 transcripts for fly body samples in high resolution. This method achieves high resolution by multiplexing a large number of transcript visualization steps and allows the authors to map the location of unique cell types identified by the Fly Cell Atlas.

Weaknesses:<br /> Combining single-nuclei sequencing (snRNA-seq) data with spatially resolved transcriptomics (SRT) data is challenging, and the methods used by the authors in this study cannot reliably distinguish between cells, especially in brain regions where the processes of different neurons are clustered, such as in neuropils. This means that a grid that the authors mark as a unique cell may actually be composed of processes from multiple cells.

Reviewer #2 (Public Review):

Summary:<br /> The landmark publication of the "Fly Atlas" in 2022 provided a single cell/nuclear transcriptomic dataset from 15 individually dissected tissues, the entire head, and the body of male and female flies. These data led to the annotation of more than 250 cell types. While certainly a powerful and data-rich approach, a significant step forward relies on mapping these data back to the organism in time and space. The goal of this manuscript is to map 150 transcripts defined by the Fly Atlas by FISH and in doing so, provide, for the first time, a spatial transcriptomic dataset of the adult fly. Using this approach (Molecular Cartography with Resolve Biosciences), the authors, furthermore, distinguish different RNA localizations within a cell type. In addition, they seek to use this approach to define previously unannotated clusters found in the Fly Atlas. As a resource for the community at large interested in the computational aspects of their pipeline, the authors compare the strengths and weaknesses of their approach to others currently being performed in the field.

Strengths:<br /> 1. The authors use Resolve Biosciences and a novel bioinformatics approach to generate a FISH-based spatial transcriptomics map. To achieve this map, they selected 150 genes (50 body; 100 head) that were highly expressed in the single nuclear RNA sequencing dataset and were used in the 2022 paper to annotate specific cell types; moreover, the authors chose several highly expressed genes characteristic of unannotated cell types. Together, the approach and generated data are important next steps in translating the transcriptomic data to spatial data in the organism.<br /> 2. Working with Resolve, the authors developed a relatively high throughput approach to analyze the location of transcripts in Drosophila adults. This approach confirmed the identification of particular cell types suggested by the FlyAtlas as well as revealed interesting subcellular locations of the transcripts within the cell/tissue type. In addition, the authors used co-expression of different RNAs to unbiasedly identify "new cell types". This pipeline and data provide a roadmap for additional analyses of other time points, female flies, specific mutants, etc.<br /> 3. The authors show that their approach reveals interesting patterns of mRNA distribution (e.g alpha- and beta-Trypsin in apical and basal regions of gut enterocytes or striped patterns of different sarcomeric proteins in body muscle). These observations are novel and reveal unexpected patterns. Likewise, the authors use their more extensive head database to identify the location of cells in the brain. They report the resolution of 23 clusters suggested by the single-cell sequencing data, given their unsupervised clustering approach. This identification supports the use of spatial cell transcriptomics to characterize cell types (or cell states).<br /> 4. Lastly, the authors compare three different approaches --- their own described in this manuscript, Tangram, and SpaGE - which allow integration of single cell/nuclear RNA-seq data with spatial localization FISH. This was a very helpful section as the authors compared the advantages and disadvantages (including practical issues, like computational time).

Weaknesses:<br /> 1. Experimental setup. It is not clear how many and, for some of the data, the sex of the flies that were analyzed. It appears that for the body data, only one male was analyzed. For the heads, methods say male and female heads, but nothing is annotated in the figures. As such, it remains unclear how robust these data are, given such a limited sample from one sex. As such, the claims of a spatial atlas of the entire fly body and its head ("a rosetta stone") are overstated. Also, the authors should clearly state in the main text and figure legends the sex, the age, how many flies, and how many replicates contributed to the data presented (not just the methods). What also adds to the confusion is the use of "n" in para 2 of the results. " ... we performed coronal sections at different depths in the head (n=13)..." 13 sections in total from 1 head or sections from 13 heads? Based on the body and what is shown in the figure, one assumes 13 sections from one head. Please clarify.<br /> 2. Probes selected: Information from the methods section should be put into the main text so that it is clear what and why the gene lists were selected. The current main text is confusing. If the authors want others to use their approach, then some testing or, at the very least, some discussion of lower expressed genes should be added. How useful will this approach be if only highly expressed genes can be resolved? In addition, while it is understood that the company has a propriety design algorithm for the probes, the authors should comment on whether the probes for individual genes detect all isoforms or subsets (exons and introns?), given the high level of splicing in tissues such as muscle.<br /> 3. Imaging: it isn't clear from the text whether the repeated rounds of imaging impacted data collection. In many of what appear to be "stitched" images, there are gradients of signal (eg, figure 2F); please comment. Also, since this a new technique, could a before and after comparison of the original images and the segmented images be shown in the supplemental data so that the reader can better appreciate how the authors assessed/chose/thresholded their data? More discussion of the accuracy of spot detection would be helpful.<br /> 4. The authors comment on how many RNAs they detected (first paragraph of results). How do these numbers compare to the total mRNA present as detected by single-cell or single-nuclear sequencing?<br /> 5. Using this higher throughput method of spatial transcriptomics, the authors discern different cell types and different localization patterns within a tissue/cell type.<br /> a. The authors should comment on the resolution provided by this approach, in terms of the detection of populations of mRNAs detected by low throughput methods, for example, in glia, motor neuron axons, and trachea that populate muscle tissue. Are these found in the images? Please show.<br /> b. The authors show interesting localization patterns in muscle tissue for different sarcomere protein-coding mRNAs, including enrichment of sls in muscle nuclei located near the muscle-tendon attachment sites. As this high throughput approach is newly being applied to the adult fly, it would increase confidence in these data, if the authors would confirm these data using a low throughput FISH technique. For example, do the authors detect such alternating "stripes" ( Act 88F, TpnC4, and Mhc) or enriched localization (sls) using FISH that doesn't rely on the repeated colorization, imaging, decolorization of the probes?<br /> 6. The authors developed an unbiased method to identify "new cell types" which relies on co-expression of different transcripts. Are these new cell types or a cell state? While expression is a helpful first step, without any functional data, the significance of what the authors found is diminished. The authors need to soften their statements.

Appraisal:<br /> The authors' goal is to map single cell/nuclear RNAseq data described in the 2022 Fly Atlas paper spatially within an organism to achieve a spatial transcriptomic map of the adult fly; no doubt, this is a critical next step in our use of 'omics approaches. While this manuscript does the hard work of trying to take this next step, including developing and testing a new pipeline for high throughput FISH and its analysis, it falls short, in its present form, in achieving this goal. The authors discuss creating a robust spatial map, based on one male fly. Moreover, they do not reveal principles of mRNA localization, as stated in the abstract; they show us patterns, but nothing about the logic or function of these patterns. This same criticism can be said of the identification of "new cell types, just based on RNA colocalization. In both cases (mRNA subcellular localization or cell type identification), further data in the form of validation with traditional low throughput FISH and genetic manipulations to assess the relation to cell function are required for the authors to make such claims.

Discussion of likely impact:<br /> If revised, these data, and importantly the approach, would impact those working on Drosophila adults as well as those working in other model systems where single cell/nuclear sequencing is being translated to the spatial localization within the organism. The subcellular localization data - for example, the size of transcripts and how that relates to localization or the patterns of sarcomeric protein localization in muscle - are intriguing, and would likely impact our thinking on RNA localization, transport, etc if confirmed. Lastly, the authors compare their computational approaches to those available in the field; this is valuable as this is a rapidly evolving field and such considerations are critical for those wishing to use this type of approach.

eLife assessment

In this valuable contribution, the authors demonstrate that the infusion of NAD+ may prevent death and reduce disease severity from lethal experimental bacterial sepsis, possibly through inflammasome inhibition, without reducing bacterial load. They provide solid evidence for these protective effects of NAD+, though the precise mechanisms involved remain unclear and need further support and elucidation. The core findings may well have clinical implications but, in addition to mechanistic clarifications, contextualised interpretation as metabolic adaptation to sepsis would create wider interest.

eLife assessment

This detailed and well powered manuscript explores auditory perception of modulated noise in the presence of transcranial alternating-current stimulation (tACS) and shows valuable results suggesting that there are subject-specific effects when the phase of 2-Hz tACS varies relative to the phase of the noise modulation. The strength of the evidence is mixed. There is convincing evidence that tACS alters perception significantly in individuals; however, the effects are inconsistent across subjects and even across sessions, frustrating attempts to draw conclusions about the underlying mechanisms of the idiosyncratic effects. Despite these limitations, the paper will be of great interest to researchers interested in determining when and how tACS influences neural processes, especially those interested in neural entrainment and its relationship to perception.

Reviewer #1 (Public Review):

Trenker et al. report cryo-EM structures of HER4/HER2 heterodimers and HER4 homodimers bound to Neuregulin-1β (Nrg1β) and Betacellulin (BTC). As observed for prior cryo-EM structures of full-length or near full-length HER-family receptors only the extracellular regions are visualized, presumably owing to flexibility in the relative orientation of extra- and intra-cellular regions. The authors observe no appreciable differences between Nrg1β and BTC bound heterodimers, both ligands, in this case being high-affinity ligands, and modest "scissor-like" differences in the subunit relationships in HER4 homodimers with Nrg1β and BTC bound.

The authors also show that, as they showed for HER3, the HER4 dimerization arm is not indispensable for forming heterodimers with HER2 despite the HER4 dimerization arm forming a more canonical interaction with HER2. Perhaps most interestingly, the authors observe glycan interactions that appear to stabilize intra- and inter-subunit interactions in HER4 homodimers but that inter-subunit glycans are not present in HER2/HER4 heterodimers. The authors speculate that these glycan interactions may contribute to the apparent propensity of HER4 to homodimerize vs. heterodimerize with HER2.

I realize that an important role of reviewers is to provide authors with informed and critical comments, but I found this manuscript a well-written, thoughtful, and important contribution. My only note is that I am not an electron microscopist so have assumed the microscopy has been carried out expertly and rely on other reviewers to vet structure determinations.

Author Response

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

We thank the reviewers for truly valuable advice and comments. We have made multiple corrections and revisions to the original pre-print accordingly per the following comments:

1. Pro1153Leu is extremely common in the general population (allele frequency in gnomAD is 0.5). Further discussion is warranted to justify the possibility that this variant contributes to a phenotype documented in 1.5-3% of the population. Is it possible that this variant is tagging other rare SNPs in the COL11A1 locus, and could any of the existing exome sequencing data be mined for rare nonsynonymous variants?

One possible avenue for future work is to return to any existing exome sequencing data to query for rare variants at the COL11A1 locus. This should be possible for the USA MO case-control cohort. Any rare nonsynonymous variants identified should then be subjected to mutational burden testing, ideally after functional testing to diminish any noise introduced by rare benign variants in both cases and controls. If there is a significant association of rare variation in AIS cases, then they should consider returning to the other cohorts for targeted COL11A1 gene sequencing or whole exome sequencing (whichever approach is easier/less expensive) to demonstrate replication of the association.

Response: Regarding the genetic association of the common COL11A1 variant rs3753841 (p.(Pro1335Leu)), we do not propose that it is the sole risk variant contributing to the association signal we detected and have clarified this in the manuscript. We concluded that it was worthy of functional testing for reasons described here. Although there were several common variants in the discovery GWAS within and around COL11A1, none were significantly associated with AIS and none were in linkage disequilibrium (R2>0.6) with the top SNP rs3753841. We next reviewed rare (MAF<=0.01) coding variants within the COL11A1 LD region of the associated SNP (rs3753841) in 625 available exomes representing 46% of the 1,358 cases from the discovery cohort. The LD block was defined using Haploview based on the 1KG_CEU population. Within the ~41 KB LD region (chr1:103365089- 103406616, GRCh37) we found three rare missense mutations in 6 unrelated individuals, Table below. Two of them (NM_080629.2: c.G4093A:p.A1365T; NM_080629.2:c.G3394A:p.G1132S), from two individuals, are predicted to be deleterious based on CADD and GERP scores and are plausible AIS risk candidates. At this rate we could expect to find only 4-5 individuals with linked rare coding variants in the total cohort of 1,358 which collectively are unlikely to explain the overall association signal we detected. Of course, there also could be deep intronic variants contributing to the association that we would not detect by our methods. However, given this scenario, the relatively high predicted deleteriousness of rs3753841 (CADD= 25.7; GERP=5.75), and its occurrence in a GlyX-Y triplet repeat, we hypothesized that this variant itself could be a risk allele worthy of further investigation.

Author response table 1.

We also appreciate the reviewer’s suggestion to perform a rare variant burden analysis of COL11A1. We did conduct pilot gene-based analysis in 4534 European ancestry exomes including 797 of our own AIS cases and 3737 controls and tested the burden of rare variants in COL11A1. SKATO P value was not significant (COL11A1_P=0.18), but this could due to lack of power and/or background from rare benign variants that could be screened out using the functional testing we have developed.

1. COL11A1 p.Pro1335Leu is pursued as a direct candidate susceptibility locus, but the functional validation involves both: (a) a complementation assay in mouse GPCs, Figure 5; and (b) cultured rib cartilage cells from Col11a1-Ad5 Cre mice (Figure 4). Please address the following:

2A. Is Pro1335Leu a loss of function, gain of function, or dominant negative variant? Further rationale for modeling this change in a Col11a1 loss of function cell line would be helpful.

Response: Regarding functional testing, by knockdown/knockout cell culture experiments, we showed for the first time that Col11a1 negatively regulates Mmp3 expression in cartilage chondrocytes, an AIS-relevant tissue. We then tested the effect of overexpressing the human wt or variant COL11A1 by lentiviral transduction in SV40-transformed chondrocyte cultures. We deleted endogenous mouse Col11a1 by Cre recombination to remove the background of its strong suppressive effects on Mmp3 expression. We acknowledge that Col11a1 missense mutations could confer gain of function or dominant negative effects that would not be revealed in this assay. However as indicated in our original manuscript we have noted that spinal deformity is described in the cho/cho mouse, a Col11a1 loss of function mutant. We also note the recent publication by Rebello et al. showing that missense mutations in Col11a2 associated with congenital scoliosis fail to rescue a vertebral malformation phenotype in a zebrafish col11a2 KO line. Although the connection between AIS and vertebral malformations is not altogether clear, we surmise that loss of the components of collagen type XI disrupt spinal development. in vivo experiments in vertebrate model systems are needed to fully establish the consequences and genetic mechanisms by which COL11A1 variants contribute to an AIS phenotype.

2B. Expression appears to be augmented compared WT in Fig 5B, but there is no direct comparison of WT with variant.

Response: Expression of the mutant (from the lentiviral expression vector) is increased compared to mutant. We observed this effect in repeated experiments. Sequencing confirmed that the mutant and wildtype constructs differed only at the position of the rs3753841 SNP. At this time, we cannot explain the difference in expression levels. Nonetheless, even when the variant COL11A1 is relatively overexpressed it fails to suppress MMP3 expression as observed for the wildtype form.

2C. How do the authors know that their complementation data in Figure 5 are specific? Repetition of this experiment with an alternative common nonsynonymous variant in COL11A1 (such as rs1676486) would be helpful as a comparison with the expectation that it would be similar to WT.

Response: We agree that testing an allelic series throughout COL11A1 could be informative, but we have shifted our resources toward in vivo experiments that we believe will ultimately be more informative for deciphering the mechanistic role of COL11A1 in MMP3 regulation and spine deformity.

2D. The y-axes of histograms in panel A need attention and clarification. What is meant by power? Do you mean fold change?

Response: Power is directly comparable to fold change but allows comparison of absolute expression levels between different genes.

2E. Figure 5: how many technical and biological replicates? Confirm that these are stated throughout the figures.

Response: Thank you for pointing out this oversight. This information has been added throughout.

1. Figure 2: What does the gross anatomy of the IVD look like? Could the authors address this by showing an H&E of an adjacent section of the Fig. 2 A panels?

Response: Panel 2 shows H&E staining. Perhaps the reviewer is referring to the WT and Pax1 KO images in Figure 3? We have now added H&E staining of WT and Pax1 KO IVD as supplemental Figure 3E to clarify the IVD anatomy.

1. Page 9: "Cells within the IVD were negative for Pax1 staining ..." There seems to be specific PAX1 expression in many cells within the IVD, which is concerning if this is indeed a supposed null allele of Pax1. This data seems to support that the allele is not null.

Response: We have now added updated images for the COL11A1 and PAX1 staining to include negative controls in which we omitted primary antibodies. As can be seen, there is faint autofluorescence in the PAX1 negative control that appears to explain the “specific staining” referred to by the reviewer. These images confirm that the allele is truly a null.

1. There is currently a lack of evidence supporting the claim that "Col11a1 is positively regulated by Pax1 in mouse spine and tail". Therefore, it is necessary to conduct further research to determine the direct regulatory role of Pax1 on Col11a1.

Response: We agree with the reviewer and have clarified that Pax1 may have either a direct or indirect role in Col11a1 regulation.

1. There is no data linking loss of COL11A1 function and spine defects in the mouse model. Furthermore, due to the absence of P1335L point mutant mice, it cannot be confirmed whether P1335L can actually cause AIS, and the pathogenicity of this mutation cannot be directly verified. These limitations need to be clearly stated and discussed. A Col11a1 mouse mutant called chondroysplasia (cho), was shown to be perinatal lethal with severe endochondral defects (https://pubmed.ncbi.nlm.nih.gov/4100752/). This information may help contextualize this study.

Response: We partially agree with the reviewer. Spine defects are reported in the cho mouse (for example, please see reference 36 Hafez et al). We appreciate the suggestion to cite the original Seegmiller et al 1971 reference and have added it to the manuscript.

1. A recent article (PMID37462524) reported mutations in COL11A2 associated with AIS and functionally tested in zebrafish. That study should be cited and discussed as it is directly relevant for this manuscript.

Response: We agree with the reviewer that this study provides important information supporting loss of function I type XI collagen in spinal deformity. Language to this effect has been added to the manuscript and this study is now cited in the paper.

1. Please reconcile the following result on page 10 of the results: "Interestingly, the AISassociated gene Adgrg6 was amongst the most significantly dysregulated genes in the RNA-seq analysis (Figure 3c). By qRT-PCR analysis, expression of Col11a1, Adgrg6, and Sox6 were significantly reduced in female and male Pax1-/- mice compared to wild-type mice (Figure 3d-g)." In Figure 3f, the downregulation of Adgrg6 appears to be modest so how can it possibly be highlighted as one of the most significantly downregulated transcripts in the RNAseq data?

Response: By “significant” we were referring to the P-value significance in RNAseq analysis, not in absolute change in expression. This language was clearly confusing, and we have removed it from the manuscript.

1. It is incorrect to refer to the primary cell culture work as growth plate chondrocytes (GPCs), instead, these are primary costal chondrocyte cultures. These primary cultures have a mixture of chondrocytes at differing levels of differentiation, which may change differentiation status during the culturing on plastic. In sum, these cells are at best chondrocytes, and not specifically growth plate chondrocytes. This needs to be corrected in the abstract and throughout the manuscript. Moreover, on page 11 these cells are referred to as costal cartilage, which is confusing to the reader.

Response: Thank you for pointing out these inconsistencies. We have changed the manuscript to say “costal chondrocytes” throughout.

Minor points

• On 10 of the Results: "These data support a mechanistic link between Pax1 and Col11a1, and the AIS-associated genes Gpr126 and Sox6, in affected tissue of the developing tail." qRT-PCR validation of Sox6, although significant, appears to be very modestly downregulated in KO. Please soften this statement in the text.

Response: We have softened this statement.

• Have you got any information about how the immortalized (SV40) costal cartilage affected chondrogenic differentiation? The expression of SV40 seemed to stimulate Mmp13 expression. Do these cells still make cartilage nodules? Some feedback on this process and how it affects the nature of the culture what be appreciated.

Response: The “+ or –“ in Figure 5 refers to Ad5-cre. Each experiment was performed in SV40-immortalized costal chondrocytes. We have removed SV40 from the figure and have clarified the legend to say “qRT-PCR of human COL11A1 and endogenous mouse Mmp3 in SV40 immortalized mouse costal chondrocytes transduced with the lentiviral vector only (lanes 1,2), human WT COL11A1 (lane 3), or COL11A1P1335L. Otherwise we absolutely agree that understanding Mmp13 regulation during chondrocyte differentiation is important. We plan to study this using in vivo systems.

• Figure 1: is the average Odds ratio, can this be stated in the figure legend?

Response: We are not sure what is being asked here. The “combined odds ratio” is calculated as a weighted average of the log of the odds.

• A more consistent use of established nomenclature for mouse versus human genes and proteins is needed.

Human:GENE/PROTEIN Mouse: Gene/PROTEIN

Response: Thank you for pointing this out. The nomenclature has been corrected throughtout the manuscript.

• There is no Figure 5c, but a reference to results in the main text. Please reconcile. -There is no Figure 5-figure supplement 5a, but there is a reference to it in the main text. Please reconcile.

Response: Figure references have been corrected.

• Please indicate dilutions of all antibodies used when listed in the methods.

Response: Antibody dilutions have been added where missing.

• On page 25, there is a partial sentence missing information in the Histologic methods; "#S36964 Invitrogen, CA, USA)). All images were taken..."

Response: We apologize for the error. It has been removed.

• Table 1: please define all acronyms, including cohort names.

Response: We apologize for the oversight. The legend to the Table has been updated with definitions of all acronyms.

• Figure 2: Indicate that blue staining is DAPI in panel B. Clarify that "-ab" as an abbreviation is primary antibody negative.

Response: A color code for DAPI and COL11A! staining has been added and “-ab” is now defined.

• Page 4: ADGRG6 (also known as GPR126)...the authors set this up for ADGRG6 but then use GPR126 in the manuscript, which is confusing. For clarity, please use the gene name Adgrg6 consistently, rather than alternating with Gpr126.

Response: Thank you for pointing this out. GPR126 has now been changed to ADGRG6 thoughout the manuscript.

• REF 4: Richards, B.S., Sucato, D.J., Johnston C.E. Scoliosis, (Elsevier, 2020). Is this a book, can you provide more clarity in the Reference listing?

Response: Thank you for pointing this out. This reference has been corrected.

• While isolation was addressed, the methods for culturing Rat cartilage endplate and costal chondrocytes are poorly described and should be given more text.

Response: Details about the cartilage endplate and costal chondrocyte isolation and culture have been added to the Methods.

• Page 11: 1st paragraph, last sentence "These results suggest that Mmp3 expression"... this sentence needs attention. As written, I am not clear what the authors are trying to say.

Response: This sentence has been clarified and now reads “These results suggest that Mmp3 expression is negatively regulated by Col11a1 in mouse costal chondrocytes.”

• Page 13: line 4 from the bottom, "ECM-clearing"? This is confusing do you mean ECM degrading?

Response: Yes and thank you. We have changed to “ECM-degrading”.

• Please use version numbers for RefSeq IDs: e.g. NM_080629.3 instead of NM_080629

Response: This change has been made in the revised manuscript.

• It would be helpful for readers if the ethnicity of the discovery case cohort was clearly stated as European ancestry in the Results main text.

Response: “European ancestry” has been added at first description of the discovery cohort in the manuscript.

• Avoid using the term "mutation" and use "variant" instead.

Response: Thank you for pointing this out. “Variant” is now used throughout the manuscript.

• Define error bars for all bar charts throughout and include individual data points overlaid onto bars.

Response: Thank you. Error bars are now clarified in the Figure legends.

Author Response

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

Reviewer #1:

1. The most important concern that I have refers to the FDTD simulations to characterize the ZMW, as shown in Appendix 2, Figure 4. So far, the explanations given in the caption of Figure 4 are confusing and misleading: the authors should provide more detailed explanations on how the simulations were performed and the actual definition of the parameters used. In particular:

a. lines 1330-1332: it is not clear to me how the fluorescence lifetime can be calculated from the detected signal S (z), and why they are horizontal, i.e., no z dependence? Which lifetimes are the authors referring to?

b. lines 1333-1335: Where do these values come from? And how do they relate to panels D & E? From what I can see in these panels the lifetimes are highly dependent on z and show the expected reduction of lifetime inside the nanostructures.

c. lines 1336-1337: Why the quantum yield of the dyes outside the ZMW differs from those reported in the literature? In particular the changes of quantum yield and lifetime for Alexa 488 are very large (also mentioned in the corresponding part of Materials & Methods but not explained in any detail).

We thank the Reviewer for his detailed questions on the FDTD simulations. We have now added the missing equation related to the computation of signal-averaged fluorescence lifetimes from the FDTD simulations. Specifically to the three points raised:

a) The fluorescence lifetime is indeed not calculated from the detected signal S(z), but from the radiative and non-radiative rates in the presence of the ZMW as given in eq. 9-10. However, we use the detected signal S(z) to compute the average fluorescence lifetime over the whole z-profile of the simulation box, which we relate to the experimentally measured fluorescence lifetimes as given in Appendix 7, Figure 1. We have now added the equation to compute the signal-weighted fluorescence lifetimes, which we denote as <𝜏>S , in eq. 13 in the methods. To clarify this point, we have added the symbol <𝜏>S to the plots in Appendix 2, Figure 4 D-E and Appendix 7, Figure 1 C-D.

b) The estimated lifetimes were obtained as the signal-weighted average over the lifetime profiles, (<𝜏>S) as given in the new eq. 13. All plotted quantities, i.e., the detection efficiency η, quantum yield ϕ, detected signal S(z), and fluorescence lifetime, are computed from the radiative and loss rates obtained from the FDTD simulation according to eqs. 8-11. To make this clearer, we have now added the new Appendix 2 – Figure 5 which shows the z-profiles of the quantities (radiative and loss rates) used to derive the experimental observables.

c) There are multiple reasons for the differences of the quantum yields of the two analytes used in this study compared to the literature values. For cyanine dyes such as Alexa647, it is well known that steric restriction (as e.g. caused by conjugation to a biomolecule) can lead to an increase of the quantum yield and fluorescence lifetime. We observe a minor increase of the fluorescence lifetime for Alexa647 from the literature value of 1.17 ns to a value of 1.37 ns when attached to Kap95, which is indicative of this effect. In the submitted manuscript, this was discussed in the methods in lines 936-938 (lines 938-945 in the revised manuscript). For the dye Alexa488, which is used to label the BSA protein, this effect is absent. Instead, we observe (as the Reviewer correctly notes) a quite drastic reduction of the fluorescence lifetime compared to the unconjugated dye from 4 ns to 2.3 ns. In cases where a single cysteine is labeled on a protein, such a drastic reduction of the quantum yield usually indicates the presence of a quenching moiety in proximity of the labeling site, such as tryptophane, which acts via the photo-induced electron transfer mechanism. Indeed, BSA contains two tryptophanes that could be responsible for the low quantum yield of the conjugated dyes. The situation is complicated by the fact that BSA contains 35 cysteines that can potentially be labeled (although 34 are involved in disulfide bridges). The labeled BSA was obtained commercially and the manufacturer lists the degree of labeling as ~6 dye molecules per protein, with a relative quantum yield of 0.2 compared to the standard fluorescein. This corresponds to an absolute quantum yield of ~0.16, which is low compared to the literature value for Alexa488 of ~0.8.

Based on the measured fluorescence lifetime, we estimate a quantum yield of 0.46, which is higher than the photometrically obtained value of 0.16 reported by the manufacturer. Fully quenched, nonfluorescent dyes will not contribute to the lifetime measurement but are detected in the photometric quantum yield estimates. The difference between the lifetime and photometric based quantum yield estimates thus suggest that part of the fluorophores are almost fully quenched. While it is unknown where the dyes are attached to the protein, the low quantum yield could be indicative of dye-dye interactions via pi-pi stacking, which can often lead to non-fluorescent dimers. This is supported by the fact that the manufacturer reports color differences between batches of labeled protein, which indicate spectral shifts of the absorption spectrum when dye-dye adducts are formed by π-π stacking. We have now added a short discussion of this effect in lines 938-941. We note that the conclusions drawn on the quenching effect of the metal nanostructure remain valid despite the drastic reduction of the quantum yield for Alexa488, which leads to a further quantum yield reduction of the partly quenched reference state.

2) A second important concern refers to Figure 3: Why is there so much variability on the burst intensities reported on panels C, D? They should correspond to single molecule translocation events and thus all having comparable intensity values. In particular, the data shown for BSA in panel D is highly puzzling, since it not only reflects a reduced number of bursts (which is the main finding) but also very low intensity values, suggesting a high degree of quenching of the fluorophore being proximal to the metal on the exit side of the pore. In fact, the count rates for BSA on the uncoated pore range form 50-100kcounts/s, while on the coated pores thy barely reach 30 kcounts/s, a clear indication of quenching. Importantly, and in direct relation to this, could the authors exclude the possibility that the low event rates measured on BSA are largely due to quenching of the dye by getting entangled in the Nsp mesh just underneath the pore but in close contact to the metal?

The Reviewer raises a valid concern, but further analysis shows that this is unproblematic. Notably, the burst intensities are in fact not reduced, in contrast to the visual impression obtained from the time traces shown in the figure. The time trace of the BSA intensity is visually dominated by high-intensity bursts which mask the low-intensity bursts in the plot. In contrast, in Figure 3 the reduced number of BSA events results in a sparser distribution of the intensity spikes, which allows low-intensity events to be seen. Different to the visual inspection, the spike-detection algorithm does not exhibit any bias in terms of the duration or the number of photons of the detected events between the different conditions for both BSA and Kap95, as shown in the new Appendix 7 – Figure 1. Using FCS analysis it can be tested whether the event duration varies between the different conditions shown in Figure 3 C-D. This did not show a significant difference in the estimated diffusion time for BSA (Appendix 7 – Figure 1 C,D). Contrary to the suggestion of the Reviewer, we also do not observe any indication of quenching by the metal between uncoated and Nsp1-coated pores for BSA. Such quenching should result in differences of the fluorescence lifetimes, which however is not evident in our experimental data (Appendix 7 – Figure 1 F).

3) Line 91: I suggest the authors remove the word "multiplexed" detection since it is misleading. Essentially the authors report on a two-color excitation/detection scheme which is far from being really multiplexing.

We have changed the word to “simultaneous” now and hope this avoids further confusion.

4) Line 121: why are the ZMW fabricated with palladium? Aluminum is the gold-standard to reduce light transmissivity. An explanation for the choice of this material would be appreciated by the community.

In a previous study (Klughammer and Dekker, Nanotechnology, 2021), we established that palladium can have distinct advantages compared to other ZMW metals such as aluminum and gold, most prominently, an increased chemical stability and reduced photoluminescence. For this study, we chose palladium over aluminum as it allowed the use of simple thiol chemistry for surface modification. In the beginning of the project, we experimented with aluminum pores as well. We consistently found that the pores got closed after measuring their ionic conductance in chlorine-containing solutions such as KCl or PBS. This problem was avoided by choosing palladium.

5) Lines 281-282: This statement is somewhat misleading, since it reads such that the molecules stay longer inside the pore. However, if I understand correctly, these results suggest that Kap95 stays closer to the metal on the exit side. This is because measurements are being performed on the exit side of the pore as the excitation field inside the pore is quite negligible.

We thank the Reviewer for this comment and have clarified the text in lines 290-292 as suggested to: “(…) this indicates that, on the exit side, Kap95 diffuses closer to the pore walls compared to BSA due to interactions with the Nsp1 mesh”

6) Lines 319-320: Although the MD simulations agree with the statement being written here, the variability could be also due to the fact that the proteins could interact in a rather heterogenous manner with the Nsp mesh on the exit side of the pore, transiently trapping molecules that then would stay longer and/or closer to the metal altering the emission rate of the fluorophores. Could the authors comment on this?

The variation mentioned in the text refers to a pore-to-pore variation and thus needs to be due to a structural difference between individual pores. This effect would also need to be stable for the full course of an experiment, typically hours. We did not find any structural changes in the fluorescence lifetimes measured on individual pores such as suggested by the Reviewer. We think that the suggested mechanism would show up as distinct clusters in Appendix 7 – Figure 1 E,F where we found no trace of such a change to happen. If we understand correctly, the Reviewer suggests a mechanism, not based on changes in the Nup layer density, that would lead to a varying amount of trapping of proteins close to the surface. Such a behavior should show up in the diffusion time of each pore ( Appendix 7 – figure 1 C,D), where we however find no trace of such an effect.

7) Lines 493-498: These claims are actually not supported by the experimental data shown in this contribution: a) No direct comparison in terms of signal-to-noise ratio between fluorescence-based and conductance-based readouts has been provided in the ms. b) I would change the word multiplexed by simultaneous since it is highly misleading. c) The results shown are performed sequentially and thus low throughput. d) Finally, the use of unlabeled components is dubious since the detection schemes relies on fluorescence and thus requiring labeling.

We thank the Reviewer for pointing this out.

a) We have now added a section in appendix 3 that discusses the signal-to-noise ratios. In brief, there are three observations that led us to conclude that ZMWs provide beneficial capabilities to resolve individual events from the background:

1. The signal-to-background ratio was determined to be 67±53 for our ZMW data of Kap95 which is an order of magnitude higher compared to the ~5.6 value for a conductance-based readout.

2. The detection efficiency for ZMWs is independent of the Kap95 occupancy within the pore. This is different from conductance based approaches that have reduced capability to resolve individual Kap95 translocations at high concentrations.

3. The fraction of detected translocations is much higher for ZMWs than for conductance-based data (where lots of translocations occur undetected) and matches closer to the theoretical predictions.

b) We have changed the wording accordingly.

c) We agree with the Reviewer that our method is still low throughput. However, the throughput is markedly increased compared to previous conductance-based nanopore measurements. This is because we can test many (here up to 8, but potentially many more) pores per chip in one experiment, whereas conductance-based readouts are limited to a single pore. We have now changed the wording to “increased throughput” in line 507 to avoid confusion.

d) We agree that only labeled components can be studied directly with our methods. However, the effect of unlabeled analytes can be assessed indirectly without any perturbation of the detection scheme due to the specificity of the fluorescent labeling. This is distinct from previous nanopore approaches using a conductance-based readout that lack specificity. In our study, we have for example used this advantage of our approach to access event rates at high concentrations (1000nM Kap95, 500nM BSA) and large pore diameters by reducing the fraction of labeled analyte in the sample. Finally, the dependence of the BSA leakage rate as a function of the concentration of Kap95 (Figure 6) relies on a specific readout of BSA events in the presence of large amounts of Kap95, which would be impossible in conductance-based experiments.

8) Line 769: specify the NA of the objective. Using a very long working distance would also affect the detection efficiency. Have the authors considered the NA of the objective on the simulations of the detection efficiency? This information should be included and it is important as the authors are detecting single molecule events.

We used an NA of 1.1 for the simulation of the Gaussian excitation field in the FDTD simulations, corresponding to the NA of the objective lens used in the experiments and as specified in the methods. The Reviewer is correct that the NA also affects the absolute detection efficiency of the fluorescence signal due to the finite opening angle of the collection cone of ~56˚. In our evaluation of the simulations, we have neglected this effect for simplicity, because the finite collection efficiency of the objective lens represents only an additional constant factor that does not depend on the parameters of the simulated system, such as the pore diameter. Instead, we focused solely the effect of the ZMW and defined the detection efficiency purely based on the fraction of the signal that is emitted towards the detection side and can potentially be detected in the experiment, which also provides the benefit that the discussed numbers are independent of the experimental setup used.

To clarify this, we have now made this clearer in the method text on lines 917-920.

9) Line 831: I guess that 1160ps is a mistake, right?

This is not a mistake. We performed a tail fit of the fluorescence decay curves, meaning that the initial rise of the decay was excluded from the fit. The initial part of the fluorescence decay is dominated by the instrument response function (IRF) of the system, with an approximate width of ~500 ps. To minimize the influence of the IRF on the tail fit, we excluded the first ~1 ns of the fluorescence decay.

10) Lines 913-917: Why are the quantum yield of Alexa 488 and lifetime so much reduced as compared to the published values in literature?

See answer to point 1. We have added a short discussion at lines 938-941 where we speculate that the reduced quantum yield is most likely caused by dye-dye interactions due to the high degree of labeling of ~6 dyes per protein.

11) Lines 1503-1509: The predicted lifetimes with the Nsp-1 coating have not been shown in Appendix 2 - Figure 4. How have they been estimated?

We have not performed predictions of fluorescence lifetimes in the presence of an Nsp1 coating. Predictions of the fluorescence lifetime in the absence of the Nsp1 coating were obtained by assuming a uniform occupancy of the molecules over the simulation box. A prediction of the fluorescence lifetimes in the presence of the Nsp1 coating would require a precise knowledge of the spatial distribution of analytes, which depends, among other factors, on the extension of the Nsp1 brushes and the interaction strengths with the FG repeats. While simulations provide some insights on this, we consider a quantitative comparison of predicted and measured fluorescence lifetimes in the presence of the Nsp1 coating beyond the scope of the present study.

12) Lines 1534-1539: I disagree with this comment, since the measurements reported here have been performed outside the nano-holes, and thus the argument of Kap95 translocating along the edges of the pore and being responsible for the reduced lifetime does not make sense to me.

In accordance with our answer to point 5 above, we have now changed the interpretation to the proximity of Kap95 to the metal surface on the exit side, rather than speculating on the path that the protein takes through the pore (lines 1662-1664), as follows:

“This indicates that, in the presence of Nsp1, Kap95 molecules diffuse closer to or spend more time in proximity of the metal nanoaperture on the exit side.”

Reviewer #2:

(Numbers indicate the line number.)

48: should cite more recent work: Timney et al. 2016 Popken et al 2015

59: should cite Zilman et al 2007, Zilman et al 2010

62: should cite Zilman et al 2010

We thank the Reviewer for the suggestions and have added them to the manuscript now.

65: one should be careful in making statements that the "slow" phase is immobile, as it likely rapidly exchanging NTRs with the "fast" phase.

We have removed this description and replaced it by “This 'slow phase' exhibits a reduced mobility due to the high affinity of NTRs to the FG-Nup mesh.” to avoid misunderstanding.

67: Schleicher 2014 does not provide evidence of dedicated channels

We agree with the Reviewer and therefore moved the reference to an earlier position in the sentence.

74-75: must cite work by Lusk & Lin et al on origami nanochannels

We thank the Reviewer for this suggestion. We have now added a reference to the nanotraps of Shen et al. 2021, JACS, in line 75. In addition, we now also refer to Shen et al. 2023, NSMB, in the discussion where viral transport is discussed.

77: Probably Jovanovic- Talisman (2009)?

We thank the Reviewer for pointing out this typo.

93; should cite Auger&Montel et al, PRL 2014

We thank the Reviewer for pointing out this reference. To give proper credit to previous ZMW, we have now incorporated a sentence in lines 100-102 citing this reference.

111-112: there appears to be some internal inconsistency between this interpretation and the BSA transport mostly taking place through the "central hole" (as seems to be implied by Equation (3). Probably it should be specified explicitly that the "central hole" in large channels is a "void".

We thank the Reviewer for this suggestion and have added a clarifying sentence.

115-177: This competition was studied in Jovanovic-Talisman 2009 and theoretically analysed in Zilman et al Plos Comp Biol 2010. The differences in the results and the interpretation should be discussed.

We agree, therefore it is discussed in the discussion section (around line 594) and now added the reference to Zilman et al.

Figure 2 Caption: "A constant flow..." - is it clear that is flow does not generate hydrodynamic flow through the pore?

The Reviewer raises an important point. Indeed, the pressure difference over the membrane generates a hydrodynamic flow through the pore that leads to a reduction of the event rate compared to when no pressure is applied. However, as all experiments were performed under identical pressures, one can expect a proportional reduction of the absolute event rates due to the hydrodynamic flow against the concentration gradient. In other words, this will not affect the conclusions drawn on the selectivity, as it is defined as a ratio of event rates.

We have now added additional data on the influence of the hydrodynamic flow on the translocation rate in Appendix 3 – Figure 2, where we have measured the signal of free fluorophores at high concentration on the exit side of the pore as a function of the applied pressure. The data show a linear dependence of the signal reduction on the applied pressure. At the pressure values used for the experiments of 50 mbar, we see a ~5% reduction compared to the absence of pressure, implying that the reported absolute event rates are underestimated only by ~5%. Additionally we have added such data for Kap95 translocations that shows a similar effect (however less consistent). Measuring the event rate at zero flow is difficult, since this leads to an accumulation of fluorophores on the detection side.

Figure 3: it would help to add how long is each translocation, and what is the lower detection limit. A short explanation of why the method detects actual translocations would be good

With our method, unfortunately, we can not assess the duration of a translocation event since we only see the particle as it exists the pore. Instead, the measured event duration is determined by the time it takes for the particle to diffuse out of the laser focus. This is confirmed by FCS analysis of translocation events that show the same order of magnitude of diffusion times as for free diffusion (Appendix 7 – Figure 1 C,D) in contrast to a massively reduced diffusion time within a nanopore. In Figure 2D we show the detection efficiency at different locations around the ZMW as obtained from FDTD simulations and discuss the light blocking. This clearly shows that the big majority of the fluorescence signal comes from the laser illuminated side and therefore only particles that translocated through the ZMW are detected as presented between lines 170-190. In Yang et al. 2023, bioRxiv (https://doi.org/10.1101/2023.06.26.546504) a more detailed discussion about the optical properties of Pd nanopores is given.

This point also explains why we see actual translocations: since the light is blocked by the ZMW, fluorophores can only be detected after they have translocated. On parts of the membrane without pores and upstream the amount of spikes found in a timetrace was found to be negligibly small. Additionally, if a significant part of the signal would be contributed by leaking fluorescence from the dark top side, there should no difference in BSA event rate found between small open and Nsp1 pores which we did not observe.

With respect to the lower detection limit for events: In the burst search algorithm we require a false positive level rate of lower than 1 event in 100. Additionally, as described in Klughammer and Dekker, Nanotechnology (2021), we apply an empirical filtering to remove low signal to noise ratio events that contain less than 5 detected photons per event or a too low event rate. From the event detection algorithm there is no lower limit set on the duration of an event. Such a limit is then set by the instrument and the maximum frequency it which it can detect photons. This time is below 1μs. Practically we don’t find events shorter than 10μs as can be seen in the distribution of events where also the detection limits can be estimated (Appendix 7 – figure 1 A and B.)

Equation (1): this is true only for passive diffusion without interactions (see eg Hoogenboom et al Physics Reports 2021 for review). Using it for pores with interactions would predict, for instance, that the inhibition of the BSA translocation comes from the decrease in D which is not correct.

We agree with the Reviewer that this equation would not reproduce the measured data in a numerically correct way. We included it to justify why we subsequently fit a quadratic function to the data. As we write in line 260 we only used the quadratic equation “as a guide to the eye and for numerical comparison” and specifically don’t claim that this fully describes the translocation process. In this quadratic function, we introduced a scaling factor α that can be fitted to the data and thus incorporates deviations from the model. In appendix 5 we added a more elaborate way to fit the data including a confinement-based reduction of the diffusion coefficient (although not incorporating interactions). Given the variations of the measured translocation rates, the data is equally well described by both the simple and the more complex model function.

Equation (1): This is not entirely exact, because the concentration at the entrance to the pore is lower than the bulk concentration, which might introduce corrections

We agree with the Reviewer and have added that the concentration difference Δc is measured at the pore entrance and exit, and this may be lower than the bulk concentration. As described in our reaction to the Reviewer’s previous comment, equation (1) only serves as a justification to use the quadratic dependence and any deviations in Δc are absorbed into the prefactor α in equation (2).

Equation (3): I don't understand how this is consistent with the further discussion of BSA translocation. Clearly BSA can translocate through the pore even if the crossection is covered by the FG nups (through the "voids" presumably?).

The Reviewer raises an important point here. Equation 3 can only be used for a pore radius r > rprot + b. b was determined to be 11.5 nm and rprot is 3.4 nm for BSA, thus it needs to be that r > 15 nm. We would like to stress, however, that b does not directly give a height of a rigid Nsp1 ring but is related to the configuration of the Nsp1 inside the pore. Equation (3) (and equation (2)) were chosen because even these simple equations could fit the experimentally measured translocation rates well, and not because they would accurately model the setup in the pore. As we found from the simulations, the BSA translocations at low pore diameters presumably happen through transient openings of the mesh. The dynamics leading to the stochastic opening of voids on average leads to the observed translocation rate.

296-297: is it also consistent with the simulations?

We compare the experimentally and simulated b values in lines 387-388 and obtained b=9.9 ± 0.1 nm from the simulations (as obtained from fitting the translocation rates and not from measuring the extension of the Nsp1 molecules) and 11.5 ± 0.4 nm from the experiments – which we find in good agreement.

331: has it been established that the FG nups equilibrate on the microsecond scale?

As an example, we have analyzed the simulation trajectory of the most dense nanopore (diameter = 40 nm, grafting = 1/200 nm2). In Author response image 1 we show for each of the Nsp1-proteins how the radius of gyration (Rg) changes in time over the full trajectory (2 μs + 5 μs). As expected, the Rg values reached the average equilibrium values very well within 2 μs simulation time, showing that the FG-Nups indeed equilibrate on the (sub)microsecond scale.

Author response image 1.

334-347: the details of the method should be explained explicitly in the supplementary (how exactly voids distributions are estimated and the PMF are calculated etc)

The void analysis was performed with the software obtained from the paper of Winogradoff et al. In our Methods we provide an overview of how this software calculates the void probability maps and how these are converted into PMFs. For a more detailed description of how exactly the analysis algorithm is implemented in the software, we refer the reader to the original work. The analysis codes with the input files that were used in this manuscript have been made public ( https://doi.org/10.4121/22059227.v1 ) along with the manuscript.

Equation (4) is only an approximation (which works fine for high barriers but not the low ones). Please provide citations/derivation.

To our knowledge, the Arrhenius relation is a valid approximation for our nanopore simulations. We are unaware of the fact that it should not work for low barriers and cannot find mention of this in the literature. It would be helpful if the Reviewer can point us to relevant literature.

Figure 4: how was transport rate for Kaps calculated?

As mentioned in lines 388-391, we assumed that the Kap95 translocation rate through Nsp1-coated pores is equal to that for open pores, as we did not observe any significant hindrance of Kap95 translocation by the Nsp1 mesh in the experiment (Figure 4 A,C).

378: It's a bit strange to present the selectivity ratio as prediction of the model when only BSA translocation rate was simulated (indirectly).

We agree with the Reviewer that ideally we should also simulate the Kap95 translocation rate to obtain an accurate selectivity measure of the simulated nanopores. However, as the experiments showed very similar Kap95 translocation rates for open pores and Nsp1-coated pores, we believe it is reasonable to take the Kap95 rates for open and Nsp1-pores to be equal.

Figure 5C and lines 397: I am a bit confused how is this consistent with Figure 4D?

Figure 5C and figure 4D both display the same experimental data, where 4D only focuses on a low diameter regime. In relation to line 397 (now 407), the Nsp1 mesh within the 60-nm pore dynamically switches between closed configurations and configurations with an open channel. When taking the temporal average of these configurations, we find that the translocation rate is higher than for a closed pore but lower than for a fully open pore. The stochastic opening and closing of the Nup mesh results in the continuous increase of the translocation rates with increasing diameter, which is in contrast to a step-wise increase that would be expected from an instantaneous collapse of the Nsp1 mesh at a certain pore diameter.

428-439: Please discuss the differences from Jovanovic-Talisman 2009.

How our results for a Kap95 induced change of the BSA translocation rate are related to previous literature is discussed extensively in the lines 598-620.

440: How many Kaps are in the pore at different concentrations?

This is a very interesting question that we were, unfortunately, not able to answer within the scope of this project. With our fluorescent based methods we could not determine this number because the excitation light does not reach well into the nanopore.

In our previous work on Nsp1-coated SiN nanopores using conductance measurements, we quantified the drop in conductance at increasing concentrations of Kap95 (Fragasso et al., 2023, NanoResearch, http://dx.doi.org/10.1007/s12274-022-4647-1). From this, we estimated that on average ~20 Kap95 molecules are present in a pore with a diameter of 55 nm at a bulk concentration of 2 µM. In these experiments, however, the height of the pore was only ~20 nm, which is much lower compared to 100 nm long channel used here, and the grafting density of 1 per 21 nm2 was high compared to the grafting density here of 1 per 300 nm2. Assuming that the Kap95 occupancy scales linearly with the number of binding sites (FG repeats) in the vicinity of the pore, and hence the amount of Nsp1 molecules bound to the pore, we would expect approximately ~7 Kap95 molecules in a pore of similar diameter under saturating (> 1 µM) concentrations.

On the other hand, the simulations showed that the density of Nsp1 within the pore is equal to the density within the 20-nm thick SiN pores (line 380). For the longer channel and lower grafting density used here, Nsp1 was also more constrained to the pore compared to thinner pores used in previous studies (Fragasso et al., 2023, NanoResearch), where the grafted protein spilled out from the nanopores. Thus assuming that the Kap95 occupancy depends on the protein density in the pore volume rather than the total protein amount grafted to the pore walls, we would estimate a number of 100 Kap95 molecules per pore.

These varying numbers already show that we cannot accurately provide an estimate of the Kap95 occupancy within the pore from our data due to limitations of the ZMW approach.

445: how is this related to the BSA translocation increase?

For the calculation of the selectivity ratio, we assumed the normalized Kap95 translocation rate to be independent of the Kap95 concentration. Hence, the observed trends of the selectivity ratios at different concentrations of Kap95, as shown in Figure 6 D, are solely due to a change in the BSA translocation rate at different concentrations of Kap95, as given in Figure 6 B,C.

462-481: it's a bit confusing how this interfaces with the "void" analysis ( see my previous comments)

We agree that the phenomenological descriptions in terms of transient openings (small, dynamic voids) that for larger pores become a constantly opened channel (a single large, static void) might cause some confusion to the reader. In the last part of the results, we aimed to relate the loss of the BSA rate to a change of the Nsp1 mesh. We acknowledge that the model of a rim of Nsp1 and an open center described in Figure 5F is highly simplifying . We now explain this in the revised paper at lines 483-486 by referring to an effective layer thickness which holds true under the simplifying assumption of a central transport channel.

Figure 6D: I think the illustration of the effect of kaps on the brush is somewhat misleading: at low pore diameters, it is possible that the opposite happens: the kaps concentrate the polymers towards the center of the pore. It should be also made clear that there are no kaps in simulations (if I understand correctly?)

Indeed, at small pore diameters we think it would be possible to observe what the Reviewer describes. The illustration should only indicate what we think is happening for large pore diameters where we observed the opening of a central channel. To avoid confusion, we now shifted the sketches to panel G where the effective layer thickness is discussed.

Indeed, as stated in lines 331-340 no Kap95 or BSA molecules were present in the simulations. We have now clarified this point in lines 872-876.

518: Please provide more explanation on the role of hydrodynamics pressure.

We have now performed additional experiments and quantified the effect of the pressure to be a ~5% reduction of the event rates, as described in the answer to a previous question above.  

Reviewer #3 (Recommendations For The Authors):

No experiments have been performed with the Ran-Mix regeneration system. It would be beneficial to add Ran-Mix to the trans compartment and see how this would affect Kap95 translocation events frequency and passive cargo diffusion. As the authors note in their outlook, this setup offers an advantage in using Ran-Mix and thus could also be considered here or in a future follow-up study.

We thank the Reviewer for this suggestion. We think, however, that it is beyond the scope of this paper and an interesting subject for a follow-up study.

Author Response

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

eLife assessment:

This important study represents a comprehensive computational analysis of Plasmodium falciparum gene expression, with a focus on var gene expression, in parasites isolated from patients; it assesses changes that occur as the parasites adapt to short-term in vitro culture conditions. The work provides technical advances to update a previously developed computational pipeline. Although the findings of the shifts in the expression of particular var genes have theoretical or practical implications beyond a single subfield, the results are incomplete and the main claims are only partially supported.

The authors would like to thank the reviewers and editors for their insightful and constructive assessment. We particularly appreciate the statement that our work provides a technical advance of our computational pipeline given that this was one of our main aims. To address the editorial criticisms, we have rephrased and restructured the manuscript to ensure clarity of results and to support our main claims. For the same reason, we removed the var transcript differential expression analysis, as this led to confusion.

Public Reviews:

Reviewer #1:

The authors took advantage of a large dataset of transcriptomic information obtained from parasites recovered from 35 patients. In addition, parasites from 13 of these patients were reared for 1 generation in vivo, 10 for 2 generations, and 1 for a third generation. This provided the authors with a remarkable resource for monitoring how parasites initially adapt to the environmental change of being grown in culture. They focused initially on var gene expression due to the importance of this gene family for parasite virulence, then subsequently assessed changes in the entire transcriptome. Their goal was to develop a more accurate and informative computational pipeline for assessing var gene expression and secondly, to document the adaptation process at the whole transcriptome level.

Overall, the authors were largely successful in their aims. They provide convincing evidence that their new computational pipeline is better able to assemble var transcripts and assess the structure of the encoded PfEMP1s. They can also assess var gene switching as a tool for examining antigenic variation. They also documented potentially important changes in the overall transcriptome that will be important for researchers who employ ex vivo samples for assessing things like drug sensitivity profiles or metabolic states. These are likely to be important tools and insights for researchers working on field samples.

One concern is that the abstract highlights "Unpredictable var gene switching..." and states that "Our results cast doubt on the validity of the common practice of using short-term cultured parasites...". This seems somewhat overly pessimistic with regard to var gene expression profiling and does not reflect the data described in the paper. In contrast, the main text of the paper repeatedly refers to "modest changes in var gene expression repertoire upon culture" or "relatively small changes in var expression from ex vivo to culture", and many additional similar assessments. On balance, it seems that transition to culture conditions causes relatively minor changes in var gene expression, at least in the initial generations. The authors do highlight that a few individuals in their analysis showed more pronounced and unpredictable changes, which certainly warrants caution for future studies but should not obscure the interesting observation that var gene expression remained relatively stable during transition to culture.

Thank you for this comment. We were happy to modify the wording in the abstract to have consistency with the results presented by highlighting that modest but unpredictable var gene switching was observed while substantial changes were found in the core transcriptome. Moreover, any differences observed in core transcriptome between ex vivo samples from naïve and pre-exposed patients are diminished after one cycle of cultivation making inferences about parasite biology in vivo impossible.

Therefore, – to our opinion – the statement in the last sentence is well supported by the data presented.

Line 43–47: “Modest but unpredictable var gene switching and convergence towards var2csa were observed in culture, along with differential expression of 19% of the core transcriptome between paired ex vivo and generation 1 samples. Our results cast doubt on the validity of the common practice of using short-term cultured parasites to make inferences about in vivo phenotype and behaviour.” Nevertheless, we would like to note that this study was in a unique position to assess changes at the individual patient level as we had successive parasite generations. This comparison is not done in most cross-sectional studies and therefore these small, unpredictable changes in the var transcriptome are missed.

Reviewer #2:

In this study, the authors describe a pipeline to sequence expressed var genes from RNA sequencing that improves on a previous one that they had developed. Importantly, they use this approach to determine how var gene expression changes with short-term culture. Their finding of shifts in the expression of particular var genes is compelling and casts some doubt on the comparability of gene expression in short-term culture versus var expression at the time of participant sampling. The authors appear to overstate the novelty of their pipeline, which should be better situated within the context of existing pipelines described in the literature.

Other studies have relied on short-term culture to understand var gene expression in clinical malaria studies. This study indicates the need for caution in over-interpreting findings from these studies.

The novel method of var gene assembly described by the authors needs to be appropriately situated within the context of previous studies. They neglect to mention several recent studies that present transcript-level novel assembly of var genes from clinical samples. It is important for them to situate their work within this context and compare and contrast it accordingly. A table comparing all existing methods in terms of pros and cons would be helpful to evaluate their method.

We are grateful for this suggestion and agree that a table comparing the pros and cons of all existing methods would be helpful for the general reader and also highlight the key advantages of our new approach. A table comparing previous methods for var gene and transcript characterisation has been added to the manuscript and is referenced in the introduction (line 107).

Author response table 1.

Comparison of previous var assembly approaches based on DNA- and RNA-sequencing.

Reviewer #3:

This work focuses on the important problem of how to access the highly polymorphic var gene family using short-read sequence data. The approach that was most successful, and utilized for all subsequent analyses, employed a different assembler from their prior pipeline, and impressively, more than doubles the N50 metric.

The authors then endeavor to utilize these improved assemblies to assess differential RNA expression of ex vivo and short-term cultured samples, and conclude that their results "cast doubt on the validity" of using short-term cultured parasites to infer in vivo characteristics. Readers should be aware that the various approaches to assess differential expression lack statistical clarity and appear to be contradictory. Unfortunately, there is no attempt to describe the rationale for the different approaches and how they might inform one another.

It is unclear whether adjusting for life-cycle stage as reported is appropriate for the var-only expression models. The methods do not appear to describe what type of correction variable (continuous/categorical) was used in each model, and there is no discussion of the impact on var vs. core transcriptome results.

We agree with the reviewer that the different methods and results of the var transcriptome analysis can be difficult to reconcile. To address this, we have included a summary table with a brief description of the rationale and results of each approach in our analysis pipeline.

Author response table 2.

Summary of the different levels of analysis performed to assess the effect of short-term parasite culturing on var and core gene expression, their rational, method, results, and interpretation.

Additionally, the var transcript differential expression analysis was removed from the manuscript, because this study was in a unique position to perform a more focused analysis of var transcriptional changes across paired samples, meaning the per-patient approach was more suitable. This allowed for changes in the var transcriptome to be identified that would have gone unnoticed in the traditional differential expression analysis.

We thank the reviewer for his highly important comment about adjusting for life cycle stage. Var gene expression is highly stage-dependent, so any quantitative comparison between samples does need adjustment for developmental stage. All life cycle stage adjustments were done using the mixture model proportions to be consistent with the original paper, described in the results and methods sections:

• Line 219–221: “Due to the potential confounding effect of differences in stage distribution on gene expression, we adjusted for developmental stage determined by the mixture model in all subsequent analyses.”

• Line 722–725: “Var gene expression is highly stage dependent, so any quantitative comparison between samples needs adjustment for developmental stage. The life cycle stage proportions determined from the mixture model approach were used for adjustment.“

The rank-expression analysis did not have adjustment for life cycle stage as the values were determined as a percentage contribution to the total var transcriptome. The var group level and the global var gene expression analyses were adjusted for life cycle stages, by including them as an independent variable, as described in the results and methods sections.

Var group expression:

• Line 321–326: “Due to these results, the expression of group A var genes vs. group B and C var genes was investigated using a paired analysis on all the DBLα (DBLα1 vs DBLα0 and DBLα2) and NTS (NTSA vs NTSB) sequences assembled from ex vivo samples and across multiple generations in culture. A linear model was created with group A expression as the response variable, the generation and life cycle stage as independent variables and the patient information included as a random effect. The same was performed using group B and C expression levels.“

• Line 784–787: “DESeq2 normalisation was performed, with patient identity and life cycle stage proportions included as covariates and differences in the amounts of var transcripts of group A compared with groups B and C assessed (Love et al., 2014). A similar approach was repeated for NTS domains.”

Gobal var gene expression:

• Line 342–347: “A linear model was created (using only paired samples from ex vivo and generation 1) (Supplementary file 1) with proportion of total gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as independent variables and the patient information included as a random effect. This model showed no significant differences between generations, suggesting that differences observed in the raw data may be a consequence of small changes in developmental stage distribution in culture.”

• Line 804–806: “Significant differences in total var gene expression were tested by constructing a linear model with the proportion of gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as an independent variables and the patient identity included as a random effect.“

The analysis of the conserved var gene expression was adjusted for life cycle stage:

• Line 766–768: “For each conserved gene, Salmon normalised read counts (adjusted for life cycle stage) were summed and expression compared across the generations using a pairwise Wilcoxon rank test.”

And life cycle stage estimates were included as covariates in the design matrix for the domain differential expression analysis:

• Line 771–773: “DESeq2 was used to test for differential domain expression, with five expected read counts in at least three patient isolates required, with life cycle stage and patient identity used as covariates.”

Reviewer #1:

1. In the legend to Figure 1, the authors cite "Deitsch and Hviid, 2004" for the classification of different var gene types. This is not the best reference for this work. Better citations would be Kraemer and Smith, Mol Micro, 2003 and Lavstsen et al, Malaria J, 2003.

We agree and have updated the legend in Figure 1 with these references, consistent with the references cited in the introduction.

1. In Figures 2 and 3, each of the boxes in the flow charts are largely filled with empty space while the text is nearly too small to read. Adjusting the size of the text would improve legibility.

We have increased the size of the text in these figures.

1. My understanding of the computational method for assessing global var gene expression indicates an initial step of identifying reads containing the amino acid sequence LARSFADIG. It is worth noting that VAR2CSA does not contain this motif. Will the pipeline therefore miss expression of this gene, and if so, how does this affect the assessment of global var gene assessment? This seems relevant given that the authors detect increased expression of var2csa during adaptation to culture.

To address this question, we have added an explanation in the methods section to better explain our analysis. Var2csa was not captured in the global var gene expression analysis, but was analyzed separately because of its unique properties (conservation, proposed role in regulating var gene switching, slightly divergent timing of expression, translational repression).

• Line 802/3: “Var2csa does not contain the LARSFADIG motif, hence this quantitative analysis of global var gene expression excluded var2csa (which was analysed separately).”

1. In Figures 4 and 7, panels a and b display virtually identical PCA plots, with the exception that panel A displays more generations. Why are both panels included? There doesn't appear to be any additional information provided by panel B.

We agree and have removed Figure 7b for the core transcriptome PCA as it did not provide any new information. The var transcript differential analysis (displayed in Figure 4) has been removed from the manuscript.

1. On line 560-567, the authors state "However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at higher levels." What are the high levels being referred to here?

We have replaced this sentence to make it clearer what the different levels are (global var gene expression, var domain and var type).

• Line 526/7: “However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at the var domain, var type and global var gene expression level.”

Reviewer #2:

The authors make no mention or assessment of previously published var gene assembly methods from clinical samples that focus on genomic or transcriptomic approaches. These include:

https://pubmed.ncbi.nlm.nih.gov/28351419/

https://pubmed.ncbi.nlm.nih.gov/34846163/

These methods should be compared to the method for var gene assembly outlined by the co-authors, especially as the authors say that their method "overcomes previous limitations and outperforms current methods" (128-129). The second reference above appears to be a method to measure var expression in clinical samples and so should be particularly compared to the approach outlined by the authors.

Thank you for pointing this out. We have included the second reference in the introduction of our revised manuscript, where we refer to var assembly and quantification from RNA-sequencing data. We abstained from including the first paper in this paragraph (Dara et al., 2017) as it describes a var gene assembly pipeline and not a var transcript assembly pipeline.

• Line 101–105: “While approaches for var assembly and quantification based on RNA-sequencing have recently been proposed (Wichers et al., 2021; Stucke et al., 2021; Andrade et al., 2020; TonkinHill et al., 2018, Duffy et al., 2016), these still produce inadequate assembly of the biologically important N-terminal domain region, have a relatively high number of misassemblies and do not provide an adequate solution for handling the conserved var variants (Table S1).”

Additionally, we have updated the manuscript with a table (Table S1) comparing these two methods plus other previously used var transcript/gene assembly approaches (see comment to the public reviews).

But to address this particular comment in more detail, the first paper (Dara et al., 2017) is a var gene assembly pipeline and not a var transcript assembly pipeline. It is based on assembling var exon 1 from unfished whole genome assemblies of clinical samples and requires a prior step for filtering out human DNA. The authors used two different assemblers, Celera for short reads (which is no longer maintained) and Sprai for long reads (>2000bp), but found that Celera performed worse than Sprai, and subsequently used Sprai assemblies. Therefore, this method does not appear to be suitable for assembling short reads from RNA-seq.

The second paper (Stucke et al. 2021) focusses more on enriching for parasite RNA, which precedes assembly. The capture method they describe would complement downstream analysis of var transcript assembly with our pipeline. Their assembly pipeline is similar to our pipeline as they also performed de novo assembly on all P. falciparum mapping and non-human mapping reads and used the same assembler (but with different parameters). They clustered sequences using the same approach but at 90% sequence identity as opposed to 99% sequence identity using our approach. Then, Stucke et al. use 500nt as a cut-off as opposed to the more stringent filtering approach used in our approach. They annotated their de novo assembled transcripts with the known amino acid sequences used in their design of the capture array; our approach does not assume prior information on the var transcripts. Finally, their approach was validated only for its ability to recover the most highly expressed var transcript in 6 uncomplicated malaria samples, and they did not assess mis-assemblies in their approach.

For the methods (619–621), were erythrocytes isolated by Ficoll gradient centrifugation at the time of collection or later?

We have updated the methods section to clarify this.

• Line 586–588: “Blood was drawn and either immediately processed (#1, #2, #3, #4, #11, #12, #14, #17, #21, #23, #28, #29, #30, #31, #32) or stored overnight at 4oC until processing (#5, #6, #7, #9, #10, #13, #15, #16, #18, #19, #20, #22, #24, #25, #26, #27, #33).”

Was the current pipeline and assembly method assessed for var chimeras? This should be described.

Yes, this was quantified in the Pf 3D7 dataset and also assessed in the German traveler dataset. For the 3D7 dataset it is described in the result section and Figure S1.

• Line 168–174: “However, we found high accuracies (> 0.95) across all approaches, meaning the sequences we assembled were correct (Figure 2 – Figure supplement 1b). The whole transcript approach also performed the best when assembling the lower expressed var genes (Figure 2 – Figure supplement 1e) and produced the fewest var chimeras compared to the original approach on P. falciparum 3D7. Fourteen misassemblies were observed with the whole transcript approach compared to 19 with the original approach (Table S2). This reduction in misassemblies was particularly apparent in the ring-stage samples.” - Figure S1:

Author response image 1.

Performance of novel computational pipelines for var assembly on Plasmodium falciparum 3D7: The three approaches (whole transcript: blue, domain approach: orange, original approach: green) were applied to a public RNA-seq dataset (ENA: PRJEB31535) of the intra-erythrocytic life cycle stages of 3 biological replicates of cultured P. falciparum 3D7, sampled at 8-hour intervals up until 40hrs post infection (bpi) and then at 4-hour intervals up until 48 (Wichers al., 2019). Boxplots show the data from the 3 biological replicates for each time point in the intra-erythrocytic life cycle: a) alignment scores for the dominantly expressed var gene (PF3D7_07126m), b) accuracy scores for the dominantly var gene (PF3D7_0712600), c) number of contigs to assemble the dominant var gene (PF3D7_0712600), d) alignment scores for a middle ranking expressed vargene (PF3D7_0937800), e) alignment scores for the lowest expressed var gene (PF3D7_0200100). The first best blast hit (significance threshold = le-10) was chosen for each contig. The alignment score was used to evaluate the each method. The alignment score represents √accuracy* recovery. The accuracy is the proportion of bases that are correct in the assembled transcript and the recovery reflects what proportion of the true transcript was assembled. Assembly completeness of the dominant vargene (PF3D7 071200, length = 6648nt) for the three approaches was assessed for each biological f) biological replicate 1, g) biological replicate 2, h) biological replicate 3. Dotted lines represent the start and end of the contigs required to assemble the vargene. Red bars represent assembled sequences relative to the dominantly whole vargene sequence, where we know the true sequence (termed “reference transcript”).

For the ex vivo samples, this has been discussed in the result section and now we also added this information to Table 1.

• Line 182/3: “Remarkably, with the new whole transcript method, we observed a significant decrease (2 vs 336) in clearly misassembled transcripts with, for example, an N-terminal domain at an internal position.”

• Table 1:

Author response table 3.

Statistics for the different approaches used to assemble the var transcripts. Var assembly approaches were applied to malaria patient ex vivo samples (n=32) from (Wichers et al., 2021) and statistics determined. Given are the total number of assembled var transcripts longer than 500 nt containing at least one significantly annotated var domain, the maximum length of the longest assembled var transcript in nucleotides and the N50 value, respectively. The N50 is defined as the sequence length of the shortest var contig, with all var contigs greater than or equal to this length together accounting for 50% of the total length of concatenated var transcript assemblies. Misassemblies represents the number of misassemblies for each approach. **Number of misassemblies were not determined for the domain approach due to its poor performance in other metrics.

Line 432: "the core gene transcriptome underwent a greater change relative to the var transcriptome upon transition to culture." Can this be shown statistically? It's unclear whether the difference in the sizes of the respective pools of the core genome and the var genes may account for this observation.

We found 19% of the core transcriptome to be differentially expressed. The per patient var transcript analysis revealed individually highly variable but generally rather subtle changes in the var transcriptome. The different methods for assessing this make it difficult to statistically compare these two different results.

The feasibility of this approach for field samples should be discussed in the Discussion.

In the original manuscript we reflected on this already several times in the discussion (e.g., line 465/6; line 471–475; line 555–568). We now have added another two sentences at the end of the paragraph starting in line 449 to address this point. It reads now:

• Line 442–451: “Our new approach used the most geographically diverse reference of var gene sequences to date, which improved the identification of reads derived from var transcripts. This is crucial when analysing patient samples with low parasitaemia where var transcripts are hard to assemble due to their low abundancy (Guillochon et al., 2022). Our approach has wide utility due to stable performance on both laboratory-adapted and clinical samples. Concordance in the different var expression profiling approaches (RNA-sequencing and DBLα-tag) on ex vivo samples increased using the new approach by 13%, when compared to the original approach (96% in the whole transcript approach compared to 83% in Wichers et al., 2021. This suggests the new approach provides a more accurate method for characterising var genes, especially in samples collected directly from patients. Ultimately, this will allow a deeper understanding of relationships between var gene expression and clinical manifestations of malaria.”

MINOR

The plural form of PfEMP1 (PfEMP1s) is inconsistently used throughout the text.

Corrected.

404-405: statistical test for significance?

Thank you for this suggestion. We have done two comparisons between the original analysis from Wichers et al., 2021 and our new whole transcript approach to test concordance of the RNAseq approaches with the DBLα-tag approach using paired Wilcoxon tests. These comparisons suggest that our new approach has significantly increased concordance with DBLα-tag data and might be better at capturing all expressed DBLα domains than the original analysis (and the DBLα-approach), although not statistically significant. We describe this now in the result section.

• Line 352–361: “Overall, we found a high agreement between the detected DBLα-tag sequences and the de novo assembled var transcripts. A median of 96% (IQR: 93–100%) of all unique DBLα-tag sequences detected with >10 reads were found in the RNA-sequencing approach. This is a significant improvement on the original approach (p= 0.0077, paired Wilcoxon test), in which a median of 83% (IQR: 79–96%) was found (Wichers et al., 2021). To allow for a fair comparison of the >10 reads threshold used in the DBLα-tag approach, the upper 75th percentile of the RNA-sequencingassembled DBLα domains were analysed. A median of 77.4% (IQR: 61–88%) of the upper 75th percentile of the assembled DBLα domains were found in the DBLα-tag approach. This is a lower median percentage than the median of 81.3% (IQR: 73–98%) found in the original analysis (p= 0.28, paired Wilcoxon test) and suggests the new assembly approach is better at capturing all expressed DBLα domains.”

Figure 4: The letters for the figure panels need to be added.

The figure has been removed from the manuscript.

Reviewer #3:

It is difficult from Table S2 to determine how many unique var transcripts would have enough coverage to be potentially assembled from each sample. It seems unlikely that 455 distinct vars (~14 per sample) would be expressed at a detectable level for assembly. Why not DNA-sequence these samples to get the full repertoire for comparison to RNA? Why would so many distinct transcripts be yielded from fairly synchronous samples?

We know from controlled human malaria infections of malaria-naive volunteers, that most var genes present in the genomic repertoire of the parasite strain are expressed at the onset of the human blood phase (heterogenous var gene expression) (Wang et al., 2009; Bachmann et al, 2016; Wichers-Misterek et al., 2023). This pattern shifts to a more restricted, homogeneous var expression pattern in semi-immune individuals (expression of few variants) depending on the degree of immunity (Bachmann et al., 2019).

Author response image 2.

In this cohort, 15 first-time infections are included, which should also possess a more heterogenous var gene expression in comparison to the pre-exposed individuals, and indeed such a trend is already seen in the number of different DBLa-tag clusters found in both patient groups (see figure panel from Wichers et al. 2021: blue-first-time infections; grey–pre-exposed). Moreover, Warimwe et al. 2013 have shown that asymptomatic infections have a more homogeneous var expression in comparison to symptomatic infections. Therefore, we expect that parasites from symptomatic infections have a heterogenous var expression pattern with multiple var gene variants expressed, which we could assemble due to our high read depth and our improved var assembly pipeline for even low expressed variants.

Moreover, the distinct transcripts found in the RNA-seq approach were confirmed with the DBLα tag data. To our opinion, previous approaches may have underestimated the complexity of the var transcriptome in less immune individuals.

Mapping reads to these 455 putative transcripts and using this count matrix for differential expression analysis seems very unlikely to produce reliable results. As acknowledged on line 327, many reads will be mis-mapped, and perhaps most challenging is that most vars will not be represented in most samples. In other words, even if mapping were somehow perfect, one would expect a sparse matrix that would not be suitable for statistical comparisons between groups. This is likely why the per-patient transcript analysis doesn't appear to be consistent. I would recommend the authors remove the DE sections utilizing this approach, or add convincing evidence that the count matrix is useable.

We agree that this is a general issue of var differential expression analysis. Therefore, we have removed the var differential expression analysis from this manuscript as the per patient approach was more appropriate for the paired samples. We validated different mapping strategies (new Figure S6) and included a paragraph discussing the problem in the result section:

• Line 237–255: “In the original approach of Wichers et al., 2021, the non-core reads of each sample used for var assembly were mapped against a pooled reference of assembled var transcripts from all samples, as a preliminary step towards differential var transcript expression analysis. This approach returned a small number of var transcripts which were expressed across multiple patient samples (Figure 3 – Figure supplement 2a). As genome sequencing was not available, it was not possible to know whether there was truly overlap in var genomic repertoires of the different patient samples, but substantial overlap was not expected. Stricter mapping approaches (for example, excluding transcripts shorter than 1500nt) changed the resulting var expression profiles and produced more realistic scenarios where similar var expression profiles were generated across paired samples, whilst there was decreasing overlap across different patient samples (Figure 3 – Figure supplement 2b,c). Given this limitation, we used the paired samples to analyse var gene expression at an individual subject level, where we confirmed the MSP1 genotypes and alleles were still present after short-term in vitro cultivation. The per patient approach showed consistent expression of var transcripts within samples from each patient but no overlap of var expression profiles across different patients (Figure 3 – Figure supplement 2d). Taken together, the per patient approach was better suited for assessing var transcriptional changes in longitudinal samples. It has been hypothesised that more conserved var genes in field isolates increase parasite fitness during chronic infections, necessitating the need to correctly identify them (Dimonte et al., 2020, Otto et al., 2019). Accordingly, further work is needed to optimise the pooled sample approach to identify truly conserved var transcripts across different parasite isolates in cross-sectional studies.” - Figure S6:

Author response image 3.

Var expression profiles across different mapping. Different mapping approaches Were used to quantify the Var expression profiles of each sample (ex Vivo (n=13), generation I (n=13), generation 2 (n=10) and generation 3 (n=l). The pooled sample approach in Which all significantly assembled van transcripts (1500nt and containing3 significantly annotated var domains) across samples were combined into a reference and redundancy was removed using cd-hit (at sequence identity = 99%) (a—c). The non-core reads of each sample were mapped to this pooled reference using a) Salmon, b) bowtie2 filtering for uniquely mapping paired reads with MAPQ and c) bowtie2 filtering for uniquely mapping paired reads with a MAPQ > 20. d) The per patient approach was applied. For each patient, the paired ex vivo and in vitro samples were analysed. The assembled var transcripts (at least 1500nt and containing3 significantly annotated var domains) across all the generations for a patient were combined into a reference, redundancy was removed using cd-hit (at sequence identity: 99%), and expression was quantified using Salmon. Pie charts show the var expression profile With the relative size of each slice representing the relative percentage of total var gene expression of each var transcript. Different colours represent different assembled var transcripts with the same colour code used across a-d.

For future cross-sectional studies a per patient analysis that attempts to group per patient assemblies on some unifying structure (e.g., domain, homology blocks, domain cassettes etc) should be performed.

Line 304. I don't understand the rationale for comparing naïve vs. prior-exposed individuals at ex-vivo and gen 1 timepoints to provide insights into how reliable cultured parasites are as a surrogate for var expression in vivo. Further, the next section (per patient) appears to confirm the significant limitation of the 'all sample analysis' approach. The conclusion on line 319 is not supported by the results reported in figures S9a and S9b, nor is the bold conclusion in the abstract about "casting doubt" on experiments utilizing culture adapted

We have removed this comparison from the manuscript due to the inconsistencies with the var per patient approach. However, the conclusion in the abstract has been rephrased to reflect the fact we observed 19% of the core transcript differentially expressed within one cycle of cultivation.

Line 372/391 (and for the other LMM descriptions). I believe you mean to say response variable, rather than explanatory variable. Explanatory variables are on the right hand side of the equation.

Thank you for spotting this inaccuracy, we changed it to “response variable” (line 324, line 343, line 805).

Line 467. Similar to line 304, why would comparisons of naïve vs. prior-exposed be informative about surrogates for in vivo studies? Without a gold-standard for what should be differentially expressed between naïve and prior-exposed in vivo, it doesn't seem prudent to interpret a drop in the number of DE genes for this comparison in generation 1 as evidence that biological signal for this comparison is lost. What if the generation 1 result is actually more reflective of the true difference in vivo, but the ex vivo samples are just noisy? How do we know? Why not just compare ex vivo vs generation 1/2 directly (as done in the first DE analysis), and then you can comment on the large number of changes as samples are less and less proximal to in vivo?

In the original paper (Wichers et al., 2021), there were differences between the core transcriptome of naïve vs previously exposed patients. However, these differences appeared to diminish in vitro, suggesting the in vivo core transcriptome is not fully maintained in vitro.

We have added a sentence explaining the reasoning behind this analysis in the results section:

• Lines 414–423: “In the original analysis of ex vivo samples, hundreds of core genes were identified as significantly differentially expressed between pre-exposed and naïve malaria patients. We investigated whether these differences persisted after in vitro cultivation. We performed differential expression analysis comparing parasite isolates from naïve (n=6) vs pre-exposed (n=7) patients, first between their ex vivo samples, and then between the corresponding generation 1 samples. Interestingly, when using the ex vivo samples, we observed 206 core genes significantly upregulated in naïve patients compared to pre-exposed patients (Figure 7 – Figure supplement 3a). Conversely, we observed no differentially expressed genes in the naïve vs pre-exposed analysis of the paired generation 1 samples (Figure 7 – Figure supplement 3b). Taken together with the preceding findings, this suggests one cycle of cultivation shifts the core transcriptomes of parasites to be more alike each other, diminishing inferences about parasite biology in vivo.”

Overall, I found the many DE approaches very frustrating to interpret coherently. If not dropped in revision, the reader would benefit from a substantial effort to clarify the rationale for each approach, and how each result fits together with the other approaches and builds to a concise conclusion.

We agree that the manuscript contains many different complex layers of analysis and that it is therefore important to explain the rationale for each approach. Therefore, we now included the summary Table 3 (see comment to public review). Additionally, we have removed the var transcript differential expression due to its limitations, which we hope has already streamlined our manuscript.

Author Response

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

We sincerely thank the reviewers for their in-depth consideration of our manuscript and their helpful reviews. Their efforts have made the paper much better. We have responded to each point. The previously provided public responses have been updated they are included after the private response for convenience.

Reviewer #1 (Recommendations For The Authors):

1. In general, the manuscript will benefit from copy editing and proof reading. Some obvious edits;

2. Page 6 line 140. Do the authors mean Cholera toxin B?

Response: We corrected this error and went through the entire paper carefully correcting for grammar and increased clarity.

• Page 8 line 173. Methylbetacyclodextrin is misspelled.

Response: Yes, corrected.

• Figure 4c is missing representative traces for electrophysiology data.

• Figure 4. Please check labeling ordering in figure legend as it does not match the panels in the figure.

Thank you for the correction and we apologize for the confusion in figure 4. We uploaded an incomplete figure legend, and the old panel ‘e’ was not from an experiment that was still in the figure. It was removed and the figure legends are now corrected.

• Please mention the statistical analysis used in all figure legends.

Response: Thank you for pointing out this omission, statistics have been added.

• Although the schematics in each figure helps guide readers, they are very inconsistent and sometimes confusing. For example, in Figure 5 the gating model is far-reaching without conclusive evidence, whereas in Figure 6 it is over simplified and unclear what the image is truly representing (granted that the downstream signaling mechanism and channel is not known).

Response: Figure 5d is the summary figure for the entire paper. We have made this clearer in the figure legend and we deleted the title above the figure that gave the appearance that the panel relates to swell only. It is the proposed model based on what we show in the paper and what is known about the activation mechanism of TREK-1.

Figure 6 is supposed to be simple. It is to help the reader understand that when PA is low mechanical sensitivity is high. Without the graphic, previous reviewers got confused about threshold going down and mechanosensitivity going up and how the levels of PA relate. Low PA= high sensitivity. We’ve added a downstream effector to the right side of the panel to avoid any biased to a putative downstream channel effector. The purpose of the experiment is to show PLD has a mechanosensitive phenotype in vivo.

Reviewer #2 (Recommendations For The Authors):

This manuscript outlines some really interesting findings demonstrating a mechanism by which mechanically driven alterations in molecular distributions can influence a) the activity of the PLD2 molecule and subsequently b) the activation of TREK-1 when mechanical inputs are applied to a cell or cell membrane.

The results presented here suggest that this redistribution of molecules represents a modulatory mechanism that alters either the amplitude or the sensitivity of TREK-1 mediated currents evoked by membrane stretch. While the authors do present values for the pressure required to activate 50% of channels (P50), the data presented provides incomplete evidence to conclude a shift in threshold of the currents, given that many of the current traces provided in the supplemental material do not saturate within the stimulus range, thus limiting the application of a Boltzmann fit to determine the P50. I suggest adding additional context to enable readers to better assess the limitations of this use of the Boltzmann fit to generate a P50, or alternately repeating the experiments to apply stimuli up to lytic pressures to saturate the mechanically evoked currents, enabling use of the Boltzmann function to fit the data.

Response: We thank the reviewer for pointing this out. We agree the currents did not reach saturation. Hence the term P50 could be misleading, so we have removed it from the paper. We now say “half maximal” current measured from non-saturating pressures of 0-60 mmHg. We also deleted the xPLD data in supplemental figure 3C since there is insufficient current to realistically estimate a half maximal response.

In my opinion, the conclusions presented in this manuscript would be strengthened by an assessment of the amount of TREK-1 in the plasma membrane pre and post application of shear. While the authors do present imaging data in the supplementary materials, these data are insufficiently precise to comment on expression levels in the membrane. To strengthen this conclusion the authors could conduct cell surface biotinylation assays, as a more sensitive and quantitative measure of membrane localisation of the proteins of interest.

1. Response: as mentioned previously, we do not have an antibody to the extracellular domain. Nonetheless to better address this concern we directly compared the levels of TREK-1, PIP2, and GM1; in xPLD2, mPLD2, enPLD2 with and without shear. The results are in supplemental figure 2. PLD2 is known to increase endocytosis1 and xPLD2 is known to block both agonist induced and constitutive endocytosis of µ-opioid receptor2. The receptor is trapped on the surface. This is true of many proteins including Rho3, ARF4, and ACE21 among others. In agreement with this mechanism, in Figure S2C,G we show that TREK increases with xPLD and the localization can clearly be seen at the plasma membrane just like in all of the other publications with xPLD overexpression. xPLD2 would be expected to inhibit the basal current but we presume the increased expression likely has compensated and there is sufficient PA and PG from other sources to allow for the basal current. It is in this state that we then conduct our ephys and monitor with a millisecond time resolution and see no activation. We are deriving conclusion from a very clear response—Figure 1b shows almost no current, even at 1-10 ms after applying pressure. There is little pressure current when we know the channel is present and capable of conducting ion (Figure 1d red bar). After shear there is a strong decrease in TREK-1 currents on the membrane in the presence of xPLD2. But it is not less than TREK-1 expression with mPLD2. And since mouse PLD2 has the highest basal current and pressure activation current. The amount of TREK-1 present is sufficient to conduct large current. To have almost no detective current would require at least a 10 fold reduction compared to mPLD2 levels before we would lack the sensitivity to see a channel open. Lasty endocytosis typically in on the order of seconds to minutes, no milliseconds.

2. We have shown an addition 2 independent ways that TREK-1 is on the membrane during our stretch experiments. Figure 1d shows the current immediately prior to applying pressure for wt TREK-1. When catalytically dead PLD is present (xPLD2) there is almost normal basal current. The channel is clearly present. And then in figure 1a we show within a millisecond there is no pressure current. As a control we added a functionally dead TREK-1 truncation (xTREK). Compared to xPLD2 there is clearly normal basal current. If this is not strong evidence the channel was available on the surface for mechanical activation please help us understand why. And if you think within 2.1 ms 100% of the channel is gone by endocytosis please provide some evidence that this is possible so we can reconsider.

3. We have TIRF super resolution imaging with ~20 nm x-y resolution and ~ 100nm z resolution and Figure 2b clearly shows the channel on the membrane. When we apply pressure in 1b, the channel is present.

4. Lastly, In our previous studies we showed activation of PLD2 by anesthetics was responsible for all of TREK-1’s anesthetic sensitivity and this was through PLD2 binding to the C-terminus of TREK-15. We showed this was the case by transferring anesthetic sensitivity to an anesthetic insensitive homolog TRAAK. This established conclusively the basic premise of our mechanism. Here we show the same C-terminal region and PLD2 are responsible for the mechanical current observed by TREK-1. TRAAK is already mechanosensitive so the same chimera will not work for our purposes here. But anesthetic activation and mechanical activation are dramatically different stimuli, and the fact that the role of PLD is robustly observed in both should be considered.

The authors discuss that the endogenous levels of TREK-1 and PLD2 are "well correlated: in C2C12 cells, that TREK-1 displayed little pair correlation with GM1 and that a "small amount of TREK-1 trafficked to PIP2". As such, these data suggest that the data outlined for HEK293T cells may be hampered by artefacts arising from overexpression. Can TREK-1 currents be activated by membrane stretch in these cells C2C12 cells and are they negatively impacted by the presence of xPLD2? Answering this question would provide more insight into the proposed mechanism of action of PLD2 outlined by the authors in this manuscript. If no differences are noted, the model would be called into question. It could be that there are additional cell-specific factors that further regulate this process.

Response: The low pair correlation of TREK-1 and GM1 in C2C12 cells was due to insufficient levels of cholesterol in the cell membrane to allow for robust domain formation. In Figure 4b we loaded C2C12 cells with cholesterol using the endogenous cholesterol transport protein apoE and serum (an endogenous source of cholesterol). As can be seen in Fig. 4b, the pair correlation dramatically increased (purple line). This was also true in neuronal cells (N2a) (Fig 4d, purple bar). And shear (3 dynes/cm2) caused the TREK-1 that was in the GM1 domains to leave (red bar) reversing the effect of high cholesterol. This demonstrates our proposed mechanism is working as we expect with endogenously expressed proteins.

There are many channels in C2C12 cells, it would be difficult to isolate TREK-1 currents, which is why we replicated the entire system (ephys and dSTORM) in HEK cells. Note, in figure 4c we also show that adding cholesterol inhibits TREK-1 whole cell currents in HEK293cells.

As mentioned in the public review, the behavioural experiments in D. melanogaster can not solely be attributed to a change in threshold. While there may be a change in the threshold to drive a different behaviour, the writing is insufficiently precise to make clear that conclusions cannot be drawn from these experiments regarding the functional underpinnings of this outcome. Are there changes in resting membrane potential in the mutant flys? Alterations in Nav activity? Without controlling for these alternate explanations it is difficult to see what this last piece of data adds to the manuscript, particularly given the lack of TREK-1 in this organism. At the very least, some editing of the text to more clearly indicate that these data can only be used to draw conclusions on the change in threshold for driving the behaviour not the change in threshold of the actual mechanotransduction event (i.e. conversion of the mechanical stimulus into an electrochemical signal).

Response: We agree; features other than PLDs direct mechanosensitivity are likely contributing. This was shown in figure 6g left side. We have an arrow going to ion channel and to other downstream effectors. We’ve added the putative alteration to downstream effectors to the right side of the panel. This should make it clear that we no more speculate the involvement of a channel than any of the other many potential downstream effectors. As mentioned above, the figure helps the reader coordinate low PA with increased mechanosensitivity. Without the graphic reviewers got confused that PA increased the threshold which corresponds to a decreased sensitivity to pain. Nonetheless we removed our conclusion about fly thresholds from the abstract and made clearer in the main text the lack of mechanism downstream of PLD in flies including endocytosis. Supplemental Figure S2H also helps emphasize this. .

Nav channels are interesting, and since PLD contribute to endocytosis and Nav channels are also regulated by endocytosis there is likely a PLD specific effect using Nav channels. There are many ways PA likely regulates mechanosensitive thresholds, but we feel Nav is beyond the scope of our paper. Someone else will need to do those studies. We have amended a paragraph in the conclusion which clearly states we do not know the specific mechanism at work here with the suggestions for future research to discover the role of lipid and lipid-modifying enzymes in mechanosensitive neurons.

There may be fundamental flaws in how the statistics have been conducted. The methods section indicates that all statistical testing was performed with a Student's t-test. A visual scan of many of the data sets in the figures suggests that they are not normally distributed, thus a parametric test such as a Student's t-test is not valid. The authors should assess if each data set is normally distributed, and if not, a non-parametric statistical test should be applied. I recommend assessing the robustness of the statistical analyses and adjusting as necessary.

Response: We thank the reviewer for pointing this out, indeed there is some asymmetry in Figure 6C-d. The p values with Mann Whitney were slightly improved p=0.016 and p=0.0022 for 6c and 6d respectively. For reference, the students t-test had slightly worse statistics p=0.040 and p=0.0023. The score remained the same 1 and 2 stars respectively.

The references provided for the statement regarding cascade activation of the TRPs are incredibly out of date. While it is clear that TRPV4 can be activated by a second messenger cascade downstream of osmotic swelling of cells, TRPV4 has also been shown to be activated by mechanical inputs at the cell-substrate interface, even when the second messenger cascade is inhibited. Recommend updating the references to reflect more current understanding of channel activation.

Response: We thank the reviewer for pointing this out. We have updated the references and changed the comment to “can be” instead of “are”. The reference is more general to multiple ion channel types including KCNQ4. This should avoid any perceived conflict with the cellsubstrate interface mechanism which we very much agree is a correct mechanism for TRP channels.

Minor comments re text editing etc:

The central messages of the manuscript would benefit from extensive work to increase the precision of the writing of the manuscript and the presentation of data in the figures, such textual changes alone would help address a number of the concerns outlined in this review, by clarifying some ambiguities. There are numerous errors throughout, ranging from grammatical issues, ambiguities with definitions, lack of scale bars in images, lack of labels on graph axes, lack of clarity due to the mode of presentation of sample numbers (it would be far more precise to indicate specific numbers for each sample rather than a range, which is ambiguous and confusing), unnecessary and repeat information in the methods section. Below are some examples but this list is not exhaustive.

Response: Thank you, reviewer # 1 also had many of these concerns. We have gone through the entire paper and improved the precision of the writing of the manuscript. We have also added the missing error bar to Figure 6. And axis labels have been added to the inset images. The redundancy in cell culture methods has been removed. Where a range is small and there are lots of values, the exact number of ‘n’ are graphically displayed in the dot plot for each condition.

Text:

I recommend considering how to discuss the various aspects of channel activation. A convention in the field is to use mechanical activation or mechanical gating to describe that process where the mechanical stimulus is directly coupled to the channel gating mechanism. This would be the case for the activation of TREK-1 by membrane stretch alone. The increase in activation by PLD2 activity then reflects a modulation of the mechanical activation of the channel, because the relevant gating stimulus is PA, rather than force/stretch. The sum of these events could be described as shear-evoked or mechanically-evoked, TREK-1 mediated currents (thus making it clear that the mechanical stimulus initiates the relevant cascade, but the gating stimulus may be other than direct mechanical input.) Given the interesting and compelling data offered in this manuscript regarding the sensitisation of TREK-1 dependent mechanicallyevoked currents by PLD2, an increase in the precision of the language would help convey the central message of this work.

Response; We agree there needs to be convention. We have taken the suggestion of mechanically evoked and we suggest the following definitions:

1. Mechanical activation of PLD2: direct force on the lipids releasing PLD2 from nonactivating lipids.

2. Mechanical activation/gating of TREK1: direct force from lipids from either tension or hydrophobic mismatch that opens the channel.

3. Mechanically evoked: a mechanical event that leads to a downstream effect. The effect is mechanically “evoked”.

4. Spatial patterning/biochemistry: nanoscopic changes in the association of a protein with a nanoscopic lipid cluster or compartment.

An example of where discussion of mechanical activation is ambiguous in the text is found at line 109: "channel could be mechanically activated by a movement from GM1 to PIP2 lipids." In this case, the sentence could be suggesting that the movement between lipids provides the mechanical input that activates the channel, which is not what the data suggest.

Response: Were possible we have replaced “movement” with “spatial patterning” and “association” and “dissociation” from specific lipid compartment. This better reflects the data we have in this paper. However, we do think that a movement mechanically activates the channel, GM1 lipids are thick and PIP2 lipids are thin, so movement between the lipids could activate the channel through direct lipid interaction. We will address this aspect in a future paper.

Inconsistencies with usage:

• TREK1 versus TREK-1

Response: corrected to TREK-1

• mPLD2 versus PLD2

Response: where PLD2 represents mouse this has been corrected.

• K758R versus xPLD2

Response: we replaced K758R in the methods with xPLD2.

• HEK293T versus HEK293t Response: we have changed all instances to read HEK293T.

• Drosophila melanogaster and D. melanogaster used inconsistently and in many places incorrectly

Response: we have read all to read the common name Drosophila.

Line 173: misspelled methylbetacyclodextrin

Response corrected

Line 174: degree symbol missing

Response corrected

Line 287: "the decrease in cholesterol likely evolved to further decrease the palmate order in the palmitate binding site"... no evidence, no support for this statement, falsely attributes intention to evolutionary processes .

Response: we have removed the reference to evolution at the request of the reviewer, it is not necessary. But we do wish to note that to our knowledge, all biological function is scientifically attributed to evolution. The fact that cholesterol decreases in response to shear is evidence alone that the cell evolved to do it.

Line 307: grammatical error

Response: the redundant Lipid removed.

Line 319: overinterpreted - how is the mechanosensitivy of GPCRs explained by this translocation?

Response: all G-alpha subunits of the GPCR complex are palmitoylated. We showed PLD (which has the same lipidation) is mechanically activated. If the palmitate site is disrupted for PLD2, then it is likely disrupted for every G-alpha subunit as well.

Line 582: what is the wild type referred to here?

Response: human full length with a GFP tag.

Methods:

• Sincere apologies if I missed something but I do not recall seeing any experiments using purified TREK-1 or flux assays. These details should be removed from the methods section

Response: Removed.

• There is significant duplication of detail across the methods (three separate instances of electrophysiology details) these could definitely be consolidated.

Response: Duplicates removed.

Figures:

• Figure 2- b box doesn't correspond to inset. Bottom panel should provide overview image for the cell that was assessed with shear. In bottom panel, circle outlines an empty space.

Response: We have widened the box slightly to correspond so the non shear box corresponds to the middle panel. We have also added the picture for the whole cell to Fig S2g and outlined the zoom shown in the bottom panel of Fig 2b as requested. The figure is of the top of a cell. We also added the whole cell image of a second sheared cell.

Author response image 1.

• Figure 3 b+c: inset graph lacking axis labels

Response; the inset y axis is the same as the main axis. We added “pair corr. (5nM)” and a description in the figure legend to make this clearer. The purpose of the inset is to show statistical significance at a single point. The contrast has been maximized but without zooming in points can be difficult to see.

• Figure 5: replicate numbers missing and individual data points lacking in panels b + c, no labels of curve in b + c, insets, unclear what (5 nm) refers to in insets.

Response: Thank you for pointing out these errors. The N values have been added. Similar to figure 3, the inset is a bar graph of the pair correlation data at 5 nm. A better explanation of the data has been added to the figure legend.

• Figure 6: no scale bar, no clear membrane localization evident from images presented, panel g offers virtually nothing in terms of insight

Response: We have added scale bars to figure 6b. Figure 6g is intentionally simplistic, we found that correlating decreased threshold with increased pain was confusing. A previous reviewer claimed our data was inconsistent. The graphic avoids this confusion. We also added negative effects of low PA on downstream effects to the right panel. This helps graphically show we don’t know the downstream effects.

Reviewer #3 (Recommendations For The Authors):

Minor suggestions:

1. line 162, change 'heat' to 'temperature'.

Response: changed.

1. in figure 1, it would be helpful to keep the unit for current density consistent among different panels. 1e is a bit confusing: isn't the point of Figure 1 that most of TREK1 activation is not caused by direct force-sensing?

Response: Yes, the point of figure 1 is to show that in a biological membrane over expressed TREK-1 is a downstream effector of PLD2 mechanosensation which is indirect. We agree the figure legend in the previous version of the paper is very confusing.

There is almost no PLD2 independent current in our over expressed system, which is represented by no ions in the conduction pathway of the channel despite there being tension on the membrane.

Purified TREK-1 is only mechanosensitive in a few select lipids, primarily crude Soy PC. It was always assumed that HEK293 and Cos cells had the correct lipids since over expressed TREK-1 responded to mechanical force in these lipids. But that does not appear to be correct, or at least only a small amount of TREK-1 is in the mechanosensitive lipids. Figure 1e graphically shows this. The arrows indicate tension, but the channel isn’t open with xPLD2 present. We added a few sentences to the discussion to further clarify.

Panels c has different units because the area of the tip was measured whereas in d the resistance of the tip was measured. They are different ways for normalizing for small differences in tip size.

1. line 178, ~45 of what?

Response: Cells were fixed for ~30 sec.

1. line 219 should be Figure 4f?

Response: thank you, yes Figure 4f.

Previous public reviews with minor updates.

Reviewer #1 (Public Review):

Force sensing and gating mechanisms of the mechanically activated ion channels is an area of broad interest in the field of mechanotransduction. These channels perform important biological functions by converting mechanical force into electrical signals. To understand their underlying physiological processes, it is important to determine gating mechanisms, especially those mediated by lipids. The authors in this manuscript describe a mechanism for mechanically induced activation of TREK-1 (TWIK-related K+ channel. They propose that force induced disruption of ganglioside (GM1) and cholesterol causes relocation of TREK-1 associated with phospholipase D2 (PLD2) to 4,5-bisphosphate (PIP2) clusters, where PLD2 catalytic activity produces phosphatidic acid that can activate the channel. To test their hypothesis, they use dSTORM to measure TREK-1 and PLD2 colocalization with either GM1 or PIP2. They find that shear stress decreases TREK-1/PLD2 colocalization with GM1 and relocates to cluster with PIP2. These movements are affected by TREK-1 C-terminal or PLD2 mutations suggesting that the interaction is important for channel re-location. The authors then draw a correlation to cholesterol suggesting that TREK-1 movement is cholesterol dependent. It is important to note that this is not the only method of channel activation and that one not involving PLD2 also exists. Overall, the authors conclude that force is sensed by ordered lipids and PLD2 associates with TREK-1 to selectively gate the channel. Although the proposed mechanism is solid, some concerns remain.

1) Most conclusions in the paper heavily depend on the dSTORM data. But the images provided lack resolution. This makes it difficult for the readers to assess the representative images.

Response: The images were provided are at 300 dpi. Perhaps the reviewer is referring to contrast in Figure 2? We are happy to increase the contrast or resolution.

As a side note, we feel the main conclusion of the paper, mechanical activation of TREK-1 through PLD2, depended primarily on the electrophysiology in Figure 1b-c, not the dSTORM. But both complement each other.

2) The experiments in Figure 6 are a bit puzzling. The entire premise of the paper is to establish gating mechanism of TREK-1 mediated by PLD2; however, the motivation behind using flies, which do not express TREK-1 is puzzling.

Response: The fly experiment shows that PLD mechanosensitivity is more evolutionarily conserved than TREK-1 mechanosensitivity. We have added this observation to the paper.

-Figure 6B, the image is too blown out and looks over saturated. Unclear whether the resolution in subcellular localization is obvious or not.

Response: Figure 6B is a confocal image, it is not dSTORM. There is no dSTORM in Figure 6. We have added the error bars to make this more obvious. For reference, only a few cells would fit in the field of view with dSTORM.

-Figure 6C-D, the differences in activity threshold is 1 or less than 1g. Is this physiologically relevant? How does this compare to other conditions in flies that can affect mechanosensitivity, for example?

Response: Yes, 1g is physiologically relevant. It is almost the force needed to wake a fly from sleep (1.2-3.2g). See ref 33. Murphy Nature Pro. 2017.

3) 70mOsm is a high degree of osmotic stress. How confident are the authors that a cell health is maintained under this condition and b. this does indeed induce membrane stretch? For example, does this stimulation activate TREK-1?

Response: Yes, osmotic swell activates TREK1. This was shown in ref 19 (Patel et al 1998). We agree the 70 mOsm is a high degree of stress. This needs to be stated better in the paper.

Reviewer #2 (Public Review):

This manuscript by Petersen and colleagues investigates the mechanistic underpinnings of activation of the ion channel TREK-1 by mechanical inputs (fluid shear or membrane stretch) applied to cells. Using a combination of super-resolution microticopy, pair correlation analysis and electrophysiology, the authors show that the application of shear to a cell can lead to changes in the distribution of TREK-1 and the enzyme PhospholipaseD2 (PLD2), relative to lipid domains defined by either GM1 or PIP2. The activation of TREK-1 by mechanical stimuli was shown to be sensi>zed by the presence of PLD2, but not a catalytically dead xPLD2 mutant. In addition, the activity of PLD2 is increased when the molecule is more associated with PIP2, rather than GM1 defined lipid domains. The presented data do not exclude direct mechanical activation of TREK-1, rather suggest a modulation of TREK-1 activity, increasing sensitivity to mechanical inputs, through an inherent mechanosensitivity of PLD2 activity. The authors additionally claim that PLD2 can regulate transduction thresholds in vivo using Drosophila melanogaster behavioural assays. However, this section of the manuscript overstates the experimental findings, given that it is unclear how the disruption of PLD2 is leading to behavioural changes, given the lack of a TREK-1 homologue in this organism and the lack of supporting data on molecular function in the relevant cells.

Response: We agree, the downstream effectors of PLD2 mechanosensitivity are not known in the fly. Other anionic lipids have been shown to mediate pain see ref 46 and 47. We do not wish to make any claim beyond PLD2 being an in vivo contributor to a fly’s response to mechanical force. We have removed the speculative conclusions about fly thresholds from the abstract.

That said we do believe we have established a molecular function at the cellular level. We showed PLD is robustly mechanically activated in a cultured fly cell line (BG2-c2) Figure 6a of the manuscript. And our previous publication established mechanosensation of PLD (Petersen et. al. Nature Com 2016) through mechanical disruption of the lipids. At a minimum, the experiments show PLDs mechanosensitivity is evolutionarily better conserved across species than TREK1.

This work will be of interest to the growing community of scientists investigating the myriad mechanisms that can tune mechanical sensitivity of cells, providing valuable insight into the role of functional PLD2 in sensi>zing TREK-1 activation in response to mechanical inputs, in some cellular systems.

The authors convincingly demonstrate that, post application of shear, an alteration in the distribution of TREK-1 and mPLD2 (in HEK293T cells) from being correlated with GM1 defined domains (no shear) to increased correlation with PIP2 defined membrane domains (post shear). These data were generated using super-resolution microticopy to visualise, at sub diffraction resolution, the localisation of labelled protein, compared to labelled lipids. The use of super-resolution imaging enabled the authors to visualise changes in cluster association that would not have been achievable with diffraction limited microticopy. However, the conclusion that this change in association reflects TREK-1 leaving one cluster and moving to another overinterprets these data, as the data were generated from sta>c measurements of fixed cells, rather than dynamic measurements capturing molecular movements.

When assessing molecular distribution of endogenous TREK-1 and PLD2, these molecules are described as "well correlated: in C2C12 cells" however it is challenging to assess what "well correlated" means, precisely in this context. This limitation is compounded by the conclusion that TREK-1 displayed little pair correlation with GM1 and the authors describe a "small amount of TREK-1 trafficked to PIP2". As such, these data may suggest that the findings outlined for HEK293T cells may be influenced by artefacts arising from overexpression.

The changes in TREK-1 sensitivity to mechanical activation could also reflect changes in the amount of TREK-1 in the plasma membrane. The authors suggest that the presence of a leak currently accounts for the presence of TREK-1 in the plasma membrane, however they do not account for whether there are significant changes in the membrane localisation of the channel in the presence of mPLD2 versus xPLD2. The supplementary data provide some images of fluorescently labelled TREK-1 in cells, and the authors state that truncating the c-terminus has no effect on expression at the plasma membrane, however these data provide inadequate support for this conclusion. In addition, the data reporting the P50 should be noted with caution, given the lack of saturation of the current in response to the stimulus range.

Response: We thank the reviewer for his/her concern about expression levels. We did test TREK-1 expression. mPLD decreases TREK-1 expression ~two-fold (see Author response image 2 below). We did not include the mPLD data since TREK-1 was mechanically activated with mPLD. For expression to account for the loss of TREK-1 stretch current (Figure 1b), xPLD would need to block surface expression of TREK-1 prior to stretch. The opposite was true, xPLD2 increased TREK-1 expression (see Figure S2c). Furthermore, we tested the leak current of TREK-1 at 0 mV and 0 mmHg of stretch. Basal leak current was no different with xPLD2 compared to endogenous PLD (Figure 1d; red vs grey bars respectively) suggesting TREK-1 is in the membrane and active when xPLD2 is present. If anything, the magnitude of the effect with xPLD would be larger if the expression levels were equal.

Author response image 2.

TREK expression at the plasma membrane. TREK-1 Fluorescence was measured by GFP at points along the plasma membrane. Over expression of mouse PLD2 (mPLD) decrease the amount of full-length TREK-1 (FL TREK) on the surface more than 2-fold compared to endogenously expressed PLD (enPLD) or truncated TREK (TREKtrunc) which is missing the PLD binding site in the C-terminus. Over expression of mPLD had no effect on TREKtrunc.

Finally, by manipulating PLD2 in D. melanogaster, the authors show changes in behaviour when larvae are exposed to either mechanical or electrical inputs. The depletion of PLD2 is concluded to lead to a reduction in activation thresholds and to suggest an in vivo role for PA lipid signaling in setting thresholds for both mechanosensitivity and pain. However, while the data provided demonstrate convincing changes in behaviour and these changes could be explained by changes in transduction thresholds, these data only provide weak support for this specific conclusion. As the authors note, there is no TREK-1 in D. melanogaster, as such the reported findings could be accounted for by other explanations, not least including potential alterations in the activation threshold of Nav channels required for action potential generation. To conclude that the outcomes were in fact mediated by changes in mechanotransduction, the authors would need to demonstrate changes in receptor potential generation, rather than deriving conclusions from changes in behaviour that could arise from alterations in resting membrane potential, receptor potential generation or the activity of the voltage gated channels required for action potential generation.

Response: We are willing to restrict the conclusion about the fly behavior as the reviewers see fit. We have shown PLD is mechanosensitivity in a fly cell line, and when we knock out PLD from a fly, the animal exhibits a mechanosensation phenotype. We tried to make it clear in the figure and in the text that we have no evidence of a particular mechanism downstream of PLD mechanosensation.

This work provides further evidence of the astounding flexibility of mechanical sensing in cells. By outlining how mechanical activation of TREK-1 can be sensitised by mechanical regulation of PLD2 activity, the authors highlight a mechanism by which TREK-1 sensitivity could be regulated under distinct physiological conditions.

Reviewer #3 (Public Review):

The manuscript "Mechanical activation of TWIK-related potassium channel by nanoscopic movement and second messenger signaling" presents a new mechanism for the activation of TREK-1 channel. The mechanism suggests that TREK1 is activated by phosphatidic acids that are produced via a mechanosensitive motion of PLD2 to PIP2-enriched domains. Overall, I found the topic interesting, but several typos and unclarities reduced the readability of the manuscript. Additionally, I have several major concerns on the interpretation of the results. Therefore, the proposed mechanism is not fully supported by the presented data. Lastly, the mechanism is based on several previous studies from the Hansen lab, however, the novelty of the current manuscript is not clearly stated. For example, in the 2nd result section, the authors stated, "fluid shear causes PLD2 to move from cholesterol dependent GM1 clusters to PIP2 clusters and this activated the enzyme". However, this is also presented as a new finding in section 3 "Mechanism of PLD2 activation by shear."

For PLD2 dependent TREK-1 activation. Overall, I found the results compelling. However, two key results are missing.

1. Does HEK cells have endogenous PLD2? If so, it's hard to claim that the authors can measure PLD2-independent TREK1 activation.

Response: yes, there is endogenous PLD (enPLD). We calculated the relative expression of xPLD2 vs enPLD. xPLD2 is >10x more abundant (Fig. S3d of Pavel et al PNAS 2020, ref 14 of the current manuscript). Hence, as with anesthetic sensitivity, we expect the xPLD to out compete the endogenous PLD, which is what we see. We added the following sentence and reference : “The xPLD2 expression is >10x the endogenous PLD2 (enPLD2) and out computes the TREK-1 binding site for PLD25.”

1. Does the plasma membrane trafficking of TREK1 remain the same under different conditions (PLD2 overexpression, truncation)? From Figure S2, the truncated TREK1 seem to have very poor trafficking. The change of trafficking could significantly contribute to the interpretation of the data in Figure 1.

Response: If the PLD2 binding site is removed (TREK-1trunc), yes, the trafficking to the plasma membrane is unaffected by the expression of xPLD and mPLD (Author response image 2 above). For full length TREK1 (FL-TREK-1), co-expression of mPLD decreases TREK expression (Author response image 2) and coexpression with xPLD increases TREK expression (Figure S2f). This is exactly opposite of what one would expect if surface expression accounted for the change in pressure currents. Hence, we conclude surface expression does not account for loss of TREK-1 mechanosensitivity with xPLD2. A few sentences was added to the discussion. We also performed dSTORM on the TREKtruncated using EGFP. TREK-truncated goes to PIP2 (see figure 2 of 6)

Author response image 3.

To better compare the levels of TREK-1 before and after shear, we added a supplemental figure S2f where the protein was compared simultaneously in all conditions. 15 min of shear significantly decreased TREK-1 except with mPLD2 where the levels before shear were already lowest of all the expression levels tested.

For shear-induced movement of TREK1 between nanodomains. The section is convincing, however I'm not an expert on super-resolution imaging. Also, it would be helpful to clarify whether the shear stress was maintained during fixation. If not, what is the >me gap between reduced shear and the fixed state. lastly, it's unclear why shear flow changes the level of TREK1 and PIP2.

Response: Shear was maintained during the fixing. xPLD2 blocks endocytosis, presumably endocytosis and or release of other lipid modifying enzymes affect the system. The change in TREK-1 levels appears to be directly through an interaction with PLD as TREK trunc is not affected by over expression of xPLD or mPLD.

For the mechanism of PLD2 activation by shear. I found this section not convincing. Therefore, the question of how does PLD2 sense mechanical force on the membrane is not fully addressed. Par>cularly, it's hard to imagine an acute 25% decrease cholesterol level by shear - where did the cholesterol go? Details on the measurements of free cholesterol level is unclear and additional/alternative experiments are needed to prove the reduction in cholesterol by shear.

Response: The question “how does PLD2 sense mechanical force on the membrane” we addressed and published in Nature Comm. In 2016. The title of that paper is “Kinetic disruption of lipid rafts is a mechanosensor for phospholipase D” see ref 13 Petersen et. al. PLD is a soluble protein associated to the membrane through palmitoylation. There is no transmembrane domain, which narrows the possible mechanism of its mechanosensation to disruption.

The Nature Comm. reviewer identified as “an expert in PLD signaling” wrote the following of our data and the proposed mechanism:

“This is a provocative report that identi0ies several unique properties of phospholipase D2 (PLD2). It explains in a novel way some long established observations including that the enzyme is largely regulated by substrate presentation which 0its nicely with the authors model of segregation of the two lipid raft domains (cholesterol ordered vs PIP2 containing). Although PLD has previously been reported to be involved in mechanosensory transduction processes (as cited by the authors) this is the 0irst such report associating the enzyme with this type of signaling... It presents a novel model that is internally consistent with previous literature as well as the data shown in this manuscript. It suggests a new role for PLD2 as a force transduction tied to the physical structure of lipid rafts and uses parallel methods of disrup0on to test the predic0ons of their model.”

Regarding cholesterol. We use a fluorescent cholesterol oxidase assay which we described in the methods. This is an appropriate assay for determining cholesterol levels in a cell which we use routinely. We have published in multiple journals using this method, see references 28, 30, 31. Working out the metabolic fate of cholesterol after sheer is indeed interesting but well beyond the scope of this paper. Furthermore, we indirectly confirmed our finding using dSTORM cluster analysis (Figure 3d-e). The cluster analysis shows a decrease in GM1 cluster size consistent with our previous experiments where we chemically depleted cholesterol and saw a similar decrease in cluster size (see ref 13). All the data are internally consistent, and the cholesterol assay is properly done. We see no reason to reject the data.

Importantly, there is no direct evidence for "shear thinning" of the membrane and the authors should avoid claiming shear thinning in the abstract and summary of the manuscript.

Response: We previously established a kinetic model for PLD2 activation see ref 13 (Petersen et al Nature Comm 2016). In that publication we discussed both entropy and heat as mechanisms of disruption. Here we controlled for heat which narrowed that model to entropy (i.e., shear thinning) (see Figure 3c). We provide an overall justification below. But this is a small refinement of our previous paper, and we prefer not to complicate the current paper. We believe the proper rheological term is shear thinning. The following justification, which is largely adapted from ref 13, could be added to the supplement if the reviewer wishes.

Justification: To establish shear thinning in a biological membrane, we initially used a soluble enzyme that has no transmembrane domain, phospholipase D2 (PLD2). PLD2 is a soluble enzyme and associated with the membrane by palmitate, a saturated 16 carbon lipid attached to the enzyme. In the absence of a transmembrane domain, mechanisms of mechanosensation involving hydrophobic mismatch, tension, midplane bending, and curvature can largely be excluded. Rather the mechanism appears to be a change in fluidity (i.e., kinetic in nature). GM1 domains are ordered, and the palmate forms van der Waals bonds with the GM1 lipids. The bonds must be broken for PLD to no longer associate with GM1 lipids. We established this in our 2016 paper, ref 13. In that paper we called it a kinetic effect, however we did not experimentally distinguish enthalpy (heat) vs. entropy (order). Heat is Newtonian and entropy (i.e., shear thinning) is non-Newtonian. In the current study we paid closer attention to the heat and ruled it out (see Figure 3c and methods). We could propose a mechanism based on kinetic disruption, but we know the disruption is not due to melting of the lipids (enthalpy), which leaves shear thinning (entropy) as the plausible mechanism.

The authors should also be aware that hypotonic shock is a very dirty assay for stretching the cell membrane. Ouen, there is only a transient increase in membrane tension, accompanied by many biochemical changes in the cells (including acidification, changes of concentration etc). Therefore, I would not consider this as definitive proof that PLD2 can be activated by stretching membrane.

Response: Comment noted. We trust the reviewer is correct. In 1998 osmotic shock was used to activate the channel. We only intended to show that the system is consistent with previous electrophysiologic experiments.

References cited:

1 Du G, Huang P, Liang BT, Frohman MA. Phospholipase D2 localizes to the plasma membrane and regulates angiotensin II receptor endocytosis. Mol Biol Cell 2004;15:1024–30. htps://doi.org/10.1091/mbc.E03-09-0673.

2 Koch T, Wu DF, Yang LQ, Brandenburg LO, Höllt V. Role of phospholipase D2 in the agonist-induced and constistutive endocytosis of G-protein coupled receptors. J Neurochem 2006;97:365–72. htps://doi.org/10.1111/j.1471-4159.2006.03736.x.

3 Wheeler DS, Underhill SM, Stolz DB, Murdoch GH, Thiels E, Romero G, et al. Amphetamine activates Rho GTPase signaling to mediate dopamine transporter internalization and acute behavioral effects of amphetamine. Proc Natl Acad Sci U S A 2015;112:E7138–47. htps://doi.org/10.1073/pnas.1511670112.

4 Rankovic M, Jacob L, Rankovic V, Brandenburg L-OO, Schröder H, Höllt V, et al. ADP-ribosylation factor 6 regulates mu-opioid receptor trafficking and signaling via activation of phospholipase D2. Cell Signal 2009;21:1784–93. htps://doi.org/10.1016/j.cellsig.2009.07.014.

5 Pavel MA, Petersen EN, Wang H, Lerner RA, Hansen SB. Studies on the mechanism of general anesthesia. Proc Natl Acad Sci U S A 2020;117:13757–66. htps://doi.org/10.1073/pnas.2004259117.

6 Call IM, Bois JL, Hansen SB. Super-resolution imaging of potassium channels with genetically encoded EGFP. BioRxiv 2023. htps://doi.org/10.1101/2023.10.13.561998.

Author Response:

Reviewer #1 (Public Review):

Force sensing and gating mechanisms of the mechanically activated ion channels is an area of broad interest in the field of mechanotransduction. These channels perform important biological functions by converting mechanical force into electrical signals. To understand their underlying physiological processes, it is important to determine gating mechanisms, especially those mediated by lipids. The authors in this manuscript describe a mechanism for mechanically induced activation of TREK-1 (TWIK-related K+ channel. They propose that force induced disruption of ganglioside (GM1) and cholesterol causes relocation of TREK-1 associated with phospholipase D2 (PLD2) to 4,5-bisphosphate (PIP2) clusters, where PLD2 catalytic activity produces phosphatidic acid that can activate the channel. To test their hypothesis, they use dSTORM to measure TREK-1 and PLD2 colocalization with either GM1 or PIP2. They find that shear stress decreases TREK-1/PLD2 colocalization with GM1 and relocates to cluster with PIP2. These movements are affected by TREK-1 C-terminal or PLD2 mutations suggesting that the interaction is important for channel re-location. The authors then draw a correlation to cholesterol suggesting that TREK-1 movement is cholesterol dependent. It is important to note that this is not the only method of channel activation and that one not involving PLD2 also exists. Overall, the authors conclude that force is sensed by ordered lipids and PLD2 associates with TREK-1 to selectively gate the channel. Although the proposed mechanism is solid, some concerns remain.

1) Most conclusions in the paper heavily depend on the dSTORM data. But the images provided lack resolution. This makes it difficult for the readers to assess the representative images.

The images were provided are at 300 dpi. Perhaps the reviewer is referring to contrast in Figure 2? We are happy to increase the contrast or resolution.

As a side note, we feel the main conclusion of the paper, mechanical activation of TREK-1 through PLD2, depended primarily on the electrophysiology in Figure 1b-c, not the dSTORM. But both complement each other.

2) The experiments in Figure 6 are a bit puzzling. The entire premise of the paper is to establish gating mechanism of TREK-1 mediated by PLD2; however, the motivation behind using flies, which do not express TREK-1 is puzzling.

The fly experiment shows that PLD mechanosensitivity is more evolutionarily conserved than TREK-1 mechanosensitivity. We should have made this clearer.

-Figure 6B, the image is too blown out and looks over saturated. Unclear whether the resolution in subcellular localization is obvious or not.

Figure 6B is a confocal image, it is not dSTORM. There is no dSTORM in Figure 6. This should have been made clear in the figure legend. For reference, only a few cells would fit in the field of view with dSTORM.

-Figure 6C-D, the differences in activity threshold is 1 or less than 1g. Is this physiologically relevant? How does this compare to other conditions in flies that can affect mechanosensitivity, for example?

Yes, 1g is physiologically relevant. It is almost the force needed to wake a fly from sleep (1.2-3.2g). See ref 33. Murphy Nature Pro. 2017.

3) 70mOsm is a high degree of osmotic stress. How confident are the authors that a. cell health is maintained under this condition and b. this does indeed induce membrane stretch? For example, does this stimulation activate TREK-1?

Yes, osmotic swell activates TREK1. This was shown in ref 19 (Patel et al 1998). We agree the 70 mOsm is a high degree of stress. This needs to be stated better in the paper.

Reviewer #2 (Public Review):

This manuscript by Petersen and colleagues investigates the mechanistic underpinnings of activation of the ion channel TREK-1 by mechanical inputs (fluid shear or membrane stretch) applied to cells. Using a combination of super-resolution microscopy, pair correlation analysis and electrophysiology, the authors show that the application of shear to a cell can lead to changes in the distribution of TREK-1 and the enzyme PhospholipaseD2 (PLD2), relative to lipid domains defined by either GM1 or PIP2. The activation of TREK-1 by mechanical stimuli was shown to be sensitized by the presence of PLD2, but not a catalytically dead xPLD2 mutant. In addition, the activity of PLD2 is increased when the molecule is more associated with PIP2, rather than GM1 defined lipid domains. The presented data do not exclude direct mechanical activation of TREK-1, rather suggest a modulation of TREK-1 activity, increasing sensitivity to mechanical inputs, through an inherent mechanosensitivity of PLD2 activity. The authors additionally claim that PLD2 can regulate transduction thresholds in vivo using Drosophila melanogaster behavioural assays. However, this section of the manuscript overstates the experimental findings, given that it is unclear how the disruption of PLD2 is leading to behavioural changes, given the lack of a TREK-1 homologue in this organism and the lack of supporting data on molecular function in the relevant cells.

We agree, the downstream effectors of PLD2 mechanosensitivity are not known in the fly. Other anionic lipids have been shown to mediate pain see ref 46 and 47. We do not wish to make any claim beyond PLD2 being an in vivo contributor to a fly’s response to mechanical force.

That said we do believe we have established a molecular function at the cellular level. We showed PLD is robustly mechanically activated in a cultured fly cell line (BG2-c2) Figure 6a of the manuscript. And our previous publication established mechanosensation of PLD (Petersen et. al. Nature Com 2016) through mechanical disruption of the lipids. At a minimum, the experiments show PLDs mechanosensitivity is evolutionarily better conserved across species than TREK1.

This work will be of interest to the growing community of scientists investigating the myriad mechanisms that can tune mechanical sensitivity of cells, providing valuable insight into the role of functional PLD2 in sensitizing TREK-1 activation in response to mechanical inputs, in some cellular systems.

The authors convincingly demonstrate that, post application of shear, an alteration in the distribution of TREK-1 and mPLD2 (in HEK293T cells) from being correlated with GM1 defined domains (no shear) to increased correlation with PIP2 defined membrane domains (post shear). These data were generated using super-resolution microscopy to visualise, at sub diffraction resolution, the localisation of labelled protein, compared to labelled lipids. The use of super-resolution imaging enabled the authors to visualise changes in cluster association that would not have been achievable with diffraction limited microscopy. However, the conclusion that this change in association reflects TREK-1 leaving one cluster and moving to another overinterprets these data, as the data were generated from static measurements of fixed cells, rather than dynamic measurements capturing molecular movements.

When assessing molecular distribution of endogenous TREK-1 and PLD2, these molecules are described as "well correlated: in C2C12 cells" however it is challenging to assess what "well correlated" means, precisely in this context. This limitation is compounded by the conclusion that TREK-1 displayed little pair correlation with GM1 and the authors describe a "small amount of TREK-1 trafficked to PIP2". As such, these data may suggest that the findings outlined for HEK293T cells may be influenced by artefacts arising from overexpression.

The changes in TREK-1 sensitivity to mechanical activation could also reflect changes in the amount of TREK-1 in the plasma membrane. The authors suggest that the presence of a leak currently accounts for the presence of TREK-1 in the plasma membrane, however they do not account for whether there are significant changes in the membrane localisation of the channel in the presence of mPLD2 versus xPLD2. The supplementary data provide some images of fluorescently labelled TREK-1 in cells, and the authors state that truncating the c-terminus has no effect on expression at the plasma membrane, however these data provide inadequate support for this conclusion. In addition, the data reporting the P50 should be noted with caution, given the lack of saturation of the current in response to the stimulus range.

We thank the reviewer for his/her concern about expression levels. We did test TREK-1 expression. mPLD decreases TREK-1 expression ~two-fold (see Author response image 1). We did not include the mPLD data since TREK-1 was mechanically activated with mPLD. For expression to account for the loss of TREK-1 stretch current (Figure 1b), xPLD would need to block surface expression of TREK-1. The opposite was true, xPLD2 increased TREK-1 expression increased (see Figure S2c). Furthermore, we tested the leak current of TREK-1 at 0 mV and 0 mmHg of stretch. Basal leak current was no different with xPLD2 compared to endogenous PLD (Figure 1d; red vs grey bars respectively) suggesting TREK-1 is in the membrane and active when xPLD2 is present. If anything, the magnitude of the effect with xPLD would be larger if the expression levels were equal.

Author response image 1.<br /> TREK expression at the plasma membrane. TREK-1 Fluorescence was measured by GFP at points along the plasma membrane. Over expression of mouse PLD2 (mPLD) decrease the amount of full-length TREK-1 (FL TREK) on the surface more than 2-fold compared to endogenously expressed PLD (enPLD) or truncated TREK (TREKtrunc) which is missing the PLD binding site in the C-terminus. Over expression of mPLD had no effect on TREKtrunc.

>

Finally, by manipulating PLD2 in D. melanogaster, the authors show changes in behaviour when larvae are exposed to either mechanical or electrical inputs. The depletion of PLD2 is concluded to lead to a reduction in activation thresholds and to suggest an in vivo role for PA lipid signaling in setting thresholds for both mechanosensitivity and pain. However, while the data provided demonstrate convincing changes in behaviour and these changes could be explained by changes in transduction thresholds, these data only provide weak support for this specific conclusion. As the authors note, there is no TREK-1 in D. melanogaster, as such the reported findings could be accounted for by other explanations, not least including potential alterations in the activation threshold of Nav channels required for action potential generation. To conclude that the outcomes were in fact mediated by changes in mechanotransduction, the authors would need to demonstrate changes in receptor potential generation, rather than deriving conclusions from changes in behaviour that could arise from alterations in resting membrane potential, receptor potential generation or the activity of the voltage gated channels required for action potential generation.

We are willing to restrict the conclusion about the fly behavior as the reviewers see fit. We have shown PLD is mechanosensitivity in a fly cell line, and when we knock out PLD from a fly, the animal exhibits a mechanosensation phenotype.

This work provides further evidence of the astounding flexibility of mechanical sensing in cells. By outlining how mechanical activation of TREK-1 can be sensitised by mechanical regulation of PLD2 activity, the authors highlight a mechanism by which TREK-1 sensitivity could be regulated under distinct physiological conditions.

Reviewer #3 (Public Review):

The manuscript "Mechanical activation of TWIK-related potassium channel by nanoscopic movement and second messenger signaling" presents a new mechanism for the activation of TREK-1 channel. The mechanism suggests that TREK1 is activated by phosphatidic acids that are produced via a mechanosensitive motion of PLD2 to PIP2-enriched domains. Overall, I found the topic interesting, but several typos and unclarities reduced the readability of the manuscript. Additionally, I have several major concerns on the interpretation of the results. Therefore, the proposed mechanism is not fully supported by the presented data. Lastly, the mechanism is based on several previous studies from the Hansen lab, however, the novelty of the current manuscript is not clearly stated. For example, in the 2nd result section, the authors stated, "fluid shear causes PLD2 to move from cholesterol dependent GM1 clusters to PIP2 clusters and this activated the enzyme". However, this is also presented as a new finding in section 3 "Mechanism of PLD2 activation by shear."

For PLD2 dependent TREK-1 activation. Overall, I found the results compelling. However, two key results are missing. 1. Does HEK cells have endogenous PLD2? If so, it's hard to claim that the authors can measure PLD2-independent TREK1 activation.

Yes, there is endogenous PLD (enPLD). We calculated the relative expression of xPLD2 vs enPLD. xPLD2 is >10x more abundant (Fig. S3d of Pavel et al PNAS 2020, ref 14 of the current manuscript). Hence, as with anesthetic sensitivity, we expect the xPLD to out compete the endogenous PLD, which is what we see. This should have been described more carefully in this paper and the studies pointed out that establish this conclusion.

1. Does the plasma membrane trafficking of TREK1 remain the same under different conditions (PLD2 overexpression, truncation)? From Figure S2, the truncated TREK1 seem to have very poor trafficking. The change of trafficking could significantly contribute to the interpretation of the data in Figure 1.

If the PLD2 binding site is removed (TREK-1trunc), yes, the trafficking to the plasma membrane is unaffected by the expression of xPLD and mPLD (Figure R1 above). For full length TREK1 (FL-TREK-1), co-expression of mPLD decreases TREK expression (Figure R1) and co-expression with xPLD increases TREK expression (Figure S2). This is exactly opposite of what one would expect if surface expression accounted for the change in pressure currents. Hence, we conclude surface expression does not account for loss of TREK-1 mechanosensitivity with xPLD2.

For shear-induced movement of TREK1 between nanodomains. The section is convincing, however I'm not an expert on super-resolution imaging. Also, it would be helpful to clarify whether the shear stress was maintained during fixation. If not, what is the time gap between reduced shear and the fixed state. lastly, it's unclear why shear flow changes the level of TREK1 and PIP2.

Shear was maintained during the fixing. We do not know why shear changes PIP2 and TREK-1 levels. Presumably endocytosis and or release of other lipid modifying enzymes affect the system. The change in TREK-1 levels appears to be directly through an interaction with PLD as TREKtrunc is not affected by over expression of xPLD or mPLD.

For the mechanism of PLD2 activation by shear. I found this section not convincing. Therefore, the question of how does PLD2 sense mechanical force on the membrane is not fully addressed. Particularly, it's hard to imagine an acute 25% decrease cholesterol level by shear - where did the cholesterol go? Details on the measurements of free cholesterol level is unclear and additional/alternative experiments are needed to prove the reduction in cholesterol by shear.

The question “how does PLD2 sense mechanical force on the membrane” we addressed and published in Nature Comm. In 2016. The title of that paper is “Kinetic disruption of lipid rafts is a mechanosensor for phospholipase D” see ref 13 Petersen et. al. PLD is a soluble protein associated to the membrane through palmitoylation. There is no transmembrane domain, which narrows the possible mechanism of its mechanosensation to disruption.

The Nature Comm. reviewer identified as “an expert in PLD signaling” wrote the following of our data and the proposed mechanism:

"This is a provocative report that identifies several unique properties of phospholipase D2 (PLD2). It explains in a novel way some long established observations including that the enzyme is largely regulated by substrate presentation which fits nicely with the authors model of segregation of the two lipid raft domains (cholesterol ordered vs PIP2 containing). Although PLD has previously been reported to be involved in mechanosensory transduction processes (as cited by the authors) this is the first such report associating the enzyme with this type of signaling... It presents a novel model that is internally consistent with previous literature as well as the data shown in this manuscript. It suggests a new role for PLD2 as a force transduction tied to the physical structure of lipid rafts and uses parallel methods of disruption to test the predictions of their model."

Regarding cholesterol. We use a fluorescent cholesterol oxidase assay which we described in the methods. This is an appropriate assay for determining cholesterol levels in a cell which we use routinely. We have published in multiple journals using this method, see references 28, 30, 31. Working out the metabolic fate of cholesterol after sheer is indeed interesting but well beyond the scope of this paper. Furthermore, we indirectly confirmed our finding using dSTORM cluster analysis (Figure 3d-e). The cluster analysis shows a decrease in GM1 cluster size consistent with our previous experiments where we chemically depleted cholesterol and saw a similar decrease in cluster size (see ref 13). All the data are internally consistent, and the cholesterol assay is properly done. We see no reason to reject the data.

Importantly, there is no direct evidence for "shear thinning" of the membrane and the authors should avoid claiming shear thinning in the abstract and summary of the manuscript.

We previously established a kinetic model for PLD2 activation see ref 13 (Petersen et al Nature Comm 2016). In that publication we discussed both entropy and heat as mechanisms of disruption. Here we controlled for heat which narrowed that model to entropy (i.e., shear thinning) (see Figure 3c). We provide an overall justification below. But this is a small refinement of our previous paper, and we prefer not to complicate the current paper. We believe the proper rheological term is shear thinning. The following justification, which is largely adapted from ref 13, could be added to the supplement if the reviewer wishes.

Justification: To establish shear thinning in a biological membrane, we initially used a soluble enzyme that has no transmembrane domain, phospholipase D2 (PLD2). PLD2 is a soluble enzyme and associated with the membrane by palmitate, a saturated 16 carbon lipid attached to the enzyme. In the absence of a transmembrane domain, mechanisms of mechanosensation involving hydrophobic mismatch, tension, midplane bending, and curvature can largely be excluded. Rather the mechanism appears to be a change in fluidity (i.e., kinetic in nature). GM1 domains are ordered, and the palmate forms van der Waals bonds with the GM1 lipids. The bonds must be broken for PLD to no longer associate with GM1 lipids. We established this in our 2016 paper, ref 13. In that paper we called it a kinetic effect, however we did not experimentally distinguish enthalpy (heat) vs. entropy (order). Heat is Newtonian and entropy (i.e., shear thinning) is non-Newtonian. In the current study we paid closer attention to the heat and ruled it out (see Figure 3c and methods). We could propose a mechanism based on kinetic disruption, but we know the disruption is not due to melting of the lipids (enthalpy), which leaves shear thinning (entropy) as the plausible mechanism.

The authors should also be aware that hypotonic shock is a very dirty assay for stretching the cell membrane. Often, there is only a transient increase in membrane tension, accompanied by many biochemical changes in the cells (including acidification, changes of concentration etc). Therefore, I would not consider this as definitive proof that PLD2 can be activated by stretching membrane.

Comment noted. We trust the reviewer is correct. In 1998 osmotic shock was used to activate the channel. We only intended to show that the system is consistent with previous electrophysiologic experiments.

Author Response

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

Reviewer #1 (Public Review):

The present work establishes 14-3-3 proteins as binding partners of spastin and suggests that this binding is positively regulated by phosphorylation of spastin. The authors show evidence that 14-3-3 >- spastin binding prevents spastin ubiquitination and final proteasomal degradation, thus increasing the availability of spastin. The authors measured microtubule severing activity in cell lines and axon regeneration and outgrowth as a prompt to spastin activity. By using drugs and peptides that separately inhibit 14-3-3 binding or spastin activity, they show that both proteins are necessary for axon regeneration in cell culture and in vivo models in rats.

The following is an account of the major strengths and weaknesses of the methods and results.

Major strengths

-The authors performed pulldown assays on spinal cord lysates using GST-spastin, then analyzed pulldowns via mass spectrometry and found 3 peptides common to various forms of 14-3-3 proteins. In co-expression experiments in cell lines, recombinant spastin co-precipitated with all 6 forms of 14-3-3 tested.

-By protein truncation experiments they found that the Microtubule Binding Domain of spastin contained the binding capability to 14-3-3. This domain contained a putative phosphorylation site, and substitutions that cannot be phosphorylated cannot bind to spastin.

-spastin overexpression increased neurite growth and branching, and so did the phospho null spastin. On the other hand, the phospho mimetic prevents all kinds of neurite development.

-Overexpression of GFP-spastin shows a turn-over of about 12 hours when protein synthesis is inhibited by cycloheximide. When 14-3-3 is co-overexpressed, GFP-spastin does not show a decrease by 12 hours. When S233A is expressed, a turn-over of 9 hours is observed, indicating that the ability to be phosphorylated increases the stability of the protein.

-In support of that notion, the phospho-mimetic S233D makes it more stable, lasting as much as the over-expression of 14-3-3.

-Authors show that spastin can be ubiquitinated, and that in the presence of ubiquitin, spastin-MT severing activity is inhibited.

-By combining FCA with Spastazoline, the authors claim that FCA increased regeneration is due to increased spastin Activity in various models of neurite outgrowth and regeneration in cell culture and in vivo, the authors show impressive results on the positive effect of FCA in regeneration, and that this is abolished when spastin is inhibited.

Major weaknesses

-However convincing the pull-downs of the expressed proteins, the evidence would be stronger if a co-immunoprecipitation of the endogenous proteins were included.

We thank the reviewer for their succinct summary of the main results and strengths of our study. We acknowledge the reviewers' valuable suggestions and agree that performing endogenous co-immunoprecipitation (co-IP) experiments in neurons is crucial for supporting our conclusions. To address this question, cortical neurons were cultured in vitro for endogenous IP experiment. The cortical neurons were cultured using a neurobasal medium supplemented with 2% B27, and using cytarabine to inhibit the proliferation of glial cells. The proteins were then extracted and subjected to the immunoprecipitation experiments using antibodies against spastin. The results, as shown in Fig.1C in the revised manuscript, clearly demonstrate that 14-3-3 protein indeed interacts with spastin within neurons.

-To better establish the impact of spastin phosphorylation in the interaction, there is no indication that the phosphomimetic (S233D) can better bind spastin, and this result is contradicting to the conclusion of the authors that spastin-14-3-3 interaction is necessary for (or increases) spastin function.

Thank you for your valuable and constructive comments. We agree with your consideration. To reinforce the importance of phosphorylated spastin in this binding model, we conducted additional experiments by transfecting S233D into 293T cells and performed immunoprecipitation experiments (Fig.2H). The results clearly demonstrate that spastin (S233D) exhibits enhanced binding to spastin, indicating that phosphorylation at the S233 site is critical for this interaction. Additionally, we observed that spastin (S233D) maintains its binding to 14-3-3 even in the presence of staurosporine. This data further supports and strengthens our conclusions.

-To fully support the authors' suggestion that 14-3-3 and spastin work in the same pathway to promote regeneration, I believe that some key observations are missing.

1-There is no evidence showing that 14-3-3 overexpression increases the total levels of spastin, not only its turnover.

Thank you for your consideration and valuable input. We have previously demonstrated that overexpression of 14-3-3 leads to an increase in the protein levels of spastin in the absence of CHX (Fig.3E&F). Furthermore, we also observed an upregulated protein levels of spastin S233D compared to the wild-type (Fig.3G). We have now included these results in the revised manuscript.

2- There is no indication that increasing the ubiquitination of spastin decreases its levels. To suggest that proteasomal activity is affecting the levels of a protein, one would expect that proteasomal inhibition (with bortezomib or epoxomycin), would increase its levels.

Thanks for your concern. We believe that this evidence is critical. Indeed, another study by our team is working to elucidate the ubiquitination degradation pathway of spastin. In addition, a previous study has shown that phosphorylation of the S233 site of spastin can affect its protein stability (Spastin recovery in hereditary spastic paraplegia by preventing neddylation-dependent degradation, doi:10.26508/lsa.202000799.). To better support our conclusions, we have supplemented the results in Fig.3L&M. The results showed that the proteasome inhibitor MG132 could significantly increase the protein level of spastin, whereas CHX could significantly decrease the protein level of spastin, and the degradation of spastin is significantly hindered in the presence of both CHX and MG132. This experiment also further showed that ubiquitination of spastin reduced its protein level.

3- Authors show that S233D increases MT severing activity, and explain that it is related to increased binding to 14-3-3. An alternative explanation is that phosphorylation at S233 by itself could increase MT severing activity. The authors could test if purified spastin S233D alone could have more potent enzymatic activity.）

We appreciate the reviewer’s consideration. After investigating the interaction between 14-3-3 and spastin, we first aimed to determine whether the S233 phosphorylation mutation of spastin influenced its microtubule-severing activity. We found that overexpression of both S233A and S233D mutants resulted in significant microtubule severing (as indicated by a significant decrease in microtubule fluorescence intensity) (Fig.S2). Furthermore, it is noteworthy that S233 is located outside the microtubule-binding domain (MTBD, 270-328 amino acids) and the AAA region (microtubule-severing region, 342-599 amino acids) of spastin. Based on our initial observations, we believe that the phosphorylation of the S233 residue in spastin does not impact its microtubule-severing function. Additionally, under the same experimental conditions, we observed that the green fluorescence intensity of GFP-spastin S233D was significantly higher than that of GFP-spastin S233A. Based on these phenomena, we speculated that phosphorylation of the S233 residue of spastin might affect its protein stability, leading us to conduct further experiments. Furthermore, we fully acknowledge the reviewer's concern; however, due to technical limitations, we were unable to perform an in vitro assay to test the microtubule-severing activity of spastin. We have provided an explanation for this consideration in the revised version.

-Finally, I consider that there are simpler explanations for the combined effect of FC-A and spastazoline. FC-A mechanism of action can be very broad, since it will increase the binding of all 14-3-3 proteins with presumably all their substrates, hence the pathways affected can rise to the hundreds. The fact that spastazoline abolishes FC-A effect, may not be because of their direct interaction, but because spastin is a necessary component of the execution of the regeneration machinery further downstream, in line with the fact that spastizoline alone prevented outgrowth and regeneration, and in agreement with previous work showing that normal spastin activity is necessary for regeneration.

We appreciate the considerations raised by the reviewer. It is evident that spastin is not the exclusive substrate protein for 14-3-3, and it is challenging to demonstrate that 14-3-3 promotes nerve regeneration and recovery of spinal cord injury directly through spastin in vivo. However, we have identified the importance of 14-3-3 and spastin in the process of nerve regeneration. Importantly, we have conducted supplementary experiments to support the stabalization of spastin by FC-A treatment within neurons (Fig.4M), as well as the repair process of spinal cord injury in vivo (Fig.5D). The results showed that FC-A treatment in cortical neurons could enhance the stability of spastin protein levels, and we also demonstrated a consistent trend of upregulated protein levels of spastin and 14-3-3 following spinal cord injury. Moreover, the protein levels were significantly elevated in the the FC-A group of mice. These results also support that 14-3-3 enhances spastin protein stability to promote spinal cord injury repair. The manuscript was revised accordingly.

Reviewer #2 (Public Review):

Summary:

The idea of harnessing small molecules that may affect protein-protein interactions to promote axon regeneration is interesting and worthy of study. In this manuscript, Liu et al. explore a 14-3-3-spastin complex and its role in axon regeneration.

Strengths:

Some of the effects of FC-A on locomotor recovery after spinal cord contusion look interesting.

Weaknesses:

The manuscript falls short of establishing that a 14-3-3-spastin complex is important for any FC-A-dependent effects and there are several issues with data quality that make it difficult to interpret the results. Importantly, the effects of the spastin inhibitor have a major impact on neurite outgrowth suggesting that cells simply cannot grow in the presence of the inhibitor and raising serious questions about any selectivity for FC-A - dependent growth. Aspects of the histology following spinal cord injury were not convincing.

We sincerely appreciate the reviewer for evaluating our manuscript. Given the multitude of substrates that interact with 14-3-3, and considering spastin's indispensable role in neuroregeneration, it is indeed challenging to experimentally establish that FC-A's neuroregenerative effect is directly mediated through spastin in vivo. Therefore, we have provided additional crucial evidence regarding the changes in spastin protein levels following spinal cord injury, as well as the application of FC-A after spinal cord injury. Furthermore, we have made relevant adjustments to the uploaded images to enhance the resolution of the presented figures, as detailed in the subsequent response.

Reviewer #3 (Public Review):

Summary: The current manuscript c laims that 14-3-3 interacts with spastin and that the 14-3-3/spastin interaction is important to regulate axon regeneration after spinal cord injury.

Strengths:

In its present form, this reviewer identified no clear strengths for this manuscript.

Weaknesses:

In general, most of the figures lack sufficient quality to allow analyses and support the author's claims (detailed below). The legends also fail to provide enough information on the figures which makes it hard to interpret some of them. Most of the quantifications were done based on pseudo-replication. The number of independent experiments (that should be defined as n) is not shown. The overall quality of the written text is also low and typos are too many to list. The original nature of the spinal cord injury-related experiments is unclear as the role of 14-3-3 (and spastin) in axon regeneration has been extensively explored in the past.

We sincerely appreciate the careful consideration and rigorous evaluation provided by the reviewer. In the revised version, we have made effort to present high-resolution figures and provide more detailed figure legends. Furthermore, we have made relevant adjustments to the statistical methods in accordance with the reviewer's suggestions. The manuscript has also undergone a thorough review and correction process to eliminate any writing-related errors. Please refer to the following response.

To the best of our knowledge, there has been no clear reports on the efficacy of 14-3-3 in the repair of spinal cord injury. Kaplan A et al. (doi: 10.1016/j.neuron.2017.02.018) reported a reduction in die-back of the corticospinal tract following spinal cord injury using FC-A as a filler in situ in the lesion site. However, the specific effects of FC-A on spinal cord injury, such as motor function and neural reactivity, as well as the expression characteristic of 14-3-3 after spinal cord injury, have not been extensively elucidated. Additionally, prior research on spastin's role in axon regeneration primarily focused on the effects in Drosophila, and its regenerative effects in the central nervous system of adult mammals after injury have not been reported. Therefore, our study provides crucial insights into the importance of 14-3-3 and spastin in the process of spinal cord injury repair in mammals.

Reviewer #1 (Recommendations For The Authors):

There are many spelling and grammar errors, please revise. Examples:

-approach revealed14-3-3

-We have detected different many 14-3-3 peptides

-Line 1057 (D) 14-3-3 agnoist FC-A

-There is a discrepancy between panel names and figure legend in Figure 4.

-There is another discrepancy between the color coding of treatments in Figure 7. All panels show "injury" in red and FC-A in orange, but in panel E, these are swapped. This is confusing to readers.

Thank you for the thorough and rigorous review. We have re-colored the relevant chart. The manuscript has also undergone a thorough review to eliminate any writing-related errors.

Most images from confocal microscopy are blurred or low resolution. They should be sharper for the type of microscopy used.

We have adjusted and re-uploaded the images with higher resolution. Additionally, we have enlarged the relevant images.

The list of all peptides retrieved in the Mass-Spec analyses of the GST-spastin pulldown must be publicly available, according to eLife rules.

Thank you for your suggestion. We have now uploaded the mass spectrometry data.

To determine where the 14-3-3/spastin protein142 complex functions in neurons, we double stained hippocampal neurons with spastin143 and 14-3-3 antibody, and found that 14-3-3 was colocalized with spastin in the entire144 cell compartment (Figure 1C).

Colocalization by confocal fluorescence microscopy is not evidence for protein complexes.

While co-localization experiments may not directly demonstrate protein-protein interactions, they can still provide valuable insights into the cellular localization of the proteins and suggest potential interactions between them. Therefore, we adjusted the statement.

Fig1F- Co-immunoprecipitation assay results confirmed that all 14-3-3 isoforms could form direct complexes with spastin.

CoIP in cells overexpressing the proteins is not evidence that it is direct. That they can interact directly with each other can be extracted from the evidence in vitro with purified proteins.

We agree with this and we have changed our statement accordingly.

For a broad audience to have a better understanding, the authors have to explain their a.a. subtitucions of Serine233, one being mimicking phosphorylation (S233D) and the other rendering the protein not being able to be phosphorylated in that position (S233A).

We appreciate the suggestion. We have provided a more detailed explanation in revised manuscript.

The panel of neuronas in Fig2G is mislabeled, because it is twice spastin S233A, instead of S233D.

We apologize for this mistake and we have corrected it in the panel.

FCA may increase the interaction of 14-3-3 with any of its substrates, including spastin. One would appreciate evidence that FCA increases the MT-severing activity of spastin, as assumed by authors

We appreciate the reviewer’s suggestion. In this study, we overexpressed spastin to investigate its microtubule severing activity. It is important to note that overexpressing spastin significantly exceeds the normal physiological concentration of the protein. Using excessive amounts of FC-A to enhance the interaction between 14-3-3 and spastin in cells can lead to cell toxicity. Therefore, we chose to overexpress 14-3-3 instead of employing excessive FC-A.

In Fig2F, the interaction of 14-3-3 with Spas-S233D would have been very informative.

Thank you for the constructive suggestions from the reviewer. We have supplemented the corresponding co-immunoprecipitation experiments (Fig.).

The functional effect of S233A and S233D does not correlate with a function of 14-3-3 in neurite outgrowth. This is because S233A does not interact with 14-3-3, however, it is as good as WT spastin... meaning that binding of 14-3-3 with spastin is not necessary...

We appreciate the reviewer's consideration. The observed phenomenon of spastin WT and S233A promoting axon growth do not align with the physiological state within neurons. This may mask the true effects of S233A or S233D on neuronal axon growth. It is documented that the proper dosage of spastin is essential for neuronal growth and regeneration, as excessive or insufficient amounts can hinder axon growth. Excessive spastin levels can disrupt the overall cellular MTs. Therefore, spastin were moderately expressed by adjusting the transfection dosage and duration. Nevertheless, we were unable to precisely control the expression levels of spastin for both WT and S233A, also resulting in an overexpression state compared to the physiological state. As a result, the crucial role of spastin S233 in neural growth under physiological conditions may be masked. We have addressed this issue in the revised version of our manuscript.

In panels 3C and D it is not clear if it does contain 14-3-3.... it seems it does not... but clarify.

We apologize for any confusion. Since there is endogenous 14-3-3 present in the cells, we utilized spastin S233A and S233D to mimic the binding pattern with 14-3-3 according to the established interaction model. This information has been clarified in the original manuscript.

Line 217 should indicate Figure 3, not Figure 5

We have made the corresponding corrections.

In F3G, it is intriguing that the input blot shows a decrease in Ubiquitin proteins when there is expression of flag ubiquitin...

We apologize for the error in our presentation. In the control group, we actually overexpressed Flag-ubiquitin and GFP instead of Flag and GFP-spastin. Additionally, to further elucidate the impact of different phosphorylation states on spastin ubiquitination and degradation, we have conducted additional ubiquitination experiments (Fig.3N), which are now included in the revised version of our manuscript.

S233 mutations seem to affect the effective turnover of spastin, but does not seem to change the levels of the spastin protein...hence, the conclusion that 14-3-3 protects from degradation is overstated.

We thank the reviewers for the careful review and we have revised the statement accordingly.

The mode of action of R18 FCA should be introduced earlier in the text.

Thank you for the reviewer's correction. We have provided a corresponding description of the effects of FC-A and R18 on the interaction between 14-3-3 and spastin in the ubiquitination experiments section of the manuscript.

Line 296 reads: Our results revealed that levels of 14-3-3 protein remained high even at 30 DPI, indicating that 14-3-3 plays an important role in the recovery of spinal cord injury.

This is overstated since it can well be that an upregulated protein is inhibitory. We thank the reviewers for their consideration and we have made adjustments accordingly.

It is not clear if 14-3-3 prevents ubiquitination of spastin, then its levels should be higher... it is noteworthy that they did not measure its levels in nerve tissue after injury. For example, in experiments shown in Figure 5A, it would have been very useful the observation of the levels of spastin.

We appreciate the reviewer's consideration. We have now included the assessment of spastin protein levels following spinal cord injury. Additionally, we have collected the injured spinal cord lysates in mice treated with FC-A for western blot analysis. The results revealed that the expression trend of 14-3-3 protein is largely consistent with spastin after spinal cord injury. Furthermore, the treatment with FC-A was found to enhance the expression of spastin after spinal cord injury (Fig. 5C&D)."

Panel 5G reads "nerve regeneration across the lesion site", but it actually measured NF levels, according to the legend.

Thanks to the reviewers for the critical review. We have revised the chart accordingly.

361 "BMS" should be explained in the results section for a better understanding of the results by non-experts.

Thank you to the reviewers for their suggestions. We have explained this in the results section accordingly.

Reviewer #2 (Recommendations For The Authors):

1. The results of the mass spec and co-IP in Figure 1 are unclear.

a) Are all of the peptides in Fig. 1A from 14-3-3 and were there only 3 14-3-3 peptides that were identified?

The mass spectrum results did identify only three 14-3-3 peptides, and these three peptides were highly conserved across all isoforms.

b) The blot in panel B needs to show the input band for spastin and 14-3-3 from the same gel and not spliced so that the level of enrichment can be evaluated in the co-IP.

Thanks to the reviewer's comments, we have presented the whole gel (Fig.1B)

c) Further, does an IP for 14-3-3 co-precipitate spastin?

Thank you for your concern. We appreciate your feedback. Our 14-3-3 antibody is capable of Western blot experiments and recognizes all subtypes (Pan 14-3-3, Cell Signaling Technology, Cat #8312). Unfortunately, it is not suitable for immunoprecipitation (IP) experiments. Therefore, we have employed additional approaches, namely immunoprecipitation and pull-down assays, to further investigate the interaction between 14-3-3 and spastin.

1. It is difficult to say anything about 14-3-3 - spastin co-localization in hippocampal neurons (1c) since 14-3-3 labels the entire hippocampal neuron so any protein will co-localize.

We appreciate the comments. The co-localization experiments have provided evidence of the relative expression of both 14-3-3 and spastin in neurons, suggesting their potential interaction within neuronal cells. We have made the necessary revisions to accurately describe the results of the co-localization experiments in the manuscript.

To further investigate the interaction between 14-3-3 and spastin within neurons, we have conducted additional co-immunoprecipitation (Co-IP) experiments using cortical neuron lysates (Fig.1C).

1. The molecular weight of 14-3-3 is 25-28 kDa but the band in panel 1B and in subsequent figures it is below 15 kDa. Fig. 1F - the spastin band also seems to be low compared to predicted molecular weight and other W. Blot reports in the literature so some indication of how the antibody was validated would be important.

Apologies for the mistakes. We have carefully re-evaluated the western blot images (See Author response image 1). We have confirmed that the molecular weight of the 14-3-3 protein is approximately 33 kDa. In the case of spastin, its molecular weight is around 55-70 kDa. Additionally, the GFP-spastin fusion protein has an estimated molecular weight of approximately 90 kDa. We have conducted a thorough verification and made appropriate adjustments to the molecular weight labels in all western blot images.

Author response image 1.

1. Fig 1G is a co-immunoprecipitation and it is not clear what the authors mean by "direct complexes" as claimed in line 150 of the results since this does not show direct binding between 14-3-3 and spastin. None of the assays in Fig. 1 assess "direct" binding between the two proteins and the authors should be clear in their interpretation.

We agree with the reviewer's comments and have removed the word "direct" from the text.

1. Fig. 1D - there is no validation that staurosporine (protein kinase inhibitor, not protein kinase as per typo in Line 167) affects the phosphorylation levels of spastin.

Thank you for your valuable comments. In our group, we have conducted another study that has confirmed the involvement of CAMKII in mediating spastin phosphorylation. Furthermore, we have found that the addition of staurosporine significantly reduces the phosphorylation levels of spastin (unpublished results). In response to the reviewer's comment, we are pleased to provide western blot experiments demonstrating the effect of staurosporine on reducing spastin phosphorylation. The phosphorylation levels of spastin were assessed using a Pan Phospho antibody (Fig.2D).

1. Fig. 2F - it would be important to test if spastin S233D interacts more robustly with 14-3-3 and if this is insensitive to staurosporine.

Thank you for your comments. The suggestion provided by the reviewer is highly significant for supporting our conclusion that "phosphorylation of spastin is a prerequisite for its interaction with 14-3-3." Therefore, we have conducted additional immunoprecipitation experiments to further supplement our findings (Fig.2H). The experimental results demonstrate that the binding affinity between spastin S233D and 14-3-3 is stronger compared to spastin WT.

1. Line 179 "Next, we transfected Ser233 mutation of spastin (spastin S233A or spastin S233D) with flag tagged 14-3-3 and generated Pearson's correlation coefficients. Results revealed that spastin 181 S233D was markedly colocalized with 14-3-3, with minimal colocalization with spastin S233A (Figure 2A-B)." Assuming the authors are referring to supplemental Figure 2, the 14-3-3 covers the entire cell thus I think measures of co-localization are uninterpretable.

We agree with the reviewer's comment. We realize that 14-3-3θ exhibits a ubiquitous cellular distribution, which renders the measurement of its co-localization coefficients inconclusive. Therefore, we have decided to remove Supplementary Figure 2 from the manuscript.

1. Line 189 "Consistent with earlier results, spastin promoted neurite outgrowth, as evidenced by both the length and total branches of neurite." - It is unclear what earlier results the authors are referring to. The authors should clarify how they determined the "moderate" expression level.

We thank the review’s suggestions. The "earlier results" mentioned here refers to previously published articles, we now have added relevant references. Existing literature indicates that an appropriate dosage of spastin is necessary for neuronal growth and regeneration. However, both excessive and insufficient amounts of spastin are detrimental to axonal growth. Excessive spastin disrupts the overall microtubule network within cells. We controlled plasmid transfection dosage and transfection durations to achieve moderate expression. We have provided an explanation of these details in the revised version.

1. The effects of WT spastin and spastin S233A were similar in spite of the fact that S233A does not bind to 14-3-3, which is inconsistent with the author's model that spastin-14-3-3 binding promotes growth. Line 191 - the authors mention that spastin S233D was toxic but I do not see any cell death measurements. I assume the bottom right panel in Fig. 2G labelled as spastin S233A is mislabeled and should be S233D.

In response to comment 8, the transfection of both wild-type (WT) spastin and S233A mutant failed to precisely control the expression levels around the physiological concentration. Consequently, we observed an overexpression of spastin in both cases, which obscured the critical role of S233 phosphorylation in neurite outgrowth. We have addressed this issue in the revised version of the manuscript.

1. Fig. 3. Does spastin(S233D) bind constitutively to 14-3-3? Why is spastin S233A not less stable than WT spastin based on the author's model?

We propose that 14-3-3 is more likely to interact with spastin S233D in a non-constitutive manner. The instability of the S233A protein is attributed to the disruption of its ubiquitination degradation process due to the absence of 14-3-3 binding.

1. The ubiquitin blot in Fig. 3G is not convincing and not quantified.

We acknowledge the mislabeling in our figures. In the control group, Flag-Ubiquitin was also overexpressed, and we transfected GFP as a control instead of GFP-spastin. To further enhance the reliability, we conducted additional ubiquitination experiments (Fig.3N), which revealed a significant increase in spastin (S233A) ubiquitination levels compared to the WT group, consistent with previous research findings (Spastin recovery in hereditary spastic paraplegia by preventing neddylation-dependent degradation, doi:10.26508/lsa.202000799). Additionally, we observed that the addition of R18 could partially enhance spastin ubiquitination levels, as quantitatively illustrated in the figure (Fig.3O). This result further underscores the inhibitory role of 14-3-3 in the ubiquitination degradation pathway of spastin.

1. I do not understand how the glutamate injury fits with the narrative (Fig. 4C).

Excessive glutamate exposure can induce severe intracellular oxidative stress reactions, leading to the disruption of physiological processes such as mitochondrial energy production. This, in turn, results in the swelling and lysis of neuronal processes, a phenomenon known as neuronal necrosis. During this state, neurite maintenance is obstructed, and neurites exhibit swelling and breakage (Glutamate-induced neuronal death: a succession of necrosis or apoptosis depending on mitochondrial function. Neuron. 1995 Oct;15(4):961-73). We have provided a more comprehensive explanation of this phenomenon in the revised version of our manuscript.

1. Some commentary about the selectivity of spastazoline to inhibit spastin should be included - it would be helpful if the authors could explain that this is a spastin inhibitor in the manuscript. FC-A still seems to promote growth in the presence of spastazoline suggesting that the FC-A effects are not dependent on spastin (Fig. 4E). The statistical analysis section of the materials and methods indicates that multiple groups were analyzed by one-way ANOVA. This seems unusual since the controls for cellular transfection are different than for small molecules (FC-A) and for peptides such as R18. As such, there is no vehicle control for the FC-A condition and it is difficult to assess the FC-A vs Spastazoline vs FA-A + Spastoazoline. The authors should clarify (Fig. 4E-J)

Thank you for the reviewer’s suggestions. In the revised version, we have provided a more detailed explanation of the specific inhibition of spastin's severing function by spastazoline.

We observed that FC-A, in combination with spastazoline, still exhibited a certain degree of promotion in neurite growth compared to the injury group under the glutamate circumstances. Evidently, spastin is not the exclusive substrate for 14-3-3, and FC-A might delay cellular oxidative stress reactions by facilitating the interaction of 14-3-3 with other substrates, such as the FOXO transcription factors as mentioned in the introduction. Nevertheless, our results still demonstrate that the addition of spastazoline significantly diminishes the promoting effect of FC-A on neurite growth, indicating that FC-A affects neuronal growth by impacting spastin.

Furthermore, in the drug-treated groups, we overexpressed GFP to trace the morphology of neurons. Culture media were exchanged following transfection, and during media exchange, drugs were added. And an equivalent amount of DMSO or ethanol were added as controls to rule out the influence of solvents on neurons.

1. There is a good possibility that spastin is required for all axon regeneration and that there is no selectivity for the FC-A pathway and this is a major issue with the interpretation of the manuscript (Fig 4K-L).

We acknowledge this point. Clearly, spastin is not the exclusive substrate for 14-3-3, and our experimental evidence does not establish that 14-3-3 solely promotes neuronal regeneration through spastin. Nevertheless, we have identified the significance of 14-3-3 and spastin in the process of neural regeneration. Furthermore, we conducted complementary experiments to support the stability of spastin by FC-A treatment both in vitro and in vivo. We found an enhanced protein expression in cortical neurons after FC-A treatment (Fig.4M). Also, the results indicate a consistent elevation trend in the protein levels of spastin and 14-3-3 following spinal cord injury (Fig.5C&H). Moreover, in the FC-A group of mice, there was a significant increase in spastin protein levels (Fig.5D&I). These results also support that 14-3-3 promotes spinal cord injury repair by enhancing spastin protein stability.

1. Fig. 5C- it is unclear where the photomicrographs were taken relative to the lesion.

We obtained tissue sections from the lesion core and the above segments for histological analysis. Given the scarcity of neural compartment at the injury center, we select tissue slices as close as possible to lesion core to illustrate the relationship between 14-3-3 and the injured neurons. We have provided an explanation of this in the revised version of the manuscript.

1. The authors need to provide some evidence that the FC-A and spastazoline compounds are accessing the CNS following IP injection.

We thank the review’s suggestion. Although direct visualization evidence of FC-A and spastazoline entering the CNS is challenging to obtain, several indicators suggest drug penetration into spinal cord tissue. Firstly, behavioral and electrophysiological experiments in vivo demonstrate that drug injections indeed affect the neural activity of mice. Secondly, following spinal cord injury, the blood-spinal cord barrier was disrupted at the injury site, combined with the fact that both FC-A (molecular weight: 680.82 Da) and spastazoline (molecular weight: 382.51 Da) are small molecule drugs, these increases the likelihood of these small molecules entering the injured spinal cord tissue. Furthermore, our microtubule staining results indicated that FC-A and spastazoline did influence the acetylation ratio of microtubules. These findings support the drug penetration into spinal cord tissue.

1. Some quantification of Fig. 5D would be important to support the contention that the lesion site is impacted by FC-A treatment.

Thank you for the suggestion. We have included quantitative analysis for Figure 5D (Figure) as recommended.

1. The NF and 5-HT staining in Fig. 5D and in Fig. 7A and B does not clearly define fibers and is not convincing.

We appreciate the concerns. While we did not present whole nerve fibers, we therefore employed NF and 5-HT immunoreactive fluorescence intensity as an indicator to assess the regeneration of nerve fibers as previously described, but not axons per square millimeter (Baltan S, et, al. J Neurosci. 2011 Mar 16;31(11):3990-9; Iwai M, et, al. Stroke. 2010 May;41(5):1032-7; Wang Y, et, al. Elife. 2018 Sep 12;7:e39016; Altmann C, et, al. Mol Neurodegeneration. 2016 Oct 22;11(1):69).

Our results showed that in the spinal cord injury group, there was strongly decreased NF-positive stainning (with a slight increase in 5-HT). In contrast, the FC-A treatment group exhibited a significant higher abundance of NF-positive signals (or an increased 5-HT signal) in the lesion site, which also suggests the reparative effect of FC-A on nerves. We also intend to refine our immunohistochemical methods in future experiments.

Minor Comments: 1. Line 80 -84. To my knowledge the only manuscripts examining the effects of spastin in axon regeneration models includes the analysis in drosophila (i.e. ref 15 and 16) and a study in sciatic nerve that reported an index of functional recovery but did not perform any histology to assess axon regeneration phenotypes. The literature should be more accurately reflected in the introduction.

We appreciate the suggestions from the reviewer. In the revised version, we have provided further clarification on the novelty of spastin in the spinal cord injury repair process.

1. Line 73: The meaning of the following statement needs to be clarified: "spastin has two major isoforms, namely M1 and M87, coded form different initial sites."

We have provided additional elaboration for this statement in the revised version.

1. Line 216: Results indicated that GFP-spastin could be ubiquitinated, while inhibiting the 217 binding of 14-3-3/spastin promoted spastin ubiquitination (Figure 5G)." - Should be Fig 3G

Sorry about the mistake. We have made the corresponding changes in the revised version.

1. Line 255: "Briefly, we established a neural injury model as previously described(31)" - the basics of the injury model need to be described in this manuscript.

In the revised version, we have provided further elaboration on the glutamate-induced neuronal injury model.

Reviewer #3 (Recommendations For The Authors):

Figure 1: A- Both legend and text fail to provide detail on this specific panel.

We have provided a more detailed and comprehensive description of the legend and results in this section.

B- Is the contribution of non-neuronal cells for co-IPs relevant? Co-IP with isolated neuronal extracts (instead of spinal cord tissue) should be performed.

We thank the review’s suggestion. To further elucidate their interaction within neurons, cortical neurons were cultured (Cultured in Neurobasal medium supplemented with 2%B27 and cytarabine was used to inhibit glial cell growth) and cells were lysed for co-IP experiments (Fig.1C), and the results demonstrated the interaction between 14-3-3 and spastin within neurons.

C- Both spastin and 14-3-3 appear to label the entire neuron with similar intensities throughout the entire cell which is rather unusual. Conditions of immunofluorescence should be improved and z-projections should be provided to support co-localization.

Thanks for the comment. Our dual-labeling experiments indicated that 14-3-3 exhibits a characteristic pattern of whole-cell distribution. Therefore, this result cannot confirm the interaction between 14-3-3 and spastin within neurons, but it does provide evidence regarding the intracellular distribution patterns of 14-3-3 and spastin. Consequently, we supplemented neuronal endogenous co-IP experiments to further demonstrate the direct interaction between 14-3-3 and spastin within neurons, and we have modified the wording in the revised version accordingly.

D- xx and yy axis information is either lacking or incomplete.

We have made the corrections to the figures.

E- It would be useful to show the conservation between the different 14-3-3 isoforms.

We appreciate the suggestions. We have included a conservation analysis of 14-3-3 to assist readers in better understanding these results (Fig.1F).

Figure 2:

D- The experiment using a general protein kinase inhibitor does not allow concluding that the specific phosphorylation of spastin is sufficient for binding to 14-3-3. An alternative phosphorylated protein might be involved in the process.

We appreciate the reviewer's consideration. We believe this serves as a prerequisite condition to demonstrate that "14-3-3 binding to spastin requires spastin phosphorylation." In fact, another project in our group has confirmed that CAMK II can mediate spastin phosphorylation, and the addition of staurosporine significantly reduces spastin phosphorylation levels (unpublished results). Here, we provide the western blot experiment showing the decrease in spastin phosphorylation under staurosporine treatment, with phosphorylation levels detected using the Pan Phospho antibody (Fig.2D).

H and I- Pseudo-replication. Only independent experiments should be plotted and not data on multiple cells obtained in the same experiment. Please indicate the number of independent experiments.

We appreciate the reviewer's correction. We now have included the mean value of three independent experiments and we have made relevant revisions to the statistical charts.

Figure 3:

The rationale for the hypothesis that spastin S233D transfection might upregulate the expression of spastin relative to WT and spastin S233A is unclear.

We appreciate the reviewer's consideration. We have supplemented the relevant results, as depicted in the Fig.3G, which demonstrates that 14-3-3 can enhance the protein levels of spastin, and phosphorylated spastin (S233D) exhibits a significantly increased protein level compared to wild-type spastin. These findings indicate that 14-3-3 not only inhibits the degradation of spastin but also increases its protein levels.

I- pseudo-replication. Please plot and do statistical analysis of independent experiments.

Thank you for the reviewer's corrections. We have made the necessary revisions.

Figure 4: E-J: I- pseudo-replication. Please plot and do statistical analysis of independent experiments.

Thank you for the reviewer's corrections. We have made the necessary revisions.

Figure 5:

B- Please show individual data points.

Thank you for the reviewer's corrections. We have made the necessary revisions.

D- Longitudinal images of spinal cords where spastazoline was used cannot correspond to contusion as there is a very sharp discontinuity between the rostral and caudal spinal cord tissue. A full transection seems to have occurred. Alternatively, technical problems with tissue collection/preservation might have occurred.

Thank you for the reviewer's consideration. The sharp discontinuity observed in the spastazoline group is not due to modeling issues but rather a result of the drug's effects on the injury site. This is primarily because spastin plays a crucial role not only in neuronal development but also in mitosis. Since the highly active proliferation of stromal cells at the injury site, . spastazoline may inhibit the proliferation of injury site-related stormal cells, thereby impeding the wound healing process following spinal cord injury, resulting in the observed discontinuous injury gap. We have made the corresponding revision accordingly.

E- Images do not have the quality to allow analysis. 5HT staining should not be considered as a clear axonal labeling is not seen. This is also the case for neurofilament staining.

We appreciate the concerns. While we did not present whole nerve fibers, we therefore employed NF and 5-HT immunoreactive fluorescence intensity as an indicator to assess the regeneration of nerve fibers as previously described, but not axons per square millimeter (Baltan S, et, al. J Neurosci. 2011 Mar 16;31(11):3990-9; Iwai M, et, al. Stroke. 2010 May;41(5):1032-7; Wang Y, et, al. Elife. 2018 Sep 12;7:e39016; Altmann C, et, al. Mol Neurodegeneration. 2016 Oct 22;11(1):69).

Our results showed that in the spinal cord injury group, there was strongly decreased NF-positive stainning (with a slight increase in 5-HT). In contrast, our FC-A treatment group exhibited a significant higher abundance of NF-positive signals (or an increased 5-HT signal) in the lesion site, which also suggests the reparative effect of FC-A on nerves. We also intend to refine our immunohistochemical methods in future experiments.

F- Images do not allow analysis. Higher magnifications are needed.

Thank you for the reviewer's consideration. We have now included higher-magnification images (Fig.5M) to address this concern.

Figure 7:

Same issues as in Figure 5.

A- Images do not have the quality to allow analysis. 5HT staining should not be considered as a clear axonal labeling is not seen.

B- Images do not have the quality to allow analysis. Neurofilament staining should not be considered as clear axonal labeling is not seen. MBP staining does not have a pattern consistent with myelin staining

We appreciate the concerns. While we did not present whole nerve fibers, we therefore employed NF and 5-HT immunoreactive fluorescence intensity as an indicator to assess the regeneration of nerve fibers as previously described, but not axons per square millimeter (Baltan S, et, al. J Neurosci. 2011 Mar 16;31(11):3990-9; Iwai M, et, al. Stroke. 2010 May;41(5):1032-7; Wang Y, et, al. Elife. 2018 Sep 12;7:e39016; Altmann C, et, al. Mol Neurodegeneration. 2016 Oct 22;11(1):69). In this study, sagittal slices were used. MBP covers the axonal surface, indicating its co-localization with the axons. However, as we did not present intact nerve fibers, so we were unable to show the typical myelin staining of MBP.

Author Response

Reviewer 1 (Public Review):

1. With respect to the predictions, the authors propose that the subjects, depending on their linguistic background and the length of the tone in a trial, can put forward one or two predictions. The first is a short-term prediction based on the statistics of the previous stimuli and identical for both groups (i.e. short tones are expected after long tones and vice versa). The second is a long-term prediction based on their linguistic background. According to the authors, after a short tone, Basque speakers will predict the beginning of a new phrasal chunk, and Spanish speakers will predict it after a long tone.

In this way, when a short tone is omitted, Basque speakers would experience the violation of only one prediction (i.e. the short-term prediction), but Spanish speakers will experience the violation of two predictions (i.e. the short-term and long-term predictions), resulting in a higher amplitude MMN. The opposite would occur when a long tone is omitted. So, to recap, the authors propose that subjects will predict the alternation of tone durations (short-term predictions) and the beginning of new phrasal chunks (long-term predictions).

The problem with this is that subjects are also likely to predict the completion of the current phrasal chunk. In speech, phrases are seldom left incomplete. In Spanish is very unlikely to hear a function-word that is not followed by a content-word (and the opposite happens in Basque). On the contrary, after the completion of a phrasal chunk, a speaker might stop talking and a silence might follow, instead of the beginning of a new phrasal chunk.

Considering that the completion of a phrasal chunk is more likely than the beginning of a new one, the prior endowed to the participants by their linguistic background should make us expect a pattern of results actually opposite to the one reported here.

Response: We acknowledge the plausibility of the hypothesis advanced by Reviewer #1. We would like to further clarify the rationale that led us to predict that the hypothesized long-term predictions should manifest at the onset of (and not within) a “phrasal chunk”. The hypothesis does not directly concern the probability of a short event to follow a long one (or the other way around), which to our knowledge has not been systematically quantified in previous cross-linguistic studies. Rather, it concerns how the auditory system forms higher-level auditory chunks based on the rhythmic properties of the native language, which is what the previous behavioral studies on perceptual grouping have addressed (e.g., Iversen 2008; Molnar et al. 2014; Molnar et al. 2016). When presented with sequences of two tones alternating in duration, Spanish speakers typically report perceiving the auditory stream as a repetition of short-long chunks separated by a pause, while speakers of Basque usually report the opposite long-short grouping bias. These results suggest that the auditory system performs a chunking operation by grouping pairs of tones into compressed, higher-level auditory units (often perceived as a single event). The way two constituent tones are combined depends on linguistic experience. Based on this background, we hypothesized the presence of (i) a short-term system that merely encodes a repetition of alternations rule and predicts transitions from one constituent tone to the other (a → b → a → b, etc.); (ii) a long-term system that encodes a repetition of concatenated alternations rule and predicts transitions from one high-level unit to the other (ab → ab, etc.). Under this view, we expect predictions based on the long-term system to be stronger at the onset of (rather than within) high-level units and therefore omissions of the first constituent tone to elicit larger responses than omissions of the second constituent tone.

In other words, the omission of the onset tone would reflect the omission of the whole chunk. On the other hand, the omission of the internal tone would be better handled by the short-term system, involved in processing the low-level structure of our sequences.

A similar concern was also raised by Reviewer #2. We will include the view proposed by Reviewer #1 and Reviewer #2 in the updated version of the manuscript.

1. The authors report an interaction effect that modulates the amplitude of the omission response, but caveats make the interpretation of this effect somewhat uncertain. The authors report a widespread omission response, which resembles the classical mismatch response (in MEG) with strong activations in sensors over temporal regions. Instead, the interaction found is circumscribed to four sensors that do not overlap with the peaks of activation of the omission response.

Response: We appreciate that all three reviewers agreed on the robustness of the data analysis pipeline. The approach employed to identify the presence of an interaction effect was indeed conservative, using a non-parametric test on combined gradiometers data, no a priori assumptions regarding the location of the effect, and small cluster thresholds (cfg.clusteralpha = 0.05) to enhance the likelihood of detecting highly localized clusters with large effect sizes. This approach led to the identification of the cluster illustrated in Figure 2c, where the interaction effect is evident. The fact that this interaction effect arises in a relatively small cluster of sensors does not alter its statistical robustness. The only partial overlap of the cluster with the activation peaks might simply reflect the fact that distinct sources contribute to the generation of the omission-MMN, which has been demonstrated in numerous prior studies (e.g., Zhang et al., 2018; Ross & Hamm, 2020).

Furthermore, the boxplot in Figure 2E suggests that part of the interaction effect might be due to the presence of two outliers (if removed, the effect is no longer significant). Overall, it is possible that the reported interaction is driven by a main effect of omission type which the authors report, and find consistently only in the Basque group (showing a higher amplitude omission response for long tones than for short tones). Because of these points, it is difficult to interpret this interaction as a modulation of the omission response.

Response: The two participants mentioned by Reviewer #1, despite being somewhat distant from the rest of the group, are not outliers according to the standard Tukey’s rule. As shown in Author response image 1 below, no participant fell outside the upper (Q3+1.5xIQR) and lower whiskers (Q1-1.5xIQR) of the boxplot.

Author response image 1.

The presence of a main effect of omission type does not impact the interpretation of the interaction, especially considering that these effects emerge over distinct clusters of channels.

The code to generate Author response image 1 and the corresponding statistics have been added to the script “analysis_interaction_data.R” in the OSF folder (https://osf.io/6jep8/).

It should also be noted that in the source analysis, the interaction only showed a trend in the left auditory cortex, but in its current version the manuscript does not report the statistics of such a trend.

Response: Our interpretation of the results for the present study is mainly driven by the effect observed on sensor-level data, which is statistically robust. The source modeling analyses (in non-invasive electrophysiology) provide a possible model of the candidate brain sources driving the effect observed at the sensor level. The source showing the interactive effect in our study is the left auditory cortex. More details and statistics will be provided in the reviewed version of the manuscript.

Reviewer #2 (Public Review):

1. Despite the evidence provided on neural responses, the main conclusion of the study reflects a known behavioral effect on rhythmic sequence perceptual organization driven by linguistic background (Molnar et al. 2016, particularly). Also, the authors themselves provide a good review of the literature that evidences the influence of long-term priors in neural responses related to predictive activity. Thus, in my opinion, the strength of the statements the authors make on the novelty of the findings may be a bit far-fetched in some instances.

Response: We will consider the suggestion of reviewer #2 for the new version of the manuscript. Overall, we believe that the novelty of the current study lies in bridging together findings from two research fields - basic auditory neuroscience and cross-linguistic research - to provide evidence for a predictive coding model in the auditory that uses long-term priors to make perceptual inferences.

1. Albeit the paradigm is well designed, I fail to see the grounding of the hypotheses laid by the authors as framed under the predictive coding perspective. The study assumes that responses to an omission at the beginning of a perceptual rhythmic pattern will be stronger than at the end. I feel this is unjustified. If anything, omission responses should be larger when the gap occurs at the end of the pattern, as that would be where stronger expectations are placed: if in my language a short sound occurs after a long one, and I perceptually group tone sequences of alternating tone duration accordingly, when I hear a short sound I will expect a long one following; but after a long one, I don't necessarily need to expect a short one, as something else might occur.

Response: A similar point was advanced by Reviewer #1. We tried to clarify our hypothesis (see above). We will consider including this interpretation in the updated version of the manuscript.

1. In this regard, it is my opinion that what is reflected in the data may be better accounted for (or at least, additionally) by a different neural response to an omission depending on the phase of an underlying attentional rhythm (in terms of Large and Jones rhythmic attention theory, for instance) and putative underlying entrained oscillatory neural activity (in terms of Lakatos' studies, for instance). Certainly, the fact that the aligned phase may differ depending on linguistic background is very interesting and would reflect the known behavioral effect.

Response: We thank the reviewer for this comment, which is indeed very pertinent. Below are some comments highlighting our thoughts on this.

1) We will explore in more detail the possibility that the aligned phase may differ depending on linguistic background, which is indeed very interesting. However, we believe that even if a phase modulation by language experience is found, it would not negate the possibility that the group differences in the MMN are driven by different long-term predictions. Rather, since the hypothesized phase differences would be driven by long-term linguistic experience, phase entrainment may reflect a mechanism through which long-term predictions are carried. On this point, we agree with the Reviewer when says that “this view would not change the impact of the results but add depth to their interpretation”.

2) Related to the point above: Despite evoked responses and oscillations are often considered distinct electrophysiological phenomena, current evidence suggests that these phenomena are interconnected (e.g., Studenova et al., 2023). In our view, the hypotheses that the MMN reflects differences in phase alignment and long-term prediction errors are not mutually exclusive.

3) Despite the plausibility of the view proposed by reviewer #2, many studies in the auditory neuroscience literature putatively consider the MMN as an index of prediction error (e.g., Bendixen et al., 2012; Heilbron and Chait, 2018). There are good reasons to believe that also in our study the MMN reflects, at least in part, an error response.

In the updated version of the manuscript, we will include a paragraph discussing the possibility that the reported group differences in the omission MMN might be partially accounted for by differences in neural entrainment to the rhythmic sound sequences.

Reviewer #3 (Public Review):

The main weaknesses are the strength of the effects and generalisability. The sample size is also relatively small by today's standards, with N=20 in each group. Furthermore, the crucial effects are all mostly in the .01>P<.05 range, such as the crucial interaction P=.03. It would be nice to see it replicated in the future, with more participants and other languages. It would also have been nice to see behavioural data that could be correlated with neural data to better understand the real-world consequences of the effect.

Response: We appreciate the positive feedback from Reviewer #3. Concerning this weakness highlighted: we agree with Reviewer #3 that it would be nice to see this study replicated in the future with larger sample sizes and a behavioral counterpart. Overall, we hope this work will lead to more studies using cross-linguistic/cultural comparisons to assess the effect of experience on neural processing. In the context of the present study, we believe that the lack of behavioral data does not undermine the main findings of this study, given the careful selection of the participants and the well-known robustness of the perceptual grouping effect (e.g., Iversen 2008; Yoshida et al., 2010; Molnar et al. 2014; Molnar et al. 2016). As highlighted by Reviewer #2, having Spanish and Basque dominant “speakers as a sample equates that in Molnar et al. (2016), and thus overcomes the lack of direct behavioral evidence for a difference in rhythmic grouping across linguistic groups. Molnar et al. (2016)'s evidence on the behavioral effect is compelling, and the evidence on neural signatures provided by the present study aligns with it.”

References

1. Bendixen, A., SanMiguel, I., & Schröger, E. (2012). Early electrophysiological indicators for predictive processing in audition: a review. International Journal of Psychophysiology, 83(2), 120-131.

2. Heilbron, M., & Chait, M. (2018). Great expectations: is there evidence for predictive coding in auditory cortex?. Neuroscience, 389, 54-73.

3. Iversen, J. R., Patel, A. D., & Ohgushi, K. (2008). Perception of rhythmic grouping depends on auditory experience. The Journal of the Acoustical Society of America, 124(4), 2263-2271.

4. Molnar, M., Lallier, M., & Carreiras, M. (2014). The amount of language exposure determines nonlinguistic tone grouping biases in infants from a bilingual environment. Language Learning, 64(s2), 45-64.

5. Molnar, M., Carreiras, M., & Gervain, J. (2016). Language dominance shapes non-linguistic rhythmic grouping in bilinguals. Cognition, 152, 150-159.

6. Ross, J. M., & Hamm, J. P. (2020). Cortical microcircuit mechanisms of mismatch negativity and its underlying subcomponents. Frontiers in Neural Circuits, 14, 13.

7. Simon, J., Balla, V., & Winkler, I. (2019). Temporal boundary of auditory event formation: An electrophysiological marker. International Journal of Psychophysiology, 140, 53-61.

8. Studenova, A. A., Forster, C., Engemann, D. A., Hensch, T., Sander, C., Mauche, N., ... & Nikulin, V. V. (2023). Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG. bioRxiv, 2023-02.

9. Yoshida, K. A., Iversen, J. R., Patel, A. D., Mazuka, R., Nito, H., Gervain, J., & Werker, J. F. (2010). The development of perceptual grouping biases in infancy: A Japanese-English cross-linguistic study. Cognition, 115(2), 356-361.

10. Zhang, Y., Yan, F., Wang, L., Wang, Y., Wang, C., Wang, Q., & Huang, L. (2018). Cortical areas associated with mismatch negativity: A connectivity study using propofol anesthesia. Frontiers in Human Neuroscience, 12, 392.

Author Response

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

Reviewer #1:

Watanuki et al used metabolomic tracing strategies of U-13C6-labeled glucose and 13C-MFA to quantitatively identify the metabolic programs of HSCs during steady-state, cell-cycling, and OXPHOS inhibition. They found that 5-FU administration in mice increased anaerobic glycolytic flux and decreased ATP concentration in HSCs, suggesting that HSC differentiation and cell cycle progression are closely related to intracellular metabolism and can be monitored by measuring ATP concentration. Using the GO-ATeam2 system to analyze ATP levels in single hematopoietic cells, they found that PFKFB3 can accelerate glycolytic ATP production during HSC cell cycling by activating the rate-limiting enzyme PFK of glycolysis. Additionally, by using Pfkfb3 knockout or overexpressing strategies and conducting experiments with cytokine stimulation or transplantation stress, they found that PFKFB3 governs cell cycle progression and promotes the production of differentiated cells from HSCs in proliferative environments by activating glycolysis. Overall, in their study, Watanuki et al combined metabolomic tracing to quantitatively identify metabolic programs of HSCs and found that PFKFB3 confers glycolytic dependence onto HSCs to help coordinate their response to stress. Even so, several important questions need to be addressed as below:

We sincerely appreciate the constructive feedback from the reviewer. Additional experiments and textual improvements have been made to the manuscript based on your valuable suggestions. In particular, the major revisions are as follows: First, we investigated the extent to which other metabolites, not limited to the glycolytic system, affect metabolism in HSCs after 5-FU treatment. Second, the extent to which PFKFB3 contributes to the expansion of the HSPC pool in the bone marrow was adjusted to make the description more accurate based on the data. Finally, we overexpressed PFKFB3 in HSCs derived from GO-ATeam2 mice and confirmed that PRMT1 inhibition did not reduce the ATP concentration. We believe that the reviewer's valuable comments have further deepened our knowledge of the significance of glycolytic activation by PFKFB3 that we have demonstrated. Our response to the "Recommendations for Authors" is listed first, followed by our responses to all "Public Review" comments as follows:

(Recommendations For The Authors):

1. The methods used in key experiments should be described in more detail. For example, in the section on ‘Conversion of GO-ATeam2 fluorescence to ATP concentration’, the knock-in strategy for GO-ATeam2 should be described, as well as U-13C6 -glucose tracer assays.

As per your recommendation, we have described the key experimental method in more detail in the revised manuscript: the GO-ATeam2 knock-in method was reported by Yamamoto et al. 1. Briefly, they used a CAG promoter-based knock-in strategy targeting the Rosa26 locus to generate GO-ATeam2 knock-in mice. A description of the method has been added to Methods and the reference has been added to the citation.

For the U-13C6-glucose tracer analysis, the following points were added to describe the details of the analysis: First, a note was added that the number of cells used for the in vitro tracer analysis was the number of cells used for each sample. Second, we added the solution from which the cells were collected by sorting. We added that the incubation was performed under 1% O2 and 5% CO2.

1. Confusing image label of Supplemental Figure 1H should be corrected in line 253.

We have corrected the incorrect figure caption on line 217 in the revised manuscript to "Supplemental Figure 1N" as you suggested.

1. The percentage of the indicated cell population should also be shown in Figure S1B.

As you indicated, we have included the percentages for each population in Supplemental Figure 1B.

Author response image 1.

1. Please pay attention to the small size of the marks in the graph, such as in Figure S1F and so on.

As you indicated, we have corrected the very small text contained in Figure S1F. Similar corrections have been made to Figures S1B and S5A.

1. Please pay attention to the label of line in Figure S6A-D.

Thank you very much for the advice. We have added line labels to the graph in the original Figures S6A–D.

(Specific comments)

1. Based on previous reports, the authors expanded the LSK gate to include as many HSCs as possible (Supplemental Figure 1B). However, while they showed the gating strategy on Day 6 after 5-FU treatment, results from other time-points should also be displayed to ensure the strict selection of time-points.

Thank you for pointing this out. First, we did not enlarge the Sca-1 gating in this study. We apologize for any confusion caused by the incomplete description. The gating of c-Kit is based on that shown by Umemoto et al (Figure EV1A) 2, who used 250 mg/kg 5-FU, so their c-Kit reduction is more pronounced than ours.

We followed this study and compared c-Kit expression in Lin-Sca-1+CD150+CD48-EPCR+ gates to BMMNCs on day 6 after 5-FU administration (150 mg/kg). The results are shown below.

Author response image 2.

>

Since the MFI of c-Kit was downregulated, we used gating that extended the c-Kit gate to lower-expression regions on day 6 after 5-FU administration (revised Figure S1C). At other time points, LSK gating was the same as in the PBS-treated group, as noted in the Methods.

1. In Figure 1, the authors examined the metabolite changes on Day 6 after 5-FU treatment. However, it is important to consider whether there are any dynamic adjustments to metabolism during the early and late stages of 5-FU treatment in HSCs compared to PBS treatment, in order to coordinate cell homeostasis despite no significant changes in cell cycle progression at other time-points.

Thank you for pointing this out. Below are the results of the GO-ATeam2 analysis during the very early phase (day 3) and late phase (day 15) after 5-FU administration (revised Figures S7A–H).

Author response image 3.

In the very early phase, such as day 3 after 5-FU administration, cell cycle progression had not started (Figure S1C) and was not preceded by metabolic changes. Meanwhile, in the late phase, such as day 15 after 5-FU administration, the cell cycle and metabolism returned to a steady state. In summary, the timing of the metabolic changes coincided with that of cell cycle progression. This point is essential for discussing the cell cycle-dependent metabolic system of HSCs and has been newly included in the Results (page 11, lines 321-323).

1. As is well known, ATP can be produced through various pathways, including glycolysis, the TCA cycle, the PPP, NAS, lipid metabolism, amino acid metabolism and so on. Therefore, it is important to investigate whether treatment with 5-FU or oligomycin affects these other metabolic pathways in HSCs.

As the reviewer pointed out, ATP production by systems other than the glycolytic system of HSCs is also essential. In this revised manuscript, we examined the effects of the FAO inhibitor (Etomoxir, 100 µM) and the glutaminolysis inhibitor 6-diazo-5-oxo-L-norleucine (DON, 2mM) alone or in combination on the ATP concentration of HSCs after PBS or 5-FU treatment. As shown below, there was no apparent decrease in ATP concentration (revised Figures S7J–M).

Author response image 4.

Fatty acid β-oxidation activity was also measured in 5-FU-treated HSCs using the fluorescent probe FAOBlue and was unchanged compared to PBS-treated HSCs (revised Figure S7N).

Author response image 5.

Notably, the addition of 100 µM etomoxir plus glucose and Pfkfb3 inhibitors resulted in a rapid decrease in ATP concentration in HSCs (revised Figures S7O–P). This indicates that etomoxir partially mimics the effect of oligomycin, suggesting that at a steady state, OXPHOS is driven by FAO, but can be compensated by the acceleration of the glycolytic system by Pfkfb3. Meanwhile, the exposure of HSCs to Pfkfb3 inhibitors in addition to 2 mM DON, which is an extremely high dose considering that the Ki value of DON for glutaminase is 6 µM, did not reduce ATP (revised Figures S7O–P). This suggests that ATP production from glutaminolysis is limited in HSCs at a steady state.

Author response image 6.

These points suggest that OXPHOS is driven by fatty acids at a steady state, but unlike the glycolytic system, FAO is not further activated by HSCs after 5-FU treatment. The results of these analyses and related descriptions are included in the revised manuscript (page 11, lines 332-344).

1. In part 2, they showed that oligomycin treatment of HSCs exhibited activation of the glycolytic system, but what about the changes in ATP concentration under oligomycin treatment? Are other metabolic systems affected by oligomycin treatment?

Thank you for your thoughtful comments. The relevant results we have obtained so far with the GO-ATeam2 system are as follows: First, OXPHOS inhibition in the absence of glucose significantly decreases the ATP concentration of HSCs (Figure 4C). Meanwhile, OXPHOS inhibition in the presence of glucose maintains the ATP concentration of HSCs (Figure 5B). Since it is difficult to imagine a completely glucose-free environment in vivo, it is thought that ATP concentration is maintained by the acceleration of the glycolytic system even under hypoxic or other conditions that inhibit OXPHOS.

Meanwhile, glucose tracer analysis shows that OXPHOS inhibition suppresses nucleic acid synthesis (NAS) except for the activation of the glycolytic system (Figures 2C–F). This is because phosphate groups derived from ATP are transferred to nucleotide mono-/di-phosphate in NAS, but OXPHOS, the main source of ATP production, is impaired, along with the enzyme conjugated with OXPHOS in the process of NAS (dihydroorotate dehydrogenase, DHODH). We have added a new paragraph in the Discussion section (page 17, lines 511-515) to provide more insight to the reader by summarizing and discussing these points.

1. In Figure 5M, it would be helpful to include a control group that was not treated with 2-DG. Additionally, if Figure 5L is used as the control, it is unclear why the level of ATP does not show significant downregulation after 2-DG treatment. Similarly, in Figure 5O, a control group with no glucose addition should be included.

Thank you for your advice. The experiments corresponding to the control groups in Figures 5M and O were in Figures 5L and N, respectively, but we have combined them into one graph (revised Figures 5L–M). The results more clearly show that PFKFB3 overexpression enhances sensitivity to 2-DG, but also enhances glycolytic activation upon oligomycin administration.

Author response image 7.

1. In this study, their findings suggest that PFKFB3 is required for glycolysis of HSCs under stress, including transplantation. In Figure 7B, the results showed that donor-derived chimerism in PB cells decreased relative to that in the WT control group during the early phase (1 month post-transplant) but recovered thereafter. Although the transplantation cell number is equal in two groups of donor cells, it is unclear why the donor-derived cell count decreased in the 2-week post-transplantation period and recovered thereafter in the Pfkgb3 KO group. Therefore, they should provide an explanation for this. Additionally, they only detected the percentage of donor-derived cells in PB but not from BM, which makes it difficult to support the argument for Increasing the HSPC pool.

As pointed out by the reviewer, it is interesting to note that the decrease in peripheral blood chimerism in the PFKFB3 knockout is limited to immediately after transplantation and then catches up with the control group (Figure 7B). We attribute this to the fact that HSPC proliferation is delayed immediately after transplantation in PFKFB3 deficiency, but after a certain time, PB cells produced by the delayed proliferating HSPCs are supplied. In support of this, the PFKFB3 knockout HSPCs did not exhibit increased cell death after transplantation (Figure 7K), while a delayed cell cycle was observed (Figures 7G–J). A description of this point has been added to the Discussion (page 19, lines 573-579).

In addition, the knockout efficiency in bone marrow cells could not be verified because the number of cells required for KO efficiency analysis was not available. Therefore, we have added a statement on this point and have toned down our overall claim regarding the extent to which PFKFB3 is involved in the expansion of the HSPC pool (page 15, lines 474-476).

1. In Figure 7E, they collected the BM reconstructed with Pfkfb3- or Rosa-KO HSPCs two months after transplantation, and then tested their resistance to 5-FU. However, the short duration of the reconstruction period makes it difficult to draw conclusions about the effects on steady-state blood cell production.

We agree that we cannot conclude from this experiment alone that PFKFB3 is completely unnecessary in steady state because, as you pointed out, the observation period of the experiment in Figure 7E is not long. We have toned down the claim by stating that PFKFB3 is only less necessary in steady-state HSCs compared to proliferative HSCs (page 15, lines 460-461).

1. PFK is allosterically activated by PFKFB, and other members of the PFKFB family could also participate in the glycolytic program. Therefore, they should investigate their function in contributing to glycolytic plasticity in HSCs during proliferation. Additionally, they should also analyze the protein expression and modification levels of other members. Although PFKFB3 is the most favorable for PFK activation, the role of other members should also be explored in HSC cell cycling to provide sufficient reasoning for choosing PFKFB3.

To further justify why we chose PFKFB3 among the PFKFB family members, we reviewed our data and the publicly available Gene Expression Commons (GEXC) 3. PFKFB3 is the most highly expressed member of the PFKFB family in HSCs (revised Figure 4F), and its expression increases with proliferation (Author response image 9). In addition to this, we have also cited the literature 4 indicating that AZ PFKFB3 26 is a Pfkfb3-specific inhibitor that we used in this paper, and added a note to this point (that it is specific) (page 11, lines 327-329). Through these revisions, we sought to strengthen the rationale for Pfkfb3 as the primary target of the analysis.

Author response image 8.

Author response image 9.

1. In this study, the authors identified PRMT1 as the upstream regulator of PFKFB3 that is involved in the glycolysis activation of HSCs. However, PRMT1 is also known to participate in various transcriptional activations. Thus, it is important to determine whether PRMT1 affects glycolysis through transcriptional regulation or through its direct regulation of PFKFB3? Additionally, the authors should investigate whether PRMT1i inhibits ATP production in normal HSCs. Moreover, could we combine Figure 6I and 6J for analysis. Finally, the authors could conduct additional rescue experiments to demonstrate that the effect of PRMT1 inhibitors on ATP production can be rescued by overexpression of PFKFB3.

Although PRMT1 inhibition reduced m-PFKFB3 levels in HSCs, 5-FU treatment also reduced or did not alter Pfkfb3 transcript levels (Figures 6B, G) and the expression of genes such as Hoxa7/9/10, Itga2b, and Nqo1, which are representative transcriptional targets of PRMT1, in proliferating HSCs after 5-FU treatment (revised Figure S9).

Author response image 10.

These results suggest that PRMT1 promotes PFKFB3 methylation, which increases independently of transcription in HSCs after 5-FU treatment.

A summary analysis of the original Figures 6I and 6J is shown below (revised Figure 6I).

Author response image 11.

Finally, we tested whether the inhibition of the glycolytic system and the decrease in ATP concentration due to PRMT1 inhibition could be rescued by the retroviral overexpression of PFKFB3. We found that PFKFB3 overexpression did not decrease the ATP concentration in HSCs due to PRMT1 inhibition (revised Figure 6J). Therefore, PFKFB3 overexpression mitigated the decrease in ATP concentration caused by PRMT1 inhibition. These data and related statements have been added to the revised manuscript (page 14, lines 427-428).

Author response image 12.

Reviewer #2:

In the manuscript Watanuki et al. want to define the metabolic profile of HSCs in stress/proliferative (myelosuppression with 5-FU), and mitochondrial inhibition and homeostatic conditions. Their conclusions are that during proliferation HSCs rely more on glycolysis (as other cell types) while HSCs in homeostatic conditions are mostly dependent on mitochondrial metabolism. Mitochondrial inhibition is used to demonstrate that blocking mitochondrial metabolism results in similar features of proliferative conditions.

The authors used state-of-the-art technologies that allow metabolic readout in a limited number of cells like rare HSCs. These applications could be of help in the field since one of the major issues in studying HSCs metabolism is the limited sensitivity of the“"standard”" assays, which make them not suitable for HSC studies.

However, the observations do not fully support the claims. There are no direct evidence/experiments tackling cell cycle state and metabolism in HSCs. Often the observations for their claims are indirect, while key points on cell cycle state-metabolism, OCR analysis should be addressed directly.

We sincerely appreciate the reviewer's constructive comments. Thank you for highlighting the importance of the highly sensitive metabolic assay developed in this study and the findings based on it. Meanwhile, the reviewer's comments have made us aware of areas where we can further improve this manuscript. In particular, in the revised manuscript, we have performed further studies to demonstrate the link between the cell cycle and metabolic state. Specifically, we further subdivided HSCs by the uptake of in vivo-administered 2-NBDG and performed cell cycle analysis. Next, HSCs after PBS or 5-FU treatment were analyzed by a Mito Stress test using the Seahorse flux analyzer, including ECAR and OCR, and a more direct relationship between the cell cycle state and the metabolic system was found. We believe that the reviewer's valuable suggestions have helped us clarify more directly the importance of the metabolic state of HSCs in response to cell cycle and stress that we wanted to show and emphasize the usefulness of the GO-ATeam2 system. Our response to "Recommendations For The Authors" is listed first, followed by our responses to all comments in "Public Review" as follows:

(Recommendations For The Authors):

In general, I believe it would be important:

1. to directly associate cell cycle state with metabolic state. For example, by sorting HSC (+/- 5FU) based on their cell cycle state (exploiting the mouse model presented in the manuscript or by defining G0/G1/G2-S-M via Pyronin/Hoechst staining which allow to sort live cells) and follow the fate of radiolabeled glucose.

Thank you for raising these crucial points. Unfortunately, it was difficult to perform the glucose tracer analysis by preparing HSCs with different cell cycle states as you suggested due to the amount of work involved. In particular, in the 5-FU group, more than 60 mice per group were originally required for an experiment, and further cell cycle-based purification would require many times that number of mice, which we felt was unrealistic under current technical standards. As an alternative, we administered 2-NBDG to mice and fractionated HSCs at the 2-NBDG fluorescence level for cell cycle analysis. The results are shown below (revised Figure S1M). Notably, even in the PBS-treated group, HSCs with high 2-NBDG uptake were more proliferative than those with low 2-NBDG uptake and are comparable to HSCs after 5-FU treatment, although the overall population of HSCs exiting the G0 phase and entering the G1 phase increased after 5-FU treatment. In both PBS/5-FU-treated groups, these large differences in cell cycle glucose utilization suggest a direct link between HSC proliferation and glycolysis activation. If a more sensitive type of glucose tracer analysis becomes available in the future, it may be possible to directly address the reviewer's comments. We see this as a topic for the future. The descriptions of the above findings and perspectives have been added to the Results and Discussion section (page 7, lines 208-214, page 20, lines 607-610).

Author response image 13.

1. Use other radio labeled substrates (fatty acid, glutamate)

Thank you very much for your suggestion. While this is an essential point for future studies, we believe it is not the primary focus of the paper. We are planning another research project on tracer analysis using labeled fatty acids and glutamates, which we will report on in the near future. We have clearly stated in the Abstract and Introduction of the revised manuscript, that the focus of this study is on changes in glucose metabolism when HSCs are stressed (page 3, line 75 and 87, page 5, lines 135).

Instead, we added the following analyses of metabolic changes in fatty acids and glutamate using the GO-ATeam2 system. HSCs derived from GO-ATeam2 mice treated with PBS or 5-FU were used to measure changes in ATP concentrations after exposure to the fatty acid beta-oxidation (FAO) inhibitor etomoxir and the glutaminolysis inhibitor 6-diazo-5-oxo-L-norleucine (DON). Etomoxir was used at 100 µM, a concentration that inhibits FAO without inhibiting mitochondrial electron transfer complex I, as previously reported 5. DON was used at 2 mM, a concentration that sufficiently inhibits the enzyme as the Ki for glutaminase is 6 µM. In this experiment, etomoxir alone, DON alone, or etomoxir and DON in combination did not decrease the ATP concentration of HSCs in the PBS and 5-FU groups (revised Figures S7J–M), suggesting that FAO and glutaminolysis were not essential for ATP production in HSCs in the short term. Thus, according to the analysis using the GO-Ateam2 system, HSCs exposed to acute stresses change the efficiency of glucose utilization (accelerated glycolytic ATP production) rather than other energy sources. Since there are reports that FAO and glutaminolysis are required for HSC maintenance in the long term 5,6, compensatory pathways may be able to maintain ATP levels in the short term. A description of these points has been added to the Discussion (page 11, lines 332-344).

Author response image 14.

1. Include OCR analyses.

In addition to the ECAR data of the Mito Stress test (original Figures 2G–H), OCR data were added to the revised manuscript (revised Figures 2H, S3D). Compared to c-Kit+ myeloid progenitors (LKS- cells), HSC showed a similar increase in ECAR, while the decrease in OCR was relatively limited. A possible explanation for this is that glycolytic and mitochondrial metabolism are coupled in c-Kit+ myeloid progenitors, whereas they are decoupled in HSCs. This is also suggested by the glucose plus oligomycin experiment in Figures 5B, C, and S6A–D (orange lines). In summary, in HSCs, glycolytic and mitochondrial ATP production are decoupled and can maintain ATP levels by glycolytic ATP production alone, whereas in progenitors including GMPs, the two ATP production systems are constantly coupled, and glycolysis alone cannot maintain ATP concentration. We have added descriptions of these points in the Results and Discussion section (page 8, lines 240-243, page 18, lines 558-561).

Author response image 15.

Next, a Mito Stress test was performed using HSCs derived from PBS- or 5-FU-treated mice in the presence or absence of oligomycin (revised Figures 1G–H, S3A–B). Without oligomycin treatment, ECAR in 5-FU-treated HSCs was higher than in PBS-treated HSCs, and OCR was unchanged. Oligomycin treatment increased ECAR in both PBS- and 5-FU-treated HSCs, whereas OCR was unchanged in PBS-treated HSCs, but significantly decreased in 5-FU-treated HSCs. Changes in ECAR in response to oligomycin differed between HSC proliferation or differentiation: ECAR increased in 5-FU-treated HSCs but not in LKS- progenitors (original Figures 2G–H). This suggests a metabolic feature of HSCs in which the coupling of OXPHOS with glycolysis seen in LKS- cells is not essential in HSCs even after cell cycle entry. The results and discussion of this experiment have been added to page 7, lines 194-201 and page 18, lines 558-561).

Author response image 16.

1. Correlate proliferation-mitochondrial inhibition-metabolic state

We agree that it is important to clarify this point. First, OXPHOS inhibition and proliferation similarly accelerate glycolytic ATP production with PFKFB3 (Figures 4G, I, and 5F–I). Meanwhile, oligomycin treatment rapidly decreases ATP in HSCs with or without 5-FU administration (Figure 4C). These results suggest that OXPHOS is a major source of ATP production both at a steady state and during proliferation, even though the analysis medium is pre-saturated with hypoxia similar to that in vivo. This has been added to the Discussion section (page 17, lines 520-523).

1. Tune down the claim on HSCs in homeostatic conditions since from the data it seems that HSCs rely more on anaerobic glycolysis.

Thanks for the advice. The original Figures S2C, D, F, and G show that HSC is dependent on the anaerobic glycolytic system even at a steady state, so we have toned down our claims (page 7, lines 192-194).

1. For proliferative HSCs mitochondrial are key. When you block mitochondria with oligomycin there's the biggest drop in ATP.

In the revised manuscript, we have tried to highlight the key findings that you have pointed out. First, we mentioned in the Discussion (page 17, lines 523-525) that previous studies suggested the importance of mitochondria in proliferating HSCs. Meanwhile, the GO-ATeam2 and glucose tracer analyses in this study newly revealed that the glycolytic system activated by PFKFB3 is activated during the proliferative phase, as shown in Figure 4C. We also confirmed that mitochondrial ATP production is vital in proliferating HSCs, and we hope to clarify the balance between ATP-producing pathways and nutrient sources in future studies.

1. To better clarify this point authors, authors should do experiments in hypoxic conditions and compare it to oligomycin treatment and showing that mito-inhibition acts differently on HSCs (considering that all these drugs are toxic for mitochondria and induce rapidly stress responses ex: mitophagy).

We apologize for any confusion caused by not clearly describing the experimental conditions. As pointed out by the reviewer, we also recognize the importance of experiments in a hypoxic environment. All GO-ATeam2 analyses were performed in a medium saturated sufficiently under hypoxic conditions and analyzed within minutes, so we believe that the medium did not become oxygenated (page S5-S6, lines 160-163 in the Methods). Despite being conducted under such hypoxic conditions, the substantial decrease in ATP after oligomycin treatment is intriguing (original Figures 4C, 5B, 5C). The p50 value of mitochondria (the partial pressure of oxygen at which respiration is half maximal) is 0.1 kPa, which is less than 0.1% of the oxygen concentration at atmospheric pressure 7. Thus, biochemically, it is consistent that OXPHOS can maintain sufficient activity even in a hypoxic environment like the bone marrow. We are currently embarking on a study to determine ATP concentration in physiological hypoxic conditions using in vivo imaging within the bone marrow, which we hope to report in a separate project. We have discussed these points, technical limitations, and perspectives in the Discussion section (page 20, lines 610-612).

• In Figure 1 C, D, E and F, the comparison should be done as unpaired t test and the control group should not be 1 as the cells comes from different individuals.

Thank you very much for pointing this out. We have reanalyzed and revised the figures (revised Figures 1C–F)

Author response image 17.

• In Figure S2A, the post-sorting bar of 6PG, R5P and S7P are missing.

Metabolites below the detection threshold (post-sorting samples of 6PG, R5P, and S7P) are now indicated as N.D. (not detected) (revised Figure S2A).

Author response image 18.

• In the 2NBDG experiments, authors should add the appropriate controls, since it has been shown that 2NBDG cellular uptake do not correctly reflect glucose uptake (Sinclair LV, Immunometabolism 2020) (a cell type dependent variations) thus inhibitors of glucose transporters should be added as controls (cytochalasin B; 4,6-O-ethylidene-a-D-glucose) it would be quite challenging to test it in vivo but it would be sufficient to show that in vitro in the different HSPCs analyzed.

We appreciate the essential technical point raised by the reviewer. In the revised manuscript, we performed a 2-NBDG assay with cytochalasin B and phloretin as negative controls. After PBS treatment, 2-NBDG uptake was higher in 5-FU-treated HSCs compared to untreated HSCs. This increase was inhibited by both cytochalasin B and phloretin. In PBS-treated HSCs, cytochalasin B did not downregulate 2-NBDG uptake, whereas phloretin did. Although cytochalasin B inhibits glucose transporters (GLUTs), it is also an inhibitor of actin polymerization. Therefore, its inhibitory effect on GLUTs may be weaker than that of phloretin. We have revised the figure (revised Figure S1L) and added the corresponding description (page 7, lines 207-208).

Author response image 19.

• S5C: authors should show the cell number for each population. If there's a decreased in % in Lin- that will be reflected in all HSPCs. Comparing the proportion of the cells doesn't show the real impact on HSPCs.

Thank you for your insightful point. In the revision, we compared the numbers, not percentages, of HSPCs and found no difference in the number of cells in the major HSPC fractions in Lin-. The figure has been revised (revised Figure S6C) and the corresponding description has been added (page 10, lines 296-299).

Author response image 20.

Minor:

1. In S1 F-G is not indicated in which day post 5FU injection is done the analysis. I assume on day 6 but it should be indicated in the figure legend and/or text.

Thank you for pointing this out. As you assumed, the analysis was performed on day 6. The description has been added to the legend of the revised Figure S1G.

1. S1K is not described in the text. What are proliferative and quiescence-maintaining conditions? The analyses are done by flow using LKS SLAM markers after culture? How long was the culture?

Thank you for your comments. First, the figure citation on line 250 was incorrect and has been corrected to Figure S1N. Regarding the proliferative and quiescence-maintaining conditions, we have previously reported on these 8. In brief, these are culture conditions that maintain HSC activity at a high level while allowing for the proliferation or maintenance of HSCs in quiescence, achieved by culturing under fatty acid-rich, hypoxic conditions with either high or low cytokine concentrations. Analysis was performed after one week of culture, with the HSC number determined by flow cytometry based on the LSK-SLAM marker. While these are mentioned in the Methods section, we have added a description in the main text to highlight these points for the reader (page 7, lines 214-217).

1. In Figure 5G, why does the blue line (PFKFB3 inhibitor) go up in the end of the real-time monitoring? Does it mean that other compensatory pathway is turned on?

As you have pointed out, we cannot rule out the possibility that other unknown compensatory ATP production pathways were activated. We have added a note in the Discussion section to address this (page 18, lines 555-556).

1. In Figure S6H&J, the reduction is marginal. Does it mean that PKM2 is not important for ATP production in HSCs?

The activity of the inhibitor is essential in the GO-ATeam2 analysis. The commercially available PKM2 inhibitors have a higher IC50 value (IC50 = 2.95 μM in this case). Nevertheless, the effect of reducing the ATP concentration was observed in progenitor cells, but not in HSCs. The report by Wang et al. 9 on the analysis using a PKM2-deficient model suggests a stronger effect on progenitor cells than on HSCs. Our results are similar to those of the previous report.

(Specific comments)

Specifically, there are several major points that rise concerns about the claims:

1. The gating strategy to select HSCs with enlarged Sca1 gating is not convincing. I understand the rationale to have a sufficient number of cells to analyze, however this gating strategy should be applied also in the control group. From the FACS plot seems that there are more HSCs upon 5FU treatment (Figure S1b). How that is possible? Is it because of the 20% more of cycling cells at day 6? To prove that this gating strategy still represents a pure HSC population, authors should compare the blood reconstitution capability of this population with a "standard" gated population. If the starting population is highly heterogeneous then the metabolic readout could simply reflect cell heterogeneity.

Thank you for pointing this out. First, we did not enlarge the Sca-1 gating in this study. We apologize for any confusion caused by the incomplete description. The gating of c-Kit is based on that shown by Umemoto et al (Figure EV1A) 2, who used 250 mg/kg 5-FU, so their c-Kit reduction is more pronounced than ours.

We followed this study and compared c-Kit expression in the Lin-Sca-1+CD150+CD48-EPCR+ gates to BMMNCs on day 6 after 5-FU administration (150 mg/kg). The results are shown below.

Author response image 21.

Since the MFI of c-Kit was downregulated, we used gating that extended the c-Kit gate to lower expression regions on day 6 after 5-FU administration (revised Figure S1C).

At other time points, LSK gating was the same as in the PBS-treated group, as noted in the Methods.

The reason why the number of HSCs appears to be higher in the 5-FU group is because most of the differentiated blood cells were lost due to 5-FU administration and the same number of cells as in the PBS group were analyzed by FACS, resulting in a relatively higher number of HSCs. The legend of Figure S1 shows that the number of HSCs in both the PBS and 5-FU groups appeared to increase because the same number of BMMNCs was obtained at the time of analysis (page S22, lines 596-598).

Regarding cellular heterogeneity, from a metabolic point of view, the heterogeneity in HSCs is rather reduced by 5-FU administration. As shown in Figure S3A–C, this is simulated under stress conditions, such as after 5-FU administration or during OXPHOS inhibition, where the flux variability in each enzymatic reaction is significantly reduced. GO-ATeam2 analysis after 5-FU treatment showed no increase in cell population variability. After 2-DG treatment, ATP concentrations in HSCs were widely distributed from 0 mM to 0.8 mM in the PBS group, while more than 80% of those in the 5-FU group were less than 0.4 mM (Figures 4B, D). HSCs may have a certain metabolic diversity at a steady state, but under stress conditions, they may switch to a more specialized metabolism with less cellular heterogeneity in order to adapt.

1. S2 does not show major differences before and after sorting. However, a key metabolite like Lactate is decreased, which is also one of the most present. Wouldn't that mean that HSCs once they move out from the hypoxic niche, they decrease lactate production? Do they decrease anaerobic glycolysis? How can quiescent HSC mostly rely on OXPHOS being located in hypoxic niche?

2. Since HSCs in the niche are located in hypoxic regions of the bone marrow, would that not mimic OxPhos inhibition (oligomycin)? Would that not mean that HSCs in the niche are more glycolytic (anaerobic glycolysis)?

3. In Figure 5B, the orange line (Glucose+OXPHOS inhibition) remains stable, which means HSCs prefer to use glycolysis when OXPHOS is inhibited. Which metabolic pathway would HSCs use under hypoxic conditions? As HSCs resides in hypoxic niche, does it mean that these steady-state HSCs prefer to use glycolysis for ATP production? As mentioned before, mitochondrial inhibition can be comparable at the in vivo condition of the niche, where low pO2 will "inhibit" mitochondria metabolism.

Thank you for the first half of comment 2 on the technical features of our approach. First, as you have pointed out, there is minimal variation and stable detection of many metabolites before and after sorting (Figure S2A), suggesting that isolation from the hypoxic niche and sorting stress do not significantly alter metabolite detection performance. This is consistent with a previous report by Jun et al. 10. Meanwhile, lactate levels decreased by sorting. Therefore, if the activity of anaerobic glycolysis was suppressed in stressed HSCs, it may be difficult to detect these metabolic changes with our tracer analysis. However, in this study, several glycolytic metabolites, including an increase in lactate, were detected in HSCs from 5-FU-treated mice compared with HSCs from PBS-treated mice that were similarly sorted and prepared, suggesting an increase in glycolytic activity. In other words, we may have been fortunate to detect the stress-induced activation of the glycolytic system beyond the characteristic of our analysis system that lactate levels tend to appear lower than they are. Given that damage to the bone marrow hematopoiesis tends to alleviate the low-oxygen status of the niche 11, we postulate that this upregulated aerobic glycolysis arises intrinsically in HSCs rather than from external conditions.

The second half of comment 2, and comments 7 and 10, are essential and overlapping comments and will be answered together. Although genetic analyses have shown that HSCs produce ATP by anaerobic glycolysis in low-oxygen environments 9,12, our GO-ATeam2 analysis in this study confirmed that HSCs also generate ATP via mitochondria. This is also supported by Ansó's prior findings where the knockout of the Rieske iron–sulfur protein (RISP), a constituent of the mitochondrial electron transport chain, impairs adult HSC quiescence and bone marrow repopulation 13. Bone marrow is a physiologically hypoxic environment (9.9–32.0 mmHg 11). However, the p50 value of mitochondria (the partial pressure of oxygen at which respiration is half maximal) is below 0.1% oxygen concentration at atmospheric pressure (less than 1 mmHg) 7. This suggests that OXPHOS can retain sufficient activity even under physiologically hypoxic conditions. We are currently initiating efforts to discern ATP concentrations in vivo within the bone marrow under physiological hypoxia. This will be reported in a separate project in the future. Admittedly, when we began this research, we did not anticipate the significant mitochondrial reliance of HSCs. As we previously reported, the metabolic uncoupling of glycolysis and mitochondria 12 may enable HSCs to activate only glycolysis, and not mitochondria, under stress conditions such as post-5-FU administration, suggesting a unique metabolic trait of HSCs. We have included these technical limitations and perspectives in the Discussion section (page 17, lines 520-523).

1. The authors performed challenging experiments to track radiolabeled glucose, which are quite remarkable. However, the data do not fully support the conclusions. Mitochondrial metabolism in HSCs can be supported by fatty acid and glutamate, thus authors should track the fate of other energy sources to fully discriminate the glycolysis vs mito-metabolism dependency. From the data on S2 and Fig1 1C-F, the authors can conclude that upon 5FU treatment HSCs increase glycolytic rate.

2. FIG.2B-C: Increase of Glycolysis upon oligomycin treatment is common in many different cell types. As explained before, other radiolabeled substrates should be used to understand the real effect on mitochondria metabolism.

Thank you for your suggestion. While this is essential for future studies, we believe it is not the primary focus of the paper. We are planning another research project on tracer analysis using labeled fatty acids and glutamates, which we will report on in the near future. We have clearly stated in the Abstract and Introduction of the revised manuscript that the focus of this study is on changes in glucose metabolism when HSCs are stressed (page 3, line 75 and 87, page 5, lines 135).

Instead, we have added the following analyses of metabolic changes in fatty acids and glutamate using the GO-ATeam2 system: HSCs derived from GO-ATeam2 mice treated with PBS or 5-FU were used to measure changes in ATP concentrations after exposure to the fatty acid beta-oxidation (FAO) inhibitor etomoxir and the glutaminolysis inhibitor 6-diazo-5-oxo-L-norleucine (DON). Etomoxir was used at 100 µM, a concentration that inhibits FAO without inhibiting mitochondrial electron transfer complex I, as previously reported 5. DON was used at 2 mM, a concentration that sufficiently inhibits the enzyme as the Ki for glutaminase is 6 µM. In this experiment, etomoxir alone, DON alone, or etomoxir and DON in combination did not decrease the ATP concentration of HSCs in the PBS and 5-FU groups (revised Figures S7J–M), suggesting that FAO and glutaminolysis were not essential for ATP production in HSCs in the short term. Thus, according to the analysis using the GO-Ateam2 system, HSCs exposed to acute stresses change the efficiency of glucose utilization (accelerated glycolytic ATP production) rather than other energy sources. Since there are reports that FAO and glutaminolysis are required for HSC maintenance in the long term 5,6, compensatory pathways may be able to maintain ATP levels in the short term. A description of these points has been added to the Discussion (page 17, lines 525-527).

Author response image 22.

Fatty acid β-oxidation activity was also measured in 5-FU-treated HSCs using the fluorescent probe FAOBlue and was unchanged compared to PBS-treated HSCs (revised Figure S7N).

Author response image 23.

Notably, the addition of 100 µM etomoxir plus glucose and Pfkfb3 inhibitors resulted in a rapid decrease in ATP concentration in HSCs (revised Figures S7O–P). This indicates that etomoxir partially mimics the effect of oligomycin, suggesting that at a steady state, OXPHOS is driven by FAO, but can be compensated by the acceleration of the glycolytic system by Pfkfb3. Meanwhile, the exposure of HSCs to Pfkfb3 inhibitors in addition to 2 mM DON did not reduce ATP (revised Figures S7O–P). This suggests that ATP production from glutaminolysis is limited in HSCs at a steady state.

Author response image 24.

These points suggest that OXPHOS is driven by fatty acids at a steady state, but unlike the glycolytic system, FAO is not further activated by HSCs after 5-FU treatment. The results of these analyses and related descriptions are included in the revised manuscript (page 11, lines 332-344).

1. In Figure S1, 5-FU leads to the induction of cycling HSCs and in figure 1, 5-FU results in higher activation of glycolysis. Would it be possible to correlate these two phenotypes together? For example, by sorting NBDG+ cells and checking the cell cycle status of these cells?

We appreciate the reviewer’s insightful comments. We administered 2-NBDG to mice and fractionated HSCs at the 2-NBDG fluorescence level for cell cycle analysis. The results are shown below (revised Figure S1M). Notably, even in the PBS-treated group, HSCs with high 2-NBDG uptake were more proliferative than HSCs with low 2-NBDG uptake and were comparable to HSCs after 5-FU treatment, although the overall population of HSCs that exited the G0 phase and entered the G1 phase increased after 5-FU treatment. In both PBS/5-FU-treated groups, these profound differences in cell cycle glucose utilization suggest a direct link between HSC proliferation and glycolysis activation. Descriptions of the above findings and perspectives have been added to the Results and Discussion section (page 7, lines 208-214, page 20, lines 607-610).

Author response image 25.

1. Why are only ECAR measurements (and not OCR measurements) shown? In Fig.2G, why are HSCs compared with cKit+ myeloid progenitors, and not with MPP1? The ECAR increased observed in HSC upon oligomycin treatment is shared with many other types of cells. However, cKit+ cells have a weird behavior. Upon oligo treatment cKit+ cells decrease ECAR, which is quite unusual. The data of both HSCs and cKit+ cells could be clarified by adding OCR curves. Moreover, it is recommended to run glycolysis stress test profile to assess the dependency to glycolysis (Glucose, Oligomycin, 2DG).

In addition to the ECAR data of the Mito Stress test (original Figures 2G–H), OCR data were added in the revised manuscript (revised Figures 2H, S3D). Compared to c-Kit+ myeloid progenitors (LKS- cells), HSC exhibited a similar increase in ECAR, while the decrease in OCR was relatively limited. This may be because glycolytic and mitochondrial metabolism are coupled in c-Kit+ myeloid progenitors, whereas they are decoupled in HSCs. This is also suggested by the glucose plus oligomycin experiment in Figures 5B, C, and S6A–D (orange lines). In summary, in HSCs, glycolytic and mitochondrial ATP production are decoupled and can maintain ATP levels by glycolytic ATP production alone, whereas in progenitors including GMPs, the two ATP production systems are constantly coupled, and glycolysis alone cannot maintain the ATP concentration. While we could not conduct a glycolysis stress test, we believe that Pfkfb3-dependent glycolytic activation, which is evident in the oligomycin+glucose+Pfkfb3i experiment, is only apparent in HSCs when subjected to glucose+oligomycin treatment (original Figures 5F–I). We have added descriptions of these points in the Results and Discussion section (page 8, lines 240-243, page 18, lines 558-561).

Author response image 26.

FIG.3 A-C. As mentioned previously, the flux analyses should be integrated with data using other energy sources. If cycling HSCs are less dependent to OXPHOS, what happen if you inhibit OXHPHOS in 5-FU condition? Since the authors are linking OXPHOS inhibition and upregulation of Glycolysis to increase proliferation, do HSCs proliferate more when treated with oligomycin?

First, please see our response to comments 3 and 5 regarding the first part of this comment about the flux analysis of other energy sources. According to the analysis using the GO-Ateam2 system, stressed HSCs change the efficiency of glucose utilization (accelerated glycolytic ATP production) rather than other energy sources. The change in ATP concentration after OXPHOS inhibition for 5-FU-treated HSCs is shown in Figures 4C and E, suggesting that the activity of OXPHOS itself does not increase. HSCs after oligomycin treatment and HSCs after 5-FU treatment are similar in that they activate glycolytic ATP production. However, inhibition of OXPHOS did not induce the proliferation of HSCs (original Figure S1K). This suggests that proliferation activates glycolysis and not that activation of the glycolytic system induces proliferation. This similarity and dissimilarity of glycolytic activation upon proliferation and OXPHOS inhibition is discussed in the Discussion section (page 16-17, lines 505-515).

1. FIG.4 shows that in vivo administration of radiolabeled glucose especially marks metabolites of TCA cycle and Glycolysis. The authors interpret enhanced anaerobic glycolysis, but I am not sure this is correct; if more glycolysis products go in the TCA cycle, it might mean that HSC start engaging mitochondrial metabolism. What do the authors think about that?

Thank you for pointing this out. We believe that the data are due to two differences in the experimental features between in vivo (Figure S5) and in vitro (Figures 1 and S2) tracer analysis. The first difference is that in in vivo tracer analysis, unlike in vitro, all cells can metabolize U-13C6-glucose. Another difference is that after glucose labeling in vivo, it takes approximately 120–180 minutes to purify HSCs to extract metabolites, and processing on ice may result in a gradual progression of metabolic reactions within HSCs. As a result, in vivo tracer analysis may detect an increased influx of labeled carbon derived from U-13C6-glucose into the TCA cycle over an extended period. However, it is difficult to interpret whether this influx of labeled carbon is derived from the direct influx of glycolysis or the re-uptake by HSCs of metabolites that have been metabolized to other metabolites in other cells. Meanwhile, as shown in Figure 4C using the GO-ATeam2 system, ATP production from mitochondria is not upregulated by 5-FU treatment. This suggests that even if the direct influx from glycolysis into the TCA cycle is increased, the rate of ATP production does not exceed that of glycolysis. Despite these technical caveats in interpretation, the results of in vivo and in vitro tracer analyses are considered essential. In particular, we consider the increased labeling of metabolites involved in glycolysis and nucleotide synthesis to be crucial. We have added a discussion of these points, including experimental limitations (page 17-18, lines 530-545).

1. FIG.4: the experimental design is not clear. Are BMNNCs stained and then put in culture? Is it 6-day culture or BMNNCs are purified at day 6 post 5FU? FIG-4B-C The difference between PBS vs 5FU conditions are the most significant; however, the effect of oligomycin in both conditions is the most dramatic one. From this readout, it seems that HSCs are more dependent on mitochondria for energy production both upon 5FU treatment and in PBS conditions.

We apologize for the incomplete description of the experimental details. The experiment involved dispensing freshly stained BMMNC with surface antigens into the medium and immediately subjecting them to flow cytometry analysis. For post-5-FU treatment HSCs, mice were administered with 5-FU (day 1), and freshly obtained BMMNCs were analyzed on day 6. The analysis of HSCs and progenitors was performed by gating each fraction within the BMMNC (original Figure S5A). We have added these details to ensure that readers can grasp these aspects more clearly (page S5, lines 155-158).

As pointed out by the reviewer, we understand that HSCs produce more ATP through OXPHOS. However, ATP production by glycolysis, although limited, is observed under steady-state conditions (post-PBS treatment HSC), and its reliance increases during the proliferation phase (post-5-FU treatment HSC) (original Figures 4B, D). Until now, discussions on energy production in HSCs have focused on either glycolysis or mitochondrial functions. However, with the GO-ATeam2 system, it has become possible for the first time to compare their contributions to ATP production and evaluate compensatory pathways. As a result, it became evident that while OXPHOS is the main source of ATP production, the reliance on glycolysis plastically increases in response to stress. This has led to a better understanding of HSC metabolism. These points are included in the Discussion as well (page 16, lines 479-488).

1. FIG.6H should be extended with cell cycle analyses. There are no differences between 5FU and ctrl groups. If 5FU induces HSCs cycling and increases glycolysis I would expect higher 2-NBDG uptake in the 5FU group. How do the authors explain this?

Thank you for your comments. In the original Figure 6H, we found that 2-NBDG uptake correlated with mPFKFB3 levels in both the 5-FU and PBS groups. mPfkfb3 levels remained low in the few HSCs with low 2-NBDG uptake in the 5-FU group.

In the revised manuscript, to directly relate glucose utilization to the cell cycle, we administered 2-NBDG to mice and fractionated HSCs at the 2-NBDG fluorescence level for cell cycle analysis. The results are shown below (revised Figure S1M). Notably, even in the PBS-treated group, HSCs with high 2-NBDG uptake were more proliferative than those with low 2-NBDG uptake and are comparable to HSCs after 5-FU treatment, although the overall population of HSCs that exited the G0 phase and entered the G1 phase increased after 5-FU treatment. The large differences in glucose utilization per cell cycle observed in both PBS/5-FU-treated groups suggest a direct link between HSC proliferation and glycolysis activation. Descriptions of the above findings have been added to the Results and Discussion ((page 7, lines 208-214, page 20, lines 607-610).

Author response image 27.

1. In S7 the experimental design is not clear. What are quiescent vs proliferative conditions? What does it mean "cell number of HSC-derived colony"? Is it a CFU assay? Then you should show colony numbers. When HSCs proliferate, they need more energy thus inhibition of metabolism will impact proliferation. What happens if you inhibit mitochondrial metabolism with oligomycin?

Regarding the proliferative and quiescence-maintaining conditions, we have previously reported on these 8. In brief, these are culture conditions that maintain HSC activity at a high level while allowing for the proliferation or maintenance of HSCs in quiescence, achieved by culturing under fatty acid-rich, hypoxic conditions with either high or low cytokine concentrations. Analysis was performed after one week of culture, with the HSC number determined by flow cytometry based on the LSK-SLAM marker. While these are mentioned in the Methods section, we have added a description in the main text to highlight these points for the reader (page 7, lines 214-217).

In vitro experiments with the oligomycin treatment of HSCs showed that OXPHOS inhibition activates the glycolytic system, but does not induce HSC proliferation (original Figure S1K). This suggests that proliferation activates glycolysis and not that activation of the glycolytic system induces proliferation. This similarity and dissimilarity of glycolytic activation upon proliferation and OXPHOS inhibition is discussed in the Discussion (page 16-17, lines 505-515).

1. In FIG 7 since homing of HSCs is influenced by the cell cycle state, should be important to show if in the genetic model for PFKFB3 in HSCs there's a difference in homing efficiency.

In response to the reviewer's comments, we knocked out PFKFB3 in HSPCs derived from Ubc-GFP mice, transplanted 200,000 HSPCs into recipients (C57BL/6 mice) post-8.5Gy irradiation, and harvested the bone marrow of recipients after 16 h to compare homing efficiency (revised Figure S10H). Even with the knockout of PFKFB3, no significant difference in homing efficiency was detected compared to the control group (Rosa knockout group). These results suggest that the short-term reduction in chimerism due to PFKFB3 knockout is not due to decreased homing efficiency or cell death by apoptosis (Figure 7K) but a transient delay in cell cycle progression. We have added descriptions regarding these findings in the Results and Discussion sections (page 15, lines 470-471, page 19, lines 576-578).

Author response image 28.

1. Yamamoto M, Kim M, Imai H, Itakura Y, Ohtsuki G. Microglia-Triggered Plasticity of Intrinsic Excitability Modulates Psychomotor Behaviors in Acute Cerebellar Inflammation. Cell Rep. 2019;28(11):2923-2938 e2928.

2. Umemoto T, Johansson A, Ahmad SAI, et al. ATP citrate lyase controls hematopoietic stem cell fate and supports bone marrow regeneration. EMBO J. 2022:e109463.

3. Seita J, Sahoo D, Rossi DJ, et al. Gene Expression Commons: an open platform for absolute gene expression profiling. PLoS One. 2012;7(7):e40321.

4. Boyd S, Brookfield JL, Critchlow SE, et al. Structure-Based Design of Potent and Selective Inhibitors of the Metabolic Kinase PFKFB3. J Med Chem. 2015;58(8):3611-3625.

5. Ito K, Carracedo A, Weiss D, et al. A PML–PPAR-δ pathway for fatty acid oxidation regulates hematopoietic stem cell maintenance. Nat Med. 2012;18(9):1350-1358.

6. Oburoglu L, Tardito S, Fritz V, et al. Glucose and glutamine metabolism regulate human hematopoietic stem cell lineage specification. Cell Stem Cell. 2014;15(2):169-184.

7. Gnaiger E, Mendez G, Hand SC. High phosphorylation efficiency and depression of uncoupled respiration in mitochondria under hypoxia. Proc Natl Acad Sci U S A. 2000;97(20):11080-11085.

8. Kobayashi H, Morikawa T, Okinaga A, et al. Environmental Optimization Enables Maintenance of Quiescent Hematopoietic Stem Cells Ex Vivo. Cell Rep. 2019;28(1):145-158 e149.

9. Wang YH, Israelsen WJ, Lee D, et al. Cell-state-specific metabolic dependency in hematopoiesis and leukemogenesis. Cell. 2014;158(6):1309-1323.

10. Jun S, Mahesula S, Mathews TP, et al. The requirement for pyruvate dehydrogenase in leukemogenesis depends on cell lineage. Cell Metab. 2021;33(9):1777-1792 e1778.

11. Spencer JA, Ferraro F, Roussakis E, et al. Direct measurement of local oxygen concentration in the bone marrow of live animals. Nature. 2014;508(7495):269-273.

12. Takubo K, Nagamatsu G, Kobayashi CI, et al. Regulation of glycolysis by Pdk functions as a metabolic checkpoint for cell cycle quiescence in hematopoietic stem cells. Cell Stem Cell. 2013;12(1):49-61.

13. Anso E, Weinberg SE, Diebold LP, et al. The mitochondrial respiratory chain is essential for haematopoietic stem cell function. Nat Cell Biol. 2017;19(6):614-625.

Author Response

We would like to thank the Editors and Reviewers for their comprehensive review of the manuscript. We appreciate your feedback, and we will carefully consider all your comments in the revision of the manuscript. Below are our provisional responses to your comments.

eLife assessment

This manuscript reveals important insights into the role of ipsilateral descending pathways in locomotion, especially following unilateral spinal cord injury. The study provides solid evidence that this method improves the injured side's ability to support weight, and as such the findings may lead to new treatments for stroke, spinal cord injuries, or unilateral cerebral injuries. However, the methods and results need to be better detailed, and some of the statistical analysis enhanced.

Thank you for your assessment. We will incorporate various textual enhancements in the final version of the manuscript to address the weaknesses you have pointed out. The specific improvements are outlined below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript provides potentially important new information about ipsilateral cortical impact on locomotion. A number of issues need to be addressed.

Strengths:

The primary appeal and contribution of this manuscript are that it provides a range of different measures of ipsilateral cortical impact on locomotion in the setting of impaired contralateral control. While the pathways and mechanisms underlying these various measures are not fully defined and their functional impacts remain uncertain, they comprise a rich body of results that can inform and guide future efforts to understand cortical control of locomotion and to develop more effective rehabilitation protocols.

Weaknesses:

1. The authors state that they used a cortical stimulation location that produced the largest ankle flexion response (lines 102-104). Did other stimulation locations always produce similar, but smaller responses (aside from the two rats that showed ipsilateral neuromodulation)? Was there any site-specific difference in response to stimulation location?

We derived motor maps in each rat, akin to the representation depicted in Fig 6. In each rat, alternative cortical sites did, indeed, produce distal or proximal contralateral leg flexion responses. Distal responses were more likely to be evoked in the rostral portion of the array, similarly to proximal responses early after injury. This distribution in responses across different cortical sites is reported in this study (Fig. 6) and is consistent with our prior work. The Results section will be revised to provide additional clarification and context for the data presented in Figure 6.

1. Figure 2: There does not appear to be a strong relationship between the percentage of spared tissue and the ladder score. For example, the animal with the mild injury (based on its ladder score) in the lower left corner of Figure 2A has less than 50% spared tissue, which is less spared tissue than in any animal other than the two severe injuries with the most tissue loss. Is it possible that the ladder test does not capture the deficits produced by this spinal cord injury? Have the authors looked for a region of the spinal cord that correlates better with the deficits that the ladder test produces? The extent of damage to the region at the base of the dorsal column containing the corticospinal tract would be an appropriate target area to quantify and compare with functional measures.

In Fig. S6 of our 2021 publication "Bonizzato and Martinez, Science Translational Medicine", we investigated the predictive value of tissue sparing in specific sub-regions of the spinal cord for ladder performance. Specifically, we examined the correlation between the accuracy of left leg ladder performance in the acute state and the preservation of the corticospinal tract (CST). Our results indicated that dorsal CST sparing serves as a mild predictor for ladder deficits, confirming the results obtain in this study.

1. Lines 219-221: The authors state that "phase-coherent stimulation reinstated the function of this muscle, leading to increased burst duration (90{plus minus}18% of the deficit, p=0.004, t-test, Fig. 4B) and total activation (56{plus minus}13% of the deficit, p=0.014, t-test, Fig. 3B). This way of expressing the data is unclear. For example, the previous sentence states that after SCI, burst duration decreased by 72%. Does this mean that the burst duration after stimulation was 90% higher than the -72% level seen with SCI alone, i.e., 90% + -72% = +18%? Or does it mean that the stimulation recovered 90% of the portion of the burst duration that had been lost after SCI, i.e., -72% * (100%-90%)= -7%? The data in Figure 4 suggests the latter. It would be clearer to express both these SCI alone and SCI plus stimulation results in the text as a percent of the pre-SCI results, as done in Figure 4.

Your assessment is correct; we intended to report that the stimulation recovered 90% of the portion of the burst duration that had been lost after SCI. This point will be addressed in the revision of the manuscript.

1. Lines 227-229: The authors claim that the phase-dependent stimulation effects in SCI rats are immediate, but they don't say how long it takes for these effects to be expressed. Are these effects evident in the response to the first stimulus train, or does it take seconds or minutes for the effects to be expressed? After the initial expression of these effects, are there any gradual changes in the responses over time, e.g., habituation or potentiation?

The effects are immediately expressed at the very first occurrence of stimulation. We never tested a rat completely naïve to stimuli, as each treadmill session involves prior cortical mapping to identify a suitable active site for involvement in locomotor experiments. Yet, as demonstrated in Supplementary Video 1 accompanying our 2021 publication on contralateral effects of cortical stimulation, "Bonizzato and Martinez, Science Translational Medicine," the impact of phase-dependent cortical stimulation on movement modulation is instantaneous and ceases promptly upon discontinuation of the stimulation. We did not quantify potential gradual changes in responsiveness over time, but we cannot exclude that for long stimulation sessions (e.g., 30 min or more), stimulus amplitude may need to be slightly increased over time to compensate habituation.

1. Awake motor maps (lines 250-277): The analysis of the motor maps appears to be based on measurements of the percentage of channels in which a response can be detected. This analytic approach seems incomplete in that it only assesses the spatial aspect of the cortical drive to the musculature. One channel could have a just-above-threshold response, while another could have a large response; in either case, the two channels would be treated as the same positive result. An additional analysis that takes response intensity into account would add further insight into the data, and might even correlate with the measures of functional recovery. Also, a single stimulation intensity was used; the results may have been different at different stimulus intensities.

We confirm that maps of cortical stimulation responsiveness may vary at different stimulus amplitudes. To establish an objective metric of excitability, we identified 100µA as a reliable stimulation amplitude across rats and used this value to build the ipsilateral motor representation results in Figure 6. This choice allows direct comparison with Figure 6 of our 2021 article, related to contralateral motor representation. The comparison reveals a lack of correlation with functional recovery metrics in the ipsilateral case, in contrast to the successful correlation achieved in the contralateral case.

Regarding the incorporation of stimulation amplitudes into the analysis, as detailed in the Method section (lines 770-771), we systematically tested various stimulation amplitudes to determine the minimal threshold required for eliciting a muscle twitch, identified as the threshold value. This process was conducted for each electrode site. Upon reviewing these data, we considered the possibility of presenting an additional assessment of ipsilateral cortical motor representation based on stimulation thresholds. However, the representation depicted in the figure did not differ significantly from the data presented in Figure 6A. Furthermore, this representation introduced an additional weakness, as it was unclear how to represent the absence of a response in the threshold scale. We chose to arbitrarily designate it as zero on the inverse logarithmic scale, where, for reference, 100 µA is positioned at 0.2 and 50 µA at 0.5.

In conclusion, we believe that the conclusions drawn from this analysis align substantially with those in the text. The addition of the threshold analysis, in our assessment, would not contribute significantly to improving the manuscript.

Author response image 1.

Threshold analysis

Author response image 2.

Original occurrence probability analysis, for comparison.

1. Lines 858-860: The authors state that "All tests were one-sided because all hypotheses were strictly defined in the direction of motor improvement." By using the one-sided test, the authors are using a lower standard for assessing statistical significance that the overwhelming majority of studies in this field use. More importantly, ipsilateral stimulation of particular kinds or particular sites might conceivably impair function, and that is ignored if the analysis is confined to detecting improvement. Thus, a two-sided analysis or comparable method should be used. This appropriate change would not greatly modify the authors' current conclusions about improvements.

Our original hypothesis, drawn from previous studies involving cortical stimulation in rats and cats, as well as other neurostimulation research for movement restoration, posited a favorable impact of neurostimulation on movement. Consistent with this hypothesis, we designed our experiments with a focus on enhancing movement, emphasizing a strict direction of improvement.

It's important to note that a one-sided test is the appropriate match for a one-sided hypothesis, and it is not a lower standard in statistics. Each experiment we conducted was constructed around a strictly one-sided hypothesis: the inclusion of an extensor-inducing stimulus would enhance extension, and the inclusion of a flexion-inducing stimulus would enhance flexion. This rationale guided our choice of the appropriate statistical test.

We acknowledge your concern regarding the potential for ipsilateral stimulation to have negative effects on locomotion, which might not be captured when designing experiments based on one-sided hypotheses. This concern is valid, and we will explicitly mention it in the statistics section. Nonetheless, even if such observations were made, they could serve as the basis for triggering an ad-hoc follow-up study.

Reviewer #2 (Public Review):

Summary:

The authors' long-term goals are to understand the utility of precisely phased cortex stimulation regimes on recovery of function after spinal cord injury (SCI). In prior work, the authors explored the effects of contralesion cortex stimulation. Here, they explore ipsilesion cortex stimulation in which the corticospinal fibers that cross at the pyramidal decussation are spared. The authors explore the effects of such stimulation in intact rats and rats with a hemisection lesion at the thoracic level ipsilateral to the stimulated cortex. The appropriately phased microstimulation enhances contralateral flexion and ipsilateral extension, presumably through lumbar spinal cord crossed-extension interneuron systems. This microstimulation improves weight bearing in the ipsilesion hindlimb soon after injury, before any normal recovery of function would be seen. The contralateral homologous cortex can be lesioned in intact rats without impacting the microstimulation effect on flexion and extension during gait. In two rats ipsilateral flexion responses are noted, but these are not clearly demonstrated to be independent of the contralateral homologous cortex remaining intact.

Strengths:

This paper adds to prior data on cortical microstimulation by the laboratory in interesting ways. First, the strong effects of the spared crossed fibers from the ipsi-lesional cortex in parts of the ipsi-lesion leg's step cycle and weight support function are solidly demonstrated. This raises the interesting possibility that stimulating the contra-lesion cortex as reported previously may execute some of its effects through callosal coordination with the ipsi-lesion cortex tested here. This is not fully discussed by the authors but may represent a significant aspect of these data. The authors demonstrate solidly that ablation of the contra-lesional cortex does not impede the effects reported here. I believe this has not been shown for the contra-lesional cortex microstimulation effects reported earlier, but I may be wrong. Effects and neuroprosthetic control of these effects are explored well in the ipsi-lesion cortex tests here.

In the revised version of the manuscript, we will incorporate various text improvements to address the points you have highlighted below. Additionally, we will integrate the suggested discussion topic on callosal coordination related to contralateral cortical stimulation.

Weaknesses:

Some data is based on very few rats. For example (N=2) for ipsilateral flexion effects of microstimulation. N=3 for homologous cortex ablation, and only ipsi extension is tested it seems. There is no explicit demonstration that the ipsilateral flexion effects in only 2 rats reported can survive the contra-lateral cortex ablation. We agree with this assessment. The ipsilateral flexion representation is here reported as a rare but consistent phenomenon, which we believe to have robustly described with Figure 7 experiments. We will underline in the text that the ablation experiment did not conclude on the unilateral-cortical nature of ipsilateral flexion effects.

Some improvements in clarity and precision of descriptions are needed, as well as fuller definitions of terms and algorithms.

Likely Impacts: This data adds in significant ways to prior work by the authors, and an understanding of how phased stimulation in cortical neuroprosthetics may aid in recovery of function after SCI, especially if a few ambiguities in writing and interpretation are fully resolved.

The manuscript text will be revised in its final version, and we seek to eliminate any ambiguity in writing, data interpretation and algorithms.

Reviewer #3 (Public Review):

Summary:

This article aims to investigate the impact of neuroprosthesis (intracortical microstimulation) implanted unilaterally on the lesion side in the context of locomotor recovery following unilateral thoracic spinal cord injury.

Strength:

The study reveals that stimulating the left motor cortex, on the same side as the lesion, not only activates the expected right (contralateral) muscle activity but also influences unexpected muscle activity on the left (ipsilateral) side. These muscle activities resulted in a substantial enhancement in lift during the swing phase of the contralateral limb and improved trunk-limb support for the ipsilateral limb. They used different experimental and stimulation conditions to show the ipsilateral limb control evoked by the stimulation. This outcome holds significance, shedding light on the engagement of the "contralateral projecting" corticospinal tract in activating not only the contralateral but also the ipsilateral spinal network.

The experimental design and findings align with the investigation of the stimulation effect of contralateral projecting corticospinal tracts. They carefully examined the recovery of ipsilateral limb control with motor maps. They also tested the effective sites of cortical stimulation. The study successfully demonstrates the impact of electrical stimulation on the contralateral projecting neurons on ipsilateral limb control during locomotion, as well as identifying important stimulation spots for such an effect. These results contribute to our understanding of how these neurons influence bilateral spinal circuitry. The study's findings contribute valuable insights to the broader neuroscience and rehabilitation communities.

Thank you for your assessment of this manuscript. The final version of the manuscript will incorporate your suggestions for improving term clarity and will also enhance the discussion on the mechanism of spinal network engagement, as outlined below.

Weakness:

The term "ipsilateral" lacks a clear definition in the title, abstract, introduction, and discussion, potentially causing confusion for the reader. In the next revision of the manuscript, we will provide a clear definition of the term "ipsilateral."

The unexpected ipsilateral (left) muscle activity is most likely due to the left corticospinal neurons recruiting not only the right spinal network but also the left spinal network. This is probably due to the joint efforts of the neuroprosthesis and activation of spinal motor networks which work bilaterally at the spinal level. However, in my opinion, readers can easily link the ipsilateral cortical network to the ipsilateral-projecting corticospinal tract, which is less likely to play a role in ipsilateral limb control in this study since this tract is disrupted by the thoracic spinal injury.

We agree with your assessment. The discussion section paragraph presenting putative mechanisms of cortico-spinal transmission in the effects presented in the results will be enhanced to reflect these suggestions.