eLife Assessment

The paper illustrates a valuable approach to generating TCR transgenic mice specific for known epitopes. Solid evidence validates the described pipeline for identification of TCRs from single-cell datasets for the generation of TCR transgenic mice, while obviating the need for generation of T-cell lines and hybridomas.

Reviewer #1 (Public review):

Summary:

Debeuf et al. introduce a new, fast method for the selection of suitable T cell clones to generate TCR transgenic mice, a method claimed to outperform traditional hybridoma-based approaches. Clone selection is based on the assessment of the expansion and phenotype of cells specific for a known epitope following immune stimulation. The analysis is facilitated by a new software tool for TCR repertoire and function analysis termed DALI. This work also introduces a potentially invaluable TCR transgenic mouse line specific for SARS-CoV-2.

Strengths:

The newly introduced method proved successful in the quick generation of a TCR transgenic mouse line. Clone selection is based on more comprehensive phenotypical information than traditional methods, providing the opportunity for a more rational T-cell clone selection.

The study provides a software tool for TCR repertoire analysis and its linkage with function.

The findings entail general practical implications in the preclinical study of a potentially very broad range of infectious diseases or vaccination.

A novel SARS-CoV-2 spike-specific TCR transgenic mouse line was generated.

Weaknesses:

The authors present a novel method to develop TCR transgenic mice and overcome the limitations of the more traditional method based on hybridomas.

The authors indicate that they did not intend to directly compare their new method with the traditional hybridoma-based approach. However, such comparison becomes inevitable when the classical method is presented as suboptimal and an alternative approach is introduced to address its limitations. Nevertheless, the explanations provided in their rebuttal have helped clarify their position. The intention behind supplementary figure 1 is to illustrate that a clone that appears suitable using traditional assays may fail to produce a successful TCR transgenic line. This is a valid point that I think should be emphasized more clearly in the manuscript, as it highlights the limitations of the traditional method.

However, the main question that remains is whether the proposed new method will reliably resolve this issue. As previously noted, only one mouse line was generated (successfully) from a single candidate, and the method presented to generate their new TCR transgenic line starts from a more advanced point (a well characterized epitope is already known, and tetramers are available to preselect specific clones). Although this approach likely increases the chances of success, it also limits applicability.

The authors suggest that tetramers are not absolutely necessary to select a clone of interest. Testing this hypothesis would have added value to this manuscript, demonstrating the ability to rapidly generate new TCR transgenic lines in response to emerging pathogens, as outlined in the introduction. They propose that, in such cases, mice could be immunised and expanded clones retested for reactivity. However, it is unclear how this strategy differs from the classic method in increasing the chances of selecting an optimal clone.

Regarding the practical value and cost-effectiveness of extensive expression profiling for T cell clone selection, it remains unclear how well a clone chosen for specific traits will retain these features when developed into a TCR transgenic line, or what traits are ideal for different applications. T cell fate is plastic, and various parameters could influence marker expression.

Issues remain concerning the statistical analysis. Data are said to have been analyzed using both parametric and non-parametric tests. The described approach of performing a normality test followed by either parametric or non-parametric tests is not a correct method for statistical data analysis.

Reviewer #2 (Public review):

Summary:

The authors seek to use single-cell sequencing approaches to identify TCRs specific for the SARS CoV2 spike protein, select a candidate TCR for cloning and use it to construct a TCR transgenic mouse. The argument is that this process is less cumbersome than the classical approach, which involves the identification of antigen-reactive T cells in vitro and the construction of T cell hybridomas prior to TCR cloning. TCRs identified by single-cell sequencing that is already paired to transcriptomic data would more rapidly identify TCRs that are likely to contribute to a functional response. The authors successfully identify TCRs that have expanded in response to SARS CoV2 spike protein immunization, bind to MHC tetramers and express genes associated with functional response. They then select a TCR for cloning and construction of a transgenic mouse in order to test the response of resulting T cells in vivo following immunization with spike protein of coronavirus infection.

Strengths:

(1) The study provides proof of principle for the identification and characterization of TCRs based on single-cell sequencing data.

(2) The authors employ a recently developed software tool (DALI) that assists in linking transcriptomic data to individual clones.

(3) The authors successfully generate a TCR transgenic animal derived from the most promising T cell clone (CORSET8) using the TCR sequencing approach.

(4) The authors provide initial evidence that CORSET8 T cells undergo activation and proliferation in vivo in response to immunization or infection.

(5) Procedures are well-described and readily reproducible.

Weaknesses:

(1) The purpose of presenting a failed attempt to generate TCR transgenic mice using a traditional TCR hybridoma method is unclear. The reasons for the failure are uncertain, and the inclusion of this data does not really provide information on the likely success rate of the hybridoma vs single cell approach for TCR identification, as only a single example is provided for either.

(2) There is little information provided regarding the functional differentiation of the CORSET8 T cells following challenge in vivo, including expression of molecules associated with effector function, cytokine production, killing activity and formation of memory. The study would be strengthened by some evidence that CORSET8 T cells are successfully recapitulating the functional features of the endogenous immune response (beyond simply proliferating and expressing CD44). This information is important to evaluate whether the presented sequencing-based identification and selection of TCRs is likely to result in T-cell responses that replicate the criteria for selecting the TCR in the first place.

(3) While I find the argument reasonable that the approach presented here has a lot of likely advantages over traditional approaches for generating TCR transgenic animals, the use of TCR sequencing data to identify TCRs for study in variety of areas, including cancer immunotherapy and autoimmunity, is in broad use. While much of this work opts for alternative methods of TCR expression in primary T cells (i.e. CRISPR or retroviral approaches), the process of generating a TCR transgenic mouse from a cloned TCR is not in itself novel. It would be helpful if the authors could provide a more extensive discussion explaining the novelty of their approach for TCR identification in comparison to other more modern approaches, rather than only hybridoma generation.

Comments on revisions:

The authors have provided additional clarification on the comparisons between the presented method for TCR transgenic generation and the hybridoma method that is more commonly used and added additional verification of the functional response in vivo of T cells expressing the selected TCR. Overall, these additions enhance the evidence that the proposed methods are likely to identify TCRs with a strong immune activation profile and are a reasonable response to the first round of review.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Debeuf et al. introduce a new, fast method for the selection of suitable T cell clones to generate TCR transgenic mice, a method claimed to outperform traditional hybridoma-based approaches. Clone selection is based on the assessment of the expansion and phenotype of cells specific for a known epitope following immune stimulation. The analysis is facilitated by a new software tool for TCR repertoire and function analysis termed DALI. This work also introduces a potentially invaluable TCR transgenic mouse line specific for SARS-CoV-2.

Strengths:

The newly introduced method proved successful in the quick generation of a TCR transgenic mouse line. Clone selection is based on more comprehensive phenotypical information than traditional methods, providing the opportunity for a more rational T cell clone selection.

The study provides a software tool for TCR repertoire analysis and its linkage with function.

The findings entail general practical implications in the preclinical study of a potentially very broad range of infectious diseases or vaccination.

A novel SARS-CoV-2 spike-specific TCR transgenic mouse line was generated.

Weaknesses:

The authors attempt to compare their novel method with a more conventional approach to developing TCR transgenic mice. In this reviewer's opinion, this comparison appears imperfect in several ways:

(1) Work presenting the "traditional" method was inadequate to justify the selection of a suitable clone. It is therefore not surprising that it yielded negative results. More evidence would have been necessary to select clone 47 for further development of the TCR transgenic line, especially considering the significant time and investment required to create such a line.

Based on Supplementary Figure 1A only, we understand the concern of the reviewer. However, the data presented in Supplementary Figure 1A is collected during the first rough screening of clones where only the production of IL-2 and IFN-y was measured as a readout for activation. Thereafter, a large selection of responsive clones was further grown and co-cultured with a dose-titration of the antigenic peptide pool. In this second co-culture, also flow cytometry readouts are included such as CD69 expression (as shown in Supplementary Figure 1B). Finally, a narrower selection of responder clones was co-cultured with the different individual peptides to unravel the specificity of the TCR of the clone. In conclusion, the clone was tested at least three times in three distinct set-ups with multiple different readouts.

However, a good evaluation of a clone in an in vitro setting does not necessarily translate in optimal functioning of the cells in a biological context. For instance, some clones survive better in an in vitro setting than others or have already a more activated profile before stimulation.

(2) The comparison is somewhat unfair, because the methods start at different points: while the traditional method was attempted using a pool of peptides whose immunogenicity does not appear to have been established, the new method starts by utilising tetramers to select T cells specific for a well-established epitope.

Given the costs and time involved, only a single clone could be tested for either method, intrinsically making a proper comparison unfeasible. Even for their new method, the authors' ability to demonstrate that the selected clone is ideal is limited unless they made different clones with varying profiles to show that a particular profile was superior to others.

In my view, there was no absolute need to compare this method with existing ones, as the proposed method holds intrinsic value.

We acknowledge the importance of the well-established hydridoma technology and in no way intended to compare these methods head-to-head, nor do not want to question the validity of the classical methods. The reason why we also wanted to show the failed CORSET8 mouse was to highlight the parts of the TCR generating process which could be rationalized. We again want to emphasize that we do not want to compare methods in any way and recognise that we started from two different bases in terms of clone selection (peptide pool stimulation versus tetramer staining). While the tetramer staining that was employed in the generation of CORSET8 mice allowed to enrich the samples for specific responder clones, this enrichment step is not an absolute requirement for the implementation of the presented method or for the successful generation of a TCR Tg mouse model. An alternative approach could be to use the described method to select for activated and expanded clones upon immunisation and test their reactivity in subsequent steps using peptide stimulation before selecting a receptor. In conclusion, we merely wish to present a novel roadmap for others to use for the generation of their TCR Tg mouse to aid in the selection of the most preferable clone for their purposes.

(3) While having more data to decide on clone selection is certainly beneficial, given the additional cost, it remains unclear whether knowing the expression profiles of different proteins in Figure 2 aids in selecting a candidate. Is a cell expressing more CD69 preferable to a cell expressing less of this marker? Would either have been effective? Are there any transcriptional differences between clonotype 1 and 2 (red colour in Figure 2G) that justify selecting clone 1, or was the decision to select the latter merely based on their different frequency? If all major clones (i.e. by clonotype count) present similar expression profiles, would it have been necessary to know much more about their expression profiles? Would TCR sequencing and an enumeration of clones have sufficed, and been a more cost-effective approach?

The method we present in the paper serves as a proof-of-concept, to be adapted to the researcher’s own needs. We agree with the reviewer that for our intentions with the CORSET8 mice, TCRseq in combination with an enumeration of the clones could also have sufficed and would lower the cost of sequencing. However, we wish to present a roadmap for others to use for the generation of their TCR Tg mouse. Important in this, is that the cellular phenotype, and activation state can be taken into consideration, which might for some projects be essential.  

Nonetheless, we do see clear interclonal differences regarding the expression of “activation” genes, where clone 1 is clearly one of the well activated and interferon producing clones (as shown in Author response image 1). As such, researchers could expand these types of analysis to probe for specific phenotypes of characteristics.

Author response image 1.

(4) Lastly, it appears that several of the experiments presented were conducted only once. This information should have been explicitly stated in the figure legends.

To control for interexperimental variation, every experiment represented in the manuscript has been performed at least two times. We have added the additional information regarding the experimental repetitions and groups in the figure legends.

Reviewer #2 (Public Review):

Summary:

The authors seek to use single-cell sequencing approaches to identify TCRs specific for the SARS CoV2 spike protein, select a candidate TCR for cloning, and use it to construct a TCR transgenic mouse. The argument is that this process is less cumbersome than the classical approach, which involves the identification of antigen-reactive T cells in vitro and the construction of T cell hybridomas prior to TCR cloning. TCRs identified by single-cell sequencing that are already paired to transcriptomic data would more rapidly identify TCRs that are likely to contribute to a functional response. The authors successfully identify TCRs that have expanded in response to SARS CoV2 spike protein immunization, bind to MHC tetramers, and express genes associated with functional response. They then select a TCR for cloning and construction of a transgenic mouse in order to test the response of resulting T cells in vivo following immunization with spike protein of coronavirus infection.

Strengths:

(1) The study provides proof of principle for the identification and characterization of TCRs based on single-cell sequencing data.

(2) The authors employ a recently developed software tool (DALI) that assists in linking transcriptomic data to individual clones.

(3) The authors successfully generate a TCR transgenic animal derived from the most promising T cell clone (CORSET8) using the TCR sequencing approach.

(4) The authors provide initial evidence that CORSET8 T cells undergo activation and proliferation in vivo in response to immunization or infection.

(5) Procedures are well-described and readily reproducible.

Weaknesses:

(1) The purpose of presenting a failed attempt to generate TCR transgenic mice using a traditional TCR hybridoma method is unclear. The reasons for the failure are uncertain, and the inclusion of this data does not really provide information on the likely success rate of the hybridoma vs single cell approach for TCR identification, as only a single example is provided for either.

We refer to comments 2 and 3 of reviewer 1 for an answer to this point.

(2) There is little information provided regarding the functional differentiation of the CORSET8 T cells following challenge in vivo, including expression of molecules associated with effector function, cytokine production, killing activity, and formation of memory. The study would be strengthened by some evidence that CORSET8 T cells are successfully recapitulating the functional features of the endogenous immune response (beyond simply proliferating and expressing CD44). This information is important to evaluate whether the presented sequencing-based identification and selection of TCRs is likely to result in T-cell responses that replicate the criteria for selecting the TCR in the first place.

We agree with the reviewer that the data in the initial manuscript included only a limited in vivo functional validation of the CORSET8 T cells. Therefore, we extended these in vivo readouts and measured IFN-g production, CD69, T-bet expression (as measure for activation) and Ki-67 expression (as alternative readout than CTV for proliferation). In the single cell data, we saw that these markers were more pronounced in the selected clone compared to other clones. We could confirm these findings in vivo, and found a stronger induction of IFN-g, CD69, T-bet and Ki-67 in CORSET8 T cells compared to endogenous CD45.2 cells and even Spike-Tetramer+ CD45.2 endogenous cells. We added these data in Figure 4.

(3) While I find the argument reasonable that the approach presented here has a lot of likely advantages over traditional approaches for generating TCR transgenic animals, the use of TCR sequencing data to identify TCRs for study in a variety of areas, including cancer immunotherapy and autoimmunity, is in broad use. While much of this work opts for alternative methods of TCR expression in primary T cells (i.e. CRISPR or retroviral approaches), the process of generating a TCR transgenic mouse from a cloned TCR is not in itself novel. It would be helpful if the authors could provide a more extensive discussion explaining the novelty of their approach for TCR identification in comparison to other more modern approaches, rather than only hybridoma generation.

By integrating the recent technological advances in single cell sequencing into the generation of TCR Tg mice, possibilities arise to rationalize clone selection regarding clonal size, lineage/phenotype and functional characteristics. Often, the selection process based on hybridoma selection yields multiple epitope specific clones that upregulate CD69 or IL-2, and only minimal functional and phenotypic parameters are checked before prioritizing one clone to proceed with. In our experience, transgenic clones selected in this way sometimes render TCR clones unable to compete with endogenous polyclonal T clones in vivo. Taken all these caveats into account, the novelty we present here is that the researcher is fully able to select clones based on several layers of information without the need for extensive or repeated screening. Moreover, the selection of the TCR Tg clone can be done via the interactive and easily interpretable DALI tool. Owing to the browser-based interactive GUI, immunologists having limited coding experience can effectively analyse their complex datasets.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Regarding Supplementary Figure 1A was the experiment conducted more than once? Clone 47 seems minimally superior to the other clones. Incorporating a positive control, such as the response of the OT-I hybridoma to SIINFEKL, could have provided a benchmark to gauge the strength of the observed responses.

Also, what was the concentration of the peptide used to restimulate the T cells in vitro? High peptide concentrations can lead to non-specific responses. Ideally, a titration should have been performed, perhaps in a subsequent experiment that only tested those clones that responded well initially. Given the resources required to create and maintain a transgenic mouse line, proceeding with the chosen clone based on the data presented seems to carry considerable risk.

The experiment has been performed three times. The data presented in Supplementary Figure 1A is collected during the first rough screening of clones where only the production of IL-2 and IFN-y was measured as a readout for activation. Thereafter, a large selection of responsive clones was further grown and co-cultured with a dose-titration of the antigenic peptide pool. In this second co-culture, also flow cytometry readouts are included such as CD69 expression (as shown in Supplementary Figure 1B). Finally, a narrower selection of responder clones was co-cultured with the different individual peptides to unravel the specificity of the TCR of the clone. In conclusion, the clone was tested at least three times in three distinct set-ups with multiple different readouts.

In Supplementary Figure 1C, no response to stimulation was detected. Ideally, this figure should have included a positive control, such as PMA/Ionomycin or aCD3/CD28 stimulation.

We agree with the reviewer that this experiment should have included a positive control to validate the non-specific responsiveness of the clone and the technical feasibility of the experiment. Unfortunately, the initial CORSET8 line is frozen and is thus not easily available to repeat the experiment.

Can the authors clarify their gating strategy in the legend of In Supplementary Figure 1D?

Plotted cells are non-debris > single cells > viable cells > CD45+. We have added the information to the legend of Supplementary Figure 1D.

In Figure 2, the figure legend should provide more detail on which cells were sorted for the single-cell RNA sequencing analysis. The materials and methods section explains that cells were stained for CD44. Were activated cells then sorted (either tetramer-positive or -negative), plus naïve CD8 T cells from a naïve mouse?

Supplementary Figure 2 contains the detailed gating strategy during the sort for the single cell experiment. We have added additional red gates to the plots to clarify which samples were sent for sequencing. This has been adapted in the figure legends of both Figure 2 and Supplementary Figure 2. 

In Figure 3, Rag1 sufficient transgenic mice display similar numbers of CD4 and CD8 T cells as WT mice in the spleen. Typically, transgenic mice present skewed frequencies of T cells towards the type generated (CD8 in this case), which the authors only found in the thymus of CORSET8 mice. Could this be discussed?

The comment of the reviewer is valid as there is indeed a skewing towards CD8 T cells in the thymi of the CORSET8 mice. We looked back into the data of the experiments and noticed that poor resolution of some markers might have resulted in improper results. We have repeated this and added another T cell marker (TCRbeta) next to the already included CD3e marker. By including both markers, we were able to show that also in spleen the skewing towards the CD8 T cell phenotype is present.

How many repetitions were performed for the experiments in Figures 3D and 3E? How many mice were analyzed for Figure 3E? Please provide this information in the figure legend. Also, include a proper quantification and statistical analysis of the data shown.

New quantification graphs with statistical analysis have been added to Figure 3E. The accompanying figure legend has been adapted. The co-culture displayed in Figure 3D is a representative experiment of two repetitions.

Figure 4C includes 3-4 mice per group. This experiment should have been replicated, and this information should be indicated in the figure legend.

We apologise for omitting this data in the figure legend. The experiment presented in Figure 4A-C has been repeated twice, yielding results following the same trend. We were unable to pool the data as two different proliferation dyes were used in the separate experiments (CFSE and CTV). Furthermore, in the in vivo BSL3 experiments represented in figure 4E-H, we always took along the Spike/CpG-group as positive control. We have added the additional information regarding the experimental repetitions and groups in the figure legend.

eLife Assessment

This useful study examines how deletion of a major DNA repair gene in bacteria may facilitate the rise of mutations that confer resistance against a range of different antibiotics. Although the phenotypic evidence is intriguing, the interpretation of the phenotypic data presented and the proposed mechanism by which these mutations are generated are incomplete, relying on untested assumptions and methodology that merits optimization. For instance, the authors cannot fully rule out the possibility that the resistance mutations are the result of selection. Nevertheless, this work could be of interest to microbiologists studying antibiotic resistance, genome integrity, and evolution, but the significance remains uncertain.

Reviewer #1 (Public review):

Summary:

Jin et al. investigated how the bacterial DNA damage (SOS) response and its regulator protein RecA affects the development of drug resistance under short-term exposure to beta-lactam antibiotics. Canonically, the SOS response is triggered by DNA damage, which results in the induction of error-prone DNA repair mechanisms. These error-prone repair pathways can increase mutagenesis in the cell, leading to the evolution of drug resistance. Thus, inhibiting the SOS regulator RecA has been proposed as means to delay the rise of resistance.

In this paper, the authors deleted the RecA protein from E. coli and exposed this ∆recA strain to selective levels of the beta-lactam antibiotic, ampicillin. After an 8h treatment, they washed the antibiotic away and allowed the surviving cells to recover in regular media. They then measured the minimum inhibitory concentration (MIC) of ampicillin against these treated strains. They note that after just 8 h treatment with ampicillin, the ∆recA had developed higher MICs towards ampicillin, while by contrast, wild-type cells exhibited unchanged MICs. This MIC increase was also observed subsequent generations of bacteria, suggesting that the phenotype is driven by a genetic change.

The authors then used whole genome sequencing (WGS) to identify mutations that accounted for the resistance phenotype. Within resistant populations, they discovered key mutations in the promoter region of the beta-lactamase gene, ampC; in the penicillin-binding protein PBP3 which is the target of ampicillin; and in the AcrB subunit of the AcrAB-TolC efflux machinery. Importantly, mutations in the efflux machinery can impact the resistances towards other antibiotics, not just beta-lactams. To test this, they repeated the MIC experiments with other classes of antibiotics, including kanamycin, chloramphenicol, and rifampicin. Interestingly, they observed that the ∆recA strains pre-treated with ampicillin showed higher MICs towards all other antibiotic tested. This suggests that the mutations conferring resistance to ampicillin are also increasing resistance to other antibiotics.

The authors then performed an impressive series of genetic, microscopy, and transcriptomic experiments to show that this increase in resistance is not driven by the SOS response, but by independent DNA repair and stress response pathways. Specifically, they show that deletion of the recA reduces the bacterium's ability to process reactive oxygen species (ROS) and repair its DNA. These factors drive accumulation of mutations that can confer resistance towards different classes of antibiotics. The conclusions are reasonably well-supported by the data, but some aspects of the data and the model need to be clarified and extended.

Strengths:

A major strength of the paper is the detailed bacterial genetics and transcriptomics that the authors performed to elucidate the molecular pathways responsible for this increased resistance. They systemically deleted or inactivated genes involved in the SOS response in E. coli. They then subjected these mutants the same MIC assays as described previously. Surprisingly, none of the other SOS gene deletions resulted an increase in drug resistance, suggesting that the SOS response is not involved in this phenotype. This led the authors to focus on the localization of DNA PolI, which also participates in DNA damage repair. Using microscopy, they discovered that in the RecA deletion background, PolI co-localizes with the bacterial chromosome at much lower rates than wild-type. This led the authors to conclude that deletion of RecA hinders PolI and DNA repair. Although the authors do not provide a mechanism, this observation is nonetheless valuable for the field and can stimulate further investigations in the future.

In order to understand how RecA deletion affects cellular physiology, the authors performed RNA-seq on ampicillin-treated strains. Crucially, they discovered that in the RecA deletion strain, genes associated with antioxidative activity (cysJ, cysI, cysH, soda, sufD) and Base Excision Repair repair (mutH, mutY, mutM), which repairs oxidized forms of guanine, were all downregulated. The authors conclude that down-regulation of these genes might result in elevated levels of reactive oxygen species in the cells, which in turn, might drive the rise of resistance. Experimentally, they further demonstrated that treating the ∆recA strain with an antioxidant GSH prevents the rise of MICs. These observations will be useful for more detailed mechanistic follow-ups in the future.

Weaknesses:

Throughout the paper, the authors use language suggesting that ampicillin treatment of the ∆recA strain induces higher levels of mutagenesis inside the cells, leading to the rapid rise of resistance mutations. However, as the authors note, the mutants enriched by ampicillin selection can play a role in efflux and can thus change a bacterium's sensitivity to a wide range of antibiotics, in what is known as cross-resistance. The current data is not clear on whether the elevated "mutagenesis" is driven ampicillin selection or by a bona fide increase in mutation rate.

Furthermore, on a technical level, the authors employed WGS to identify resistance mutations in the treated ampicillin-treated wild-type and ∆recA strains. However, the WGS methodology described in the paper is inconsistent. Notably, wild-type WGS samples were picked from non-selective plates, while ΔrecA WGS isolates were picked from selective plates with 50 μg/mL ampicillin. Such an approach biases the frequency and identity of the mutations seen in the WGS and cannot be used to support the idea that ampicillin treatment induces higher levels of mutagenesis.

Finally, it is important to establish what the basal mutation rates of both the WT and ∆recA strains are. Currently, only the ampicillin-treated populations were reported. It is possible that the ∆recA strain has inherently higher mutagenesis than WT, with a larger subpopulation of resistant clones. Thus, ampicillin treatment might not in fact induce higher mutagenesis in ∆recA.

Comments on revisions:

Thank you for responding to the concerns raised previously. The manuscript overall has improved.

Reviewer #2 (Public review):

Summary:

This study aims to demonstrate that E. coli can acquire rapid antibiotic resistance mutations in the absence of a DNA damage response. The authors employed a modified Adaptive Laboratory Evolution (ALE) workflow to investigate this, initiating the process by diluting an overnight culture 50-fold into an ampicillin selection medium. They present evidence that a recA- strain develops ampicillin resistance mutations more rapidly than the wild-type, as indicated by the Minimum Inhibitory Concentration (MIC) and mutation frequency. Whole-genome sequencing of recA- colonies resistant to ampicillin showed predominant inactivation of genes involved in the multi-drug efflux pump system, contrasting with wild-type mutations that seem to activate the chromosomal ampC cryptic promoter. Further analysis of mutants, including a lexA3 mutant incapable of inducing the SOS response, led the authors to conclude that the rapid evolution of antibiotic resistance occurs via an SOS-independent mechanism in the absence of recA. RNA sequencing suggests that antioxidative response genes drive the rapid evolution of antibiotic resistance in the recA- strain. They assert that rapid evolution is facilitated by compromised DNA repair, transcriptional repression of antioxidative stress genes, and excessive ROS accumulation.

Strengths:

The experiments are well-executed and the data appear reliable. It is evident that the inactivation of recA promotes faster evolutionary responses, although the exact mechanisms driving this acceleration remain elusive and deserve further investigation.

Weaknesses:

Some conclusions are overstated. For instance, the conclusion regarding the LexA3 allele, indicating that rapid evolution occurs in an SOS-independent manner (line 217), contradicts the introductory statement that attributes evolution to compromised DNA repair. The claim made in the discussion of Figure 3 that the hindrance of DNA repair in recA- is crucial for rapid evolution is at best suggestive, not demonstrative. Additionally, the interpretation of the PolI data implies its role, yet it remains speculative. In Figure 2A table, mutations in amp promoters are leading to amino acid changes! The authors' assertion that ampicillin significantly influences persistence pathways in the wild-type strain, affecting quorum sensing, flagellar assembly, biofilm formation, and bacterial chemotaxis, lacks empirical validation. Figure 1G suggests that recA cells treated with ampicillin exhibit a strong mutator phenotype; however, it remains unclear if this can be linked to the mutations identified in Figure 2's sequencing analysis.

Reviewer #3 (Public review):

Summary:

In the present work, Zhang et al investigate involvement of the bacterial DNA damage repair SOS response in the evolution of beta-lactam drug resistance evolution in Escherichia coli. Using a combination of microbiological, bacterial genetics, laboratory evolution, next-generation, and live-cell imaging approaches, the authors propose short-term (transient) drug resistance evolution can take place in RecA-deficient cells in an SOS response-independent manner. They propose the evolvability of drug resistance is alternatively driven by the oxidative stress imposed by accumulation of reactive oxygen species and compromised DNA repair. Overall, this is a nice study that addresses a growing and fundamental global health challenge (antimicrobial resistance).

Strengths:

The authors introduce new concepts to antimicrobial resistance evolution mechanisms. They show short-term exposure to beta-lactams can induce durably fixed antimicrobial resistance mutations. They propose this is due to comprised DNA repair and oxidative stress. Antibiotic resistance evolution under transient stress is poorly studied, so the authors' work is a nice mechanistic contribution to this field.

Weaknesses:

The authors do not show any direct evidence of altered mutation rate or accumulated DNA damage in their model.

Author response:

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

Public Reviews:

Review #1:

Summary:

Jin et al. investigated how the bacterial DNA damage (SOS) response and its regulator protein RecA affect the development of drug resistance under short-term exposure to beta-lactam antibiotics. Canonically, the SOS response is triggered by DNA damage, which results in the induction of error-prone DNA repair mechanisms. These error-prone repair pathways can increase mutagenesis in the cell, leading to the evolution of drug resistance. Thus, inhibiting the SOS regulator RecA has been proposed as a means to delay the rise of resistance. 

In this paper, the authors deleted the RecA protein from E. coli and exposed this ∆recA strain to selective levels of the beta-lactam antibiotic, ampicillin. After an 8-hour treatment, they washed the antibiotic away and allowed the surviving cells to recover in regular media. They then measured the minimum inhibitory concentration (MIC) of ampicillin against these treated strains. They note that after just 8-hour treatment with ampicillin, the ∆recA had developed higher MICs towards ampicillin, while by contrast, wild-type cells exhibited unchanged MICs. This MIC increase was also observed in subsequent generations of bacteria, suggesting that the phenotype is driven by a genetic change.

The authors then used whole genome sequencing (WGS) to identify mutations that accounted for the resistance phenotype. Within resistant populations, they discovered key mutations in the promoter region of the beta-lactamase gene, ampC; in the penicillin-binding protein PBP3 which is the target of ampicillin; and in the AcrB subunit of the AcrAB-TolC efflux machinery. Importantly, mutations in the efflux machinery can impact the resistance towards other antibiotics, not just beta-lactams. To test this, they repeated the MIC experiments with other classes of antibiotics, including kanamycin, chloramphenicol, and rifampicin. Interestingly, they observed that the ∆recA strains pre-treated with ampicillin showed higher MICs towards all other antibiotics tested. This suggests that the mutations conferring resistance to ampicillin are also increasing resistance to other antibiotics.

The authors then performed an impressive series of genetic, microscopy, and transcriptomic experiments to show that this increase in resistance is not driven by the SOS response, but by independent DNA repair and stress response pathways. Specifically, they show that deletion of the recA reduces the bacterium's ability to process reactive oxygen species (ROS) and repair its DNA. These factors drive the accumulation of mutations that can confer resistance to different classes of antibiotics. The conclusions are reasonably well-supported by the data, but some aspects of the data and the model need to be clarified and extended.

We sincerely appreciate your overall summary of the manuscript and their positive evaluation of our work.

Strengths:

A major strength of the paper is the detailed bacterial genetics and transcriptomics that the authors performed to elucidate the molecular pathways responsible for this increased resistance. They systemically deleted or inactivated genes involved in the SOS response in E. coli. They then subjected these mutants to the same MIC assays as described previously. Surprisingly, none of the other SOS gene deletions resulted in an increase in drug resistance, suggesting that the SOS response is not involved in this phenotype. This led the authors to focus on the localization of DNA PolI, which also participates in DNA damage repair. Using microscopy, they discovered that in the RecA deletion background, PolI co-localizes with the bacterial chromosome at much lower rates than wild-type. This led the authors to conclude that deletion of RecA hinders PolI and DNA repair. Although the authors do not provide a mechanism, this observation is nonetheless valuable for the field and can stimulate further investigations in the future.

In order to understand how RecA deletion affects cellular physiology, the authors performed RNA-seq on ampicillin-treated strains. Crucially, they discovered that in the RecA deletion strain, genes associated with antioxidative activity (cysJ, cysI, cysH, soda, sufD) and Base Excision Repair repair (mutH, mutY, mutM), which repairs oxidized forms of guanine, were all downregulated. The authors conclude that down-regulation of these genes might result in elevated levels of reactive oxygen species in the cells, which in turn, might drive the rise of resistance. Experimentally, they further demonstrated that treating the ∆recA strain with an antioxidant GSH prevents the rise of MICs. These observations will be useful for more detailed mechanistic follow-ups in the future.

We are grateful to you for your positive assessment of the strengths of our manuscript and your recognition of its potential future applications.

Weaknesses:

Throughout the paper, the authors use language suggesting that ampicillin treatment of the ∆recA strain induces higher levels of mutagenesis inside the cells, leading to the rapid rise of resistance mutations. However, as the authors note, the mutants enriched by ampicillin selection can play a role in efflux and can thus change a bacterium's sensitivity to a wide range of antibiotics, in what is known as cross-resistance. The current data is not clear on whether the elevated "mutagenesis" is driven ampicillin selection or by a bona fide increase in mutation rate.

We greatly appreciate you for raising this issue, as it is an important premise that must be clearly stated throughout the entire manuscript. To verify that the observed increase in mutation rate is a bona fide increase and not due to experimental error, we used a non-selective antibiotic, rifampicin, to evaluate the mutation frequency after drug induction, as it is a gold-standard method documented in other studies [Heterogeneity in efflux pump expression predisposes antibiotic-resistant cells to mutation, Science, 362, 6415, 686-690, 2018.]. In the absence of ampicillin treatment, the natural mutation rates detected using rifampicin were consistent between the wild-type and the ΔrecA strain. However, after ampicillin treatment, the mutation rate detected using rifampicin was significantly elevated only in the ΔrecA strain (Fig. 1G). We also employed other antibiotics, such as ciprofloxacin and chloramphenicol, in our experiments to treat the cells (data not shown). However, we observed that beta-lactam antibiotics specifically induced the emergence of resistance or altered the MIC in our bacterial populations. If resistance had pre-existed before antibiotic exposure or a bona fide increase in mutation rate, we would expect other antibiotics to exhibit a similar selective effect, particularly given the potential for cross-resistance to multiple antibiotics.

Furthermore, on a technical level, the authors employed WGS to identify resistance mutations in the treated ampicillin-treated wild-type and ∆recA strains. However, the WGS methodology described in the paper is inconsistent. Notably, wild-type WGS samples were picked from non-selective plates, while ΔrecA WGS isolates were picked from selective plates with 50 μg/mL ampicillin. Such an approach biases the frequency and identity of the mutations seen in the WGS and cannot be used to support the idea that ampicillin treatment induces higher levels of mutagenesis.

We appreciate your concern regarding potential inconsistencies in the WGS methodology. However, we would like to clarify that the primary aim of the WGS experiment was to identify the types of mutations present in the wild-type and ΔrecA strains after treatment of ampicillin, rather than to quantify or compare mutation frequencies. This purpose was explicitly stated in the manuscript.

Furthermore, the choice of selective and non-selective conditions was made to ensure the successful isolation of mutants in both strains. Specifically, if selective conditions (50 μg/mL ampicillin) were applied to the wild-type strain, it would have been nearly impossible to recover colonies for WGS analysis, as wild-type cells are highly susceptible to ampicillin at this concentration (Top, Author response image 1). Conversely, under non-selective conditions, ΔrecA mutants carrying resistance mutations may not have been effectively isolated, which would have limited our ability to identify resistance mutations in these strains (Bottom, Author response image 1 Thus, the use of different selection pressures was essential for achieving the objective of mutation identification in this study.

Author response image 1.

After 8 hours of antibiotic treatment, the wild type or the ΔrecA cells were plated on agar plates either without ampicillin or with 50 μg/mL ampicillin and incubated for 24-48 hours. Top: Under selective conditions, no wild type colonies were recovered, indicating high susceptibility to the antibiotic, preventing further analysis. Bottom: In non-selective conditions, both ΔrecA resistant mutants and non-resistant cells grew, making it difficult to distinguish and isolate the mutants carrying resistance mutations.

Finally, it is important to establish what the basal mutation rates of both the WT and ∆recA strains are. Currently, only the ampicillin-treated populations were reported. It is possible that the ∆recA strain has inherently higher mutagenesis than WT, with a larger subpopulation of resistant clones. Thus, ampicillin treatment might not in fact induce higher mutagenesis in ∆recA.

Thanks for this suggestion. The basal mutation frequency of the wild-type and the ∆recA strain have been measured using rifampicin (Fig. 1G), and there is no significant difference between them.

Reviewer #2:

Summary:

This study aims to demonstrate that E. coli can acquire rapid antibiotic resistance mutations in the absence of a DNA damage response. To investigate this, the authors employed a sophisticated experimental framework based on a modified Adaptive Laboratory Evolution (ALE) workflow. This workflow involves numerous steps culminating in the measurement of antibiotic resistance. The study presents evidence that a recA strain develops ampicillin resistance mutations more quickly than the wild-type, as shown by measuring the Minimum Inhibitory Concentration (MIC) and mutation frequency. Whole-genome sequencing of 15 recA-colonies resistant to ampicillin revealed predominantly inactivation of genes involved in the multi-drug efflux pump system, whereas, in the wild-type, mutations appear to enhance the activity of the chromosomal ampC cryptic promoter. By analyzing mutants involved in the SOS response, including a lexA3 mutant incapable of inducing the SOS response, the authors conclude that the rapid evolution of antibiotic resistance occurs in an SOS-independent manner when recA is absent.

Furthermore, RNA sequencing (RNA-seq) of the four experimental conditions suggests that genes related to antioxidative responses drive the swift evolution of antibiotic resistance in the recA-strain.

We greatly appreciate your overall summary of the manuscript and their positive evaluation of our work.

Weaknesses:

However, a potential limitation of this study is the experimental design used to determine the 'rapid' evolution of antibiotic resistance. It may introduce a significant bottleneck in selecting ampicillin-resistant mutants early on. A recA mutant could be more susceptible to ampicillin than the wild-type, and only resistant mutants might survive after 8 hours, potentially leading to their enrichment in subsequent steps. To address this concern, it would be critical to perform a survival analysis at various time points (0h, 2h, 4h, 6h, and 8h) during ampicillin treatment for both recA and wild-type strains, ensuring there is no difference in viability.

We appreciate your suggestion. We measured the survival fraction at 0, 2, 4, 6, and 8 hours after ampicillin treatment. The results show no significant difference in antibiotic sensitivity between the wild-type and ΔrecA strain (Fig. S2). We therefore added a description int the main text, “Meanwhile, after 8 hours of treatment with 50 μg/mL ampicillin, the survival rates of both wild type and ΔrecA strain were consistent (Fig. S2)”.

The observation that promoter mutations are absent in ΔrecA strains could be explained by previous research indicating that amplification of the AmpC genes is a mechanism for E. coli resistance to ampicillin, which does not occur in a recA-deficient background (PMID# 19474201).

We are very grateful to you for providing this reference. We did examine the amplification of the ampC gene in both wild-type and _recA-_deficient strains, but we found no significant changes in its copy number after ampicillin treatment (Author response image 2). Therefore, the results and discussion regarding gene copy number were not included in this manuscript.

Author response image 2.

Copy number variations of genes in the chromosome before and after exposure to ampicillin at 50 µg/mL for 8 hours in the wild type and ΔrecA strain.

The section describing Figure 3 is poorly articulated, and the conclusions drawn are apparent. The inability of a recA strain to induce the SOS response is well-documented (lines 210 and 278). The data suggest that merely blocking SOS induction is insufficient to cause 'rapid' evolution in their experimental conditions. To investigate whether SOS response can be induced independently of lexA cleavage by recA, alternative experiments, such as those using a sulA-GFP fusion, might be more informative.

Thanks for your suggestion. We agree that detecting the expression level of SulA can provide valuable information to reveal the impact of the SOS system on rapid drug resistance. In addition to fluorescence visualization and quantification of SulA expression, regulating the transcription level of the sulA gene can achieve the same objective. Therefore, in our transcriptome sequencing analysis, we focused on evaluating the transcription level of sulA (Fig. 4E).

In Figure 4E, the lack of increased SulA gene expression in the wild-type strain treated with ampicillin is unexpected, given that SulA is an SOS-regulated gene. The fact that polA (Pol I) is going down should be taken into account in the interpretation of Figures 2D and 2E.

Thank you for your observation regarding the lack of increased SulA gene expression in the wild-type strain treated with ampicillin in Figure 4E. We agree that SulA is typically an SOS-regulated gene, and its expression is expected to increase in response to DNA damage induced by antibiotics like ampicillin. However, in our experimental conditions, the observed lack of increased SulA expression could be due to different factors. One possibility is that the concentration of ampicillin used, or the duration of treatment, was not applicable to induce a strong SOS response in the wild type strain under the specific conditions tested. Additionally, differences in experimental setups such as timing, sampling, or cellular stress responses could account for the lack of a pronounced upregulation of SulA.

You may state that the fact that polA (Pol I) is going down should be taken into account in the interpretation of Figures 3D and 3E, and we agree with you.

The connection between compromised DNA repair, the accumulation of Reactive Oxygen Species (ROS) based on RNA-seq data, and accelerated evolution is merely speculative at this point and not experimentally established.

We greatly appreciate your comments. First, the correlation between DNA mutations and the accumulation of reactive oxygen species (ROS) has been experimentally confirmed. As shown in Fig. 4I, after the addition of the antioxidant GSH, DNA resistance mutations were not detected in the ΔrecA strain treated with ampicillin for 8 hours, compared to those without the addition of GSH, proving that the rapid accumulation of ROS induces the enhancement of DNA resistance mutations. Second, the enhancement of DNA resistance mutations in relation to bacterial resistance has been widely validated and is generally accepted. Finally, we appreciate the your suggestion to strengthen the evidence supporting ROS enhancement. To address this, we have added an experiment to measure ROS levels. Through flow cytometry, we found that ROS levels significantly increased in both the wild-type and ΔrecA strain after 8 hours of ampicillin treatment. However, ROS levels in the ΔrecA strain showed a significant further increase compared to the wild-type strain (Fig. 4G). Additionally, with the addition of 50 mM glutathione, no significant change in ROS levels was observed in either the wild-type or ΔrecA strain before and after ampicillin treatment (Fig. 4H). This result further confirms our finding in Fig. 4I, where adding GSH inhibited the development of antibiotic resistance.

Reviewer #3:

Summary:

In the present work, Zhang et al investigate the involvement of the bacterial DNA damage repair SOS response in the evolution of beta-lactam drug resistance evolution in Escherichia coli. Using a combination of microbiological, bacterial genetics, laboratory evolution, next-generation, and live-cell imaging approaches, the authors propose short-term drug resistance evolution that can take place in RecA-deficient cells in an SOS response-independent manner. They propose the evolvability of drug resistance is alternatively driven by the oxidative stress imposed by the accumulation of reactive oxygen species and inhibition of DNA repair. Overall, this is a nice study that addresses a growing and fundamental global health challenge (antimicrobial resistance). However, although the authors perform several multi-disciplinary experiments, there are several caveats to the authors' proposal that ultimately do not fully support their interpretation that the observed antimicrobial resistance evolution phenotype is due to compromised DNA repair.

We greatly appreciate your overall summary of the manuscript and positive evaluation of our work.

Strengths:

The authors introduce new concepts to antimicrobial resistance evolution mechanisms. They show short-term exposure to beta-lactams can induce durably fixed antimicrobial resistance mutations. They propose this is due to comprised DNA repair and oxidative stress. This is primarily supported by their observations that resistance evolution phenotypes only exist for recA deletion mutants and not other genes in the SOS response.

Thanks for your positive comments.

Weaknesses:

The authors do not show any direct evidence (1) that these phenotypes exist in strains harboring deletions in other DNA repair genes outside of the SOS response, (2) that DNA damage is increased, (3) that reactive oxygen species accumulate, (4) that accelerated resistance evolution can be reversed by anything other than recA complementation. The authors do not directly test alternative hypotheses. The conclusions drawn are therefore premature.

We sincerely thank you for your insightful comments. First, in this study, our primary focus is on the role of recA deficiency in bacterial antibiotic resistance evolution. Therefore, we conducted an in-depth investigation on E. coli strains lacking RecA and found that its absence promotes resistance evolution through mechanisms involving increased ROS accumulation and downregulation of DNA repair pathways. While we acknowledge the importance of other DNA repair genes outside of the SOS response, exploring them is beyond the scope of this paper. However, in a separate unpublished study, we have identified the involvement of another DNA recombination protein, whose role in resistance evolution is not yet fully elucidated, in promoting resistance development. This finding is part of another independent investigation.

Regarding DNA damage and repair, our paper emphasizes that resistance-related mutations in DNA are central to the development of antibiotic resistance. These mutations are a manifestation of DNA damage. To demonstrate this, we measured mutation frequency and performed whole-genome sequencing, both of which confirmed an increase in DNA mutations.

We appreciate the reviewer's suggestion to provide additional evidence for ROS accumulation, and we have now supplemented our manuscript with relevant experiments. Through flow cytometry, we found that ROS levels significantly increased in both the wild type and ΔrecA strains after 8 hours of ampicillin treatment. However, ROS levels in the ΔrecA strain showed a significant further increase compared to the wild-type strain (Fig. 4G). Additionally, with the addition of 50 mM glutathione, no significant change in ROS levels was observed in either the wild-type or ΔrecA strain before and after ampicillin treatment (Fig. 4H). This result further confirms our finding in Fig. 4I, where adding GSH inhibited the development of antibiotic resistance.

Finally, in response to your question about reversing accelerated resistance evolution, we would like to highlight that, in addition to recA complementation, we successfully suppressed rapid resistance evolution by supplementing with an antioxidant, GSH (Fig. 4I). This further supports our hypothesis that increased ROS levels play a key role in driving accelerated resistance evolution in the absence of RecA.

Recommendations for the authors:

Reviewer #1:

The author's model asserts that deletion of recA impairs DNA repair in E. coli, leading to an accumulation of ROS in the cell, and ultimately driving the rapid rise of resistance mutations. However, the experimental evidence does not adequately address whether the resistance mutations are true, de novo mutations that arose due to beta-lactam treatment, or mutations that confer cross-resistance enriched by ampicillin selection.

a. Major: In Figure 1F & G, the authors show that the ∆recA strain, following ampicillin treatment, has higher resistance and mutation frequency towards rifampicin than WT. However, it is not clear whether the elevated resistance and mutagenesis are driven by mutations enriched by the ampicillin treatment (e.g. mutations in acrB, as seen in Figure 2) or by "new" mutations in the rpoB gene. As the authors note, the mutants enriched by ampicillin selection can play a role in efflux and can thus change a bacterium's sensitivity to a wide range of antibiotics, including rifampicin, in what is known as cross-resistance. Therefore, the mutation frequency calculation, which relies on quantifying rifampicin-resistant clones, might be confounded by bacteria with mutations that confer cross-resistance. A better approach to calculate mutation frequency would be to employ an assay that does not require antibiotic selection, such as a lac-reversion assay. This would mitigate the confounding effects of cross-resistance of drug-resistant mutations.

We appreciate your thoughtful comments regarding the potential for cross-resistance to confound the mutation frequency calculation based on rifampicin-resistant clones. Indeed, as noted, ampicillin selection can enrich for mutants with enhanced efflux activity, which may confer cross-resistance to a range of antibiotics, including rifampicin.

However, we believe that the current approach of calculating mutation frequency using rifampicin-resistant mutants is still valid in our specific context. Rifampicin targets the RNA polymerase β subunit, and resistance typically arises from specific mutations in the rpoB gene. These mutations are well-characterized and distinct from those typically associated with efflux-related cross-resistance. Thus, the likelihood of cross-resistance affecting our mutation frequency calculation is minimized in this scenario.

Additionally, while the lac-reversion assay could be an alternative, it focuses on specific metabolic pathway mutations (such as those affecting lacZ) and would not necessarily capture the same types of mutations relevant to rifampicin resistance or antibiotic-induced mutagenesis. Given our experimental objective of understanding how ampicillin induces mutations that confer antibiotic resistance, the current approach of using rifampicin selection provides a direct and relevant measurement of mutation frequency under antibiotic stress.

b. Major: It is important to establish what the basal mutation frequencies/rates of both the WT and ∆recA strains are. Currently, only the ampicillin-treated populations were reported. It is possible that the ∆recA strain has an inherently higher mutagenesis than WT. Thus, ampicillin treatment might not in fact induce higher mutagenesis in ∆recA.

Thanks for your suggestion. The basal mutation frequency of the wild-type and the ∆recA strain have been measured using rifampicin (Fig. 1G), and there is no significant difference between them.

c. Major: In the text, the authors write, "To verify whether drug resistance associated DNA mutations have led to the rapid development of antibiotic resistance in recA mutant strain, we randomly selected 15 colonies on non-selected LB agar plates from the wild type surviving isolates, and antibiotic screening plates containing 50 μg/mL ampicillin from the ΔrecA resistant isolates, respectively." Why were the WT clones picked from non-selective plates and the recA mutant from selective ones for WGS? It appears that such a procedure would bias the recA mutant clones to show more mutations (caused by selection on the ampicillin plate). The authors need to address this discrepancy.

We appreciate your concern regarding potential inconsistencies in the WGS methodology. However, we would like to clarify that the primary aim of the WGS experiment was to identify the types of mutations present in the wild-type and ΔrecA strains after treatment of ampicillin, rather than to quantify or compare mutation frequencies. This purpose was explicitly stated in the manuscript.

Furthermore, the choice of selective and non-selective conditions was made to ensure the successful isolation of mutants in both strains. Specifically, if selective conditions (50 μg/mL ampicillin) were applied to the wild type strain, it would have been nearly impossible to recover colonies for WGS analysis, as wild-type cells are highly susceptible to ampicillin at this concentration (Top, Author response image 1). Conversely, under non-selective conditions, ΔrecA mutants carrying resistance mutations may not have been effectively isolated, which would have limited our ability to identify resistance mutations in these strains (Bottom, Author response image 1). Thus, the use of different selection pressures was essential for achieving the objective of mutation identification in this study.

d. Major: In some instances, the authors do not use accurate language to describe their data. In Figure 2A, the authors randomly selected 15 ∆recA clones from a selective plate with 50 µg/mL of ampicillin. These clones were then subjected to WGS, which subsequently identified resistant mutations. Based on the described methods, these mutations are a result of selection: in other words, resistant mutations were preexisting in the bacterial population, and the addition of ampicillin selection killed off the sensitive cells, enabling the proliferation of the resistant clones. However, the in Figure 2 legend and associated text, the authors suggest that these mutations were "induced" by beta-lactam exposure, which is misleading. The data does not support that.

We appreciate your detailed feedback on the language used to describe our data. We understand the concern regarding the use of the term "induced" in relation to beta-lactam exposure. To clarify, we employed not only beta-lactam antibiotics but also other antibiotics, such as ciprofloxacin and chloramphenicol, in our experiments (data not shown). However, we observed that beta-lactam antibiotics specifically induced the emergence of resistance or altered the MIC in our bacterial populations. If resistance had pre-existed before antibiotic exposure, we would expect other antibiotics to exhibit a similar selective effect, particularly given the potential for cross-resistance to multiple antibiotics.

Furthermore, we used two different ∆recA strains, and the results were consistent between the strains (Fig. S3). Given that spontaneous mutations can occur with significant variability in populations, if resistance mutations pre-existed before antibiotic exposure, the selective outcomes should have varied between the two strains.

Most importantly, we found that the addition of anti-oxidative compound GSH prevented the evolution of antibiotic from the treatment of ampicillin in the ΔrecA strain. If we assume that resistant bacteria preexist in the ∆recA strain, then the addition of GSH should not affect the evolution of resistance. Therefore, we believe that the resistance mutations we detected were not simply the result of selection from preexisting mutations but were indeed induced by beta-lactam exposure.

e. Major: For Figure 4J, using WGS the authors show that the addition of GSH to WT and ∆recA cells inhibited the rise of resistance mutations; no resistance mutations were reported. However, in the "Whole genome sequencing" section under "Materials and Methods", they state that "Resistant clones were isolated by selection using LB agar plates with the supplementation of ampicillin at 50 μg/mL". These clones were then genome-extracted and sequenced. Given the methodology, it is surprising that the WGS did not reveal any resistance mutations in the GSH-treated cells. How were these cells able to grow on 50 μg/mL ampicillin plates for isolation in the first place? The authors need to address this.

We sincerely apologize for the confusion caused by the incorrect expression in the "Materials and Methods" section. Indeed, when bacteria were treated with the combination of antibiotics and GSH, resistance was significantly suppressed, and no resistant clones could be isolated from selective plates (i.e., LB agar supplemented with 50 μg/mL ampicillin).

To address this, we instead plated the bacteria treated with antibiotics and GSH onto non-selective plates (without ampicillin) and randomly selected 15 colonies for WGS. None of them showed resistance mutations. We will revise the text in the "Materials and Methods" section to accurately reflect this procedure and provide clarity.

f. Minor: for Figure 1G, it is misleading to have both "mutation frequency" and "mutant rate" in the y-axis; the two are defined and calculated differently. Based on the Materials and Materials, "mutation frequency" would be the appropriate term. Also, for the ∆recA strain, it is a bit unusual to see mutation frequencies that are tightly clustered. Usually, mutation frequencies follow the Luria-Delbruck distribution. Can the authors explain why the ∆recA data looks so different compared to, say, the WT mutation frequencies?

Thank you for your insightful feedback. We agree that having both "mutation frequency" and "mutant rate" on the y-axis is misleading, as these terms are defined and calculated differently. To avoid confusion, we will revise Figure 1G to use only "mutation frequency" as the correct term, in line with the methods described in the Materials and Methods section.

Regarding the ∆recA strain's mutation frequencies, we acknowledge that the data appear more tightly clustered compared to the expected Luria-Delbruck distribution seen in the wild type strain. In fact, the y-axis of the Figure 1G is logarithmic, this causes the data to appear more clustered.

We further added the basal mutation frequency in the wild type and ∆recA strains before the exposure to ampicillin. The basal mutation frequency of the wild-type and the ∆recA strain have been measured using rifampicin (Fig. 1G), and there is no significant difference between them.

g. Minor: It needs to be made clear in the Main Text what the selective antibiotic agar plate used was, rifampicin or ampicillin. I am assuming it was rifampicin, as ampicillin plates would yield resistance frequencies close to 100%, given the prior treatment of the culture with ampicillin.

Thanks for your comments. Depending on the objective, we used different selective plates. For example, when testing the mutation frequency of antibiotic resistance, we used a selective plate containing rifampicin in order to utilize a non-inducing antibiotic, which is the standard method for calculating resistance mutation frequency. In the WGS experiment, to obtain mutations specific to ampicillin resistance, we selected a selective plate containing ampicillin.

Reviewer #2:

The Y-axis label (log10 mutant rate) in Figure 1G is misleading or incorrect.

Thanks for your comments and we apologize for this misleading information. The Figure 1G has been revised accordingly.

In line 393 of the discussion, the authors claim that excessive ROS accumulation drives the evolution of ampicillin resistance, which has not been conclusively demonstrated. Additional experiments are needed to support this statement.

We greatly appreciate your comments. First, the correlation between DNA mutations and the accumulation of reactive oxygen species (ROS) has been experimentally confirmed. As shown in Fig. 4I, after the addition of the antioxidant GSH, DNA resistance mutations were not detected in the ΔrecA strain treated with ampicillin for 8 hours, compared to those without the addition of GSH, proving that the rapid accumulation of ROS induces the enhancement of DNA resistance mutations. Second, the enhancement of DNA resistance mutations in relation to bacterial resistance has been widely validated and is generally accepted. Finally, we appreciate the your suggestion to strengthen the evidence supporting ROS enhancement. To address this, we have added an experiment to measure ROS levels. Through flow cytometry, we found that ROS levels significantly increased in both the wild-type and ΔrecA strain after 8 hours of ampicillin treatment. However, ROS levels in the ΔrecA strain showed a significant further increase compared to the wild-type strain (Fig. 4G). Additionally, with the addition of 50 mM glutathione, no significant change in ROS levels was observed in either the wild-type or ΔrecA strain before and after ampicillin treatment (Fig. 4H). This result further confirms our finding in Fig. 4I, where adding GSH inhibited the development of antibiotic resistance.

The abstract is overly complex and difficult to read, e.g. "Contrary to previous findings, it is shown that this accelerated resistance development process is dependent on the hindrance of DNA repair, which is completely orthogonal to the SOS response").

Thank you for the valuable feedback regarding the complexity of the abstract. We agree that certain sections could be simplified for clarity. In response, we have revised the abstract to make it more concise and easier to understand. For example, the sentence “Contrary to previous findings, it is shown that this accelerated resistance development process is dependent on the hindrance of DNA repair, which is completely orthogonal to the SOS response” has been rewritten as: "Unlike earlier studies, we found that the rapid development of resistance relies on the hindrance of DNA repair, a mechanism that operates independently of the SOS response."

Reviewer #3:

As indicated above, direct evidence is needed to show (1) that these phenotypes exist in strains harboring deletions in other DNA repair genes outside of the SOS response, (2) that DNA damage is increased, (3) that reactive oxygen species accumulate, (4) that accelerated resistance evolution can be reversed by anything other than recA complementation. There are also other resistance evolution mechanisms untested here, including transcription-coupled repair (TCR) mechanisms involving Mfd. These need to be shown in order to draw the conclusions proposed.

We sincerely thank you for your insightful comments. First, in this study, our primary focus is on the role of recA deficiency in bacterial antibiotic resistance evolution. Therefore, we conducted an in-depth investigation on E. coli strains lacking RecA and found that its absence promotes resistance evolution through mechanisms involving increased ROS accumulation and downregulation of DNA repair pathways. While we acknowledge the importance of other DNA repair genes outside of the SOS response and other resistance evolution mechanisms including the TCR mechanism, exploring them is beyond the scope of this paper. However, in a separate unpublished study, we have identified the involvement of another DNA recombination protein, whose role in resistance evolution is not yet fully elucidated, in promoting resistance development. This finding is part of another independent investigation.

Regarding DNA damage and repair, our paper emphasizes that resistance-related mutations in DNA are central to the development of antibiotic resistance. These mutations are a manifestation of DNA damage. To demonstrate this, we measured mutation frequency and performed whole-genome sequencing, both of which confirmed an increase in DNA mutations.

We appreciate the reviewer's suggestion to provide additional evidence for ROS accumulation, and we have now supplemented our manuscript with relevant experiments. Through flow cytometry, we found that ROS levels significantly increased in both the wild type and ΔrecA strains after 8 hours of ampicillin treatment. However, ROS levels in the ΔrecA strain showed a significant further increase compared to the wild-type strain (Fig. 4G). Additionally, with the addition of 50 mM glutathione, no significant change in ROS levels was observed in either the wild-type or ΔrecA strain before and after ampicillin treatment (Fig. 4H). This result further confirms our finding in Fig. 4I, where adding GSH inhibited the development of antibiotic resistance.

Finally, in response to your question about reversing accelerated resistance evolution, we would like to highlight that, in addition to recA complementation, we successfully suppressed rapid resistance evolution by supplementing with an antioxidant, GSH (Fig. 4I). This further supports our hypothesis that increased ROS levels play a key role in driving accelerated resistance evolution in the absence of RecA.

eLife Assessment

The authors analyze the relationship between human mobility and genomic data of SARS-CoV-2 using mobile phone mobility data and sequence data and present a solid proof of concept. This useful work was conducted on a fine spatial scale and provides suggestions on how mobility-derived surveillance could be conducted, although these results are mixed. The primary significance of this work is the strong use of large datasets that were highly granular. The authors provide a rigorous study, but with less clear predictive power of mobility to inform transmission patterns.

Reviewer #1 (Public review):

Summary:

In this manuscript Spott et al. combine SARS-CoV-2 genomic data alongside granular mobility data to retrospectively evaluate the spread of SARS-CoV-2 alpha lineages throughout Germany and specifically Thuringia. They further prospectively identified districts with strong mobility links to the first district in which BQ.1.1 was observed to direct additional surveillance efforts to these districts. The additional surveillance effort resulted in the earlier identification of BQ.1.1 in districts with strong links to the district in which BQ.1.1 was first observed.

Strengths:

There are two important strengths of this work. The first, is the scale and detail in the data that has been generated an analyzed as part of this study. Specifically, the authors use 6,500 SARS-CoV-2 sequences and district level mobility data within Thuringia. I applaud the authors for making a subset of their analyses public e.g. on the associated micro react page.

Further, the main focus of the article is on the potential utility of mobility-directed surveillance sequence. While I may certainly be mistaken, I have not seen this proposed elsewhere, at least in the context of SARS-CoV-2. The authors were further able to test this concept in a real world setting during the emergence of BQ.1.1 and compare it to the "gold standard" of random sampling. This is a unique real-world evaluation of a novel surveillance sequencing strategy and there is considerable value in publishing this analysis. Given the increased focus on optimizing sampling strategies for genomic surveillance, this work provides a novel strategy and will hopefully motivate additional modeling and real-world implementations.

Weaknesses:

The article is quite strong and I find the analyses to generally be rigorous. Limitations of the analysis, particularly due to the fact that BQ.1.1 remained a low-prevalence variant, are adequately addressed. The results do not provide quantitative, definitive proof that mobility-guided sampling is an optimal strategy, but they also do not claim to nor do I think they need to to make an important contribution to the field.

Reviewer #2 (Public review):

In the manuscript, the authors combine SARS-CoV-2 sequence data from a state in Germany and mobility data to help in understanding the movement of virus and the potential to help decide where to focus sequencing. The global expansion in sequencing capability is a key outcome of the public health response. However, there remains uncertainty how to maximise the insights the sequence data can give. Improved ability to predict the movement of emergent variants would be a useful public health outcome.

However, I remain unconvinced that changing surveillance strategies is necessarily sensible as it remains unclear what the ultimate benefit of variant hunting is. Decisions to adapt surveillance strategies should not be taken lightly as there are substantial benefits of maintaining a stable and as representative as possible, system over time. It's unclear what public health action would result of detecting a few more sequences of a variant. Once a variant has been identified (arguably anywhere in the world/region), we already have the necessary information to motivate the development of updated vaccines/monoclonals.

Author response:

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

Thank you for your assessment and constructive critique, which helped us to improve the manuscript and its clarity. Upon carefully reading through the comments, we noticed that, based on the Reviewer's questions, some of our answers were already available but “hidden” as supplementary data. Thus, we changed the following two figures and text accordingly to showcase our results to the reader better:

A) To highlight how mobile service data can indicate the spread of highly prevalent variants, we added a high-prevalence subcluster to Figure 2 (previously shown in Supplementary Figures S4 and S5) and, in exchange, moved one low-prevalence subcluster from Figure 2 back into the supplement. The figure is now showing a low and a high prevalent subcluster instead of two low prevalent subclusters.

B) Based on Reviewer 1’s question about where samples were taken in regards to the mobility data from the community of the first identification (negative controls), we now highlight all the mobility data that was available to us in Figure 3 (as triangles) instead of just a few top mobility hits for both - mobility guided and random surveillance (serving as a negative control for the former). This way, we think, it is clearer how random sampling was also performed in some regions where mobility was coming from the community of origin (as asked by Reviewer 1) - the detailed trips and sampling are now part of the supplement for data transparency reasons. We also noticed a typo in the GPS coordinates, aligning one of the arrows falsely, which is corrected in the improved Figure 3.

We have also included the R-Scripts used to generate all the figures in the manuscript in an OSF repository (we updated the “Data sharing statement”). We also updated Figure 1 slightly and extended the supplemental material. The remaining comments to reviewers are addressed point-by-point below.

Reviewer 1 (Public Review):

In "1 Exploring the Spatial Distribution of Persistent SARS-CoV-2 Mutations -Leveraging mobility data for targeted sampling" Spott et al. combine SARS-CoV-2 genomic data alongside granular mobility data to retrospectively evaluate the spread of SARS-CoV-2 alpha lineages throughout Germany and specifically Thuringia. They further prospectively identified districts with strong mobility links to the first district in which BQ.1.1 was observed to direct additional surveillance efforts to these districts. The additional surveillance effort resulted in the earlier identification of BQ.1.1 in districts with strong links to the district in which BQ.1.1 was first observed.

Thank you for taking the time to review our work.

(1) It seems the mobility-guided increased surveillance included only districts with significant mobility links to the origin district and did not include any "control" districts (those without strong mobility links). As such, you can only conclude that increasing sampling depth increased the rate of detection for BQ.1.1., not necessarily that doing so in a mobility-guided fashion provided an additional benefit. I absolutely understand the challenges of doing this in a real-world setting and think that the work remains valuable even with this limitation, but I would like the lack of control districts to be more explicitly discussed.

Thank you for the critical assessment of our work. We agree that a control is essential for interpreting the results. In our case, randomized surveillance (“the gold standard”) served as a control with a total sampling depth seven times higher than the mobility-guided sampling. To better reflect the sampling in regards to the available mobility data, we revisited Figure 3 and added all the mobility information from the origin that was available to us. We also added this information to the random surveillance to provide a clearer picture to the reader. This now clearly shows how randomized surveillance covered communities with varying degrees of incoming mobility from the community of first occurrences, thereby underlining its role as a negative control. We updated the manuscript to reflect these changes and included the October 2020 and June 2021 mobility datasets in Supplementary Table S6. We agree that the sampling depth increases the detection, which is the point of guided sampling to increase sampling, specifically in areas where mobility points towards a possible spread. In regards to the negative control: Random surveillance (not Mobility-guided) in October covered 40 samples in the northwest region of Thuringia (Mobility-guided covered 19 samples). Thus, random surveillance also contained 31 out of 132 samples with a mobility link towards the first occurrence of BQ1.1 but with varying amounts of mobility (low to high).

We added this information to the main text:

Line 270 to 293:

Following its first Thuringian identification, we utilized the latest available dataset of the past two years of mobile service data (October 2020 and June 2021) to investigate the residential movements for the community of first detection. Considering the highest incoming mobility from both datasets, we identified 18 communities with high (> 10,000), 34 with medium (2,001-10,000), and 82 with low (30-2,000) number of incoming one-way trips from the originating community (purple triangles in Figure 3a). As a result, we specifically requested all the available samples from the eight communities with the highest incoming mobility. Still, we were restricted to the submission of third parties over whom we had no influence. This led to the inclusion of the following eight communities with the most residential movement from the originating community: four in central and three in NW of Thuringia, one in NW-neighboring state Saxony-Anhalt. The samples requested from central Thuringia were also due to their geographic arrangement as a “belt” in central Thuringia, linking three major cities (see Supplementary Figure S1). Subsequently, we collected 19 additional samples (isolated between the 17th and 25th of October 2022; see “Guided Sampling” for October 2022, Figure 3a) besides the randomized sampling strategy. Thus, the sampling depth was increased in communities with high incoming mobility from the first origin.

As part of the general Thuringian surveillance, we collected 132 samples for October (covering dates between the 5th and 31st) and 69 samples in November (covering dates between the 1st and 25th; see Figure 3b and c). Randomized sampling was not influenced or adjusted based on the mobility-guided sample collection. Thus, it also contains samples from communities with a mobility link towards the first occurrence of BQ.1.1, as they were part of the regular random collection (see gray triangles in Figure 3b). A complete overview of all samples is provided in Supplementary Table S5. The mobility datasets from October 2020 and June 2021 for all sampled communities are provided in Supplementary Table S6.

Line 305 to 313:

Among the 19 samples specifically collected based on mobile service data, we identified one additional sample of the specific Omicron sublineage BQ.1.1 in a community with high incoming mobility (n = 14, number of trips = 37,499) with a distance of approximately 16 km between both towns. Our randomly sampled routine surveillance strategy did not detect another sample during the same period. This was despite a seven times higher overall sample rate, which included 31 samples from communities with an identified incoming mobility from the community of the first occurrence (October 2022, Figure 3b). Only in the one-month follow-up were four other samples identified across Thuringia through routine surveillance (November 2022, Figure 3c).

Line 325 to 333:

In summary, increasing the sampling depth in the suspected regions successfully identified the specified lineage using only a fraction of the samples from the randomized sampling. Conversely, randomized surveillance, the “gold standard” acting as our negative control, did not identify additional samples with similar sampling depths in regions with no or low incoming mobility or even in high mobility regions with less sampling depth. Implementing such an approach effectively under pandemic conditions poses difficult challenges due to the fluctuating sampling sizes. Although the finding of the sample may have been coincidental, our proof of concept demonstrated how we can leverage the potential of mobile service data for targeted surveillance sampling.

(2) Line 313: While this work has reliably shown that the spread of Alpha was slower in Thuringia, I don't think there have been sufficient analyses to conclude that this is due to the lack of transportation hubs. My understanding is that only mobility within Thuringia has been evaluated here and not between Thuringia and other parts of Germany.

Thank you for pointing this out. We noticed that the original sentence lacked the necessary clarity. The statement in line 313 was based on the observation that Alpha first occurred in federal states with major transport hubs, such as international airports and ports, which Thuringia lacks, as demonstrated in the Microreact dataset. For clarification, we adjusted the sentence as follows:

Line 340 and following:

A plausible explanation for the delayed spread of the Alpha lineage in Thuringia is the lack of major transport hubs, as Alpha first occurred in federal states with such hubs. Previous studies have already highlighted the impact of major transportation hubs in the spread of Sars-CoV-2.

(3) Line 333 (and elsewhere): I'm not convinced, based on the results presented in Figure 2, that the authors have reliably identified a sampling bias here. This is only true if you assume (as in line 235) that the variant was in these districts, but that hasn't actually been demonstrated here. While I recognize that for high-prevalence variants, there is a strong correlation between inflow and variant prevalence, low-prevalence variants by definition spread less and may genuinely be missing from some districts. To support this conclusion that they identified a bias, I'd like to see some type of statistical model that is based e.g. on the number of sequences, prevalence of a given variant in other districts, etc. Alternatively, the language can be softened ("putative sampling bias").

Thank you for addressing this legitimate point of criticism in our interpretation. Due to the retrospective nature of the analysis and the fact that we found no additional samples of the clusters after the specified timeframes, we were limited to the samples in our dataset. Therefore, it is impossible to demonstrate if a variant was present in the relevant districts afterward. We agree that the variant’s low prevalence means they may genuinely not have spread to some districts. For clarification, we added the following statements and changed the wording accordingly:

Additional statement in line 248:

However, due to their low prevalence, it is also possible that these subclusters have not spread to the indicated districts.

Adjusted wording in line 361:

We exemplified this approach with the Alpha lineage, where mobile service data indicated a putative sampling bias and partially predicted the spread of our Thuringian subclusters.

Recommendations:

(1) I applaud the use of the microreact page to make the data public, however, I don't see any reference to a GitHub or Zenodo repository with the analysis code. The NextStrain code is certainly appreciated but there is presumably additional code used to identify the clusters, generate figures, etc. I generally prefer this code be made public and it is recommended by eLife.

Thank you for your appreciation. We have now included the R-scripts in the manuscript’s OSF repository. These were used to create the figures in the manuscript and supplement utilizing the supplementary tables 1-6, which are also stored in the repository. To clearly communicate which data is provided, we changed lines 513 and 514 of the “Data sharing statement” as follows:

Line 513 and following:

Supplementary tables and the R-scripts used to generate all figures are also provided in the repository under https://osf.io/n5qj6/. These include the mobile service data used in this study, which is available in processed and anonymized form.

The subcluster identification was performed manually. By adding each sample's mutation profile to the Microreact metadata file, we visually screened the phylogenetic time tree for all non-Alpha specific mutations present in at least 20 Thuringian genomes. We then applied the criteria described in the Methods section to identify the nine Alpha subclusters. For clarification, we changed line 436:

Line 436:

We then manually screened for mutations present in at least 20 genomes with a small phylogenetic distance and a time occurrence of at least two months.

Reviewer 2 (Public Review):

In the manuscript, the authors combine SARS-CoV-2 sequence data from a state in Germany and mobility data to help in understanding the movement of the virus and the potential to help decide where to focus sequencing. The global expansion in sequencing capability is a key outcome of the public health response. However, there remains uncertainty about how to maximise the insights the sequence data can give. Improved ability to predict the movement of emergent variants would be a useful public health outcome. Also knowing where to focus sequencing to maximising insights is also key. The presented case study from one State in Germany is therefore a useful addition to the literature. Nevertheless, I have a few comments.

Thank you for taking the time to review our work.

(1) One of the key goals of the paper is to explore whether mobile phone data can help predict the spread of lineages. However, it appears unclear whether this was actually addressed in the analyses. To do this, the authors could hold out data from a period of time, and see whether they can predict where the variants end up being found.

Based on your feedback, we noticed that the results of the other seven clusters presented in the supplement were not appropriately highlighted, causing them to be overlooked. We indeed demonstrated that predicting viral spread based on mobility data is possible, as shown for the high-prevalence subcluster 7 (Cluster “ORF1b:A520V”, 811 samples). This was briefly mentioned in lines 240-242, but the cluster was only shown in Supplementary Figures S4 and S5. Instead, we focused more on the putative sampling bias that the mobility for low-prevalence subclusters could indicate as an interesting use case of mobility data. This addresses a concrete problem of every surveillance: successfully identifying low-prevalence targets. However, based on your feedback, we revisited Figure 2, adding the plots of the high-prevalence subcluster: “ORF1b:A520V” from Supplementary Figures S4 and S5 while moving the low-prevalence subcluster “S:N185D” from Figure 2 into the Supplementary Figures S4 and S5. Additionally, we changed line 229 to highlight this result properly.

line 229 and following:

The mobile service data-based prediction of a subcluster’s spread aligned well with the subsequent regional coverage of fast-spreading, highly prevalent subclusters, such as subcluster 7, which covered 811 samples (see Figure 2). In contrast, the predicted spread for the low-prevalence subclusters did not correspond well with the actual occurrence.

(2) The abstract presents the mobility-guided sampling as a success, however, the results provide a much more mixed result. Ultimately, it's unclear what having this strategy really achieved. In a quickly moving pandemic, it is unclear what hunting for extra sequences of a specific, already identified, variant really does. I'm not sure what public health action would result, especially given the variant has already been identified.

Thank you for your critical assessment of the presented results and their interpretation.

Here, we aimed to provide an alternative to the standard randomized surveillance strategy. Through mobility-guided sampling, we sought to increase identification chances while necessitating fewer samples and decreasing costs, ultimately enhancing surveillance efficiency. The Omicron-lineage BQ.1.1 was the perfect example to prove this concept under actual pandemic conditions. Yet, the strategy is not limited to low-prevalence sublineages but can be applied to virtually any surveillance case. However, from your question, we recognize that this conclusion was unclear from the text. Therefore, we adapted the conclusion to better communicate the real implications of our proof of concept. Additionally, we altered line 42 in the abstract for clarification.

However, we did not assess the benefits of surveillance itself, as the German Robert Koch Institute (RKI) already had outlined its importance for tracking different viral variants. This tracking served several reasons, like monitoring vaccine escapism, mutational progress, and assessing available antibodies for treatment.

Line 42:

The latter concept was successfully implemented as a proof-of-concept for a mobility-guided sampling strategy in response to the surveillance of Omicron sublineage BQ.1.1.

Line 364 to 374:

Another approach is actively guiding the sampling process through mobile service data, which we demonstrated with our proof of principle focusing on the Omicron-lineage BQ.1.1 as a real-life example. This approach could allow for a flexible allocation of surveillance resources, enabling adaptation to specific circumstances and increasing sampling depth in regions where a variant is anticipated. By incorporating guided sampling, much fewer resources may be needed for unguided or random sampling, thereby reducing overall surveillance costs.

Additionally, while this approach is particularly useful for identifying low-prevalence variants, it is not limited to such variants. Still, it can provide a guided, more cost-efficient, low-sampling alternative to general randomized surveillance that can also be applied to other viruses or lineages.

(3) Relatedly, it is unclear to me whether simply relying on spatial distance would not be an alternative simpler approach than mobile phone data. From Figure 2, it seems clear that a simple proximity matrix would work well at reconstructing viral flow. The authors could compare the correlation of spatial, spatial proximity, and CDR data.

Thank you for pointing this out. While proximity data might appear to be an obvious choice, it has significant limitations compared to mobility data, especially in the context of our study. Proximity data assumes that spatial distance alone can accurately represent movement patterns, which would only be true in a normally distributed traffic network. Geographic features such as mountains, cities, and highways affect traffic flows, leading to variability over distance and time, which are beyond the scope of spatial proximity but efficiently captured by mobility data. In Figure 2, we presented a simplified view of the mobility data. Hence, proximity and mobility data appear to provide the same insights. However, as shown in the updated Figure 3, a detailed overview of the available mobility data reveals obvious and non-obvious spatial connections that proximity data can not capture. Incorporating such a level of detail in Figure 2 would have cluttered the figure and reduced its clarity (e.g., adding triangles for each Thuringian community).

While a comparison between proximity data and mobility data would indeed be informative, it is beyond the scope of our current study, as our primary focus was to examine the useability of mobility data in explaining our subcluster’s spread in the first place. However, we agree it would be a valuable direction for future research. We summarized our thoughts from above in the following additional sentence:

Line 374:

Pre-generated mobility networks automatically tailored to each state's unique infrastructure and population dynamics could provide better-targeted sampling guidance rather than simple geographical proximity.

Recommendations:

(1) Line 128: What do these percentages mean - the proportion of States with at least one Alpha variant? Please clarify.

We clarified the values at their first appearance in the text:

Line 127:

By March, Alpha had spread to nearly all states and districts (districts are similar to counties or provinces) in Germany (Median: 76·47 % Alpha samples among a federal states total sequenced samples compared to 36·03 % in February, excluding Thuringia) and Thuringia (Median: 85·29 %, up from 50·00 % in February).

(2) Line 134: It's a little strange to compare the dynamics of a state with that of the whole country. For it lagged as compared to all other States?

Line 134: “In summary, the spread of the Alpha lineage in Thuringia lagged roughly two weeks behind the general spread in the rest of Germany but showed similar proportions.”

Thank you for the feedback. The statement refers to the comparison of Alpha-lineage proportions across federal states, excluding Thuringia, in lines 118 to 130. To simplify, we collectively referred to these federal states as “Germany” in the text. However, we recognize that this formulation is misleading, so we adjusted line 135 for clarification:

Line 135:

In summary, the spread of the Alpha lineage in Thuringia lagged roughly two weeks behind the general spread of other German federal states but showed similar proportions.

eLife Assessment

In their important manuscript, Costa et al. establish an in vitro model for dorsal root ganglion (DRG) axonal asymmetry, revealing that central and peripheral axon branches have distinct patterns of microtubule populations that are linked to their differential regenerative capacities. The authors employ creative tissue culture methods to demonstrate how these branches develop uniquely in vitro, offering a potential explanation for long-observed regeneration disparities. The evidence provides a solid contribution to our understanding of the neuronal cytoskeleton and axonal regeneration, but the paper would benefit from additional methodology details and controls.

Reviewer #1 (Public review):

Summary:

This paper describes a new in vitro model for DRG neurons that recapitulates several key differences between the peripheral and central branches of DRG axons in vivo. These differences include morphology (with one branch being thinner than the other), and regenerative capacity (with the peripheral branch displaying higher regenerative capacity). The authors analyze the abundance of various microtubule-associated protein (MAPs) in each branch, as well as the microtubule dynamics in each branch, and find significant differences between branches. Importantly, they found that a well-known conditioning paradigm (prior lesion of the peripheral branch improves the regenerative capacity of the central branch) is not only reproduced in this system but also leads to loss of the asymmetry of MAPs between branches. Zooming in on one MAP that shows differential abundance between the axons, they find that the severing enzyme Spastin is required for the asymmetry in microtubule dynamics and in regenerative capacity following a conditioning lesion.

Strengths:

The establishment of an experimental system that recapitulates DRG axon asymmetry in vitro is an important step that is likely to be useful for other studies. In addition, identifying key molecular signatures that differ between central and peripheral branches, and determining how they are lost following a conditioning lesion adds to our understanding of why peripheral axons have a better regenerative capacity. Last, the author's use of an in vivo model system to support some of their in vitro findings is a strength of this work.

Weaknesses:

The main weakness of the manuscript is that to a large degree, one of its main conclusions (MAP symmetry underlies differences in regenerative capacity) relies mainly on a correlation, without firmly establishing a causal link. However, this weakness is relatively minor because (1) it is partially addressed with the Spastin KO and (2) there isn't a trivial way to show a causal relationship in this case.

Reviewer #2 (Public review):

Summary:

The authors set out to develop a tissue culture method in which to study the different regenerative abilities of the central and peripheral branch of sensory axons. Neurons developed a small and large branch, which have different regenerative abilities, different transport rates, and different microtubule properties. The study provides convincing evidence that the two axonal branches differ in a way to correspond to in vivo. The different regenerative abilities of the two branches are an important observation because until now it has not been clear whether this difference is intrinsic to the neuron and axons or due to differences in the environment surrounding the axons. The authors have then looked for molecular explanations of the differences between the branches. They find different transport rates and different microtubule dynamics. The different microtubule dynamics are explained by differing levels of spastin, an enzyme that severs microtubules encouraging dynamics.

Strengths:

The differences between the two branches are clearly shown, together with differences in transport, microtubule dynamics, and regeneration. The in vitro model is novel and could be widely used. The methods used are robust and generally accepted.

Weaknesses:

In order for the method to be used it needs to be better described. For instance what proportion of neurons develop just two axonal branches, one of which is different? How selective are the researchers in finding appropriate neurons?

Reviewer #3 (Public review):

Summary:

In this manuscript, Costa and colleagues investigate how asymmetry in dorsal root ganglion (DRG) neurons is established. The authors developed an in vitro system that mimics the pseudo-unipolar morphology and asymmetry of DRG neurons during the regeneration of the peripheral and central branch axons. They suggest that central-like DRG axons exhibit a higher density of growing microtubules. By reducing the polymerization of microtubules in these central-like axons, they were able to eliminate the asymmetry in DRG neurons.

Strengths:

The authors point out a distinct microtubule-associated protein signature that differentiates between DRG neurons' central and peripheral axonal branches. Experimental results demonstrate that genetic deletion of spastin eliminated the differences in microtubule dynamics and axon regeneration between the central and peripheral branches.

Weaknesses:

While some of the data are compelling, experimental evidence only partially supports the main claims.

In its current form, the study is primarily descriptive and lacks convincing mechanistic insights. It misses important controls and further validation using 3D in vitro models.

Given the heterogeneity of dorsal root ganglion (DRG) neurons, it is unclear whether the in vitro model described in this study can be applied to all major classes of DRG neurons. Also unclear is the inconsistency with embryonic DRG cultures with embryonic (E)16 from rats and E13 from mice (spastin knockout and wild-type controls). Furthermore, the authors stated (line 393) that only a small subset of cultured DRG neurons exhibited a pseudo-unipolar morphology. The authors should include the percentage of the neurons that exhibit a pseudo-unipolar morphology.

The significance of studying microtubule polymerization to DRG asymmetry in vitro is questionable, especially considering the model's validity. The authors might consider eliminating the in vitro data and instead focus on characterizing DRG asymmetry in vivo both before and after a conditioning lesion. If the authors choose to retain the in vitro data, classifying the central and peripheral-like branches in cultured DRG neurons will require further in-depth characterization. Additional validation should be performed in adult DRG neuron cultures not aged in vitro.

The comparison of asymmetry associated with a regenerative response between in vitro and in vivo paradigms has significant limitations due to the nature of the in vitro culture system. When cultured in isolation, DRG neurons fail to form functional connections with appropriate postsynaptic target neurons (the central branch) or to differentiate the peripheral domains associated with the innervation of target organs. Rather than growing neurons on a flat, hard surface like glass, more physiologically relevant substrates and/or culturing conditions should be considered. This approach could help eliminate potential artifacts caused by plating adult DRG neurons on a flat surface. Additionally, the authors should consider replicating their findings in a 3D culture model or using dorsal root ganglia explants, where both centrally and peripherally projecting axons are present.

Panels 5H-J require additional processing with astrocyte markers to accurately define the lesion borders. Furthermore, including a lower magnification would facilitate a direct comparison of the lesion site. The use of cholera toxin subunit B (CTB) to trace dorsal column sensory axons is prone to misinterpretation, as the tracer accumulates at the axon's tip. This limitation makes it extremely challenging to distinguish between regenerating and degenerating axons.

Author response:

Reviewer #1 (Public review)

Weaknesses:

The main weakness of the manuscript is that to a large degree, one of its main conclusions (MAP symmetry underlies differences in regenerative capacity) relies mainly on a correlation, without firmly establishing a causal link. However, this weakness is relatively minor because (1) it is partially addressed with the Spastin KO and (2) there isn't a trivial way to show a causal relationship in this case.

We thank Reviewer #1 for their positive assessment of our manuscript. To further strengthen the claim that MAP asymmetry underlies differences in regenerative capacity, we could investigate the effect of depleting other MAPs that lose asymmetry after conditioning lesion (CRMP5 and katanin). One expects that similarly to spastin, this would disrupt the physiological asymmetry of DRG axons and impair axon regeneration. We will further discuss this issue in the revised version of the manuscript.

Reviewer #2 (Public review):

Weaknesses:

In order for the method to be used it needs to be better described. For instance what proportion of neurons develop just two axonal branches, one of which is different? How selective are the researchers in finding appropriate neurons?

We thank Reviewer #2 for their positive assessment of our manuscript. As suggested, we will include further methodological details on the in vitro system in the revised version of the manuscript. We have evaluated the percentage of DRG neurons exhibiting different morphologies in our cultures: multipolar (4%), bipolar, (35%) bell-shaped (17%), and pseudo-unipolar neurons (43%). This will be included in the revised manuscript. All the pseudo-unipolar neurons analysed had distinct axonal branches in terms of diameter and microtubule dynamics. For imaging purposes, we selected pseuso-unipolar neurons with axons unobstructed from other cells or neurites within a distance of at least 20–30 μm from the bifurcation point, to ensure optimal imaging. In the case of laser axotomy experiments, this distance was increased to 100–200 μm to ensure clear analysis of regeneration. These selection criteria will be detailed in the Methods of the revised manuscript.

Reviewer #3 (Public review):

Weaknesses:

While some of the data are compelling, experimental evidence only partially supports the main claims. In its current form, the study is primarily descriptive and lacks convincing mechanistic insights. It misses important controls and further validation using 3D in vitro models.

We recognize the importance of further exploring the contribution of other MAPs to microtubule asymmetry and regenerative capacity of DRG axons. In future work, we plan to investigate this issue by using knockout mice for katanin and CRMP5. To understand the mechanisms underlying the differential localization of MAPs in DRG axons, we performed in-situ hybridization to assess the availability of axonal mRNA but no differences were found between central and peripheral DRG axons (Figure 4 – figure supplement 2). To address whether differences in protein transport exist, we attempted to transduce DRG neurons with GFP-tagged spastin both in vitro and in vivo. However, these experiments were inconclusive as very low levels of spastin-GFP were detected. We are actively optimizing these approaches and will address this challenge in future studies. This will be further discussed in the revised manuscript.

Given the heterogeneity of dorsal root ganglion (DRG) neurons, it is unclear whether the in vitro model described in this study can be applied to all major classes of DRG neurons.

We acknowledge the diversity of DRG neurons and agree that assessing the presence of different DRG subtypes in our culture system will enrich its future use. Despite this heterogeneity, we focused on DRG neuron features that are common to all subtypes i.e, pseudo-unipolarization and higher regenerative capacity of peripheral branches. This will be further discussed in the revised version of the manuscript.

Also unclear is the inconsistency with embryonic DRG cultures with embryonic (E)16 from rats and E13 from mice (spastin knockout and wild-type controls).

Given our previous experience in establishing DRG neuron cultures from Wistar rats and C57BL/6 mice, these developmental stages are equivalent, yielding cultures of DRG neurons with similar percentages of different morphologies. Of note, in our colonies, gestation length is ~19 days in C57BL/6 mice (background of the spastin knockout line) and ~22 days in Wistar Han rats. This will be further clarified in the Methods.

Furthermore, the authors stated (line 393) that only a small subset of cultured DRG neurons exhibited a pseudo-unipolar morphology. The authors should include the percentage of the neurons that exhibit a pseudo-unipolar morphology.

We have previously evaluated the percentage of DRG neurons exhibiting different morphologies in our cultures: multipolar (4%), bipolar, (35%) bell-shaped (17%), and pseudo-unipolar neurons (43%). This will be included in the revised manuscript. In line 393, we referred specifically to an experimental setup where DRG neuron transduction was done and 30 transduced neurons were randomly selected for longitudinal imaging. From these, the number of viable pseudo-unipolar DRG neurons was limited by both the random nature of viral transduction and light-induced toxicity as continuous imaging over seven consecutive days at hourly intervals was done. This will be clarified in the revised manuscript.

The significance of studying microtubule polymerization to DRG asymmetry in vitro is questionable, especially considering the model's validity. The authors might consider eliminating the in vitro data and instead focus on characterizing DRG asymmetry in vivo both before and after a conditioning lesion. If the authors choose to retain the in vitro data, classifying the central and peripheral-like branches in cultured DRG neurons will require further in-depth characterization. Additional validation should be performed in adult DRG neuron cultures not aged in vitro.

The in vitro system here presented reliably reproduces several key features of DRG neurons observed in vivo, including asymmetry in axon diameter, regenerative capacity, axonal transport, and microtubule dynamics. Of note, most studies in the field were developed using multipolar DRG neurons that do not recapitulate in vivo morphology and asymmetries. Thus, the current in vitro system serves as a versatile tool for advancing our understanding of DRG biology and associated diseases. This system is particularly suited to study axon regeneration, and enables research on mechanisms occurring at the stem axon bifurcation, which are challenging to examine in vivo due to the length of the stem axon and the difficulty of locating the DRG T-junction. Optimizing similar cultures using adult DRG neurons comes with challenges, such as lower cell viability and decreased percentage of pseudo-unipolarization. This is the case with multiple other neuron types for which the vast majority of cultures are obtained from embryonic tissue. These embryonic cultures (as is the case with cortical and hippocampal neurons) are widely used to understand neuronal polarization, axon growth and/or regeneration. This will be further addressed in the revised manuscript.

The comparison of asymmetry associated with a regenerative response between in vitro and in vivo paradigms has significant limitations due to the nature of the in vitro culture system. When cultured in isolation, DRG neurons fail to form functional connections with appropriate postsynaptic target neurons (the central branch) or to differentiate the peripheral domains associated with the innervation of target organs. Rather than growing neurons on a flat, hard surface like glass, more physiologically relevant substrates and/or culturing conditions should be considered. This approach could help eliminate potential artifacts caused by plating adult DRG neurons on a flat surface. Additionally, the authors should consider replicating their findings in a 3D culture model or using dorsal root ganglia explants, where both centrally and peripherally projecting axons are present.

We agree that a more sophisticated system, such as a compartmentalized culture, holds great potential for future research. In this respect, we are currently engaged in developing such models. A compartmentalized system would enable the separation of three compartments: central nervous system neurons, DRG neurons, and peripheral targets. While previous efforts to create compartmentalized DRG cultures have been reported, these systems have not demonstrated the development of pseudo-unipolar morphology. Incorporating non-neuronal DRG cells into the DRG neuron compartment, may successfully support the development of a pseudo-unipolar morphology.

We also recognize the importance of dimensionality in fostering pseudo-unipolar morphology. Of note, our model provides a 3D-like environment, as DRG glial cells are continuously replicating over the 21 days in culture. In relation to DRG explants, we attempted their use but encountered limitations with confocal microscopy as the axial resolution was insufficient to resolve adequately processes at the DRG T-junction or within individual branches. While tissue clearing could improve resolution, it would be incompatible with live imaging, which is essential for our experiments.

The above issues will be further discussed in the revised manuscript.

Panels 5H-J require additional processing with astrocyte markers to accurately define the lesion borders. Furthermore, including a lower magnification would facilitate a direct comparison of the lesion site.

In our study, we relied on the alignment of nuclei to delineate the lesion site as in our accumulated experience, this provides an accurate definition of the lesion boarder. Outside the lesion, the nuclei are well-aligned, while at the lesion site, they become randomly distributed. Additionally, CTB staining further supports the identification of the rostral boarder of the lesion, as most injured central DRG axons stop their growth at the injury site. This will be further detailed in the Methods.

The use of cholera toxin subunit B (CTB) to trace dorsal column sensory axons is prone to misinterpretation, as the tracer accumulates at the axon's tip. This limitation makes it extremely challenging to distinguish between regenerating and degenerating axons.

While alternative methods to trace or label regenerating axons exist, CTB is a well-established and widely used tracer for central sensory projections, as shown in multiple studies. Regarding the concern of possible CTB labeling in degenerating axons, we believe this is unlikely to be the case in our study as in spinal cord injury controls, CTB-positive axons are nearly absent. Also, as regeneration was investigated six weeks after injury, axon degeneration has most likely already occurred, as shown in (PMID: 15821747 and PMID: 25937174).

Author response:

Reviewer #1 (Public review):

Summary:

The manuscript by Rühling et al analyzes the mode of entry of S. aureus into mammalian cells in culture. The authors propose a novel mechanism of rapid entry that involves the release of calcium from lysosomes via NAADP-stimulated activation of TPC1, which in turn causes lysosomal exocytosis; exocytic release of lysosomal acid sphingomyelinase (ASM) is then envisaged to convert exofacial sphingomyelin to ceramide. These events not only induce the rapid entry of the bacteria into the host cells but are also described to alter the fate of the intracellular S. aureus, facilitating escape from the endocytic vacuole to the cytosol.

Strengths:

The proposed mechanism is novel and could have important biological consequences.

Weaknesses:

Unfortunately, the evidence provided is unconvincing and insufficient to document the multiple, complex steps suggested. In fact, there appear to be numerous internal inconsistencies that detract from the validity of the conclusions, which were reached mostly based on the use of pharmacological agents of imperfect specificity.

We thank the reviewer for the detailed evaluation of our manuscript. We will address the criticism below.

We agree with the reviewer that many of the experiments presented in our study rely on the usage of inhibitors. However, we want to emphasize that the main conclusion (invasion pathway affects the intracellular fate/phagosomal escape) was demonstrated without the use of inhibitors or genetic ablation in two key experiments (Figure4 G/H). These experiments were in line with the results we obtained with inhibitors (amitriptyline [Supp. Figure 4E], ARC39, PCK310, [Figure 4c] and Vacuolin-1 [Supp. Figure4f]). Importantly, the hypothesis was also supported by another key experiment, in which we showed the intracellular fate of bacteria is affected by removal of SM from the plasma membrane before invasion, but not by removal of SM from phagosomal membranes after bacteria internalization (Figure4d-f). Taken together, we thus believe that the main hypothesis is strongly supported by our data.

Moreover, we either used different inhibitors for the same molecule (ASM was inhibited by ARC39, amitriptyline and PCK310 with similar outcome) or supported our hypothesis with gene-ablated cell pools (TPC1, Syt7, SARM1), as we will point out in more detail below.

Firstly, the release of calcium from lysosomes is not demonstrated. Localized changes in the immediate vicinity of lysosomes need to be measured to ascertain that these organelles are the source of cytosolic calcium changes. In fact, 9-phenantrol, which the authors find to be the most potent inhibitor of invasion and hence of the putative calcium changes, is not a blocker of lysosomal calcium release but instead blocks plasmalemmal TRPM4 channels. On the other hand, invasion is seemingly independent of external calcium. These findings are inconsistent with each other and point to non-specific effects of 9-phenantrol. The fact that ionomycin decreases invasion efficiency is taken as additional evidence of the importance of lysosomal calcium release. It is not clear how these observations support involvement of lysosomal calcium release and exocytosis; in fact treatment with the ionophore should itself have induced lysosomal exocytosis and stimulated, rather than inhibited invasion. Yet, manipulations that increase and others that decrease cytosolic calcium both inhibited invasion.

With respect to lysosomal Ca2+ release, we agree with the reviewer that direct visual demonstration of lysosomal Ca2+ release upon infection will improve the manuscript. We therefore will perform additional experimentation to show alterations of Ca2+ at the lysosomes during infection.

As to the TRPM4 involvement in S. aureus host cell internalization, it has been reported that TRPM4 is activated by cytosolic Ca2+. However, the channel conducts monovalent cations such as K+ or Na+ but is impermeable for Ca2+ 1, 2. The following of our observations are supporting this:

i) S. aureus invasion is dependent on intracellular Ca2+, but is independent from extracellular Ca2+  (Figure 1c).

ii) 9-phenantrol treatment reduces S. aureus internalization by host cells, illustrating the dependence of this process on TRPM4 (Figure 1b). We therefore hypothesize that TRPM4 is activated by Ca2+ released from lysosomes (see above).

TRPM4 is localized to focal adhesions and is connected to actin cytoskeleton3, 4 – a requisite of host cell entry of S. aureus.5, 6 This speaks for an important function of TRPM4 in uptake of S. aureus in general, but does not necessarily have to be involved exclusively in the rapid uptake pathway.

TRPM4 itself is not permeable for Ca2+ but is activated by the cation.  Thus, it is unlikely to cause lysosomal exocytosis. The stronger bacterial uptake reduction by treatment with 9-phenantrol when compared to Ned19 thus may be caused by the involvement of TRPM4 in additional pathways of S. aureus host cell entry involving that association of TRPM4 with focal adhesions or, as pointed out by the reviewer, unspecific side effects of 9-phenantrol that we currently cannot exclude. We will include this information in the revised manuscript.

Regarding the reduced S. aureus invasion after ionomycin treatment, we agree with the reviewer that ionomycin is known to lead to lysosomal exocytosis as was previously shown by others7 as well as our laboratory8.

We hypothesized that pretreatment with ionomycin would trigger lysosomal exocytosis and thus would reduce the pool of lysosomes that can undergo exocytosis before host cells are contacted by S. aureus. As a result, we should observe a marked reduction of S. aureus internalization in such “lysosome-depleted cells”, if the lysosomal exocytosis is coupled to bacterial uptake. Our observation of reduced bacterial internalization after ionomycin treatment supports this hypothesis.

However, ionomycin treatment and S. aureus infection of host cells are distinct processes.

While ionomycin results in strong global and non-directional lysosomal exocytosis of all “releasable” lysosomes (~5-10 % of all lysosomes according to previous observations)7, we hypothesize that lysosomal exocytosis upon contact with S. aureus only involves a very small proportion of lysosomes at host-bacteria contact sites.

Since ionomycin disturbs the overall cellular Ca2+ homeostasis, we agree with the reviewer that this does not directly show lysosomal Ca2+ liberation. We will discuss this in more detail in the revised manuscript.

The proposed role of NAADP is based on the effects of "knocking out" TPC1 and on the pharmacological effects of Ned-19. It is noteworthy that TPC2, rather than TPC1, is generally believed to be the primary TPC isoform of lysosomes. Moreover, the gene ablation accomplished in the TPC1 "knockouts" is only partial and rather unsatisfactory. Definitive conclusions about the role of TPC1 can only be reached with proper, full knockouts. Even the pharmacological approach is unconvincing because the high doses of Ned-19 used should have blocked both TPC isoforms and presumably precluded invasion. Instead, invasion is reduced by only ≈50%. A much greater inhibition was reported using 9-phenantrol, the blocker of plasmalemmal calcium channels. How is the selective involvement of lysosomal TPC1 channels justified?

As to partial gene ablation of TPC1: To avoid clonal variances, we usually perform pool sorting to obtain a cell population that predominantly contains cells -here- deficient in TPC1, but also a small proportion of wildtype cells as seen by the residual TPC1 protein on the Western blot. We observe a significant reduction of bacterial uptake in this cell pool suggesting that the uptake reduction in a pure K.O. population may be even larger.

As to the inhibition by Ned19: We agree with the reviewer that Ned19 inhibits TPC1 and TPC2. Since ablation of TPC1 reduced invasion of S. aureus, we concluded that TPC1 is important for S. aureus host cell invasion. We thus agree with the reviewer that a role for TPC2 cannot be excluded. We will clarify this in the reviewed manuscript. It needs to be noted, however, that deficiency in either TPC1 or TPC2 alone was sufficient to prevent Ebola virus infection9, which is in line with our observations.

The 50% reduction of invasion upon Ned19 treatment (Figure 1d) is comparable with the reduction caused by other compounds that influence the ASM-dependent pathway (such as amitriptyline, ARC39 [Figure 2c], BAPTA-AM [Figure 1c], Vacuolin-1 [Figure 2a], β-toxin [Figure 2e] and ionomycin [Figure 1a]). Further, the partial reduction of invasion is most likely due to the concurrent activity of multiple internalization pathways which are not all targeted by the used compounds.

Invoking an elevation of NAADP as the mediator of calcium release requires measurements of the changes in NAADP concentration in response to the bacteria. This was not performed. Instead, the authors analyzed the possible contribution of putative NAADP-generating systems and reported that the most active of these, CD38, was without effect, while the elimination of SARM1, another potential source of NAADP, had a very modest (≈20%) inhibitory effect that may have been due to clonal variation, which was not ruled out. In view of these data, the conclusion that NAADP is involved in the invasion process seems unwarranted.

Our results from two independent experimental set-ups (Ned19 [Figure 1d] and TPC1 K.O. [Figure 1e & Figure 2f]) indicate the involvement of NAADP in the process. However, the measurement of NAADP concentration is non-trivial. However, we can rule out clonal variation in the SARM1 mutant since experiments were conducted with a cell pool as described above in order to avoid clonal variation of single clones.

The mechanism behind biosynthesis of NAADP is still debated. CD38 was the first enzyme discovered to possess the ability of producing NAADP. However, it requires acidic pH to produce NAADP10 -which does not match the characteristics of a cytosolic NAADP producer. HeLa cells do not express CD38 and hence, it is not surprising that inhibition of CD38 had no effect on S. aureus invasion in HeLa cells. However, NAADP production by HeLa cells was observed in absence of CD3811. Thus CD38-independent NAADP generation is likely. SARM1 can produce NAADP at neutral pH12 and is expressed in HeLa, thus providing a more promising candidate.

We agree with the reviewer that the reduction of S. aureus internalization after ablation of SARM1 is less pronounced than in other experiments of ours. This may be explained by NAADP originating from other enzymes, such as the recently discovered DUOX1, DUOX2, NOX1 and NOX213, which – with exception of DUOX2- possess a low expression even in HeLa cells. We will discuss this in the revised manuscript.

The involvement of lysosomal secretion is, again, predicated largely on the basis of pharmacological evidence. No direct evidence is provided for the insertion of lysosomal components into the plasma membrane, or for the release of lysosomal contents to the medium. Instead, inhibition of lysosomal exocytosis by vacuolin-1 is the sole source of evidence. However, vacuolin-1 is by no means a specific inhibitor of lysosomal secretion: it is now known to act primarily as a PIKfyve inhibitor and to cause massive distortion of the endocytic compartment, including gross swelling of endolysosomes. The modest (20-25%) inhibition observed when using synaptotagmin 7 knockout cells is similarly not convincing proof of the requirement for lysosomal secretion.

We agree that the manuscript will strongly benefit from a functional analysis of lysosomal exocytosis. We therefore will conduct assays to investigate exocytosis in the revision. However, we previously showed i) by addition of specific antisera that LAMP1 transiently is exposed on the plasma membrane during ionomycin and pore-forming toxin challenge and ii) demonstrated the release of ASM activity into the culture medium under these conditions.8 Both measurements are not compatible with S. aureus infection, since LAMP1 antibodies also are non-specifically bound by protein A and another IgG-binding protein on the S. aureus surface, which would bias the results. Since protein A also serves as an adhesin, we cannot simply delete the ORF without changing other aspects of staphylococcal virulence. Further, FBS contains a ASM background activity that impedes activity measurements of cell culture medium. We previously removed this background activity by a specific heat-inactivation protocol.8 However, S. aureus invasion is strongly reduced in culture medium containing this heat-inactivated FBS.

We agree with the reviewer that Vacuolin-1 has unspecific side effects. We will address this in the revised version of the manuscript.

As to the involvement of synaptotagmin 7:

Synaptotagmin 7 is not the only protein possibly involved in Ca-dependent exocytosis. For instance, SYT1 has been shown to possess an overlapping function.14 This may explain the discrepancy between our vacuolin-1 and SYT7 ablation experiments. We will add an according section to the discussion.

ASM is proposed to play a central role in the rapid invasion process. As above, most of the evidence offered in this regard is pharmacological and often inconsistent between inhibitors or among cell types. Some drugs affect some of the cells, but not others. It is difficult to reach general conclusions regarding the role of ASM. The argument is made even more complex by the authors' use of exogenous sphingomyelinase (beta-toxin). Pretreatment with the toxin decreased invasion efficiency, a seemingly paradoxical result. Incidentally, the effectiveness of the added toxin is never quantified/validated by directly measuring the generation of ceramide or the disappearance of SM.

Although pharmacological inhibitors can have unspecific side effects, we want to emphasize that the inhibitors used in our study act on the enzyme ASM by completely different mechanisms. Amitriptyline is a so called functional inhibitor of ASM (FIASMA) which induces the detachment of ASM from lysosomal membranes resulting in degradation of the enzyme.15 By contrast, ARC39 is a competitive inhibitor.16, 17

We do not see inconsistencies in our data obtained with ASM inhibitors. Amitriptyline and ARC39 both reduce the invasion of S. aureus in HuLEC, HuVEC and HeLa cells (Figure 2c). ARC39 needs a longer pre-incubation, since its uptake by host cells is slower (data not shown). We observe a different outcome in 16HBE14o- and Ea.Hy 926 cells, with 16HBE14o- even demonstrating a slightly increased invasion of S. aureus upon ARC39 treatment. Amitriptyline had no effect (Figure 2c). Moreover, both inhibitors affected the invasion dynamics (Figure 3d), phagosomal escape (Figure 4c and Supp. Figure 4e) and Rab7 recruitment (Figure 4a and Supp. Figure 4b) in a similar fashion. Proper inhibition of ASM by both compounds in all cell lines used was validated by enzyme assays (Supp. Figure 2e), which suggests that the ASM-dependent pathway does only exist in specific cell lines. This also may serve as an argument that we here do not observe unspecific side effects of the compounds. We will clarify this in the revised manuscript.

ASM is a key player for SM degradation and recycling. In clinical context, deficiency in ASM results in the so-called Niemann Pick disease type A/B. The lipid profile of ASM-deficient cells is massively altered18, which will result in severe side effects. Short-term inhibition by small molecules therefore poses a clear benefit when compared to the usage of ASM K.O. cells.

As to the treatment with a bacterial sphingomyelinase:

Treatment with the bacterial SMase (bSMase, here: β-toxin) was performed in two different ways:

i) Pretreatment of host cells with β-toxin to remove SM from the host cell surface before infection. This removes the substrate of ASM from the cell surface prior to addition of the bacteria (Figure 2e, Figure 4d-f). Since SM is not present on the extracellular plasma membrane leaflet after treatment, a release of ASM cannot cause localized ceramide formation at the sites of lysosomal exocytosis. Similar observations were made by others.19

ii) Addition of bSMase to host cells together with the bacteria to complement for the absence of ASM (Figure 2f).

Removal of the ASM substrate before infection (i) prevents localized ASM-mediated conversion of SM to Cer during infection and resulted in a decreased invasion, while addition of the SMase during infection resulted in an increased invasion in TPC1 and SYT7 ablated cells. Thus, both experiments are consistent with each other and in line with our other observations.

Removal of SM from the plasma membrane by β-toxin was indirectly demonstrated by the absence of Lysenin recruitment to phagosomes/escaped bacteria when host cells were pretreatment with the toxin before infection (Figure4F). In another publication, we recently quantified the effectiveness of β-toxin treatment, even though with slightly longer treatment times (75 min vs. 3h).20 We will repeat the measurements also for shorter treatment times.

To clarify our experimental approaches to the readership we will add an explanatory section to the revised manuscript.

As to the general conclusions regarding the role of ASM: ASM and lysosomal exocytosis has been shown to be involved in uptake of a variety of pathogens19, 21-25 supporting its role in the process.

The use of fluorescent analogs of sphingomyelin and ceramide is not well justified and it is unclear what conclusions can be derived from these observations. Despite the low resolution of the images provided, it appears as if the labeled lipids are largely in endomembrane compartments, where they would presumably be inaccessible to the secreted ASM. Moreover, considering the location of the BODIPY probe, the authors would be unable to distinguish intact sphingomyelin from its breakdown product, ceramide. What can be concluded from these experiments? Incidentally, the authors report only 10% of BODIPY-positive events after 10 min. What are the implications of this finding? That 90% of the invasion events are unrelated to sphingomyelin, ASM, and ceramide?

During the experiments with fluorescent SM analogues (Figure 3a,b), S. aureus was added to the samples immediately before start of video recording. Hence, bacteria are slowly trickling onto the host cells and we thus can image the initial contact between them and the bacteria, for instance, the bacteria depicted in Figure 3a contact the host cell about 9 min before becoming BODIPY-FL-positive (see Supp. Video 1, 55 min). Hence, we think that in these cases we see the formation of phagosomes around bacteria rather than bacteria in endomembrane compartments. Since generation of phagosomes happens at the plasma membrane, SM is accessible to secreted ASM.

The “trickling” approach for infection is an experimental difference to our invasion measurements, in which we synchronized the infection by a very slow centrifugation. This ensures that all bacteria have contact to host cells and are not just floating in the culture medium. However, live cell imaging of initial bacterial-host contact and synchronization of infection is technically not combinable.

In our invasion measurements -with synchronization-, we typically see internalization of ~20% of all added bacteria after 30 min. Hence, most bacteria that are visible in our videos likely are still extracellular and only a small proportion was internalized. This explains why only 10% of total bacteria are positive for BODIPY-FL-SM after 10 min. The proportion of internalized bacteria that are positive for BODIPY-FL-SM should be way higher but cannot be determined with this method.

We agree with the reviewer that we cannot observe conversion of BODIPY-FL-SM by ASM. In order to do that, we attempted to visualize the conversion of a visible-range SM FRET probe (Supp. Figure 3), but the structure of the probe is not compatible with measurement of conversion on the plasma membrane, since the FITC fluorophore released into the culture medium by the ASM activity thereby gets lost for imaging. In general, the visualization of SM conversion with subcellular resolution is challenging and even with novel tools developed in our lab26 visualization of SM on the plasma membrane is difficult.

The conclusion we draw from these experiments are that i.) S. aureus invasion is associated with SM and ii.) SM-associated invasion can be very fast, since bacteria are rapidly engulfed by BODIPY-FL-SM containing membranes.

It is also unclear how the authors can distinguish lysenin entry into ruptured vacuoles from the entry of RFP-CWT, used as a criterion of bacterial escape. Surely the molecular weights of the probes are not sufficiently different to prevent the latter one from traversing the permeabilized membrane until such time that the bacteria escape from the vacuole.

We here want to clarify that both, the Lysenin as well as the CWT reporter have access to rupture vacuoles (Figure 4b). We used the Lysenin reporter in these experiments for estimation of SM content of phagosomal membranes. If a vacuole is ruptured, both the bacteria and the luminal leaflet of the phagosomal membrane remnants get in contact with the cytosol and hence with the cytosolically expressed reporters YFP-Lysenin as well as RFP-CWT resulting in “Lysenin-positive escape” when phagosomes contained SM (see Figure 4f). By contrast, either β-toxin expression by S. aureus or pre-treatment with the bSMase resulted in absence of Lysenin recruitment suggesting that the phagosomal SM levels were decreased/undetectable (Figure 4f, Supp Figure 5f, g, i, j).

This approach does not enable a quantitative measurement of phagosomal SM and rather gives a “yes or no” answer. However, we think this method is sufficient to show that β-toxin expression and pretreatment markedly decreased phagosomal SM levels in the host cells.

The approach we used here to analyze “Lysenin-positive escape” can clearly be distinguished from Lysenin-based methods that were used by others.27 There Lysenin was used to show trans-bilayer movement of SM before rupture of bacteria-containing phagosomes.

To clarify the function of Lysenin in our approach we will add an additional figure to the revised manuscript.

Both SMase inhibitors (Figure 4C) and SMase pretreatment increased bacterial escape from the vacuole. The former should prevent SM hydrolysis and formation of ceramide, while the latter treatment should have the exact opposite effects, yet the end result is the same. What can one conclude regarding the need and role of the SMase products in the escape process?

As pointed out above, pretreatment of host cells with SMase removes SM from the plasma membrane and hence, ASM does not have access to its substrate. Hence, both treatment with either ASM inhibitors or pretreatment with bacterial SMase prevent ASM from being active on the plasma membrane and hence block the ASM-dependent uptake (Figure 2 c, e). Although overall less bacteria were internalized by host cells under these conditions, the bacteria that invaded host cells did so in an ASM-independent manner.

Since blockage of the ASM-dependent internalization pathway (with ASM inhibitor [Figure 4c], SMase pretreatment [Figure 4e] and Vacuolin-1[Supp. Fig.4f]) always resulted in enhanced phagosomal escape, we conclude that bacteria that were internalized in an ASM-independent fashion cause enhanced escape. Vice versa, bacteria that enter host cells in an ASM-dependent manner demonstrate lower escape rates.

This is supported by comparing the escape rates of “early” and “late” invaders [Figure 4g/h], which in our opinion is a key experiment that supports this hypothesis. The “early” invaders are predominantly ASM-dependent (see e.g. Figure 3e) and thus, bacteria that entered host cell in the first 10 min of infection should have been internalized predominantly in an ASM-dependent fashion, while slower entry pathways are active later during infection. The early ASM dependent invaders possessed lower escape rates, which is in line with the data obtained with inhibitors (e.g. Figure 4c and Supp. Fig. 4f).

We hypothesize that the activity of ASM on the plasma membrane during invasion mediates the recruitment of a specific subset of receptors, which then influence downstream phagosomal maturation and escape. This hypothesis is supported by the fact that the subset of receptors interacting with S. aureus is altered upon inhibition of the ASM-dependent uptake pathway. We describe this in another study that is currently under evaluation elsewhere.

Reviewer #2 (Public review):

Summary:

In this manuscript, Ruhling et al propose a rapid uptake pathway that is dependent on lysosomal exocytosis, lysosomal Ca2+ and acid sphingomyelinase, and further suggest that the intracellular trafficking and fate of the pathogen is dictated by the mode of entry.

The evidence provided is solid, methods used are appropriate and results largely support their conclusions, but can be substantiated further as detailed below. The weakness is a reliance on chemical inhibitors that can be non-specific to delineate critical steps.

Specific comments:

A large number of experiments rely on treatment with chemical inhibitors. While this approach is reasonable, many of the inhibitors employed such as amitriptyline and vacuolin1 have other or non-defined cellular targets and pleiotropic effects cannot be ruled out. Given the centrality of ASM for the manuscript, it will be important to replicate some key results with ASM KO cells.

We thank the reviewer for the critical evaluation of our manuscript and plenty of constructive comments.

We agree with the reviewer, that ASM inhibitors such as functional inhibitors of ASM (FIASMA) like amitriptyline used in our study have unspecific side effects given their mode-of-action. FIASMAs induce the detachment of ASM from lysosomal membranes resulting in degradation of the enzyme.15  However, we want to emphasize that we also used the competitive inhibitor ARC39 in our study16, 17 which acts on the enzyme by a completely different mechanism. All phenotypes (reduced invasion [Figure 2c, d], effect on invasion dynamics [Figure 3d], enhanced escape [Figure 4c and Supp Figure 4e] and differential recruitment of Rab7 [Supp. Figure 4b]) were observed with both inhibitors thereby supporting the role of ASM in the process.

We further agree that experiments with genetic evidence usually support and improve scientific findings. However, ASM is a cellular key player for SM degradation and recycling. In a clinical context, deficiency in ASM results in a so-called Niemann Pick disease type A/B. The lipid profile of ASM-deficient cells is massively altered18, which in itself will result in severe side effects. Thus, the usage of inhibitors provides a clear benefit when compared to ASM K.O. cells, since ASM activity can be targeted in a short-term fashion thereby preventing larger alterations in cellular lipid composition.

Most experiments are done in HeLa cells. Given the pathway is projected as generic, it will be important to further characterize cell type specificity for the process. Some evidence for a similar mechanism in other cell types S. aureus infects, perhaps phagocytic cell type, might be good.

Whenever possible we performed the experiments not only in HeLa but also in HuLECs. For example, we refer to experiments concerning the role of Ca2+ (Figure 1c/Supp.Figure1e), lysosomal Ca2+/Ned19 (Figure1d/Supp Figure 1g), lysosomal exocytosis/Vacuolin-1 (Figure 2a/Supp. Figure2a), ASM/ARC39 and amitriptyline (Figure 2c), surface SM/β-toxin (Figure 2e/Supp. Figure 2g), analysis of invasion dynamics (complete Figure 3) and measurement of cell death during infection (Figure 5c-e, Supp. Figure 6a+b).

HuLECs, however, are not really genetically amenable and hence we were not able to generate gene deletions in these cells and upon introduction of the fluorescence escape reporter the cells are not readily growing.

As to ASM involvement in phagocytic cells: a role for ASM during the uptake of S. aureus by macrophages was previously reported by others.23 However, in professional phagocytes S. aureus does not escape from the phagosome and replicates within the vacuole.28

I'm a little confused about the role of ASM on the surface. Presumably, it converts SM to ceramide, as the final model suggests. Overexpression of b-toxin results in the near complete absence of SM on phagosomes (having representative images will help appreciate this), but why is phagosomal SM detected at high levels in untreated conditions? If bacteria are engulfed by SM-containing membrane compartments, what role does ASM play on the surface? If surface SM is necessary for phagosomal escape within the cell, do the authors imply that ASM is tuning the surface SM levels to a certain optimal range? Alternatively, can there be additional roles for ASM on the cell surface? Can surface SM levels be visualized (for example, in Figure 4 E, F)?

We initially hypothesized that we would detect higher phagosomal SM levels upon inhibition of ASM, since our model suggests SM cleavage by ASM on the host cell surface during bacterial cell entry. However, we did not detect any changes in our experiments (Supp. Figure 4d). We currently favor the following explanation: SM is the most abundant sphingolipid in human cells.29 If peripheral lysosomes are exocytosed and thereby release ASM, only a localized and relative small proportion of SM may get converted to Cer, which most likely is below our detection limit. In addition, the detection of cytosolically exposed phagosomal SM by YFP-Lysenin is not quantitative and provides a “Yes or No” measurement. Hence, we think that the rather limited SM to Cer conversion in combination with the high abundance of SM in cellular membranes does not visibly affect the recruitment of the Lysenin reporter.

In our experiments that employ BODIPY-FL-SM (Figure 3a+b), we cannot distinguish between native SM and downstream metabolites such as Cer. Hence, again we cannot make any assumptions on the extent to which SM is converted on the surface during bacterial internalization. Although our laboratory recently used trifunctional sphingolipid analogs to analyze the SM to Cer conversion20, the visualization of this process on the plasma membrane is currently still challenging.

Overall, we hypothesize that the localized generation of Cer on the surface by released ASM leads to generation of Cer-enriched platforms. Subsequently, a certain subset of receptors may be recruited to these platforms and influence the uptake process. These platforms are supposed to be very small, which also would explain that we did not detect changes in Lysenin recruitment.

Related to that, why is ASM activity on the cell surface important? Its role in non-infectious or other contexts can be discussed.

ASM release by lysosomal exocytosis is implied in plasma membrane repair upon injury. We will this discuss this in the revised version of the manuscript.

If SM removal is so crucial for uptake, can exocytosis of lysosomes alone provide sufficient ASM for SM removal? How much or to what extent is lysosomal exocytosis enhanced by initial signaling events? Do the authors envisage the early events in their model happening in localized confines of the PM, this can be discussed.

Ionomycin treatment led to a release of ~10 % of all lysosomes and also increased extracellular ASM activity.7, 8 However, it is currently unclear– to our knowledge -to which extent the released ASM affects surface SM levels. Also, it is unknown which percentage of the lysosomes is released during infection with S. aureus. However, one has to speculate that this will be only a fraction of the “releasable lysosomes” as we assume that the effects (lysosomal Ca2+ liberation, lysosomal exocytosis and ASM activity) are very localized and take place only at host-pathogen contact sites (see also above). In initial experimentation we attempted to visualize the local ASM activity on the cell surface by using a visible range FRET probe (Supp. Fig. 3). Cleavage of the probe by ASM on the surface leads to release of FITC into the cell culture medium which does not contribute a measurable signal at the surface.

How are inhibitor doses determined? How efficient is the removal of extracellular bacteria at 10 min? It will be good to substantiate the cfu experiments for infectivity with imaging-based methods. Are the roles of TPC1 and TPC2 redundant? If so, why does silencing TPC1 alone result in a decrease in infectivity? For these and other assays, it would be better to show raw values for infectivity. Please show alterations in lysosomal Ca2+ at the doses of inhibitors indicated. Is lysosomal Ca2+ released upon S. aureus binding to the cell surface? Will be good to directly visualize this.

Concerning the inhibitor concentrations, we either used values established in published studies or recommendations of the suppliers (e.g. 2-APB, Ned19, Vacuolin-1). For ASM inhibitors, we determined proper inhibition of ASM by activity assays. Concentrations of ionomycin resulting in Ca2+ influx and lysosomal exocytosis was determined in earlier studies of our lab.8, 30

As to the removal of bacteria at 10 min p.i.: Lysostaphin is very efficient for removal of extracellular S. aureus and sterilizes the tissue culture supernatant. It significantly lyses bacteria within a few minutes, as determined by turbidity assays.31

As to imaging-based infectivity assays: We will add an analysis of imaging-based invasion assays in the revised manuscript.

Regarding the roles of TPC1 and TPC2: from our data we cannot conclude whether the roles of TPC1 and TPC2 are redundant. One could speculate that since blockage of TPC1 alone is sufficient to reduce internalization of bacteria, that both channels may have distinct roles. On the other hand, there might be a Ca2+ threshold in order to initiate lysosomal exocytosis that can only be attained if TPC1 and TPC2 are activated in parallel. Thus, our observations are in line with another study that shows reduced Ebola virus infection in absence of either TPC1 or TPC2.32

As to raw CFU counts: whereas the observed effects upon blocking the invasion of S. aureus are stable, the number of internalized bacteria varies between individual biological replicates, for instance, by differences in host cell fitness or growth differences in bacterial cultures, which are prepared freshly for each experiment.

With respect to visualization of lysosomal Ca2+ release: we agree with the reviewer that direct visual demonstration of lysosomal Ca2+ release upon infection will improve the manuscript. We therefore will perform additional experimentation to show alterations of Ca2+ at the lysosomes during infection.

The precise identification of cytosolic vs phagosomal bacteria is not very easy to appreciate. The methods section indicates how this distinction is made, but how do the authors deal with partial overlaps and ambiguities generally associated with such analyses? Please show respective images. The number of events (individual bacteria) for the live cell imaging data should be clearly mentioned.

We apologize for not having sufficiently explained the technology to detect escaped S. aureus. The cytosolic location of S. aureus is indicated by recruitment of RFP-CWT.33 CWT is the cell wall targeting domain of lysostaphin, which efficiently binds to the pentaglycine cross bridge in the peptidoglycan of S. aureus. This reporter is exclusively and homogenously expressed in the host cytosol. Only upon rupture of phagoendosomal membranes the reporter can be recruited to the cell wall of now cytosolically located bacteria. S. aureus mutants, for instance in the agr quorum sensing system, cannot break down the phagosomal membrane in non-professional phagocytes and thus stay unlabeled by the CWT-reporter.33 We will include respective images/movies of escape events and the bacteria numbers for live cell experiments in the revised version of the manuscript.

In the phagosome maturation experiments, what is the proportion of bacteria in Rab5 or Rab7 compartments at each time point? Will the decreased Rab7 association be accompanied by increased Rab5? Showing raw values and images will help appreciate such differences. Given the expertise and tools available in live cell imaging, can the authors trace Rab5 and Rab7 positive compartment times for the same bacteria?

We will include the proportion of Rab7-associated bacteria in the revised manuscript. Usually, we observe that Rab5 is only transiently (for a few minutes) present on phagosomes and only afterwards the phagosomes become positive for Rab7. We do not think that a decrease in Rab7-positive phagosomes would increase the proportion of Rab5-positive phagosomes. However, we cannot exclude this hypothesis with our data.

We can achieve tracing of individual bacteria for recruitment of Rab5/Rab7 only manually, which impedes a quantitative evaluation. However, we will include information that illustrates the consecutive recruitment of the GTPases.

The results with longer-term infection are interesting. Live cell imaging suggests that ASM-inhibited cells show accelerated phagosomal escape that reduces by 6 hpi. Where are the bacteria at this time point ? Presumably, they should have reached lysosomes. The relationship between cytosolic escape, replication, and host cell death is interesting, but the evidence, as presented is correlative for the populations. Given the use of live cell imaging, can the authors show these events in the same cell?

We think that most bacteria-containing phagoendosomes should have fused with lysosomes 6 h p.i. as we have previously shown by acidification to pH of 5 and LAMP1 decoration.34

We will provide images/videos to show the correlation between escape and replication in the revised manuscript.

Given the inherent heterogeneity in uptake processes and the use of inhibitors in most experiments, the distinction between ASM-dependent and independent pathways might not be as clear-cut as the authors suggest. Some caution here will be good. Can the authors estimate what fraction of intracellular bacteria are taken up ASM-dependent?

We agree with the reviewer that an overlap between internalization pathways is likely. A clear distinction is therefore certainly non-trivial. Alternative to ASM-dependent and ASM-independent pathways, the ASM activity may also accelerate one or several internalization pathways. We will address this limitation in the revised manuscript. 

Early in infection (~10 min after contact with the cells), the proportion of bacteria that enter host cells ASM-dependently is relatively high amounting to roughly 75% in HuLEC. After 30 min, this proportion is decreasing to about 50%. We will include this information in the revised version of the manuscript.

References

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eLife Assessment

This valuable study proposes a novel rapid-entry mechanism of S. aureus that involves the rapid release of calcium from lysosomes. The strength of the paper lies in a very interesting hypothesis; what diminishes enthusiasm is the lack of appropriate methodology, thus making the study incomplete. The methods used are deficient: they are largely reliant on the use of chemical inhibitors and do not adequately support the conclusions.

Reviewer #1 (Public review):

Summary:

The manuscript by Rühling et al analyzes the mode of entry of S. aureus into mammalian cells in culture. The authors propose a novel mechanism of rapid entry that involves the release of calcium from lysosomes via NAADP-stimulated activation of TPC1, which in turn causes lysosomal exocytosis; exocytic release of lysosomal acid sphingomyelinase (ASM) is then envisaged to convert exofacial sphingomyelin to ceramide. These events not only induce the rapid entry of the bacteria into the host cells but are also described to alter the fate of the intracellular S. aureus, facilitating escape from the endocytic vacuole to the cytosol.

Strengths:

The proposed mechanism is novel and could have important biological consequences.

Weaknesses:

Unfortunately, the evidence provided is unconvincing and insufficient to document the multiple, complex steps suggested. In fact, there appear to be numerous internal inconsistencies that detract from the validity of the conclusions, which were reached mostly based on the use of pharmacological agents of imperfect specificity.

Firstly, the release of calcium from lysosomes is not demonstrated. Localized changes in the immediate vicinity of lysosomes need to be measured to ascertain that these organelles are the source of cytosolic calcium changes. In fact, 9-phenantrol, which the authors find to be the most potent inhibitor of invasion and hence of the putative calcium changes, is not a blocker of lysosomal calcium release but instead blocks plasmalemmal TRPM4 channels. On the other hand, invasion is seemingly independent of external calcium. These findings are inconsistent with each other and point to non-specific effects of 9-phenantrol. The fact that ionomycin decreases invasion efficiency is taken as additional evidence of the importance of lysosomal calcium release. It is not clear how these observations support involvement of lysosomal calcium release and exocytosis; in fact treatment with the ionophore should itself have induced lysosomal exocytosis and stimulated, rather than inhibited invasion. Yet, manipulations that increase and others that decrease cytosolic calcium both inhibited invasion.

The proposed role of NAADP is based on the effects of "knocking out" TPC1 and on the pharmacological effects of Ned-19. It is noteworthy that TPC2, rather than TPC1, is generally believed to be the primary TPC isoform of lysosomes. Moreover, the gene ablation accomplished in the TPC1 "knockouts" is only partial and rather unsatisfactory. Definitive conclusions about the role of TPC1 can only be reached with proper, full knockouts. Even the pharmacological approach is unconvincing because the high doses of Ned-19 used should have blocked both TPC isoforms and presumably precluded invasion. Instead, invasion is reduced by only ≈50%. A much greater inhibition was reported using 9-phenantrol, the blocker of plasmalemmal calcium channels. How is the selective involvement of lysosomal TPC1 channels justified?

Invoking an elevation of NAADP as the mediator of calcium release requires measurements of the changes in NAADP concentration in response to the bacteria. This was not performed. Instead, the authors analyzed the possible contribution of putative NAADP-generating systems and reported that the most active of these, CD38, was without effect, while the elimination of SARM1, another potential source of NAADP, had a very modest (≈20%) inhibitory effect that may have been due to clonal variation, which was not ruled out. In view of these data, the conclusion that NAADP is involved in the invasion process seems unwarranted.

The involvement of lysosomal secretion is, again, predicated largely on the basis of pharmacological evidence. No direct evidence is provided for the insertion of lysosomal components into the plasma membrane, or for the release of lysosomal contents to the medium. Instead, inhibition of lysosomal exocytosis by vacuolin-1 is the sole source of evidence. However, vacuolin-1 is by no means a specific inhibitor of lysosomal secretion: it is now known to act primarily as a PIKfyve inhibitor and to cause massive distortion of the endocytic compartment, including gross swelling of endolysosomes. The modest (20-25%) inhibition observed when using synaptotagmin 7 knockout cells is similarly not convincing proof of the requirement for lysosomal secretion.

ASM is proposed to play a central role in the rapid invasion process. As above, most of the evidence offered in this regard is pharmacological and often inconsistent between inhibitors or among cell types. Some drugs affect some of the cells, but not others. It is difficult to reach general conclusions regarding the role of ASM. The argument is made even more complex by the authors' use of exogenous sphingomyelinase (beta-toxin). Pretreatment with the toxin decreased invasion efficiency, a seemingly paradoxical result. Incidentally, the effectiveness of the added toxin is never quantified/validated by directly measuring the generation of ceramide or the disappearance of SM.

The use of fluorescent analogs of sphingomyelin and ceramide is not well justified and it is unclear what conclusions can be derived from these observations. Despite the low resolution of the images provided, it appears as if the labeled lipids are largely in endomembrane compartments, where they would presumably be inaccessible to the secreted ASM. Moreover, considering the location of the BODIPY probe, the authors would be unable to distinguish intact sphingomyelin from its breakdown product, ceramide. What can be concluded from these experiments? Incidentally, the authors report only 10% of BODIPY-positive events after 10 min. What are the implications of this finding? That 90% of the invasion events are unrelated to sphingomyelin, ASM, and ceramide?

It is also unclear how the authors can distinguish lysenin entry into ruptured vacuoles from the entry of RFP-CWT, used as a criterion of bacterial escape. Surely the molecular weights of the probes are not sufficiently different to prevent the latter one from traversing the permeabilized membrane until such time that the bacteria escape from the vacuole.

Both SMase inhibitors (Figure 4C) and SMase pretreatment increased bacterial escape from the vacuole. The former should prevent SM hydrolysis and formation of ceramide, while the latter treatment should have the exact opposite effects, yet the end result is the same. What can one conclude regarding the need and role of the SMase products in the escape process?

Reviewer #2 (Public review):

Summary:

In this manuscript, Ruhling et al propose a rapid uptake pathway that is dependent on lysosomal exocytosis, lysosomal Ca2+ and acid sphingomyelinase, and further suggest that the intracellular trafficking and fate of the pathogen is dictated by the mode of entry.

The evidence provided is solid, methods used are appropriate and results largely support their conclusions, but can be substantiated further as detailed below. The weakness is a reliance on chemical inhibitors that can be non-specific to delineate critical steps.

Specific comments:

A large number of experiments rely on treatment with chemical inhibitors. While this approach is reasonable, many of the inhibitors employed such as amitriptyline and vacuolin1 have other or non-defined cellular targets and pleiotropic effects cannot be ruled out. Given the centrality of ASM for the manuscript, it will be important to replicate some key results with ASM KO cells.

Most experiments are done in HeLa cells. Given the pathway is projected as generic, it will be important to further characterize cell type specificity for the process. Some evidence for a similar mechanism in other cell types S. aureus infects, perhaps phagocytic cell type, might be good.

I'm a little confused about the role of ASM on the surface. Presumably, it converts SM to ceramide, as the final model suggests. Overexpression of b-toxin results in the near complete absence of SM on phagosomes (having representative images will help appreciate this), but why is phagosomal SM detected at high levels in untreated conditions? If bacteria are engulfed by SM-containing membrane compartments, what role does ASM play on the surface? If surface SM is necessary for phagosomal escape within the cell, do the authors imply that ASM is tuning the surface SM levels to a certain optimal range? Alternatively, can there be additional roles for ASM on the cell surface? Can surface SM levels be visualized (for example, in Figure 4 E, F)?

Related to that, why is ASM activity on the cell surface important? Its role in non-infectious or other contexts can be discussed.

If SM removal is so crucial for uptake, can exocytosis of lysosomes alone provide sufficient ASM for SM removal? How much or to what extent is lysosomal exocytosis enhanced by initial signaling events? Do the authors envisage the early events in their model happening in localized confines of the PM, this can be discussed.

How are inhibitor doses determined? How efficient is the removal of extracellular bacteria at 10 min? It will be good to substantiate the cfu experiments for infectivity with imaging-based methods. Are the roles of TPC1 and TPC2 redundant? If so, why does silencing TPC1 alone result in a decrease in infectivity? For these and other assays, it would be better to show raw values for infectivity. Please show alterations in lysosomal Ca2+ at the doses of inhibitors indicated. Is lysosomal Ca2+ released upon S. aureus binding to the cell surface? Will be good to directly visualize this.

The precise identification of cytosolic vs phagosomal bacteria is not very easy to appreciate. The methods section indicates how this distinction is made, but how do the authors deal with partial overlaps and ambiguities generally associated with such analyses? Please show respective images. The number of events (individual bacteria) for the live cell imaging data should be clearly mentioned.

In the phagosome maturation experiments, what is the proportion of bacteria in Rab5 or Rab7 compartments at each time point? Will the decreased Rab7 association be accompanied by increased Rab5? Showing raw values and images will help appreciate such differences. Given the expertise and tools available in live cell imaging, can the authors trace Rab5 and Rab7 positive compartment times for the same bacteria?

The results with longer-term infection are interesting. Live cell imaging suggests that ASM-inhibited cells show accelerated phagosomal escape that reduces by 6 hpi. Where are the bacteria at this time point ? Presumably, they should have reached lysosomes. The relationship between cytosolic escape, replication, and host cell death is interesting, but the evidence, as presented is correlative for the populations. Given the use of live cell imaging, can the authors show these events in the same cell?

Given the inherent heterogeneity in uptake processes and the use of inhibitors in most experiments, the distinction between ASM-dependent and independent pathways might not be as clear-cut as the authors suggest. Some caution here will be good. Can the authors estimate what fraction of intracellular bacteria are taken up ASM-dependent?

Reviewer #1 (Public review):

Summary of Key Findings:

The authors identified 20 ancient molluscan linkage groups (MLGs) that are largely conserved in other molluscan groups but highly dynamic and rearranged in chitons. This contrasts with the stability seen in other animal groups.

Significant chromosome rearrangements, fusions, and duplications were observed in chitons, particularly in the most basal clades like Lepidopleurida, indicating that chitons undergo more extensive genomic changes than expected.

Chitons exhibit extremely high levels of genomic heterozygosity, exceeding that of other molluscan species and even Lepidoptera. This presents challenges for assembling high-quality genomes but also points to genetic diversity as a driver of evolutionary processes.

Partial genome duplications, particularly in Liolophura japonica, extend the knowledge of gene duplication events within the broader Mollusca clade.

The paper speculates that these genomic rearrangements may contribute to maintaining species boundaries in sympatric and parapatric radiations, as observed in certain Acanthochitona species.

Strengths:

The use of high-quality genomic data, including four de novo genome assemblies, provides robust evidence for the conclusions.

The research challenges the common assumption that chitons are evolutionarily conservative, showing that their genomes are highly dynamic despite their morphological stasis.

The study adds to the understanding of how chromosomal rearrangements might contribute to speciation, a concept that can be applied to other taxa.

Limitations:

The paper acknowledges that the limited availability of high-quality genomes across molluscs may restrict the scope of comparative analyses. More genomic data from other molluscan groups could strengthen the conclusions.

The role of high heterozygosity in chitons is highlighted, but more information is needed to clarify how this affects genome assembly and evolutionary outcomes.

Implications for Future Research:

The research raises important questions about the relationship between genomic instability and phenotypic stasis, which can inform studies in other animal groups.

The findings call for a re-evaluation of how we define and measure biodiversity, particularly in "neglected" clades like chitons. Further studies could focus on linking the observed genomic changes to specific adaptive traits or ecological niches.

Reviewer #2 (Public review):

Summary:

The authors provide four new annotated genomes for an important taxon within Mollusca known as Polyplacophora (chitons). They provide an impressive analysis showing syntenic relationships between the chromosomes of these four genomes but also other available chiton genome sequences and analysis of 20 molluscan linkage groups to expand this analysis across Mollusca.

Strengths:

The authors have selected particular chiton species for genome sequencing and annotation that expand what is known about genomes across portions of chiton phylogenetic diversity lacking genome sequences. The manuscript is well-written and illustrated in a concise manner. The figures are mostly clear, allowing a reader to visually compare the syntenic relationships of chromosomes, especially within chitons. Their phylogenetic analysis provides a simple manner to map important events in molluscan genome evolution. This study greatly expands what is known about molluscan and chiton comparative genomics.

Weaknesses:

I am not especially convinced that chitons have experienced more substantial genomic rearrangements or other genomic events than other molluscan classes, and for this reason, I did not personally find the title compelling: "Still waters run deep: Large scale genome rearrangements in the evolution of morphologically conservative Polyplacophora." Are the documented events "large scale genomic rearrangements"? It seems that mostly they found two cases of chromosome fusion, plus one apparent case of whole genome duplication. What do they mean by "Still waters run deep"? I have no idea. I guess they consider chitons to be morphologically conservative in their appearance and lifestyle so they are calling attention to this apparent paradox. However, most chiton genomes seem to be relatively conserved, but there are unexpected chromosome fusion events within a particular genus, Acanthochitona. Likewise, they found a large-scale gene duplication event in Acanthopleurinae, a different subfamily of chitons, which is quite interesting but these seem to be geologically recent events that do not especially represent the general pattern of genome evolution across this ancient molluscan taxon.

eLife Assessment

This important study reports new insights into the roles of a long noncoding RNA, lnc-FANCI-2, in the progression of cervical cancer induced by a type of human papillomavirus. Through a blend of cell biological, biochemical, and genetic analyses of RNA and protein expression, protein-protein interaction, cell signaling, and cell morphology, the authors provide convincing evidence that lnc-FANCI-2 affects cervical cancer outcome by regulating the RAG signaling pathway. These findings will be of interest to scientists in the fields of cervical cancer, long noncoding RNA, and cell signaling.

Reviewer #1 (Public review):

Summary:

The authors attempted to dissect the function of a long non-coding RNA, lnc-FANCI-2, in cervical cancer. They profiled lnc-FANCI-2 in different cell lines and tissues, generated knockout cell lines, and characterized the gene using multiple assays.

Strengths:

A large body of experimental data has been presented and can serve as a useful resource for the scientific community, including transcriptomics and proteomics datasets. The reported results also span different parts of the regulatory network and open up multiple avenues for future research.

Weaknesses:

The write-up is somewhat unfocused and lacks deep mechanistic insights in some places.

Reviewer #2 (Public review):

The study by Liu et al provides a functional analysis of lnc-FANCI-2 in cervical carcinogenesis, building on their previous discovery of FANCI-2 being upregulated in cervical cancer by HPV E7.

The authors conducted a comprehensive investigation by knocking out (KO) FANCI-2 in CaSki cells and assessing viral gene expression, cellular morphology, altered protein expression and secretion, altered RNA expression through RNA sequencing (verification of which by RT-PCR is well appreciated), protein binding, etc. Verification experiments by RT-PCR, Western blot, etc are notable strengths of the study.

The KO and KD were related to increased Ras signaling and EMT and reduced IFN-y/a responses.

Although the large amount of data is well acknowledged, it is a limitation that most data come from CaSki cells, in which FANCI-2 localization is different from SiHa cells and cancer tissues (Figure 1). The cytoplasmic versus nuclear localization is somewhat puzzling.

Reviewer #3 (Public review):

Summary:

A long noncoding RNA, lnc-FANCI-2, was reported to be regulated by HPV E7 oncoprotein and a cell transcription factor, YY1 by this group. The current study focuses on the function of lnc-FANCI-2 in HPV-16 positive cervical cancer is to intrinsically regulate RAS signaling, thereby facilitating our further understanding of additional cellular alterations during HPV oncogenesis. The authors used advanced technical approaches such as KO, transcriptome and (IRPCRP) and LC- MS/MS analyses in the current study and concluded that KO Inc-FANCI-2 significantly increases RAS signaling, especially phosphorylation of Akt and Erk1/2.

Strengths:

(1) HPV E6E7 are required for full immortalization and maintenance of the malignant phenotype of cervical cancer, but they are NOT sufficient for full transformation and tumorigenesis. This study helps further understanding of other cellular alterations in HPV oncogenesis.

(2) lnc-FANCI-2 is upregulated in cervical lesion progression from CIN1, CIN2-3 to cervical cancer, cancer cell lines, and HPV transduced cell lines.

(3) Viral E7 of high-risk HPVs and host transcription factor YY1 are two major factors promoting lnc-FANCI-2 expression.

(4) Proteomic profiling of cytosolic and secreted proteins showed inhibition of MCAM, PODXL2, and ECM1 and increased levels of ADAM8 and TIMP2 in KO cells.

(5) RNA-seq analyses revealed that KO cells exhibited significantly increased RAS signaling but decreased IFN pathways.

(6) Increased phosphorylated Akt and Erk1/2, IGFBP3, MCAM, VIM, and CCND2 (cyclin D2) and decreased RAC3 were observed in KO cells.

Weaknesses:

(1) The authors observed the increased Inc-FANCI-2 in HPV 16 and 18 transduced cells, and other cervical cancer tissues as well, HPV-18 positive HeLa cells exhibited different expressions of Inc-FANCI-2.

(2) Previous studies and data in the current showed a steadily increased Inc-FANCI-2 during cancer progression, however, the authors did not observe significant changes in cell behaviors (both morphology and proliferation) in KO Inc-FANCI-2.

(3) The authors observed the significant changes of RAS signaling (downstream) in KO cells, but they provided limited interpretations of how these results contributed to full transformation or tumorigenesis in HPV-positive cancer.

eLife Assessment

In this potentially important study, the authors employed advanced computational techniques to explore a detailed atomistic description of the mechanism and energetics of substrate translocation in the MelB transporter. The overall approach is solid and reveals the coupling between sodium binding and melibiose transport through a series of conformational transitions, and the results for a mutant are also in qualitative agreement with the experiment, providing further support to the computational analyses. Nevertheless, the level of evidence is considered incomplete since there are concerns regarding the convergence and initial guess of the string calculations, leaving doubts that the computed pathway does not reflect the most energetically favorable mechanism.

Reviewer #1 (Public review):

Summary:

Liang and Guan have studied the transport mechanism of Melbiose transporter MelB using the string method in collective variables and replica-exchange umbrella sampling simulations. The authors study the mechanism of substrate binding to the outward-facing state, conformational change of the transporter from outward-facing to inward-facing, and substrate unbinding from inward-facing state. In their analysis, they also highlight the effects of mutant D59C and the effect of sodium binding on the substrate transport process.

Strengths:

The authors employ a combination of string method and replica-exchange umbrella sampling simulation techniques to provide a complete map of the free energy landscape for sodium-coupled melibiose transport in MelB.

Weaknesses:

(1) Free energy barriers appear to be very high for a substrate transport process. In Figure 3, the transitions from IF (Inward facing) to OF (Outward facing) state appear to have a barrier of 12 kcal/mol. Other systems with mutant or sodium unbound have even higher barriers. This does not seem consistent with previous studies where transport mechanisms of transporters have been explored using molecular dynamics.

(2) Figure 2b: The PMF between images 20-30 shows the conformation change from OF to IF, where the occluded (OC) state is the highest barrier for transition. However, OC state is usually a stable conformation and should be in a local minimum. There should be free energy barriers between OF and OC and in between OC and IF.

(3) String method pathway is usually not the only transport pathway and alternate lower energy pathways should be explored. The free energy surface looks like it has not deviated from the string pathway. Longer simulations can help in the exploration of lower free energy pathways.

(4) The conformational change in transporters from OF to IF state is a complicated multi-step process. First, only 10 images in the string pathway are used to capture the transition from OF to IF state. I am not sure is this number is enough to capture the process. Second, the authors have used geodesic interpolation algorithm to generate the intermediate images. However, looking at Figure 3B, it looks like the transition pathway has not captured the occluded (OC) conformation, where the transport tunnel is closed at both the ends. Transporters typically follow a stepwise conformational change mechanism where OF state transitions to OC and then to IF state. It appears that the interpolation algorithm has created a hourglass-like state, where IF gates are opening and OF gates are closing simultaneously thereby creating a state where the transport tunnel is open on both sides of the membrane. These states are usually associated with high energy. References 30-42 cited in the manuscript reveal a distinct OC state for different transporters.

Reviewer #2 (Public review):

Summary:

The manuscript by Liang and Guan provides an impressive attempt to characterize the conformational free energy landscape of a melibiose permease (MelB), a symporter member of major facilitator superfamily (MFS) of transporters. Although similar studies have been conducted previously for other members of MFS, each member or subfamily has its own unique features that make the employment of such methods quite challenging. While the methodology is indeed impressive, characterizing the coupling between large-scale conformational changes and substrate binding in membrane transporters is quite challenging and requires a sophisticated methodology. The conclusions obtained from the three sets of path-optimization and free energy calculations done by the authors are generally supported by the provided data and certainly add to our understanding of how sodium binding facilitates the transport of melibiose in MelB. However, the data is not generated reliably which questions the relevance of the conclusions as well. I particularly have some concerns regarding the implementation of the methodology that I will discuss below.

(1) In enhanced sampling techniques, often much attention is given to the sampling algorithm. Although the sampling algorithm is quite important and this manuscript has chosen an excellent pair: string method with swarms of trajectories (SMwST) and replica-exchange umbrella sampling (REUS) for this task, there are other important factors that must be taken into account. More specifically, the collective variables used and the preparation of initial conformations for sampling. I have objectives for both of these (particularly the latter) that I detail below. Overall, I am not confident that the free energy profiles generated (summarized in Figure 5) are reliable, and unfortunately, much of the data presented in this manuscript heavily relies on these free energy profiles.

(2) The authors state that they have had an advantage over other similar studies in that they had two endpoints of the string to work from experimental data. I agree that this is an advantage. However, this could lead to some dangerous flaws in the methodology if not appropriately taken into account. Proteins such as membrane transporters have many slow degrees of freedom that can be fully captured within tens of nanoseconds (90 ns was the simulation time used here for the REUS). Biased sampling allows us to overcome this challenge to some extent, but it is virtually impossible to take into account all slow degrees of freedom in the enhanced sampling protocol (e.g., the collective variables used here do not represent anything related to sidechain dynamics). Therefore, if one mixes initial conformations that form different initial structures (e.g., an OF state and an IF state from two different PDB files), it is very likely that despite all equilibration and relaxation during SMwST and REUS simulations, the conformations that come from different sources never truly mix. This is dangerous in that it is quite difficult to detect such inconsistencies and from a theoretical point of view it makes the free energy calculations impossible. Methods such as WHAM and its various offshoots all rely on overlap between neighboring windows to calculate the free energy difference between two windows and the overlap should be in all dimensions and not just the ones that we use for biasing. This is related to well-known issues such as hidden barriers and metastability. If one uses two different structures to generate the initial conformations, then the authors need to show their sampling has been long enough to allow the two sets of conformations to mix and overlap in all dimensions, which is a difficult task to do.

(3) I also have concerns regarding the choice of collective variables. The authors have split the residues in each transmembrane helix into the cyto- and periplasmic sides. Then they have calculated the mass center distance between the cytoplasmic sides of certain pairs of helices and have also done the same for the periplasmic side. Given the shape of a helix, this does not seem to be an ideal choice since rather than the rotational motion of the helix, this captures more the translational motion of the helix. However, the transmembrane helices are more likely to undergo rotational motion than the translational one.

(4) Convergence: String method convergence data does not show strong evidence for convergence (Figure S2) in my opinion. REUS convergence is also not discussed. No information is provided on the exchange rate or overlap between the windows.

Reviewer #3 (Public review):

The paper from Liang and Guan details the calculation of the potential mean force for the transition between two key states of the melibiose (Mel) transporter MelB. The authors used the string method along with replica-exchange umbrella sampling to model the transition between the outward and inward-facing Mel-free states, including the binding and subsequent release of Mel. They find a barrier of ~6.8 kcal/mol and an overall free-energy difference of ~6.4 kcal/mol. They also investigate the same process without the co-transported Na+, finding a higher barrier, while in the D59C mutant, the barrier is nearly eliminated.

I found this to be an interesting and technically competent paper. I was disappointed actually to see that the authors didn't try to complete the cycle. I realize this is beyond the scope of the study as presented.

The results are in qualitative agreement with expectations from experiments. Could the authors try to make this comparison more quantitative? For example, by determining the diffusivity along the path, the authors could estimate transition rates.

Relatedly, could the authors comment on how typical concentration gradients of Mel and Na+ would affect these numbers?

Author response:

Reviewer 1:

(1) Free energy barriers appear to be very high for a substrate transport process. In Figure 3, the transitions from IF (Inward facing) to OF (Outward facing) state appear to have a barrier of 12 kcal/mol. Other systems with mutant or sodium unbound have even higher barriers. This does not seem consistent with previous studies where transport mechanisms of transporters have been explored using molecular dynamics. 

First, in Figure 3, the transition from IF to OF state doesn’t have a barrier of 12 kcal/mol. The IFF to OFB transition is almost barrierless, and from OFB to OFF is ~5 kcal/mol, which is also evident in Figure 2.

If the reviewer was referring to the transition from OFB to IFB states, the barrier is 6.8 kcal/mol (Na+ bound state), and the rate-limiting barrier in the entire sugar transport process (Na+ bound state) is 8.4 kcal/mol, as indicated in Figure 2 and Table 1, which is much lower than the 12 kcal/mol barrier the reviewer mentioned. When the Na+ is unbound, the barrier can be as high as 12 kcal/mol, but it is this high barrier that leads to our conclusion that the Na+ binding is essential for sugar transport, and the 12 kcal/mol barrier indicates an energetically unfavorable sugar translocation process when the Na+ is unbound, which is unlikely to be the major translocation process in nature. 

Even for the 12 kcal/mol barrier reported for the Na+ unbound state, it is still not too high considering the experimentally measured MelB sugar active transport rate, which is estimated to be on the order of 10 to 100 s-1. This range of transport rate is typical for similar MFS transporters such as the lactose permease (LacY), which has an active transport rate of 20 s-1. The free energy barrier associated with the active transport is thus on the order of ~15-16 kcal/mol based on transition state theory assuming kBT/h as the prefactor. This experimentally estimated barrier is higher than all of our calculated barriers. Our calculated barrier for the sugar translocation with Na+ bound is 8.4 kcal/mol, which means an additional ~7-8 kcal/mol barrier is contributed by the Na+ release process after sugar release in the IFF state. This is a reasonable estimation of the Na+ unbinding barrier.

Therefore, whether the calculated barrier is too high depends on the experimental kinetics measurements, which are often challenging to perform. Based on the existing experimental data, the MFS transporters are

usually relatively slow in their active transport cycle. The calculated barrier thus falls within the reasonable range considering the experimentally measured active transport rates.

(2) Figure 2b: The PMF between images 20-30 shows the conformation change from OF to IF, where the occluded (OC) state is the highest barrier for transition. However, OC state is usually a stable conformation and should be in a local minimum. There should be free energy barriers between OF and OC and in between OC and IF.  

First, the occluded state (OCB) is not between images 20-30, it is between images 10 to 20. Second, there is no solid evidence that the OCB state is a stable conformation and a local minimum. Existing experimental structures of MFS transporters seldom have the fully occluded state resolved.

(3) String method pathway is usually not the only transport pathway and alternate lower energy pathways should be explored. The free energy surface looks like it has not deviated from the string pathway. Longer simulations can help in the exploration of lower free energy pathways. 

We agree with the reviewer that the string method pathway is usually not the only transport pathway and alternate lower energy pathways could exist. However, we also note that even if the fully occluded state is a local minimum and our free energy pathway does visit this missing local minimum after improved sampling, the overall free energy barrier will not be lowered from our current calculated value. This is because the current rate-limiting barrier arises from the transition from the OFB state to the IFF state, and the barrier top corresponds to the sugar molecule passing through the most constricted region in the cytoplasmic region, i.e., the IFC intermediate state visited after the IFB state is reached. Therefore, the free energy difference between the OFB state and the IFC state will not be changed by another hypothetical local minimum between the OFB and IFB states, i.e., the occluded OCB state. In other words, a hypothetical local minimum corresponding to the occluded state, even if it exists, will not decrease the overall rate-limiting barrier and may even increase it further, depending on the depth of the local minimum and the additional barriers of entering and escaping from this new minimum. 

(4) The conformational change in transporters from OF to IF state is a complicated multi-step process. First, only 10 images in the string pathway are used to capture the transition from OF to IF state. I am not sure is this number is enough to capture the process. Second, the authors have used geodesic interpolation algorithm to generate the intermediate images. However, looking at Figure 3B, it looks like the transition pathway has not captured the occluded (OC) conformation, where the transport tunnel is closed at both the ends. Transporters typically follow a stepwise conformational change mechanism where OF state transitions to OC and then to IF state. It appears that the interpolation algorithm has created a hourglasslike state, where IF gates are opening and OF gates are closing simultaneously thereby creating a state where the transport tunnel is open on both sides of the membrane. These states are usually associated with high energy. References 30-42 cited in the manuscript reveal a distinct OC state for different transporters. 

In our simulations, even with 10 initial images representing the OF to IF conformational transition, the occluded state is sampled in the final string pathway. There is an ensemble of snapshots where the extracellular and intracellular gates are both relatively narrower than the OF and IF states, preventing the sugar from leaking into either side of the bulk solution. In contrast to the reviewer’s guess, we never observed an hourglass-like state in our simulation where both gates are open. Figure 3B is a visual representation of the backbone structure of the OCB state without explicitly showing the actual radius of the gating region, which also depends on the side chain conformations. Thus, Figure 3B alone cannot be used to conclude that we are dominantly sampling an hourglass-like intermediate conformation instead of the occluded state, as mentioned by the reviewer. 

Moreover, not all references in 30-42 have sampled the occluded state since many of them did not even simulate the substrate translocation process at all. For the ones that did sample substrate translocation processes, only two of them were studying the cation-coupled MFS family symporter (ref 38, 40) and they didn’t provide the PMF for the entire translocation process. There is no strong evidence for a stable minimum corresponding to a fully occluded state in these two studies.  In fact, different types of transporters with different coupling cations may exhibit different stability of the fully occluded state. For example, the fully occluded state has been experimentally observed for some MFS transporters, such as multidrug transporter EmrD, but not for others, such as lactose permease LacY. Thus, it is not generally true that a stable, fully-occluded state exists in all transporters, and it highly depends on the specific type of transporter and the coupling ion under study. 

Reviewer 2:

The manuscript by Liang and Guan provides an impressive attempt to characterize the conformational free energy landscape of a melibiose permease (MelB), a symporter member of major facilitator superfamily (MFS) of transporters. Although similar studies have been conducted previously for other members of MFS, each member or subfamily has its own unique features that make the employment of such methods quite challenging. While the methodology is indeed impressive, characterizing the coupling between large-scale conformational changes and substrate binding in membrane transporters is quite challenging and requires a sophisticated methodology. The conclusions obtained from the three sets of path-optimization and free energy calculations done by the authors are generally supported by the provided data and certainly add to our understanding of how sodium binding facilitates the transport of melibiose in MelB. However, the data is not generated reliably which questions the relevance of the conclusions as well. I particularly have some concerns regarding the implementation of the methodology that I will discuss below. 

(1) In enhanced sampling techniques, often much attention is given to the sampling algorithm. Although the sampling algorithm is quite important and this manuscript has chosen an excellent pair: string method with swarms of trajectories (SMwST) and replica-exchange umbrella sampling (REUS) for this task, there are other important factors that must be taken into account. More specifically, the collective variables used and the preparation of initial conformations for sampling. I have objectives for both of these (particularly the latter) that I detail below. Overall, I am not confident that the free energy profiles generated (summarized in Figure 5) are reliable, and unfortunately, much of the data presented in this manuscript heavily relies on these free energy profiles. 

Since comments (1) and (2) from this review are related, please see our response to (2) below. 

(2) The authors state that they have had an advantage over other similar studies in that they had two endpoints of the string to work from experimental data. I agree that this is an advantage. However, this could lead to some dangerous flaws in the methodology if not appropriately taken into account. Proteins such as membrane transporters have many slow degrees of freedom that can be fully captured within tens of nanoseconds (90 ns was the simulation time used here for the REUS). Biased sampling allows us to overcome this challenge to some extent, but it is virtually impossible to take into account all slow degrees of freedom in the enhanced sampling protocol (e.g., the collective variables used here do not represent anything related to sidechain dynamics). Therefore, if one mixes initial conformations that form different initial structures (e.g., an OF state and an IF state from two different PDB files), it is very likely that despite all equilibration and relaxation during SMwST and REUS simulations, the conformations that come from different sources never truly mix. This is dangerous in that it is quite difficult to detect such inconsistencies and from a theoretical point of view it makes the free energy calculations impossible. Methods such as WHAM and its various offshoots all rely on overlap between neighboring windows to calculate the free energy difference between two windows and the overlap should be in all dimensions and not just the ones that we use for biasing. This is related to well-known issues such as hidden barriers and metastability. If one uses two different structures to generate the initial conformations, then the authors need to show their sampling has been long enough to allow the two sets of conformations to mix and overlap in all dimensions, which is a difficult task to do. 

We partly agree with the reviewer in that it is challenging to investigate whether the structures generated from the two different initial structures are sufficiently mixed in terms of orthogonal degrees of freedom outside the CV space during our string method and REUS simulations. We acknowledge that our simulations are within 100 ns for each REUS window, and there could be some slow degrees of freedom that are not fully sampled within this timescale. However, the conjectures and concerns raised by the reviewer are somewhat subjective in that they are almost impossible to be completely disproven. In a sense, these concerns are essentially the same as the general suspicion that the biomolecular simulation results are not completely converged, which cannot be fully ruled out for relatively complex biomolecular systems in any computational study involving MD simulations.  We also note that comparison among the PMFs of different cation bound/unbound states will have some error cancellation effects because of the consistent use of the same sampling methods for all three systems. Our main conclusions regarding the cooperative binding and transport of the two substrates lie in such comparison of the PMFs and additionally on the unbiased MD simulations. Thus, although there could be insufficient sampling, our key conclusions based on the relative comparison between the PMFs are more robust and less likely to suffer from insufficient sampling.

(3) I also have concerns regarding the choice of collective variables. The authors have split the residues in each transmembrane helix into the cyto- and periplasmic sides. Then they have calculated the mass center distance between the cytoplasmic sides of certain pairs of helices and have also done the same for the periplasmic side. Given the shape of a helix, this does not seem to be an ideal choice since rather than the rotational motion of the helix, this captures more the translational motion of the helix. However, the transmembrane helices are more likely to undergo rotational motion than the translational one. 

Our choice of CVs not only captures the translational motion but also the rotational motion of the helix. Consider a pair of helices. If there is a relative rotation in the angle between the two helices, causing the extracellular halves of the two helices to get closer and the intracellular halves to be more separated, this rotational motion can be captured as the decrease of one CV describing the extracellular distance and increase in the other CV describing the intracellular distance between the two helices. Reversely, if one of the two CVs is forced to increase and the other one forced to decrease, it can, in principle, bias the relative rotation of the two helices with respect to each other. Indeed, comparing Figure 3 with Figure S4, the reorientation of the helices with respect to the membrane normal (Fig. S4) is accompanied by the simultaneous decrease and increase in the pairwise distances between different segments of the helices. Therefore, our choice of CVs in the string method and REUS are not biased against the rotation of the helices, as the reviewer assumed.

(4) Convergence: String method convergence data does not show strong evidence for convergence (Figure S2) in my opinion. REUS convergence is also not discussed. No information is provided on the exchange rate or overlap between the windows.

The convergence of string method, REUS, the exchange rate and overlap between windows will be discussed in the reviewed manuscript.

Reviewer 3:

The paper from Liang and Guan details the calculation of the potential mean force for the transition between two key states of the melibiose (Mel) transporter MelB. The authors used the string method along with replica-exchange umbrella sampling to model the transition between the outward and inwardfacing Mel-free states, including the binding and subsequent release of Mel. They find a barrier of ~6.8 kcal/mol and an overall free-energy difference of ~6.4 kcal/mol. They also investigate the same process without the co-transported Na+, finding a higher barrier, while in the D59C mutant, the barrier is nearly eliminated.

For Na+ bound state, the rate-limiting barrier is 8.4 kcal/mol instead of 6.8 kcal/mol. The overall free energy difference is 3.7 kcal/mol instead of 6.4 kcal/mol. These numbers need to be corrected in the public review.

I found this to be an interesting and technically competent paper. I was disappointed actually to see that the authors didn't try to complete the cycle. I realize this is beyond the scope of the study as presented.

We agree with the reviewer that characterizing the complete cycle is our eventual goal. However, in order to characterize the complete cycle of the transporter, the free energy landscapes of the Na+ binding and unbinding process in the sugar-bound and unbound states, as well as the OF to IF conformational transition in the apo state. These additional calculations are expensive, and the amount of work devoted to these new calculations is estimated to be at least the same as the current study. Therefore, we prefer to carry out and analyze these new simulations in a future study.  

The results are in qualitative agreement with expectations from experiments. Could the authors try to make this comparison more quantitative? For example, by determining the diffusivity along the path, the authors could estimate transition rates.

In our revised manuscript, we will determine the diffusivity along the path and estimate transition rates.

Relatedly, could the authors comment on how typical concentration gradients of Mel and Na+ would affect these numbers?

The concentration gradient of Mel and Na+ can be varied in different experimental setups. In a typical active transport essay, the Na+ has a higher concentration outside the cell, and the melibiose has a higher concentration inside the cell. In the steady state, depending on the experiment setup, the extracellular Na+ concentration is in the range of 10-20 mM, and the intracellular concentration is self-balanced in the range of 3-4 mM due to the presence of other ion channels and pumps. In addition to the Na+ concentration gradient, there is also a transmembrane voltage potential of -200 mV (the intracellular side being more negative than the extracellular side), which facilitates the Na+ release into the intracellular side. In the steady state, the extracellular concentration of melibiose is ~0.4 mM, and the intracellular concentration is at least 1000 times the extracellular concentration, greater than 0.4 M. In this scenario, the free energy change of intracellular melibiose translocation will be increased by about ~5 kcal/mol at 300K temperature, leading to a total ∆𝐺 of ~8 kcal/mol. The total barrier for the melibiose translocation is expected to be increased by less than 5 kcal/mol. However, the increase in ∆𝐺 for intracellular melibiose translocation will be compensated by a decrease in ∆𝐺 of similar magnitude ( ~5 kcal/mol) for intracellular Na+ translocation. In a typical sugar self-exchange essay, there is no net gradient in the melibiose or Na+ across the membrane, and the overall free energy changes we calculated apply to this situation.

eLife Assessment

This fundamental work provides new mechanistic insight in regulation of PDGF signaling through splicing controls. The evidence is compelling to demonstrate functional involvement of Srsf3, an RNA binding protein to this new and interesting mechanism. The work will be of broad interest to developmental biologists in general and molecular biologists/biochemists in the field of growth factor signaling and RNA processing.

Reviewer #1 (Public review):

In their manuscript "PDGFRRa signaling regulates Srsf3 transcript binding to affect PI3K signaling and endosomal trafficking" Forman and colleagues use iMEPM cells to characterize the effects of PDGF signaling on alternative splicing. They first perform RNA-seq using a one-hour stimulation with Pdgf-AA in control and Srsf3 knockdown cells. While Srsf3 manipulation results in a sizeable number of DE genes, PDGF does not. They then turn to examine alternative splicing, due to findings from this lab. They find that both PDGF and Srsf3 contribute much more to splicing than transcription. They find that the vast majority of PDGF-mediated alternative splicing depends upon Srsf3 activity and that skipped exons are the most common events with PDGF stimulation typically promoting exon skipping in the presence of Srsf3. They used eCLIP to identify RNA regions bound to Srsf3. Under both PDGF conditions, the majority of peaks were in exons with +PDGF having a substantially greater number of these peaks. Interestingly, they find differential enrichment of sequence motifs and GC content in stimulated versus unstimulated cells. They examine 2 transcripts encoding PI3K pathway (enriched in their GO analysis) members: Becn1 and Wdr81. They then go on to examine PDGFRRa and Rab5, an endosomal marker, colocalization. They propose a model in which Srsf3 functions downstream of PDGFRRa signaling to, in part, regulate PDGFRa trafficking to the endosome. The findings are novel and shed light on the mechanisms of PDGF signaling and will be broadly of interest. This lab previously identified the importance of PDGF naling on alternative splicing. The combination of RNA-seq and eCLIP is an exceptional way to comprehensively analyze this effect. The results will be of great utility to those studying PDGF signaling or neural crest biology.

Comments on the revised version:

The authors have fully addressed my previous comments and I have no further concerns.

Reviewer #2 (Public review):

Summary:

This manuscript builds upon the work of a previous study published by the group (Dennison, 2021) to further elucidate the coregulatory axis of Srsf3 and PDGFRa on craniofacial development. The authors in this study investigated the molecular mechanisms by which PDGFRa signaling activates the RNA-binding protein Srsf3 to regulate alternative splicing (AS) and gene expression (GE) necessary for craniofacial development. PDGFRa signaling-mediated Srsf3 phosphorylation drives its translocation into the nucleus and affect binding affinity to different proteins and RNA, but the exact molecular mechanisms were not known. The authors performed RNA sequencing on immortalized mouse embryonic mesenchyme (MEPM) cells treated with shRNA targeting 3' UTR of Srsf3 or scramble shRNA (to probe AS and DE events that are Srsf3 dependent) and with and without PDGF-AA ligand treatment (to probe AS and DE events that are PDGFRa signaling dependent). They found that PDGFRa signaling has more effect on AS than on DE. A matching eCLIP-seq experiment was performed to investigate how Srsf3 binding sites change with and without PDGFRa signaling.

Strengths:

(1) The work builds well upon the previous data and the authors employ a variety of appropriate techniques to answer their research questions.

(2) The authors show that Srsf3 binding pattern within the transcript as well as binding motifs change significantly upon PDGFRa signaling, providing a mechanistic explanation for the significant changes in AS.

(3) By combining RNA-seq and eCLIP datasets together, the authors identified a list of genes that are directly bound by Srsf3 and undergo changes in GE and/or AS. Two examples are Becn1 and Wdr81, which are involved in early endosomal trafficking.

Weaknesses:

(1) The authors identify two genes whose AS are directly regulated by Srsf3 and involved in endosomal trafficking; however, they do not validate the differential AS results and whether changes in these genes can affect endosomal trafficking. In Figure 6, they show that PDGFRa signaling is involved in endosome size and Rab5 colocalization, but do not show how Srsf3 and the two genes are involved.

(2) The proposed model does not account for other proteins mediating the activation of Srsf3 after Akt phosphorylation. How do we know this is a direct effect (and not secondary or tertiary effect)?

This is a thoroughly revised manuscript. I would like to congratulate the authors to have invested a lot of time, resources, new data, and a more refined discussion to make this a compelling piece of work. I have no further concerns.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

In their manuscript "PDGFRRa signaling regulates Srsf3 transcript binding to affect PI3K signaling and endosomal trafficking" Forman and colleagues use iMEPM cells to characterize the effects of PDGF signaling on alternative splicing. They first perform RNA-seq using a one-hour stimulation with Pdgf-AA in control and Srsf3 knockdown cells. While Srsf3 manipulation results in a sizeable number of DE genes, PDGF does not. They then turn to examine alternative splicing, due to findings from this lab. They find that both PDGF and Srsf3 contribute much more to splicing than transcription. They find that the vast majority of PDGF-mediated alternative splicing depends upon Srsf3 activity and that skipped exons are the most common events with PDGF stimulation typically promoting exon skipping in the presence of Srsf3. They used eCLIP to identify RNA regions bound to Srsf3. Under both PDGF conditions, the majority of peaks were in exons with +PDGF having a substantially greater number of these peaks. Interestingly, they find differential enrichment of sequence motifs and GC content in stimulated versus unstimulated cells. They examine 2 transcripts encoding PI3K pathway (enriched in their

GO analysis) members: Becn1 and Wdr81. They then go on to examine PDGFRRa and Rab5, an endosomal marker, colocalization. They propose a model in which Srsf3 functions downstream of PDGFRRa signaling to, in part, regulate PDGFRa trafficking to the endosome. The findings are novel and shed light on the mechanisms of PDGF signaling and will be broadly of interest. This lab previously identified the importance of PDGF naling on alternative splicing. The combination of RNA-seq and eCLIP is an exceptional way to comprehensively analyze this effect. The results will be of great utility to those studying PDGF signaling or neural crest biology. There are some concerns that should be considered, however. 

We thank the Reviewer for these supportive comments.

(1) It took some time to make sense of the number of DE genes across the results section and Figure 1. The authors give the total number of DE genes across Srsf3 control and loss conditions as 1,629 with 1,042 of them overlapping across Pdgf treatment. If the authors would add verbiage to the point that this leaves 1,108 unique genes in the dataset, then the numbers in Figure 1D would instantly make sense. The same applies to PDGF in Figure 1F and the Venn diagrams in Figure 2. 

We have edited the relevant sentence for Figure 1D as follows: “There was extensive overlap (521 out of 1,108; 47.0%) of Srsf3-dependent DE genes across ligand treatment conditions, resulting in a total of 1,108 unique genes within both datasets (Fig. 1C,D; Fig. S1A).” Similarly, we edited the relevant sentence for Figure 1F as follows: “There was limited overlap (4 out of 47; 8.51%) of PDGF-AA-dependent DE genes across Srsf3 conditions, resulting in a total of 47 unique genes within both datasets (Fig. 1E,F; Fig. S1B).” We edited the relevant sentence for Figure 2B as follows: “There was limited overlap (203 out of 1,705; 11.9%) of Srsf3-dependent alternatively-spliced transcripts across ligand treatment conditions, resulting in a total of 1,705 unique events within both datasets (Fig. 2A,B).” Finally, we edited the relevant sentence for Figure 2D as follows: “There was negligible overlap (9 out of 622; 1.45%) of PDGF-AA-dependent alternatively-spliced transcripts across Srsf3 conditions, resulting in a total of 622 unique events within both datasets (Fig. 2C,D).”

(2) The percentage of skipped exons in the +DPSI on the righthand side of Figure 2F is not readable.  

We have moved the label for the percentage of skipped exon events with a +DPSI for the -PDGF-AA vs +PDGF-AA (scramble) alternatively-spliced transcripts in Figure 2E so that it is legible.

(3) It would be useful to have more information regarding the motif enrichment in Figure 3. What is the extent of enrichment? The authors should also provide a more complete list of enriched motifs, perhaps as a supplement. 

We have added P values beneath the motifs in Figure 3F and 3G. Further, we have added a new Supplementary Figure, Figure S5, that lists the occurrence of the top 10 most enriched motifs in the unstimulated and, separately, stimulated samples in the eCLIP dataset and in a control dataset, as well as their P values.

(4) It is unclear what subset of transcripts represent the "overlapping datasets" on lines 280-315. The authors state that there are 149 unique overlapping transcripts, but the Venn diagram shows 270. Also, it seems that the most interesting transcripts are the 233 that show alternative splicing and are bound by Srsf3. Would the results shown in Figure 5 change if the authors focused on these transcripts? 

The Reviewer is correct that 233 of the alternatively-spliced transcripts had an Srsf3 eCLIP peak, as indicated in Figure 5A. However, several of these eCLIP peaks were a large distance from an alternatively-spliced element in the rMATS datasets, indicating that Srsf3 binding may not be contributing to the splicing outcomes in these cases. Instead, we correlated the eCLIP peaks with AS events by identifying transcripts in which Srsf3 bound within an alternatively-spliced exon or within 250 bp of the neighboring introns. We have added additional text clarifying this point in the Results: “We next sought to identify high-confidence transcripts for which Srsf3 binding had an increased likelihood of contributing to AS. Previous studies revealed enrichment of functional RBP motifs near alternatively-spliced exons (Yee et al., 2019). As such, we correlated the eCLIP peaks with AS events across all four treatment comparisons by identifying transcripts in which Srsf3 bound within an alternatively-spliced exon or within 250 bp of the neighboring introns (Tables S12-S15).” Further, we have relabeled Figure 5B as “Highconfidence, overlapping datasets biological process GO terms”.

(5) In general, there is little validation of the sequencing results, performing qPCR on Arhgap12 and Cep55. The authors should additionally validate the PI3K pathway members that they analyze. Related, is Becn1 expression downregulated in the absence of Srsf3, as would be predicted if it is undergoing NMD? 

We have added two new figure panels, Figure 5F-5G, assessing Wdr81 AS and Wdr81 protein sizes, as this gene has previously been implicated in craniofacial development. We have added the following text to the Results section: “Finally, as Wdr81 protein levels are predicted to regulate RTK trafficking between early and late endosomes, we confirmed the differential AS of Wdr81 transcripts between unstimulated scramble cells and scramble cells treated with PDGFAA ligand for 1 hour by qPCR using primers within constitutively-expressed exons flanking alternatively-spliced exon 9. This analysis revealed a decreased PSI for Wdr81 in each of three biological replicates upon PDGF-AA ligand treatment (Fig. 5F). Relatedly, we assessed the ratio of larger isoforms of Wdr81 protein (containing the WD3 domain) to smaller isoforms (missing the WD3 domain) via western blotting. Consistent with our RNA-seq and qPCR results, PDGFAA stimulation for 24 hours in the presence of Srsf3 led to an increase in smaller Wdr81 protein isoforms (Fig. 5G).”

(6) What is the alternative splicing event for Acap3?  

We have added the following text to the Results section and updated Figure 5E with Acap3 eCLIP peak visualization and the predicted alternative splicing outcome: “Finally, Acap3 is a GTPase-activating protein (GAP) for the small GTPase Arf6, converting Arf6 to an inactive, GDP-bound state (Miura et al., 2016). Arf6 localizes to the plasma membrane and endosomes, and has been shown to regulate endocytic membrane trafficking by increasing PI(4,5)P2 levels at the cell periphery (D’Souza-Schorey and Chavrier, 2006). Further, constitutive activation of Arf6 leads to upregulation of the gene encoding the p85 regulatory subunit of PI3K and increased activity of both PI3K and AKT (Yoo et al., 2019)… Srsf3 binding was additionally increased in Acap3 exon 19 upon PDGF-AA stimulation, at an enriched motif within the highconfidence, overlapping datasets, and we observed a corresponding increase in excision of adjacent intron 19 (Fig. 5D,E). As Acap3 intron 19 contains a PTC, this event is predicted to result in more transcripts encoding full-length protein (Fig. 5E).”

(7) The insets in Figure 6 C"-H" are useful but difficult to see due to their small size. Perhaps these could be made as their own figure panels. 

We have increased the size of the previous insets in new Figure 6 panels C’’’-H’’’.

(8) In Figure 6A, it is not clear which groups have statistically significant differences. A clearer visualization system should be used. 

We have added bracket shapes to Figure 6A indicating the statistically significant differences between scramble 0 minutes and scramble 60 minutes, and between scramble 60 minutes and shSrsf3 60 minutes.

(9) Similarly in Figure 6B, is 15 vs 60 minutes in the shSrsf3 group the only significant difference? Is there a difference between scramble and shSrsf3 at 15 minutes? Is there a difference between 0 and 15 minutes for either group? 

We have added a bracket shape to Figure 6B indicating the statistically significant difference between shSrsf3 at 15 minutes and shSrsf3 at 60 minutes. No other pairwise comparisons between treatments or timepoints were statistically significantly different.

Reviewer #2 (Public Review): 

Summary: 

This manuscript builds upon the work of a previous study published by the group (Dennison, 2021) to further elucidate the coregulatory axis of Srsf3 and PDGFRa on craniofacial development. The authors in this study investigated the molecular mechanisms by which PDGFRa signaling activates the RNA-binding protein Srsf3 to regulate alternative splicing (AS) and gene expression (GE) necessary for craniofacial development. PDGFRa signaling-mediated Srsf3 phosphorylation drives its translocation into the nucleus and affects binding affinity to different proteins and RNA, but the exact molecular mechanisms were not known. The authors performed RNA sequencing on immortalized mouse embryonic mesenchyme (MEPM) cells treated with shRNA targeting 3' UTR of Srsf3 or scramble shRNA (to probe AS and DE events that are Srsf3 dependent) and with and without PDGF-AA ligand treatment (to probe AS and DE events that are PDGFRa signaling dependent). They found that PDGFRa signaling has more effect on AS than on DE. A matching eCLIP-seq experiment was performed to investigate how Srsf3 binding sites change with and without PDGFRa signaling. 

Strengths: 

(1) The work builds well upon the previous data and the authors employ a variety of appropriate techniques to answer their research questions. 

(2) The authors show that Srsf3 binding pattern within the transcript as well as binding motifs change significantly upon PDGFRa signaling, providing a mechanistic explanation for the significant changes in AS. 

(3) By combining RNA-seq and eCLIP datasets together, the authors identified a list of genes that are directly bound by Srsf3 and undergo changes in GE and/or AS. Two examples are Becn1 and Wdr81, which are involved in early endosomal trafficking.  We thank the Reviewer for these supportive comments.

Weaknesses: 

(1) The authors identify two genes whose AS are directly regulated by Srsf3 and involved in endosomal trafficking; however, they do not validate the differential AS results and whether changes in these genes can affect endosomal trafficking. In Figure 6, they show that PDGFRa signaling is involved in endosome size and Rab5 colocalization, but do not show how Srsf3 and the two genes are involved. 

We have added two new figure panels, Figure 5F-5G, assessing Wdr81 AS and Wdr81 protein sizes, as this gene has previously been implicated in craniofacial development. We have added the following text to the Results section: “Finally, as Wdr81 protein levels are predicted to regulate RTK trafficking between early and late endosomes, we confirmed the differential AS of Wdr81 transcripts between unstimulated scramble cells and scramble cells treated with PDGFAA ligand for 1 hour by qPCR using primers within constitutively-expressed exons flanking alternatively-spliced exon 9. This analysis revealed a decreased PSI for Wdr81 in each of three biological replicates upon PDGF-AA ligand treatment (Fig. 5F). Relatedly, we assessed the ratio of larger isoforms of Wdr81 protein (containing the WD3 domain) to smaller isoforms (missing the WD3 domain) via western blotting. Consistent with our RNA-seq and qPCR results, PDGFAA stimulation for 24 hours in the presence of Srsf3 led to an increase in smaller Wdr81 protein isoforms (Fig. 5G).” The experiments in Figure 6 compare early endosome size, PDGFRa localization in early endosomes and phospho-Akt levels in response to PDGF-AA stimulation in scramble versus shSrsf3 cells, demonstrating that Srsf3-mediated PDGFRa signaling leads to enlarged early endosomes, retention of PDGFRa in early endosomes and increased downstream phospho-Akt signaling. Though we agree with the Reviewer that functionally linking the AS events to the endosomal phenotype would strengthen our conclusions, these are technically challenging experiments for several reasons. First, this approach has typically relied on tiling oligos against a region of interest to find the optimal sequence. We identified several transcripts that are bound by Srsf3 and undergo alternative splicing upon PDGFRa signaling to potentially contribute to the regulation of PI3K signaling and early endosomal trafficking. We do not expect that these effects are mediated by a single transcript but may instead by mediated by a combination of alternative splicing changes. As such, these experiments would require us to identify and validate multiple splice-switching antisense oligonucleotides (ASOs). Second, ASOs designed against a specific target may not lead to alternative splicing of that target, even in cases of high predicted binding affinities (Scharner et al., 2020, Nucleic Acid Res 48(2), 802816). Third, ASOs have been shown to result in off-target mis-splicing effects, which are hard to predict (Scharner et al., 2020, Nucleic Acid Res 48(2), 802-816). The design of functional ASOs is thus a long-standing challenge in the field, and likely beyond the scope of this manuscript. We have added the following text to the Discussion to highlight this potential future direction: “In the future, it will be worthwhile to attempt to functionally link the AS of transcripts such as Becn1, Wdr81 and/or Acap3 to the endosomal trafficking changes observed above using spliceswitching antisense oligonucleotides (ASOs).”

(2) The proposed model does not account for other proteins mediating the activation of Srsf3 after Akt phosphorylation. How do we know this is a direct effect (and not a secondary or tertiary effect)? 

This point is introduced in the Discussion: “Whether phosphorylation of Srsf3 directly influences its binding to target RNAs or acts to modulate Srsf3 protein-protein interactions which then contribute to differential RNA binding remains to be determined, though findings from Schmok et al., 2024 may argue for the latter mechanism. Studies identifying proteins that differentially interact with Srsf3 in response to PDGF-AA ligand stimulation are ongoing and will shed light on these mechanisms…. Again, this shift could be due to loss of RNA binding owing to electrostatic repulsion and/or changes in ribonucleoprotein composition and will be the subject of future studies.” We have added a potential change in Srsf3 protein-protein interactions upon Akt phosphorylation in the model in Figure 6J.

Reviewer #2 (Recommendations For The Authors): 

Suggestions: 

(1) It would strengthen the paper and improve the connection with the other sections of the paper if the authors show: 

a)  validation of PDGFRa signaling leading to AS of Becn1 and Wdr81 and corresponding changes in protein, and  

We have added two new figure panels, Figure 5F-5G, assessing Wdr81 AS and Wdr81 protein sizes, as this gene has previously been implicated in craniofacial development. We have added the following text to the Results section: “Finally, as Wdr81 protein levels are predicted to regulate RTK trafficking between early and late endosomes, we confirmed the differential AS of Wdr81 transcripts between unstimulated scramble cells and scramble cells treated with PDGFAA ligand for 1 hour by qPCR using primers within constitutively-expressed exons flanking alternatively-spliced exon 9. This analysis revealed a decreased PSI for Wdr81 in each of three biological replicates upon PDGF-AA ligand treatment (Fig. 5F). Relatedly, we assessed the ratio of larger isoforms of Wdr81 protein (containing the WD3 domain) to smaller isoforms (missing the WD3 domain) via western blotting. Consistent with our RNA-seq and qPCR results, PDGFAA stimulation for 24 hours in the presence of Srsf3 led to an increase in smaller Wdr81 protein isoforms (Fig. 5G).”

b)  functionally link the AS event(s) to endosomal phenotype using ASOs, etc. 

Though we agree with the Reviewer that such results would strengthen our conclusions, these are technically challenging experiments for several reasons. First, this approach has typically relied on tiling oligos against a region of interest to find the optimal sequence. We identified several transcripts that are bound by Srsf3 and undergo alternative splicing upon PDGFRa signaling to potentially contribute to the regulation of PI3K signaling and early endosomal trafficking. We do not expect that these effects are mediated by a single transcript but may instead by mediated by a combination of alternative splicing changes. As such, these experiments would require us to identify and validate multiple splice-switching antisense oligonucleotides (ASOs). Second, ASOs designed against a specific target may not lead to alternative splicing of that target, even in cases of high predicted binding affinities (Scharner et al., 2020, Nucleic Acid Res 48(2), 802-816). Third, ASOs have been shown to result in off-target mis-splicing effects, which are hard to predict (Scharner et al., 2020, Nucleic Acid Res 48(2), 802-816). The design of functional ASOs is thus a long-standing challenge in the field, and likely beyond the scope of this manuscript. We have added the following text to the Discussion to highlight this potential future direction: “In the future, it will be worthwhile to attempt to functionally link the AS of transcripts such as Becn1, Wdr81 and/or Acap3 to the endosomal trafficking changes observed above using splice-switching antisense oligonucleotides (ASOs).”

(2) The Venn diagram in Figure 5A and the description of the analysis the authors did to combine the RNA-seq and eCLIP-seq data are a little confusing. The authors say that they correlated eCLIP peaks with GE or AS events across all four treatment comparisons. The purpose of looking at both datasets was to find genes that are directly bound by Srsf3 and also have significantly affected GE and/or AS. Therefore, the data with and without PDGF-AA should be considered separately. For example, eCLIP peaks in the PDGF-AA condition can be correlated to Srsf3-dependent AS differences (comparing shSrsf3 and scramble) in the -PDGF-AA condition, and eCLIP peaks in the +PDGF-AA condition can be correlated to Srsf3-dependent AS differences in the +PDGF-AA condition. In the Venn diagram and the description, it seems like all comparisons were combined and it is not clear how the data were analyzed.

As indicated in Figure 5A, 233 of the alternatively-spliced transcripts uniquely found in one of the four treatment comparisons had an Srsf3 eCLIP peak. However, several of these eCLIP peaks were a large distance from an alternatively-spliced element in the rMATS datasets, indicating that Srsf3 binding may not be contributing to the splicing outcomes in these cases. Instead, we correlated the eCLIP peaks with AS events by identifying transcripts in which Srsf3 bound within an alternatively-spliced exon or within 250 bp of the neighboring introns. We have added additional text clarifying this point in the Results: “We next sought to identify highconfidence transcripts for which Srsf3 binding had an increased likelihood of contributing to AS.

Previous studies revealed enrichment of functional RBP motifs near alternatively-spliced exons (Yee et al., 2019). As such, we correlated the eCLIP peaks with AS events across all four treatment comparisons by identifying transcripts in which Srsf3 bound within an alternativelyspliced exon or within 250 bp of the neighboring introns (Tables S12-S15).” Further, we have relabeled Figure 5B as “High-confidence, overlapping datasets biological process GO terms”. We respectfully disagree with the Reviewer’s suggested comparisons. A comparison of the PDGF-AA eCLIP data with the scramble vs shSrsf3 (-PDGF-AA) data from the list of highconfidence transcripts resulted in only 7 transcripts. Similarly, a comparison of the +PDGF-AA eCLIP data with the scramble vs shSrsf3 (+PDGF-AA) data from the list of high-confidence transcripts resulted in only 14 transcripts. Separate gene ontology analyses of these lists of 7 and 14 transcripts revealed 21 and 40 significant terms for biological process, respectively, the majority of which encompassed one, and never more than two, transcripts. Had we separately examined the -PDGF-AA and +PDGF-AA data, we would not have detected the changes in Becn1, Wdr81 and Acap3 in Figure 5E.

eLife Assessment

This valuable manuscript presents a spatiotemporal genetic analysis of malaria-infected individuals from four villages in The Gambia, covering the period between December 2014 and May 2017. Overall, laboratory and data analyses are solid, although details of the methods are lacking. This study offers evidence to advance the understanding of malaria epidemiology in sub-Saharan Africa, but would benefit from additional analysis to strengthen the findings.

Reviewer #1 (Public review):

Summary:

The manuscript titled "Household clustering and seasonal genetic variation of Plasmodium falciparum at the community-level in The Gambia" presents a valuable genetic spatio-temporal analysis of malaria-infected individuals from four villages in The Gambia, covering the period between December 2014 and May 2017. The majority of samples were analyzed using a SNP barcode with the Spotmalaria panel, with a subset validated through WGS. Identity-by-descent (IBD) was calculated as a measure of genetic relatedness and spatio-temporal patterns of the proportion of highly related infections were investigated. Related clusters were detected at the household level, but only within a short time period.

Strengths:

This study offers a valuable dataset, particularly due to its longitudinal design and the inclusion of asymptomatic cases. The laboratory analysis using the Spotmalaria platform combined and supplemented with WGS is solid, and the authors show a linear correlation between the IBD values determined with both methods, although other studies have reported that at least 200 SNPs are required for IBD analysis. Data-analysis pipelines were created for (1) variant filtering for WGS and subsequent IBD analysis, and (2) creating a consensus barcode from the spot malaria panel and WGS data and subsequent SNP filtering and IBD analysis.

Weaknesses:

Further refining the data could enhance its impact on both the scientific community and malaria control efforts in The Gambia.

(1) The manuscript would benefit from improved clarity and better explanation of results to help readers follow more easily. Despite familiarity with genotyping, WGS, and IBD analysis, I found myself needing to reread sections. While the figures are generally clear and well-presented, the text could be more digestible. The aims and objectives need clearer articulation, especially regarding the rationale for using both SNP barcode and WGS (is it to validate the approach with the barcode, or is it to have less missing data?). In several analyses, the purpose is not immediately obvious and could be clarified.

(2) Some key results are only mentioned briefly in the text without corresponding figures or tables in the main manuscript, referring only to supplementary figures, which are usually meant for additional detail, but not main results. For example, data on drug resistance markers should be included in a table or figure in the main manuscript.

(3) The study uses samples from 2 different studies. While these are conducted in the same villages, their study design is not the same, which should be addressed in the interpretation and discussion of the results. Between Dec 2014 and Sept 2016, sampling was conducted only in 2 villages and at less frequent intervals than between Oct 2016 to May 2017. The authors should assess how this might have impacted their temporal analysis and conclusions drawn. In addition, it should be clarified why and for exactly in which analysis the samples from Dec 2016 - May 2017 were excluded as this is a large proportion of your samples.

(4) Based on which criteria were samples selected for WGS? Did the spatiotemporal spread of the WGS samples match the rest of the genotyped samples? I.e. were random samples selected from all times and places, or was it samples from specific times/places selected for WGS?

(5) The manuscript would benefit from additional detail in the methods section.

(6) Since the authors only do the genotype replacement and build consensus barcode for 199 samples, there is a bias between the samples with consensus barcode and those with only the genotyping barcode. How did this impact the analysis?

(7) The linear correlation between IBD-values of barcode vs genome is clear. However, since you do not use absolute values of IBD, but a classification of related (>=0.5 IBD) vs. unrelated (<0.5), it would be good to assess the agreement of this classification between the 2 barcodes. In Figure S6 there seem to be quite some samples that would be classified as unrelated by the consensus barcode, while they have IBD>0.5 in the Genome-IBD; in other words, the barcode seems to be underestimating relatedness.<br /> a. How sensitive is this correlation to the nr of SNPs in the barcode?

(8) With the sole focus on IBD, a measure of genetic relatedness, some of the conclusions from the results are speculative.<br /> a. Why not include other measures such as genetic diversity, which relates to allele frequency analysis at the population level (using, for example, nucleotide diversity)? IBD and the proportion of highly related pairs are not a measure of genetic diversity. Please revise the manuscript and figures accordingly.<br /> b. Additionally, define what you mean by "recombinatorial genetic diversity" and explain how it relates to IBD and individual-level relatedness.<br /> c. Recombination is one potential factor contributing to the loss of relatedness over time. There are several other factors that could contribute, such as mobility/gene flow, or study-specific limitations such as low numbers of samples in the low transmission season and many months apart from the high transmission samples.<br /> d. By including other measures such as linkage disequilibrium you could further support the statements related to recombination driving the loss of relatedness.

(9) While the authors conclude there is no seasonal pattern in the drug-resistant markers, one can observe a big fluctuation in the dhps haplotypes, which go down from 75% to 20% and then up and down again later. The authors should investigate this in more detail, as dhps is related to SP resistance, which could be important for seasonal malaria chemoprofylaxis, especially since the mutations in dhfr seem near-fixed in the population, indicating high levels of SP resistance at some of the time points.

(10) I recommend that raw data from genotyping and WGS should be deposited in a public repository.

Reviewer #2 (Public review):

Summary:

Malaria transmission in the Gambia is highly seasonal, whereby periods of intense transmission at the beginning of the rainy season are interspersed by long periods of low to no transmission. This raises several questions about how this transmission pattern impacts the spatiotemporal distribution of circulating parasite strains. Knowledge of these dynamics may allow the identification of key units for targeted control strategies, the evaluation of the effect of selection/drift on parasite phenotypes (e.g., the emergence or loss of drug resistance genotypes), and analyze, through the parasites' genetic nature, the duration of chronic infections persisting during the dry season. Using a combination of barcodes and whole genome analysis, the authors try to answer these questions by making clever use of the different recombination rates, as measured through the proportion of genomes with identity-by-descent (IBD), to investigate the spatiotemporal relatedness of parasite strains at different spatial (i.e., individual, household, village, and region) and temporal (i.e., high, low, and the corresponding the transitions) levels. The authors show that a large fraction of infections are polygenomic and stable over time, resulting in high recombinational diversity (Figure 2). Since the number of recombination events is expected to increase with time or with the number of mosquito bites, IBD allows them to investigate the connectivity between spatial levels and to measure the fraction of effective recombinational events over time. The authors demonstrate the epidemiological connectivity between villages by showing the presence of related genotypes, a higher probability of finding similar genotypes within the same household, and how parasite-relatedness gradually disappears over time (Figure 3). Moreover, they show that transmission intensity increases during the transition from dry to wet seasons (Figure 4). If there is no drug selection during the dry season and if resistance incurs a fitness cost it is possible that alleles associated with drug resistance may change in frequency. The authors looked at the frequencies of six drug-resistance haplotypes (aat1, crt, dhfr, dhps, kelch13, and mdr1), and found no evidence of changes in allele frequencies associated with seasonality. They also find chronic infections lasting from one month to one and a half years with no dependence on age or gender.

The use of genomic information and IBD analytic tools provides the Control Program with important metrics for malaria control policies, for example, identifying target populations for malaria control and evaluation of malaria control programs.

Strength:

The authors use a combination of high-quality barcodes (425 barcodes representing 101 bi-allelic SNPs) and 199 high-quality genome sequences to infer the fraction of the genome with shared Identity by Descent (IBD) (i.e. a metric of recombination rate) over several time points covering two years. The barcode and whole genome sequence combination allows full use of a large dataset, and to confidently infer the relatedness of parasite isolates at various spatiotemporal scales.

Reviewer #3 (Public review):

This study aimed to investigate the impact of seasonality on the malaria parasite population genetic. To achieve this, the researchers conducted a longitudinal study in a region characterized by seasonal malaria transmission. Over a 2.5-year period, blood samples were collected from 1,516 participants residing in four villages in the Upper River Region of The Gambia and tested the samples for malaria parasite positivity. The parasites from the positive samples were genotyped using a genetic barcode and/or whole genome sequencing, followed by a genetic relatedness analysis.

The study identified three key findings:

(1) The parasite population continuously recombines, with no single genotype dominating, in contrast to viral populations;

(2) The relatedness of parasites is influenced by both spatial and temporal distances; and

(3) The lowest genetic relatedness among parasites occurs during the transition from low to high transmission seasons. The authors suggest that this latter finding reflects the increased recombination associated with sexual reproduction in mosquitoes.

The results section is well-structured, and the figures are clear and self-explanatory. The methods are adequately described, providing a solid foundation for the findings. While there are no unexpected results, it is reassuring to see the anticipated outcomes supported by actual data. The conclusions are generally well-supported; however, the discussion on the burden of asymptomatic infections falls outside the scope of the data, as no specific analysis was conducted on this aspect and was not stated as part of the aims of the study. Nonetheless, the recommendation to target asymptomatic infections is logical and relevant.

eLife Assessment

This manuscript describes a novel magnetic steering technique to target human adipose derived mesenchymal stem cells (hAMSC) or induce pluripotent stem cells to the TM (iPSC-TM). The authors demonstrate the valuable findings that delivery of the stem cells compared to baseline lowered IOP, increased outflow facility, and increased TM cellularity. Although the methods, data, and analysis are solid, there is an overall weakness in the experimental controls, and questions around the transgenic mouse model. If these issues are addressed, the manuscript will be significantly improved.

Reviewer #1 (Public review):

Summary:

This manuscript describes a novel magnetic steering technique to target human adipose derived mesenchymal stem cells (hAMSC) or induce pluripotent stem cells to the TM (iPSC-TM). The authors show that delivery of the stem cells lowered IOP, increased outflow facility, and increased TM cellularity.

Strengths:

The technique is novel and shows promise as a novel therapeutic to lower IOP in glaucoma. hAMSC are able to lower IOP below the baseline as well as increase outflow facility above baseline with no tumorigenicity. These data will have a positive impact on the field and will guide further research using hAMSC in glaucoma models.

Weaknesses:

The transgenic mouse model of glaucoma the authors used did not show ocular hypertensive phenotypes at 6-7 months of age as previously reported. Therefore, if there is no pathology in these animals the authors did not show a restoration of function, but rather a decrease in pressure below normal IOP.

Reviewer #2 (Public review):

Summary:

This observational study investigates the efficacy of intracameral injected human stem cells as a means to re-functionalize the trabecular meshwork for the restoration of intraocular pressure homeostasis. Using a murine model of glaucoma, human adipose-derived mesenchymal stem cells are shown to be biologically safer and functionally superior at eliciting a sustained reduction in intraocular pressure (IOP). The authors conclude that the use of human adipose-derived mesenchymal stem cells has the potential for long-term treatment of ocular hypertension in glaucoma.

Strengths:

A noted strength is the use of a magnetic steering technique to direct injected stem cells to the iridocorneal angle. An additional strength is the comparison of efficacy between two distinct sources of stem cells: human adipose-derived mesenchymal vs. induced pluripotent cell derivatives. Utilizing both in vivo and ex vivo methodology coupled with histological evidence of introduced stem cell localization provides a consistent and compelling argument for a sustainable impact exogenous stem cells may have on the re-functionalization of a pathologically compromised TM.

Weaknesses:

A noted weakness of the study, as pointed out by the authors, includes the unanticipated failure of the genetic model to develop glaucoma-related pathology (elevated IOP, TM cell changes). While this is most unfortunate, it does temper the conclusion that exogenous human adipose derived mesenchymal stem cells may restore TM cell function. Given that TM cell function was not altered in their genetic model, it is difficult to say with any certainty that the introduced stem cells would be capable of restoring pathologically altered TM function. A restoration effect remains to be seen. Another noted complication to these findings is the observation that sham intracameral-injected saline control animals all showed elevated IOP and reduced outflow facility, compared to WT or Tg untreated animals, which allowed for more robust statistically significant outcomes. Additional comments/concerns that the authors may wish to address are elaborated in the Private Review section.

Reviewer #3 (Public review):

Summary:

The purpose of the current manuscript was to investigate a magnetic cell steering technique for efficiency and tissue-specific targeting, using two types of stem cells, in a mouse model of glaucoma. As the authors point out, trabecular meshwork (TM) cell therapy is an active area of research for treating elevated intraocular pressure as observed in glaucoma. Thus, further studies determining the ideal cell choice for TM cell therapy is warranted. The experimental protocol of the manuscript involved the injection of either human adipose derived mesenchymal stem cells (hAMSCs) or induced pluripotent cell derivatives (iPSC-TM cells) into a previously reported mouse glaucoma model, the transgenic MYOCY437H mice and wild-type littermates followed by the magnetic cell steering. Numerous outcome measures were assessed and quantified including IOP, outflow facility, TM cellularity, retention of stem cells, and the inner wall BM of Schlemm's canal.

Strengths:

All of these analyses were carefully carried out and appropriate statistical methods were employed. The study has clearly shown that the hAMSCs are the cells of choice over the iPSC-TM cells, the latter of which caused tumors in the anterior chamber. The hAMSCs were shown to be retained in the anterior segment over time and this resulted in increased cellular density in the TM region and a reduction in IOP and outflow facility. These are all interesting findings and there is substantial data to support it.

Weaknesses:

However, where the study falls short is in the MYOCY437H mouse model of glaucoma that was employed. The authors clearly state that a major limitation of the study is that this model, in their hands, did not exhibit glaucomatous features as previously reported, such as a significant increase in IOP, which was part of the overall purpose of the study. The authors state that it is possible that "the transgene was silenced in the original breeders". The authors did not show PCR, western blot, or immuno of angle tissue of the tg to determine transgenic expression (increased expression of MYOC was shown in the angle tissue of the transgenics in the original paper by Zode et al, 2011). This should be investigated given that these mice were rederived. Thus, it is clearly possible that these are not transgenic mice. If indeed they are transgenics, the authors may want to consider the fact that in the Zode paper, the most significant IOP elevation in the mutant mice was observed at night and thus this could be examined by the authors. Other glaucomatous features of these mice could also have been investigated such as loss of RGCs, to further determine their transgenic phenotype. Finally, while increased cellular density in the TM region was observed, proliferative markers could be employed to determine if the transplanted cells are proliferating.

eLife Assessment

In this potentially valuable study, the authors employed in vivo experiments and theoretical modeling to study the growth dynamics of nuclear condensates. They observed that condensates can exhibit distinct growth modes, as dictated by the competition between condensate surface tension and local elasticity of chromatin. While the theoretical model appears to capture the experimental observations, the level of evidence supporting the proposed growth mechanism is incomplete due to, among other limitations, the multiple fitting parameters and poorly justified Neo-Hookean elasticity.

Reviewer #1 (Public review):

Summary:

The manuscript "Interplay of condensate material properties and chromatin heterogeneity governs nuclear condensate ripening" presents experiments and theory to explain the dynamic behavior of nuclear condensates. The authors present experimental data that shows the size of multiple artificially induced condensates as a function of time for various conditions. They identify different dynamic regimes, which all differ from traditional Ostwald ripening. By careful analysis and comparison with a quantitative model, the authors conclude that the elastic effects of the chromatin are relevant and the interplay between (heterogeneous) elasticity and surface tension governs the droplets' behavior. However, since they apply a simple model to a complex system, I think that the work is sometimes prone to over-interpretation, which I detail below. In summary, since droplet growth in a heterogeneous, elastic environment is unavoidable for condensates, this work achieves an important step toward understanding this complex setting. The work will likely stimulate more experiments (using different methods or alternative settings) as well as theory (accounting for additional effects, like spatial correlations).

Strengths:

A particularly strong point of the work is the tight integration between experiment and theory. Both parts are explained well at an appropriate level with more details in the methods section and the supplementary information. I cannot comment much on the experiments, but they seem convincing to me and the authors quantify the relevant parameters. Concerning the theory, they derive a model at the appropriate level of description. The analysis of the model is performed and explained well. Even though spatial correlations are not taken into account, the model will serve as a useful basis for developing more complicated models in the future. It is also worth mentioning that the clear classification into different growth regimes is helpful since such results, with qualitative predictions for parameter dependencies, likely also hold in more complex scenarios.

Weaknesses:

I think that the manuscript would profit from more precise definitions and explanations in multiple points, as detailed below. Clearly, not all these points can be fully incorporated in a model at this point, but I think it would be helpful to mention weaknesses in the manuscript and to discuss the results a bit more carefully.

(1) The viscosity analysis likely over-interprets the data. First, the FRAP curves do not show clear exponential behavior. For Figure 1C, there are at least two time scales and it is not clear to me why the shorter time scale right after bleaching is not analyzed. If the measured time scale were based on the early recovery, the differences between the two cases would likely be very small. For Figure 1D, the recovery is marginal, so it is not clear how reliable the measurements are. More generally, the analysis was performed on condensates of very different sizes, which can surely affect the measurements; see https://doi.org/10.7554/eLife.68620 for many details on using FRAP to analyze condensate dynamics. Second, the relaxation dynamics are likely not purely diffusive in a viscous environment since many condensates show elastic properties (https://doi.org/10.1126/science.aaw4951). I could very well imagine that the measured recovery time is related to the viscoelastic time scale. Third, the assumption of the Stokes-Einstein-Sutherland equation to relate diffusivity and viscosity is questionable because of viscoelasticity and the fact that the material is clearly interacting, so free diffusion is probably not expected.

(2) A large part of the paper is spent on the difference between different dynamic regimes, which are called "fusion", "ripening", and "diffusion-based" (with slightly different wording in different parts). First, I would welcome consistent language, e.g., using either fusion or coalescence. Second, I would welcome an early, unambiguous definition of the regimes. A definition is given at the end of page 2, but this definition is not clear to me: Does the definition pertain to entire experiments (e.g., is something called "fusion" if any condensates fuse at any time in the experiment?), or are these labels used for different parts of the experiment (e.g., would the data in Figure 1H first be classified as "ripening" and then "diffusion-based")? More generally, the categorization seems to depend on the observed system size (or condensate count) and time scale. Third, I find the definition of the ripening time a bit strange since it is clearly correlated with droplet size. Is this dependency carefully analyzed in the subsequent parts?

(3) The effect of the elastic properties of the chromatin is described by a Neo-Hookean model, but the strains R/\xi used in the theory are of the order of 100, which is huge. At such high strains, the Neo-Hookean model essentially has a constant pressure 5E/6, so the mesh size \xi does not matter. It is not clear to me whether chromatin actually exhibits such behavior, and I find it curious that the authors varied the stiffness E but not the mesh size \xi when explaining the experiments in the last section although likely both parameters are affected by the experimental perturbations. In any case, https://doi.org/10.1073/pnas.2102014118 shows that non-linear elastic effects related to breakage and cavitation could set in, which might also be relevant to the problem described here. In particular, the nucleation barrier discussed in the later part of the present manuscript might actually be a cavitation barrier due to elastic confinement. In any case, I would welcome a more thorough discussion of these aspects (in particular the large strains).

(4) The description of nucleation on page 7 is sloppy and might be misleading. First, at first reading I understood the text as if droplets of any radius could nucleate with probability p_nuc related to Eq. 7. This must be wrong since large droplets have ΔG<0 implying p_nuc > 1. Most likely, the nucleation rate only pertains to the critical radius (which is what might be meant by R_0, but it is unclear from the description). In this case, the critical radius and its dependence on parameters should probably be discussed. It might also help to give the value of the supersaturation S in terms of the involved concentrations, and it should be clarified whether P_E depends on R_0 or not (this might also relate to the cavitation barrier raised in point 3 above). Secondly, it is a bit problematic that E is sampled from a normal distribution, which allows for negative stiffnesses! More importantly, the exact sampling protocol is important since sampling more frequently (in the simulations) leads to a larger chance of hitting a soft surrounding, which facilitates nucleation. I could not find any details on the sampling in the numerical simulations, but I am convinced that it is a crucial aspect. I did find a graphical representation of the situation in Figure S4A, but I think it is misleading since there is no explicit space in the model and stiffnesses are not correlated.

Reviewer #2 (Public review):

Summary:

The authors used a chemical linker to induce phase separation in U2OS cell nuclei with two different proteins, a coiled-coil protein (Mad1) and a disordered domain (from LAF-1), whose condensates were purported to have different material properties. First, they performed Fluorescence Recovery After Photobleaching (FRAP) and estimated the viscosity via the Stokes-Einstein equation. Combined with droplet fusion assays, this yielded an estimate of the surface tension, wherein the disordered condensates were found to have 130 times higher surface tension than the coiled-coil condensates. Confocal fluorescence microscopy was used to follow condensates over time, enabling classification of growth events as either fusion-, ripening-, or diffusion-based, and subsequent comparison of the relative abundances of these growth events between the two condensate types. Coiled-coil condensates grew primarily by diffusive processes, whereas disordered condensates grew primarily by ripening processes. The coarsening rates were described by growth exponents extracted from power-law fits of average normalized condensate radius over time. In both cases, these growth exponents were smaller than those predicted by theory, leading the authors to propose that nuclear condensate growth is generally suppressed by chromatin mechanics, as found in previous studies albeit with different exponents. The authors developed a theory to understand how the extent of this effect may depend on condensate material properties like surface tension. Treating chromatin as a neo-Hookean elastic solid, the authors assume a form of mechanical pressure that plateaus with increasing condensate size, and the resulting theory is used to analyze the observed condensate growth dynamics. A linearized extension of the theory is used to distinguish between suppressed, elastic, and Ostwald ripening. Finally, the authors consider the impact of different chromatin environments on condensate growth patterns and dynamics, which is achieved experimentally with another cell type (HeLa) and with a drug that decondenses chromatin (TSA). They find that condensate growth patterns are not significantly changed in either condensate type, but that the number of condensates nucleated and their related growth exponent are more sensitive to variations in chromatin stiffness in the coiled-coil system due to its low surface tension.

Strengths:

This work provides evidence that nuclear condensates can coarsen not only by fusion but also by continuous diffusive growth processes, predominant in coiled-coil condensates, and ripening, predominant in disordered condensates. Across these different condensate types and coarsening mechanisms, the authors find growth exponents lower than theoretical expectations, reinforcing the notion that elastic media can suppress condensate growth in the nucleus. Combined with theory, these observed differences in growth patterns and rates are argued to originate from differences in material properties, namely, surface tension relative to local chromatin stiffness. The authors further suggest that the few ripening events that are seen in coiled-coil condensates may be elastic in nature due to gradients in chromatin stiffness as opposed to Ostwald ripening. If this assertion proves to be robust, it would mark an early observation of elastic ripening in living cells.

Weaknesses:

(1) The assertion that nuclear condensates experience an external pressure from the chromatin network implies that chromatin should be excluded from the condensates (Nott et al., Molecular Cell (2015); Shin et al., Cell (2018)). This has not been shown or discussed here. While Movie 1 suggests the coiled-coil condensates may exclude chromatin, Movie 2 suggests the disordered condensates do not. LAF-1, as an RNA helicase, interacts with RNA, and RNA can be associated with chromatin in the nucleus. RNA can also modulate droplet viscosity. The authors' analysis of the disordered condensate data only makes sense if these condensates exclude chromatin, which they have not demonstrated, and which appears not to be the case.

(2) Critical physical parameters like viscosity and surface tension have not been directly measured but rather are estimated indirectly using FRAP and the Stokes-Einstein equation. While not uncommon in the field, this approach is flawed as droplet viscosity is not simply determined by the size of the composing particles. Rather, in polymeric systems, viscosity strongly depends on the local protein concentration and intermolecular interactions (Rubinstein & Semenov Macromolecules (2001)). This unjustified approach propagates to the surface tension estimate since only the ratio of viscosity to surface tension is explicitly measured. Since the paper's conclusions strongly hinge on the magnitude of the surface tension, a more accurate estimate or direct measurement of this salient material property is called for.

(3) The phase diagram of growth modes very much depends on the assumption of neo-Hookean elasticity of the chromatin network. This assumption is poorly justified and calls into question the general conclusions about possible growth phases. The authors need to either provide evidence for neo-Hookean elasticity, or, alternatively, consider a model in which strain stiffening or thinning continues as droplets grow, which would likely lead to very different conclusions, and acknowledge this uncertainty.

(4) There is limited data for the elastic ripening claim. In Figure 3E, only one data point resides in the elastic ripening (δ < 0) range, with a few data points very close to zero.

(5) The authors claim that "our work shows that the elastic chromatin network can stabilize condensates against Ostwald ripening but only when condensate surface tension is low." This claim also depends on the details of the chosen neo-Hookean model of chromatic elasticity, and it is not studied here whether these results are robust to other models.

(6) It is also not clear how the total number of Mad1 proteins and LAF-1 disordered regions change while the condensates evolve with time. As the experiments span longer than 6 hours, continued protein production could lead to altered condensate coarsening dynamics. For example, continued production of Mad1 can lead to the growth of all Mad1 condensates, mimicking the diffusive growth process.

Author response:

We appreciate the reviewer’s recognition of the strengths of our work as well as their constructive critiques and insightful suggestions for improvement. In this provisional response, we outline how we plan to address the reviewer’s comments in the revised manuscript. 

(1) Viscosity and surface tension are not accurately measured. 

We thank the reviewers for bringing up this important point. We are aware that FRAP is not the best method to accurately measure condensate viscoelasticity due to the problems the reviewers and others in the field have pointed out. More accurate methods of measuring fluorescent protein mobility, such as single-molecule tracking or fluorescence correlation spectroscopy, can be used; however, they cannot accurately reflect the time scale dependence of viscoelasticity in the condensate either. Other methods such as rheology and micropipette aspiration that have been used to measure condensate viscoelasticity in vitro are not accessible in living cells yet. Similarly, there is no readily available method to directly measure the surface tension of condensates in live cells. Therefore, we used FRAP and fusion assays to estimate the ratio of surface tension between the two condensates. This ratio was then used to determine the surface tension of the coiled coil condensates in the model after estimating the surface tension for disordered condensate from in vitro measurements (https://doi.org/10.1016/j.bpr.2021.100011). In the revision, we will adjust our FRAP fitting and use condensates with similar sizes to make our FRAP data more accurate. However, based on the large difference we observed for these two condensates, we do not believe these FRAP improvements would change the conclusions. 

We are also aware that the stokes-einstein relation strictly applies to purely viscous systems. One can apply the generalized Stokes-Einstein relation, which links the diffusion coefficient to the complex viscoelastic modulus of the medium. However, the complex modulus is difficult to determine in cells through live imaging. We thus used the Stokes-Einstein relation to estimate the ratio of effective viscosities, assuming elastic deformations relax faster. In the revision, we will add these assumptions to our discussion. 

(2) Justification of a Neo-Hookean elasticity model for chromatin. 

We thank the reviewer for highlighting this important aspect of our work. The observation that the strains R/ξ in our initial model are of the order of 100 is valid and raises questions about the applicability of the Neo-Hookean model. While it is true that at such high strains, the pressure becomes nearly constant (5E/6), our model remains applicable within the range of strains relevant to chromatin, particularly for small droplets where R/ξ values are more moderate. This is explicitly considered in the section “Effect of mechanical heterogeneity on condensate nucleation and growth,” where we also account for heterogeneous mesh sizes correlated with local stiffness. While these points are discussed in the supplementary material, we acknowledge that these details are not clearly presented in the main text, and we will revise the manuscript to explicitly discuss the strain regime and model applicability.

We agree that varying both the stiffness E and mesh size ξ would provide a more comprehensive understanding of the system, as both parameters are likely affected by experimental perturbations. We will revisit our analysis to incorporate variations in ξ alongside E and discuss the potential effects on our results.

Furthermore, the stabilization of condensate size by chromatin elasticity arises from the size-dependent pressure exerted by the elastic network, which is a feature of strain-stiffening elastic media rather than a specific property of the Neo-Hookean model. However, we agree that exploring the robustness of our results under alternative elasticity models would strengthen the manuscript. In the revised version, we will analyze additional elasticity models, including strain stiffening and thinning, to evaluate how these might influence our conclusions and to provide a broader context for the predicted growth phases.

The connection between the nucleation barrier and the cavitation barrier is particularly intriguing. The referenced study (https://doi.org/10.1073/pnas.2102014118) highlights non-linear elastic effects, including breakage and cavitation, which may be relevant in our system. We will explore whether cavitation effects due to elastic confinement play a role in the nucleation dynamics observed here and include a discussion of these mechanisms in the revised manuscript.

(3) Unclear description of nucleation in the model. 

We thank the reviewer for pointing out the lack of clarity in our description of nucleation. R_0​ represents the critical radius for nucleation, beyond which droplets grow spontaneously. The nucleation probability p_nuc​ is evaluated at R_0​, which depends on the free energy barrier ΔG, supersaturation S, and the elastic properties of the surrounding medium. We will include a clearer explanation of R_0​, its dependence on parameters, and its role in nucleation in the revised manuscript.

We ensure that the stiffness is sampled from a truncated normal distribution, preventing negative stiffness values. Sampling is performed at fixed intervals, and we will clarify the protocol to avoid bias and ensure consistency in the simulations.

Supersaturation S will be defined regarding solute and solvent concentrations, and we will discuss its influence on ΔG and R_0​.

The dependence of the elastic pressure P_E​ on R_0​, with stiffer surroundings leading to smaller nucleated droplets, will be explicitly clarified. We also agree that Figure S4A may be misleading, as it suggests spatial correlations in stiffness. We will revise the figure and caption to better represent the model assumptions.

(4) Limited data for the elastic ripening claim.

We acknowledge the reviewer’s concern regarding the limitation of support for the claim in the current manuscript. We believe our data do indicate elastic ripening. Particularly, the data points very close to zero are not necessarily artifacts of the fitting, as the elastic ripening can be very slow due to small differences in the local stiffness values around the droplets. We have mentioned this at the end of the section “Condensate material properties and chromatin heterogeneity determine the modes of ripening”. We shall revisit these results and remedy this concern with more data and analysis in the revised manuscript. 

(5) Confusion for dynamic regimes such as "fusion", "ripening", and "diffusion-based" and the problem with using “ripening time” to compare ripening speed.

We will clear up our definitions of the dynamic regimes and ensure consistent language use. The ripening time was defined as the time it takes per length of droplets to shrink. This way, the size dependence of the absolute ripening time is decoupled and thus can be used to compare the speed of ripening between two condensates. This is not well-explained in our current version. In the revision, we will redefine the normalized ripening time to avoid this confusion. 

(6) Chromatin should be excluded from the condensates 

We have data to support that chromatin is excluded from the condensates. We will add the data in the revision. 

(7) Effect of protein production on the diffusive growth process.

From the experiment, we do not believe that protein production is a significant source of the diffusive growth because for coiled-coil condensates nucleated with Hotag3 there was little diffusive growth. In the model also, condensates can grow for hours in the absence of protein production, depending on chromatin stiffness and surface tension. We aim to address the effect of protein production on growth in the revised manuscript.

eLife Assessment

This study presents important advances in the discovery and assessment of microcins that improve our understanding of their prevalence and roles. The bioinformatics analysis, expression, and antimicrobial assays are solid, although the diverging evaluations also indicated the need for additional support regarding the sequence analysis and validation to fully back some of the claims and conclusions. This study will appeal to researchers working on the discovery and analysis of novel peptide natural products.

Reviewer #1 (Public review):

Summary:

Enterobacteriaceae produce microcins to target their competitors. Using informatics approaches, the authors identified 12 new microcins. They expressed them in E. coli, demonstrating that the microcins have antimicrobial activity against other microbes, including plant pathogens and the ESKAPE pathogens Pseudomonas aeruginosa and Acinetobacter baumannii.

Strengths:

Overall, this study has the merit of identifying new potential antimicrobial molecules that could be used to target important pathogens. The bioinformatics analysis, the expression system used, and the antimicrobial assays performed are solid, and the data presented are convincing. This work will set the basis for new studies to investigate the potential role of these microcins in vivo.

Weaknesses:

The work has been performed in vitro, which is a valid approach for identifying the antimicrobial peptides and assessing their antimicrobial activity. Future studies will need to address whether these new microcins exhibit antimicrobial activity in vivo (e.g., in the context of infection models), and to identify the targets (receptor and mechanisms of action) for the new microcins.

Reviewer #2 (Public review):

Mortzfeld et al. describe their study of class IIb microcins. Furthering our awareness of the presence and action of microcins is an important line of research. However, several issues related to the premise, sequence analysis, and validation require attention to support the claims.

(1) Previous studies have been published on the broader distribution of microcins across bacteria. The software has been published for their identification. Comparison to this software and/or discussion of previous work should be included to place this work in the context of the field.

(2) It is not clear how immunity proteins were identified and there does not appear to be functional confirmation to show these predicted immunity proteins are real. Thus, it is premature to state that immunity genes have been found. This may also confound some of the validation studies below if proper immunity proteins have not been included.

(3) Please show the nt alignment used to generate the tree. Without seeing it, one would guess that the sequences are either quite similar (making the results from this study less novel) or there would be concerns that the phylogenetic relationship derived from the nt alignment is spurious.

(4) Figure 1 B-C: There are numerous branches that do not have phylogenetic support (values <50%). These are not statistically valid phylogenetic relationships and should be collapsed. The resulting tree should be used in the description of clades.

(5) The discovered microcins are not being directly tested since they are expressed heterologous and reliant on non-native modification systems. The results present the statement that novel microcins have been validated. This should be described accordingly.

(6) The key finding of this paper is the claim that 12 novel class IIb microcins have been validated. To substantiate this claim, original images showing evidence of antibacterial activity must be made available rather than a presence/absence chart. The negative controls for this table are unclear and should be included with the original images.

(7) Further data for the purified microcin is needed. The purification method described is standard practice and should allow for product quantification, which should be included. Standard practice includes an SDS page showing the purity of the microcin, or at least the TEV digest to show microcin has been produced, and importantly a control sample (scrambled sequence, empty vector purification, etc) to show that observed activity (Figure 2B) is not from a purification carry over. This data should be included to support that microcin has been purified and is active.

Reviewer #3 (Public review):

Summary:

In this study, several novel class IIb microcin biosynthetic gene clusters have been discovered by specific homology searches and manual curation. Using a specific E. coli expression system, the microcins were expressed and conjugated to monoglycosylated enterobactin as siderophore moiety. While this synthetic biology approach cannot account for other siderophores being coupled to the microcin core peptide in the original producing strains, it nonetheless allows for a general screening for the activity of the heterologously produced compounds. Through this approach, the activity of several predicted microcins has been confirmed and three novel class IIb microcin clades were identified.

Strengths:

The experimental design is sound, the results are corroborated by suitable controls, and the findings have a high level of novelty and significance. Furthermore, the comments of the initial round of peer review have been answered satisfactorily by the authors.

Author response:

We thank the anonymous very much for dedicating their time to thoroughly review our manuscript. We sincerely appreciate their thoughtful consideration and detailed assessment. Regarding the raised concerns, we acknowledge the importance of exploring the full scope of class IIb microcins, however, we believe that in depth characterization, purification, and in vivo application of the 12 novel compounds goes beyond the scope of this short report and discovery article.

At the same time, the reviewers acknowledge that the analysis, experimental design, the expression system as well as the performed assays are “sound”, “convincing”, and “corroborated by suitable controls”. In the present manuscript we sought to identify novel antimicrobials and to comprehensively verify their antimicrobial activity in E. coli irrespective of the siderophore-dependent delivery mechanism. Notably, none of the reviewers questioned that we describe new antimicrobials, the characteristics we used to find them, that they are class IIb microcins, or that they do exhibit antimicrobial activity against Gram-negative ESKAPE and plant pathogens.

We believe that our discovery study can serve as a steppingstone towards the application of bacterially produced antimicrobial compounds to target Gram negative pathogens in numerous plant and animal species, including humans.

Author response:

Our response to Reviewer #1:

We appreciate the reviewer’s comments to clarify the strengths and weaknesses of our work. Whether the effect of GM-CSF/IL-3 on the bowel is pro-inflammatory or anti-inflammatory has been controversial. In the present study, we have shown that CD131 mediated a pro-inflammatory effect of GM-CSF on the intestine, which may have worked in synergy with tissue-infiltrating macrophages. While its down-stream signaling has been investigated back and forth, we did not put effort into it. Using macrophage-specific CD131-deficient animals is important to clarify the effects of macrophage-specific CD131 on bowel inflammation. Our present work is indeed incomplete, and we anticipate to work on it further in future research. Concerning the results on human subjects, it is indeed that results from animal experiments were not completely reproduced. We believe that CD131 does have an effect on ulcerative colitis; however, due to the use of biological agents (e.g. anti-TNFs), the need for surgery in the treatment of ulcerative colitis has dramatically decreased and we could not get enough samples to reach a more convincing statistical analysis. Twenty-nine patients shown in the present study were all that received surgical intervention at our center during the past decade, and more human subjects will be needed in future research, possibly from multi-center study.

Our response to Reviewer #2:

Many appreciations for the valuable reviewer’s comments and suggestions. We realized that the number of animals per group was not indicated in each figure; in order to clarify the experimental rigor, we have deposited data used to generate the results of the present study in Dryad. Concerning the heterozygous CD131 knock-out animals, we think that others have used the homozygous mice in their studies; however, we observed premature deaths in those animals and we could not get any single homozygous mouse. We could not tell the exact reason, but we did observe robust phenotypes in these heterozygous mice. We do realize that our present work is incomplete, and more experiments need to be done to establish a causal relationship between CD131 and down-stream effects. We anticipate to use macrophage-specific homozygous CD131-deficient mice in our future research, which we believe will produce more meaningful and convincing results.

eLife Assessment

Ulcerative colitis (UC) is a chronic gut inflammatory condition affecting the colon in humans. This study uses human samples as well as a mouse model of colitis induced by a chemical, DSS, to investigate the role of an immune marker, CD131, in UC pathogenesis. The study, as presented, is incomplete, as experimental details are lacking, the statistical analyses are deficient, and there is not yet direct evidence for a CD131-mediated mechanism of gut inflammation.

Reviewer #1 (Public review):

Summary:

This study investigates the role of CD131, a receptor subunit for GM-CSF and IL-3, in ulcerative colitis pathogenesis using a DSS-induced murine colitis model. By comparing wild-type and CD131-deficient mice, the authors demonstrate that CD131 contributes to DSS-induced colitis, working in concert with tissue-infiltrating macrophages.

Strengths:

The research shows that CD131's influence on macrophage and T cell chemotaxis is mediated by CCL4. The authors conclude by proposing a pro-inflammatory role for CD131 in murine colitis and suggest potential clinical relevance in human inflammatory bowel disease.

Weaknesses:

The statistical association between increased CD131 expression and clinical IBD was not observed in Table 1, indicating that the main results from animal experiments were not reproduced in human subjects. Additionally, due to the absence of experimental results regarding the downstream signaling pathways through CD131, it is difficult to infer the precise differentiated outcomes of this study. Furthermore, the effects of CD131 on immune cells other than macrophages were not presented, and the results specific to macrophage-selective CD131 were not shown. Therefore, I conclude that it is challenging to provide a detailed review as there is a lack of supporting evidence for the core arguments made in this paper.

Reviewer #2 (Public review):

Summary:

This study investigates the potential role of CD131, a cytokine receptor subunit shared by GM-CSF and IL-3, in intestinal inflammation. Using heterozygous mice with an inactivating mutation on this gene, the study demonstrates ameliorated inflammation, associated with less infiltration of macrophages. Moreover, the depletion of macrophages prevented many of the inflammatory effects of DSS and made both WT and mutant mice equivalent in terms of inflammation severity. Correlative data showing increased CD131+ cells in tissues of patients with ulcerative colitis is also demonstrating, evidence for plausibility for these pathways in human disease.

Strengths:

The phenotype of mutant mice seems quite robust and the pathways proposed, GM-CSF signaling in macrophages with CCL4 as a downstream pathway, are all plausible and concordant with existing models. Many of the experiments included meaningful endpoints and were overall well performed.

Weaknesses:

(1) Experimental rigor was lacking in this manuscript, which provided limited or no details on the number of independent iterations that each experiment was done, the number of animals per group, the number of technical or biological replicates in each graph, etc.

(2) Details of animal model validation showing that this particular mutant allele results in a lack of CD131 protein expression were not shown. Moreover, since the paper uses heterozygous mice, it is critical to show that at the protein level, there is indeed reduced expression of CD131 in het mice compared to controls (many heterozygous states do not lead to appreciable protein depletion).

(3) Another major weakness is that the paper asserts a causal relationship between CD131 signaling and CCL4 production: the data shown indicates that the phenotypes of CCL4 deficiency (through Ab blockade) and CD131 partial deficiency (in het mice) are similar. However, this does not establish that CD131 signaling acts through CCL4.

(4) Lastly, while the paper claims that CD131 acts through macrophage recruitment, the evidence is circumstantial and not direct. DSS-induced acute colitis is largely mediated by macrophages, so any manipulation associated with less severe inflammation is accompanied by lesser macrophage infiltration in this model: this does not directly establish that CD131 acts directly on macrophages, which would require cell-specific knockout or complex cell reconstitution experiments.

eLife Assessment

This important paper reports functional interactions between L1TD1, an RNA binding protein (RBP), and its ancestral LINE-1 retrotransposon which is not modulated at the translational level. The evidence for the association between L1TD1 and LINE-1 ORF1p is solid. The work implies that the a transposon-derived RNA binding protein in the human genome can interact with the ancestral transposable element from which this protein was initially derived. This work spurs interesting questions for cancer types, where LINE1 and L1TD1 are aberrantly expressed.

Reviewer #1 (Public review):

Summary:

In their manuscript entitled 'The domesticated transposon protein L1TD1 associates with its ancestor L1 ORF1p to promote LINE-1 retrotransposition', Kavaklıoğlu and colleagues delve into the role of L1TD1, an RNA binding protein (RBP) derived from a LINE1 transposon. L1TD1 proves crucial for maintaining pluripotency in embryonic stem cells and is linked to cancer progression in germ cell tumors, yet its precise molecular function remains elusive. Here, the authors uncover an intriguing interaction between L1TD1 and its ancestral LINE-1 retrotransposon.

The authors delete the DNA methyltransferase DNMT1 in a haploid human cell line (HAP1), inducing widespread DNA hypo-methylation. This hypomethylation prompts abnormal expression of L1TD1. To scrutinize L1TD1's function in a DNMT1 knock-out setting, the authors create DNMT1/L1TD1 double knock-out cell lines (DKO). Curiously, while the loss of global DNA methylation doesn't impede proliferation, additional depletion of L1TD1 leads to DNA damage and apoptosis.

To unravel the molecular mechanism underpinning L1TD1's protective role in the absence of DNA methylation, the authors dissect L1TD1 complexes in terms of protein and RNA composition. They unveil an association with the LINE-1 transposon protein L1-ORF1 and LINE-1 transcripts, among others.

Surprisingly, the authors note fewer LINE-1 retro-transposition events in DKO cells compared to DNMT1 KO alone.

Strengths:

The authors present compelling data suggesting the interplay of a transposon-derived human RNA binding protein with its ancestral transposable element. Their findings spur interesting questions for cancer types, where LINE1 and L1TD1 are aberrantly expressed.

Weaknesses:

Suggestions for refinement:

The initial experiment, inducing global hypo-methylation by eliminating DNMT1 in HAP1 cells, is intriguing and warrants more detailed description. How many genes experience mis-regulation or aberrant expression? What phenotypic changes occur in these cells? Why did the authors focus on L1TD1? Providing some of this data would be helpful to understand the rationale behind the thorough analysis of L1TD1.

The finding that L1TD1/DNMT1 DKO cells exhibit increased apoptosis and DNA damage but decreased L1 retro-transposition is unexpected. Considering the DNA damage associated with retro-transposition and the DNA damage and apoptosis observed in L1TD1/DNMT1 DKO cells, one would anticipate the opposite outcome. Could it be that the observation of fewer transposition-positive colonies stems from the demise of the most transposition-positive colonies? Further exploration of this phenomenon would be intriguing.

Reviewer #2 (Public review):

In this study, Kavaklıoğlu et al. investigated and presented evidence for a role for domesticated transposon protein L1TD1 in enabling its ancestral relative, L1 ORF1p, to retrotranspose in HAP1 human tumor cells. The authors provided insight into the molecular function of L1TD1 and shed some clarifying light on previous studies that showed somewhat contradictory outcomes surrounding L1TD1 expression. Here, L1TD1 expression was correlated with L1 activation in a hypomethylation dependent manner, due to DNMT1 deletion in HAP1 cell line. The authors then identified L1TD1 associated RNAs using RIP-Seq, which display a disconnect between transcript and protein abundance (via Tandem Mass Tag multiplex mass spectrometry analysis). The one exception was for L1TD1 itself, is consistent with a model in which the RNA transcripts associated with L1TD1 are not directly regulated at the translation level. Instead, the authors found L1TD1 protein associated with L1-RNPs and this interaction is associated with increased L1 retrotransposition, at least in the contexts of HAP1 cells. Overall, these results support a model in which L1TD1 is restrained by DNA methylation, but in the absence of this repressive mark, L1TD1 is expression, and collaborates with L1 ORF1p (either directly or through interaction with L1 RNA, which remains unclear based on current results), leads to enhances L1 retrotransposition. These results establish feasibility of this relationship existing in vivo in either development or disease, or both.

Comments on revised version:

In general, the authors did an acceptable job addressing the major concerns throughout the manuscript. This revision is much clearer and has improved in terms of logical progression.

Author response:

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

Recommendations for the Authors:

Reviewer #2:

(1) In my previous review, I noted that using three different movies to conclude that different genres evoke different thought patterns is an overinterpretation with only one instance per genre. In the rebuttal letter, the authors state that they provide "evidence that is necessary but not sufficient to conclude that we can distinguish different genres of films" (page 15). Accordingly, I suggest refraining from statements such as "There was a significant main effect of movie genre on memory" (page 13) in the manuscript.

Thank you for this point. We have removed any reference to genre.

Page 18 (referring to page 13) [354-355] “First, there was a significant main effect of movie on memory, F(2, 254.12) = 49.33, p <.001, η2 = .28.”

Reviewer #3:

The revised manuscript is easier to read and better contextualized.

Thank you for this comment and for your feedback to allow us to make the manuscript more clear.

Public Reviews:

Reviewer #1:

The lack of direct interrogation of individual differences/reliability of the mDES scores warrants some pause.

Our study's goal was to understand how group-level patterns of thought in one group of participants relate to brain activity in a different group of participants. To this end, we decomposed trial-level mDES data to show dimensions that are common across individuals, which demonstrated excellent split-half reliability. Then we used these data in two complementary ways. First, we established that these ratings reliably distinguished between the different films (showing that our approach is sensitive to manipulations of semantic and affective features in a film) and that these group-level patterns were also able to predict patterns of brain activity in a different group of participants (suggesting that mDES dimensions are also sensitive to the way brain activity emerges during movie watching). Second, we established that variation across individuals in their mDES scores predicted their comprehension of information from films. Thus our study establishes that when applied to movie-watching, mDES is sensitive to individual differences in the movie-watching experience (as determined by an individual's comprehension). Given the success of this study and the relative ease with which mDES can be performed, it will be possible in the future to conduct mDES studies that hone in on both the general features of the movie-watching experience, as well as aspects that are more unique to an individual.

Reviewer #2:

(1) The distinction between thinking and stimulus processing (in the sense of detecting and assigning meaning to features, modulated by factors such as attention) remains unclear. Is "thinking" a form of conscious access or a reportable read-out from sensory and higher-level stimulus processing? Or does it simply refer to the method used here to identify different processing states?

Thank you for highlighting this first point, which is an important consideration when attempting to map cognitive states. We have added some additional comments to our discussion section to expand on this point.

Page 35-36 [698-711] “It is possible, therefore, that the identification of regions of visual and auditory cortex by our study reflects the participants attention to sensory input, rather than the complex analysis of these inputs that may be required for certain features of the movie watching experience. On the other hand, it is possible that the movie-watching state is a qualitatively different type of mental state to those that emerge in typical task situations. For example, unlike tasks, the movie-watching state is characterized by multi-modal sensory input, semantically rich themes, that evolve together to reveal a continuous narrative to the viewer. It is possible, therefore, that movies engender an absorbed state which depends more on processing in sensory cortex than would occur in traditional task paradigms such as a working memory task (when systems in association cortex may be needed to maintain information related to task rules). Important headway into addressing this uncertainty can be achieved by using mDES to compare the types of states that occur in different contexts (including both movies and tasks) and comparing the topography of brain activity associated with different experiential states.”

(2) The dimensions of thought appear to be directly linked to brain areas traditionally associated with core faculties of perception and cognition. For example, superior temporal cortex codes for speech information, which is also where thought reports on verbal detail localize in this study. This raises the question of whether the present study truly captures mechanisms specific to thinking and distinct from processing, especially given that individual variations in reports were not considered and movie-specific features were not controlled for.

Thank you for this point, we have added an additional paragraph to the discussion to expand on this.

Page 35 [692-698] “Finally, it is worth considering whether the patterns of brain activity identified by our analysis reflect the stimuli that are processed during movie watching, or the cognitive and affective processing of this information. On the one hand, the regions we found were often within regions of sensory cortex, areas of the brain which are often ascribed basic stimulus processing functions [1]. Moreover, according to perspectives on cognition derived from more traditional task paradigms, complex features of cognition, such as the regulation of thought, are often attributed to regions of association cortex, such as the dorsolateral prefrontal cortex [2].”

Reviewer #3:

This paper is framed as presenting a new paradigm but it does little to discuss what this paradigm serves, what are its limitations and how it should have been tested. The novelty appears to be in using experience sampling from 1 sample to model the responses of a second sample.

Thank you for this comment, we have since made clear what the novelty of the methodology is, as you have correctly identified, by expanding this point beyond the methods section to clearly orient the reader to the application and limitation of our methodological approach with our paradigm.

Page 7-8 [149-174] “One challenge that arises when attempting to map the dynamics of thought onto brain activity during movie-watching is accounting for the inherently disruptive nature of experience sampling: to measure experience with sufficient frequency to map experiential reports during movies would inherently disrupt the natural processes of the brain and alter the viewer’s experience (for example, by pausing the film at a moment of suspense). Therefore, if we periodically interrupt viewers to acquire a description of their thoughts while recording brain activity, this could impact on the ability to capture important dynamic features of the brain. On the other hand, if we measured fMRI activity continuously over movie-watching (as is usually the case), we would lack the capacity to directly relate brain signals to the corresponding experiential states. Thus, to overcome these obstacles, we developed a novel methodological approach using two independent samples of participants. In the current study, one set of 120 participants was probed with mDES five times across the three ten-minute movie clips (11 minutes total, no sampling in the first minute). We used a jittered sampling technique where probes were delivered at different intervals across the film for different people depending on the condition they were assigned. Probe orders were also counterbalanced to minimize the systematic impact of prior and later probes at any given sampling moment. We used these data to construct a precise description of the dynamics of experience for every 15 seconds of three ten-minute movie clips. These data were then combined with fMRI data from a different sample of 44 participants who had already watched these clips without experience sampling [3]. By combining data from two different groups of participants, our method allows us to describe the time series of different experiential states (as defined by mDES) and relate these to the time series of brain activity in another set of participants who watched the same films with no interruptions. In this way, our study set out to explicitly understand how the patterns of thoughts that dominate different moments in a film in one group of participants relate to the brain activity at these time points in a second set of participants and, therefore, better understand the contribution of different neural systems to the movie-watching experience.”

Page 33-35 [658-691] “Importantly, our study provides a novel method for answering these questions and others regarding the brain basis of experiences during films that can be applied simply and cost-effectively. As we have shown, mDES can be combined with existing brain activity, allowing information about both brain activity and experience to be determined at a relatively low cost.  For example, the cost-effective nature of our paradigm makes it an ideal way to explore the relationship between cognition and neural activity during movie-watching during different genres of film. In neuroimaging, conclusions are often made using one film in naturalistic paradigm studies [4]. Although the current study only used three movie clips, restraining our ability to form strong conclusions regarding how different patterns of thought relate to specific genres of film, in the future, it will be possible to map cognition across a more extensive set of movies and discern whether there are specific types of experience that different genres of films engage. One of the major strengths of our approach, therefore, is the ability to map thoughts across groups of participants across a wide range of movies at a relatively low cost.

Nonetheless, this paradigm is not without limitations. This is the first study, as far as we know, that attempts to compare experiential reports in one sample of participants with brain activity in a second set of participants, and while the utility of this method enables us to understand the relationship between thought and brain activity during movies, it will be important to extend our analysis to mDES data during movie-watching while brain activity is recorded. In addition, our study is correlational in nature, and in the future, it could be useful to generate a more mechanistic understanding of how brain activity maps onto the participants experience. Our analysis shows that mDES is able to discriminate between films, highlighting its broad sensitivity to variation in semantic or affective content. Armed with this knowledge, we propose that in the future, researchers could derive mechanistic insights into how the semantic features may influence the mDES data. For example, it may be possible to ask participants to watch movies in a scrambled order to understand how the structure of semantic or information influences the mapping between brains and ongoing experience as measured by mDES. Finally, our study focused on mapping group-level patterns of experience onto group-level descriptions of brain activity. In the future it may be possible to adopt a “precision-mapping” approach by measuring longer periods of experience using mDES and determining how the neural correlates of experience vary across individuals who watched the same movies while brain activity was collected [5]. In the future, we anticipate that the ease with which our method can be applied to different groups of individuals and different types of media will make it possible to build a more comprehensive and culturally inclusive understanding of the links between brain activity and movie-watching experience.”

What are the considerations for treating high-order thought patterns that occur during film viewing as stable enough to use across participants? What would be the limitations of this method? (Do all people reading this paper think comparable thoughts reading through the sections?) This is briefly discussed in the revised manuscript and generally treated as an opportunity rather than as a limitation.

It is likely, based on our study, that films can evoke both stereotyped thought patterns (i.e. thoughts that many people will share) and others that are individualistic. It is clear that, in principle, mDES is capable of capturing empirical information on both stereotypical thoughts and idiosyncratic thoughts. For example, clear differences in experiences across films and, in particular, during specific periods within a film, show that movie-watching can evoke broadly similar thought patterns in different groups of participants (see Figure 3 right-hand panel). On the other hand, the association between comprehension and the different mDES components indicate that certain individuals respond to the same film clip in different ways and that these differences are rooted in objective information (i.e. their memory of an event in a film clip). A clear example of these more idiosyncratic features of movie watching experience can be seen in the association between “Episodic Knowledge” and comprehension. We found that “Episodic Knowledge” was generally high in the romance clip from 500 Days of Summer but was especially high for individuals who performed the best, indicating they remembered the most information. Thus good comprehends responded to the 500 Days of Summer clip with responses that had more evidence of “Episodic Knowledge” In the future, since the mDES approach can account for both stereotyped and idiosyncratic features of experience, it will be an important tool in understanding the common and distinct features that movie watching experiences can have, especially given the cost effective manner with which these studies can be run.  

In conclusion, this study tackles a highly interesting subject and does it creatively and expertly. It fails to discuss and establish the utility and appropriateness of its proposed method.

Thank you very much for your feedback and critique. In our revision and our responses to these questions, we provided more information about the method's robustness utility and application to understanding cognition. Thank you for bringing these points to our attention.

References

(1) Kaas, J.H. and C.E. Collins, The organization of sensory cortex. Current Opinion in Neurobiology, 2001. 11(4): p. 498-504.

(2) Turnbull, A., et al., Left dorsolateral prefrontal cortex supports context-dependent prioritisation of off-task thought. Nature Communications, 2019. 10.

(3) Aliko, S., et al., A naturalistic neuroimaging database for understanding the brain using ecological stimuli. Scientific Data, 2020. 7(1).

(4) Yang, E., et al., The default network dominates neural responses to evolving movie stories. Nature Communications, 2023. 14(1): p. 4197.

(5) Gordon, E.M., et al., Precision Functional Mapping of Individual Human Brains. Neuron, 2017. 95(4): p. 791-807.e7.

eLife Assessment

This study presents a valuable methodological advancement in quantifying thoughts over time. A novel multi-dimensional experience-sampling approach is presented, identifying data-driven patterns that the authors use to interrogate fMRI data collected during naturalistic movie-watching. The experimentation is inventive and the analyses carried out and results presented are convincing.

Reviewer #1 (Public review):

The authors used a novel multi-dimensional experience sampling (mDES) approach to identify data-driven patterns of experience samples that they use to interrogate fMRI data collected during naturalistic movie-watching data. They identify a set of multi-sensory features of a set of movies that delineate low-dimensional gradients of BOLD fMRI signal patterns that have previously been linked to fundamental axes of cortical organization.

Reviewer #2 (Public review):

The present study explores how thoughts map onto brain activity, a notoriously challenging question because of the dynamic, subjective, and abstract nature of thoughts. To tackle this question, the authors collected continuous thought ratings from participants watching a movie, and additionally made use of an open-source fMRI dataset recorded during movie watching as well as five established gradients of brain variation as identified in resting state data. Using a voxel-space approach, the results show that episodic knowledge, verbal detail, and sensory engagement of thoughts commonly modulate visual and auditory cortex, while intrusive distraction modulates the frontoparietal network. Additionally, sensory engagement mapped onto a gradient from primary to association cortex, while episodic knowledge mapped onto a gradient from the dorsal attention network to visual cortex. Building on the association between behavioral performance and neural activation, the authors conclude that sensory coupling to external input and frontoparietal executive control are key to comprehension in naturalistic settings.

The manuscript stands out for its methodological advancements in quantifying thoughts over time and its aim to study the implementation of thoughts in the brain during naturalistic movie watching.

Strengths:

(1) The study raises a question that has been difficult to study in naturalistic settings so far but is key to understanding human cognition, namely how thoughts map onto brain activation.

(2) The thought ratings introduce a novel method for continuously tracking thoughts, promising utility beyond this study.

(3) The authors used diverse data types, metrics, and analyses to substantiate the effects of thinking from multiple perspectives.

Reviewer #3 (Public review):

This study attempted to investigate the relations between processing in the human brain during movie watching and corresponding thought processes. This is a highly interesting question, as movie watching presents a semi-constrained task, combining naturally occurring thoughts and common processing of sensory inputs across participants. This task is inherently difficult because in order to know what participants are thinking at any given moment, one has to interrupt the same thought process which is the object of study.

This study attempts to deal with this issue by aggregating staggered experience sampling data across participants in one behavioral study and using the population level thought patterns to model brain activity in different participants in an open access fMRI dataset.

The behavioral data consist of 120 participants who watched 3 11-minute movie clips. Participants responded to the mDES questionnaire: 16 visual scales characterizing ongoing thought 5 times, two minutes apart, in each clip. The 16 items are first reduced to 4 factors using PCA, and their levels are compared across the different movies. The factors are "episodic knowledge", "intrusive distraction", "verbal detail", and "sensory engagement". The factors differ between the clips, and distraction is negatively correlated with movie comprehension and sensory engagement is positively correlated with comprehension.

The components are aggregated across participants (transforming single subject mDES answers into PCA space and concatenating responses of different participants) and are used as regressors in a GLM analysis. This analysis identifies brain regions corresponding to the components. The resulting brain maps reveal activations that are consistent with the proposed mental processes (e.g. negative loading for intrusion in frontoparietal network, positive loadings for visual and auditory cortices for sensory engagement).

Then, the coordinates for brain regions which were significant for more than one component are entered into a paper search in neurosynth. It is not clear what this analysis demonstrates beyond the fact that sensory engagement contained both visual and auditory components.

The next analysis projected group-averaged brain activation onto gradients (based on previous work) and used gradient timecourses to predict the behavioral report timecourses. This revealed that high activations in gradient 1 (sensory→association) predicted high sensory engagement, and that "episodic knowledge" thought patterns were predicted by increased visual cortex activations. Then, permutation tests were performed to see whether these thought pattern related activations corresponded to well defined regions on a given cluster.

In conclusion, this study tackles a highly interesting subject and does it creatively and expertly.

eLife Assessment

Rachubinski and colleagues provide an important manuscript that includes two major advances in understanding immune dysregulation in a large cohort of individuals with Down syndrome. The work comprises compelling, comprehensive, and state-of-the-art clinical, immunological, and autoantibody assessment of autoimmune/inflammatory manifestations. Additionally, the authors report promising results from a clinical trial with the JAK inhibitor tofacitinib for individuals with dermatological autoimmune disease.

Reviewer #1 (Public review):

Summary:

This paper represents a huge amount of work on a condition whose patients' health and well-being have not always been prioritized, and only relatively recently has the immune dysregulation seen in patients with Down Syndrome (DS) been garnering major research interest.

This paper provides an unparalleled examination of immune disorder in patients with DS. The authors also report the results from a clinical trial with the JAK inhibitor tofacitinib in DS patients.

Strengths:

This manuscript report an herculean effort and provides an unparalleled examination of immune disorder in a large number of patients with DS.

Weaknesses:

Not a major weakness but, apart from finding an elevation of CD4 T central memory cells and more differentiated plasmablast, several of the alteration reported in this manuscript had already been suggested by a few case reports and very small series. On the other hand, the number of patients (and controls) utilized for this study is remarkable and allows to draw much firmer conclusions.

Comments on revised version:

I don't have any further comments.

Reviewer #2 (Public review):

In this manuscript, Rachubinski and colleagues provide a comprehensive clinical, immunological, and autoantibody assessment of autoimmune/inflammatory manifestations of patients with Down syndrome (DS) in a large number of patients with this disorder. These analyses confirm prior results of excess interferon and cytokine signals in DS patients and extend these observations to highlight early-onset immunological aberrancies, far before symptoms occur, as well as characterizing novel autoantibody reactivities in this patient population. Then, the authors report the interim analysis of an open label, Phase II, clinical trial of the JAK1/3 inhibitor, tofacitinib, that aims to define the safety, clinical efficacy, and immunological outcomes of DS patients who suffer from inflammatory conditions of the skin. The clinical trial analysis indicates that the treatment is tolerated without serious adverse effects and that the majority of patients have experienced clinical improvement or remission in their corresponding clinical cutaneous manifestations as well as improvement or normalization of aberrant immunological signals such as cytokines.

The major strength of the study is the recruitment and uniform, systematic evaluation of an impressive number of DS patients. Moreover, the promising early results from the tofacitinib clinical trial pave the way for analysis of a larger number of patients within the Phase II trial and otherwise, which may lead to improved clinical outcomes of affected patients. An inherent weakness of such studies is the descriptive nature of several parameters and the relatively small size of tofacitinib-treated DS patients. However, the descriptive nature of some of the correlative research analyses are of scientific interest and are useful to generate hypotheses for future additional (including mechanistic) work and treatment of 10 DS patients in a formal clinical trial at interim analysis is not a trivial task for a disease like this. The manuscript achieves the aims of the authors and the results support their conclusions. The authors appropriately acknowledge areas that require more research and areas that are not well understood. The results are represented in a useful manner and statistical methods and analyses appear sound.

Comments on revised version:

The authors have satisfactorily addressed my comments in the revised manuscript.

Reviewer #3 (Public review):

Summary:

Individuals with Down syndrome (DS) have high rates of autoimmunity and can have exaggerated immune responses to infection that can unfortunately cause significant medical complications. Prior studies from these authors and others have convincingly demonstrated that individuals with DS have immune dysregulation including increased Type I IFN activity, elevated production of inflammatory cytokines (hypercytokinemia), increased autoantibodies, and populations of dysregulated adaptive immune cells that pre-dispose to autoimmunity. Prior studies have demonstrated that using JAK inhibitors to treat patient samples in vitro, in small case series of patients, and in mouse models of DS leads to improvement of immune phenotype and/or clinical disease. This manuscript provides two major advances in our understanding of the immune dysregulation and therapy for patients. First, they perform deep immune phenotyping on several hundred individuals with DS and demonstrate that immune dysregulation is present from infancy. Second, they report promising interim analysis of a Phase II clinical trial of a JAK inhibitor in 10 people with DS and moderate to severe skin autoimmunity.

Strengths and weaknesses:

The relatively large cohort and careful clinical annotation here provides new insights into the immune phenotype of patients with DS. For example, it is interesting that regardless of autoimmune disease or autoantibody status, individuals with DS have elevated cytokines and CRP. Analysis of the cohorts by age demonstrated that some cytokines are significant elevated in people with DS starting in infancy (e.g., IL-9 and IL-17C). Nearly all adults with DS in this study had autoantibodies (98%) and most had six or more autoantibodies (63%), which differed significantly from euploid study participants. This implies that all patients with DS might benefit from early intervention with therapy to reduce inflammation. However, it is also worth considering that an alternative interpretation that since hypercytokinemia does not vary based on disease state in individuals with DS, that this may not be a key factor driving autoimmunity (although it may be relevant for other clinical symptoms such as neuroinflammation).

Small case series have suggested the benefit of JAK inhibitors to treat autoimmunity in DS. This is the first report of a prospective clinical trial to test a JAK inhibitor in this setting. The clinical trial entry criteria included moderate to severe autoimmune skin disease in patients aged 12-50 years with DS, and treatment was with the JAK1/3 inhibitor tofacitinib. This clinical trial is a critically important step for the field. The early results support that treatment is well tolerated with improvement of interferon scores in patients and reduction of autoantibodies. Most patients experienced clinical improvement, with alopecia areata having the greatest response. Treatment may not affect all skin disease equally, for example of the 5 patients with hidradenitis suppurativa, only 1 showed clinical improvement based on skin score. While very promising, the clinical trial results reported here are preliminary and based on interim analysis of 10 patients at 16 weeks. Individuals with DS have a lifelong risk of immune dysregulation and thus it is unclear how long therapy, if of benefit, would need to be continued. Results of longer-term therapy will be informative when considering the risks/benefits of this therapy.

Comments on revised version:

The authors have made appropriate revisions to this important contribution to the literature.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This paper represents a huge amount of work on a condition whose patients' health and well-being have not always been prioritized, and only relatively recently has the immune dysregulation seen in patients with Down Syndrome (DS) been garnering major research interest.

This paper provides an unparalleled examination of immune disorders in patients with DS. The authors also report the results from a clinical trial with the JAK inhibitor tofacitinib in DS patients.

Strengths:

This manuscript reports a herculean effort and provides an unparalleled examination of immune disorders in a large number of patients with DS.

Weaknesses:

Not a major weakness but, apart from finding an elevation of CD4 T central memory cells and more differentiated plasmablast, several of the alterations reported in this manuscript had already been suggested by a few case reports and a very small series. On the other hand, the number of patients (and controls) utilized for this study is remarkable and allows for drawing much firmer conclusions.

We are grateful for the Reviewer’s very positive assessment of the work and results presented in this manuscript. We agree that many of the changes in the peripheral immune system reported here had been previously documented by our team and others using smaller sample sizes. However, as the Reviewer appreciated, this study involves an order of magnitude more research participants than previous studies (i.e., ~400 total participants, ~300 of them with trisomy 21 versus ~100 controls), which enabled us to investigate associations between immune changes and clinical variables, while also helping us draw much firmer conclusions.

Reviewer #2 (Public Review):

In this manuscript, Rachubinski and colleagues provide a comprehensive clinical, immunological, and autoantibody assessment of autoimmune/inflammatory manifestations of patients with Down syndrome (DS) in a large number of patients with this disorder. These analyses confirm prior results of excess interferon and cytokine signals in DS patients and extend these observations to highlight early-onset immunological aberrancies, far before symptoms occur, as well as characterizing novel autoantibody reactivities in this patient population. Then, the authors report the interim analysis of an open-label, Phase II, clinical trial of the JAK1/3 inhibitor, tofacitinib, that aims to define the safety, clinical efficacy, and immunological outcomes of DS patients who suffer from inflammatory conditions of the skin. The clinical trial analysis indicates that the treatment is tolerated without serious adverse effects and that the majority of patients have experienced clinical improvement or remission in their corresponding clinical cutaneous manifestations as well as improvement or normalization of aberrant immunological signals such as cytokines.

The major strength of the study is the recruitment and uniform, systematic evaluation of an impressive number of DS patients. Moreover, the promising early results from the tofacitinib clinical trial pave the way for analysis of a larger number of patients within the Phase II trial and otherwise, which may lead to improved clinical outcomes for affected patients. An inherent weakness of such studies is the descriptive nature of several parameters and the relatively small size of tofacitinib-treated DS patients. However, the descriptive nature of some of the correlative research analyses is of scientific interest and is useful to generate hypotheses for future additional (including mechanistic) work, and treatment of 10 DS patients in a formal clinical trial at interim analysis is not a trivial task for a disease like this. The manuscript achieves the aims of the authors and the results support their conclusions. The authors appropriately acknowledge areas that require more research and areas that are not well understood. The results are represented in a useful manner and statistical methods and analyses appear sound.

We appreciate the very positive evaluation by this Reviewer. We agree with the Reviewer on the descriptive nature of many of the analyses completed and on the value of a larger cohort of individuals with Down syndrome treated with a JAK inhibitor. The clinical trial will involve a total of 40 participants, and we look forward to reporting the results from the full cohort in the near future.

Reviewer #3 (Public Review):

Summary:

Individuals with Down syndrome (DS) have high rates of autoimmunity and can have exaggerated immune responses to infection that can unfortunately cause significant medical complications. Prior studies from these authors and others have convincingly demonstrated that individuals with DS have immune dysregulation including increased Type I IFN activity, elevated production of inflammatory cytokines (hypercytokinemia), increased autoantibodies, and populations of dysregulated adaptive immune cells that pre-dispose to autoimmunity. Prior studies have demonstrated that using JAK inhibitors to treat patient samples in vitro, in small case series of patients, and in mouse models of DS leads to improvement of immune phenotype and/or clinical disease. This manuscript provides two major advances in our understanding of immune dysregulation and therapy for patients. First, they perform deep immune phenotyping on several hundred individuals with DS and demonstrate that immune dysregulation is present from infancy. Second, they report a promising interim analysis of a Phase II clinical trial of a JAK inhibitor in 10 people with DS and moderate to severe skin autoimmunity.

Strengths and weaknesses:

The relatively large cohort and careful clinical annotation here provide new insights into the immune phenotype of patients with DS. For example, it is interesting that regardless of autoimmune disease or autoantibody status, individuals with DS have elevated cytokines and CRP. Analysis of the cohorts by age demonstrated that some cytokines are significantly elevated in people with DS starting in infancy (e.g., IL-9 and IL-17C). Nearly all adults with DS in this study had autoantibodies (98%) and most had six or more autoantibodies (63%), which differed significantly from euploid study participants. This implies that all patients with DS might benefit from early intervention with therapy to reduce inflammation. However, it is also worth considering that an alternative interpretation that since hypercytokinemia does not vary based on disease state in individuals with DS, this may not be a key factor driving autoimmunity (although it may be relevant for other clinical symptoms such as neuroinflammation).

Small case series have suggested the benefit of JAK inhibitors to treat autoimmunity in DS. This is the first report of a prospective clinical trial to test a JAK inhibitor in this setting. The clinical trial entry criteria included moderate to severe autoimmune skin disease in patients aged 12-50 years with DS, and treatment was with the JAK1/3 inhibitor tofacitinib. This clinical trial is a critically important step for the field. The early results support that treatment is well tolerated with an improvement of interferon scores in patients and reduction of autoantibodies. Most patients experienced clinical improvement, with alopecia areata having the greatest response. Treatment may not affect all skin diseases equally, for example of the 5 patients with hidradenitis suppurativa, only 1 showed clinical improvement based on skin score. While very promising, the clinical trial results reported here are preliminary and based on an interim analysis of 10 patients at 16 weeks. Individuals with DS have a lifelong risk of immune dysregulation and thus it is unclear how long therapy, if of benefit, would need to be continued. The results of longer-term therapy will be informative when considering the risks/benefits of this therapy.

We thank the Reviewer for the very positive evaluation. We agree with the Reviewer that the hypercytokinemia of Down syndrome may contribute to other pathophysiological processes beyond autoimmune conditions. Although many cytokines elevated in Down syndrome have well demonstrated pathogenic roles in the etiology of autoimmune diseases in the general population (e.g., TNF-a, IL-6), their consistent upregulation in DS regardless of clinical evidence of autoimmune pathology indicates the existence of a prolonged pre-clinical period, where the hypercytokinemia likely precedes evident tissue damage and symptomology. Alternatively, it is possible that these elevated cytokines are contributing the overall pathophysiology of DS (e.g., neuroinflammation, cognitive impairments, complications from viral infections) without formal diagnosis of an autoimmune disease. We also agree with the Reviewer that not all immune skin conditions would respond equally to JAK inhibition. Based on recent approvals for JAK inhibitors in the immunodermatology field, it is expected that JAK inhibition would show the greatest benefits for alopecia areata, atopic dermatitis, and psoriasis, with less clear results for hidradenitis suppurativa. We hope to contribute to this field through the analysis of the full clinical trial cohort in the near future. Lastly, we strongly agree with the need to assess the value of long-term therapy with JAK inhibitors or other immune therapies in people with Down syndrome for various clinical endpoints.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

This paper represents a huge amount of work on a condition whose patients' health and well-being have not always been prioritized, and only relatively recently has the immune dysregulation seen in patients with Down Syndrome (DS) been garnering major research interest.

This paper provides an unparalleled examination of immune disorder in patients with DS. In a truly herculean effort, the authors provided the cumulative examination of over 440 patients with DS, confirmed the alterations in immune cell subsets (n=292, 96 controls) and multi-organ autoimmunity seen in these patients as they age, and identified autoantibody production that could contribute to conditions co-occurring in patients with DS. They also sought to look at whether the early immunosenescence seen in DS was due to the inflammatory profile by comparing age-associated markers in DS patients and euploid controls separately, finding that several markers are regulated with age regardless of group, while comparing the effect of age versus DS status on cytokine status identified inflammatory markers elevated in DS patients across the lifespan that do not increase with age or that increase with age only in the DS cohort. This is very interesting in the context of DS in particular, and immunity during aging in general.

The second part of the manuscript presents the results from a clinical trial with the JAK inhibitor tofacitinib in DS patients. While the number of DS patients treated with tofacitinib was small, the results were often quite striking. Treatment was well-tolerated and the improvement of dermatological conditions was clear. The less responsive patients AA4 and AA2 provide a very clear illustration that these patients are sensitive to immune triggers during treatment. Additionally, the demonstration that patients' IFN scores and cytokine levels decreased without clear immunosuppression with tofacitinib treatment is encouraging, since treatment with this drug would need to be continuous. I would be curious to see if the patients added past the cutoff for interim analysis follow a similar trajectory. I would not ask the authors to add any data; the paper is well-written and logically constructed.

I only have a small comment: I really did not like how Figure 2 a, d, and g tethered the coloring to the magnitude of fold change to show the effect of DS particularly for 2a and 2g. Given that these fold changes are quite modest, the coloring is very light and hard to distinguish. The clear takeaway is that the effect on T cells is greatest, but there must be a better way to illustrate this. Perhaps displaying this graph on a non-white background could help with contrast.

We are grateful for the Reviewer’s very positive assessment of the manuscript and constructive feedback. We want to assure the Reviewer that similar analyses will be completed in the future for the entire cohort recruited into the trial to determine if similar trajectories and results are observed with the larger sample size. Additionally, following Reviewer’s guidance, we have modified the color scales in Figures 2a, d and g so that each panel is on its own dynamic range, thus emphasizing the differences within each immune cell lineage.

Reviewer #2 (Recommendations For The Authors):

• Although the focus of the patients in the first part of the paper is on autoimmune/inflammatory conditions, it will be useful to also list the non-autoimmune infectious manifestations for reference with prevalence data. For example, otitis media, or lung infections (mentioned within the paper), or mucosal candidiasis. Same for other manifestations such as cardiac or malignant conditions. Given the impressive number of patients, it will be useful to the readers to have prevalence data for these as well, even in brief statements within the results.

We appreciate this inquiry by the Reviewer. Following Reviewer’s guidance, we have included information on recurrent otitis media, frequent/recurrent pneumonia, congenital heart defects requiring repair, and various forms of leukemia. These additional data are presented in a revised Supplementary file 1 and briefly discussed in the results.

• Have the authors looked at DN T cells and whether they may be enriched in DS patients, given their enrichment in some autoimmune conditions?

Thanks for this inquiry. We did examine DN T cells (double negative T cells), which we referred to in our Figure 2 and Figure 2 – figure supplement 1 as non-CD4+ CD8+ T cells. Although this T cell subset is mildly elevated (in terms of frequency among T cells) in individuals with Down syndrome, the result did not reach statistical significance after multiple hypothesis correction. This negative result is shown in the heatmap in Figure 2 – figure supplement 1d.

• It would be useful to move the segment of the discussion that discusses the interim predefined analysis of the phase 2 trial to the corresponding segment of the results. As this reviewer was reading the paper, it was unclear why the interim analysis was done, whether it was predefined and it was not until the discussion that it became apparent. I believe it will help the readers to have a brief mention that this interim analysis was predefined and set to occur at the first 10 DS enrollees. Also, it would be helpful to state what is the total number of DS patients planned for enrollment in the Phase 2 trial which is continuing recruitment.

We appreciate this comment. Following the Reviewer’s guidance, we have revised the text to explain in the Results section that the interim analysis was predefined and triggered once the first 10 participants completed the 16 weeks of treatment. We also explain that the trial will be considered complete once a total of 40 participants undergo 16-weeks of treatment.

• Although the authors present data on TPO autoantibodies before and after tofacitinib, it remains unclear whether the other non-TPO autoantibodies were altered during treatment or whether this was a TPO autoantibody-specific phenomenon. Was there an alteration in mature B cells or plasmablast populations after tofacitinib? If these data are available, they would further enhance the manuscript. If they are not available, it would be useful for the authors to discuss those in the discussion of the manuscript.

We are grateful for this comment, which strongly aligns with our future research interests and plans for the analysis of the full cohort once the trial is completed. In the interim analysis, we analyzed only auto-antibodies related to autoimmune thyroid disease and celiac disease, as shown in the manuscript. However, we plan to complete a more comprehensive analysis of the effects of JAK inhibition on autoantibody production once the full sample set is available at the end of the trial. Likewise, the clinical trial protocol contemplates collection and processing of blood samples for immune mapping using mass cytometry, which will enable us to answer the question from the Reviewer about potential changes in B cells or plasmablast populations. Following Reviewer’s guidance, we discuss these planned analyses in the Discussion of the revised manuscript.

Reviewer #3 (Recommendations For The Authors):

(1) Cellular immune phenotyping data in Figure 2 presents a large number of patients with DS versus euploid controls (292 and 96 respectively). Given the relatively large cohort there would seem to be an opportunity to determine whether age or sex alters the immune phenotype shown, for example, TEMRAs, etc. Was the data analyzed in this way?

We welcome this comment, which clearly aligns with our research interests and planned additional analyses of these datasets generated by the Human Trisome Project. We can share with the Reviewer that although sex as a biological variable has minimal impacts on the strong immune dysregulation observed in Down syndrome, there are clear age-dependent effects, with some immune changes occurring early during childhood versus others taking place later in adult life. A manuscript describing a complete analysis of age-dependent effects on the multi-omics datasets in the Human Trisome Project is currently under preparation.

(2) The authors should strongly consider incorporating/discussing the findings from Gansa et al, Journal of Clinical Immunology May 2024 - where they reviewed the immune phenotype of 1299 patients with Down syndrome.

Thanks for this publication to our attention, which is not cited in the revised manuscript.

(3) It is difficult to differentiate patients Hs2 and Ps1 in Figure 5d.

Thanks for this observation, we have modified the labels for greater clarity in the revised manuscript.

(4) Given their finding of no correlation between cytokine levels/immune phenotype and autoimmunity, some additional discussion of the relevance of hypercytokinemia in the pathogenesis of autoimmunity would seem relevant (given that this was the basis for the clinical trial). The authors mention that cytokine levels may not be appropriate measures of disease in the patients.

We welcome this suggestion and have revised the Discussion along these lines.

(5) Data availability statement: appropriate.

eLife Assessment

This valuable study reports a novel function of ATG14 in preventing pyroptosis and inflammation in oviduct cells, thus allowing smooth transport of the early embryo to the uterus and implantation. The data supporting the main conclusion are solid. This work will be of interest to reproductive biologists and physicians practicing reproductive medicine.

Reviewer #1 (Public review):

This study by Popli et al. evaluated the function of Atg14, an autophagy protein, in reproductive function using a conditional knockout mouse model. The authors showed that female mice lacking Atg14 were infertile partly due to defective embryo transport function of the oviduct and faulty uterine receptivity and decidualization using PgrCre/+;Atg14f/f mice. The findings from this work are exciting and novel. The authors demonstrated that a loss of Atg14 led to an excessive pyroptosis in the oviductal epithelial cells that compromises cellular integrity and structure, impeding the transport function of the oviduct. In addition, the authors use both genetic and pharmacological approaches to test the hypothesis. Therefore, the findings from this study are high-impact and likely reproducible. However, there are multiple major concerns that need to be addressed to improve the quality of the work.

Reviewer #2 (Public review):

In this manuscript, Popli et al investigated the roles of autophagy related gene, Atg14, in the female reproductive tract (FRT) using conditional knockout mouse models. By ablation of Atg14 in both oviduct and uterus with PR-Cre (Atg14 cKO), authors discovered that such females are completely infertile. They went on to show that Atg14 cKO females have impaired embryo implantation as well as embryo transport from oviduct to uterus. Further analysis showed that Atg14 cKO leads to increased pyroptosis in oviduct, which disrupts oviduct epithelial integrity and leads to obstructive oviduct lumen and impaired embryo transport. The authors concluded that Atg14 is critical for maintaining the oviduct homeostasis and keeping the inflammation under check to enable proper embryo transport.

The authors have barely addressed most of my concerns in this revised version with a few minor issues remaining to be addressed:<br /> (1) The authors tried to address my first concern regarding the statement that "autophagy is critical for maintaining the oviduct homeostasis". The revised statement in Line 53-54 "we report that Atg14-dependent autophagy plays a crucial role in maintaining..." is still not correct. It should be corrected as " we report that autophagy-related protein Atg14 plays a crucial role in maintaining...".<br /> (2) Line 349-351 described 80-90% of blastocysts retrieved from oviducts of cKO mice, which is in consistent with Figure 3B (showing more than 98%).<br /> (3) Line 447, "Fig. 5E" should be Fig. 6A. In addition, grammar error in the next sentence.<br /> (4) In Figure 6D, why the composition of blastocysts in chemical treated group do not add up to 100%.

Reviewer #3 (Public review):

Summary:

The manuscript by Pooja Popli and co-authors tested the importance of Atg14 in the female reproductive tract by conditionally deleting Atg14 use PrCre and also Foxj1cre. The authors showed that loss of Atg14 leads to infertility due to the retention of embryos within the oviduct. The authors further concluded that the retention of embryos within the oviduct is due to pyroptosis in oviduct cells leading to defective cellular integrity. The revised manuscript has included new experimental data (Figs. S2B, 5B, 5C, and S3) that satisfied the concerns of this reviewer. The manuscript should provide important advancement to the field.

Author response:

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

We greatly appreciate the opportunity to submit a revision of our manuscript entitled: "The Autophagy Protein, ATG14 Safeguards Against Unscheduled Pyroptosis Activation to Enable Embryo Transport During Early Pregnancy" by Popli et al. We thank all three Referees for underscoring the importance of our findings as well as the constructive critiques that we used to improve our paper. Most notably, we added the following new data:

· To provide more insight into whether pyroptosis activation occurs distinctly in the oviduct, we looked for GSDMD, (primary executioner of the pyroptosis pathway) expression in the uterus and ovary too. We observed no signs of pyroptosis activation in response to ATG14 loss in either the uterus or ovary of Atg14 cKO mice compared to control ones suggesting that ATG14 plays a distinct role in regulating pyroptosis specifically in the oviduct (Revised Figure 5F).

· To better understand the molecular mechanisms of pyroptosis activation in the oviducts, we examined various key markers of mitochondrial integrity, architecture, and function in control and Atg14 cKO oviducts. Our findings indicate a significant loss of mitochondrial structural and functional integrity, possibly contributing to the embryo retention phenotype via activating the pyroptosis pathway in the oviduct. (Revised Figure 5B & C).

· To address the spatiotemporal and region-specific expression of ATG14 in the oviduct, we performed immunofluorescence analysis and observed the consistent expression of ATG14 in all the cellular compartments of oviducts including ciliary epithelial cells, secretory epithelial cells, and smooth muscle cells. Moreover, the region-specific expression analysis revealed that distinct expression of ATG14 in the ampullary region of cKO mice oviduct helps to preserve its structural integrity. Conversely, its loss in the isthmus region of the oviduct in concordance with active PR-cre activity causes completely distorted epithelial structures with luminal obliteration or narrowing resulting in an unorganized and obstructed lumen leading to embryo retention, suggesting that ATG14 is essential for maintaining the structural integrity of the oviduct (Revised Figure 3F & S2A).

· Considering the expression of PR-cre in the pituitary, which could potentially influence hormonal secretion and ovulation, we evaluated the levels of E2 and P4 during pregnancy. Our findings show that these hormone levels remained unchanged in Atg14 cKO mice, indicating that the absence of ATG14 does not negatively affect the HPG axis or pituitary function (Revised Figure 2F).

· ATG14 is an essential factor for the initiation of autophagy, and its loss can lead to reduced or inhibited autophagic activity. Consistently, we observed elevated levels of LC3b and p62 proteins, two well-known markers of autophagic flux in the oviducts of Atg14-deficient mice implying that loss of ATG14 leads to defective autophagy potentially disturbing the structural integrity of oviductal epithelial cells and impairing embryo transport. (New Supplementary Figure S2B).   

Reviewer #1 (Public Review):

This study by Popli et al. evaluated the function of Atg14, an autophagy protein, in reproductive function using a conditional knockout mouse model. The authors showed that female mice lacking Atg14 were infertile partly due to defective embryo transport function of the oviduct and faulty uterine receptivity and decidualization using PgrCre/+; Atg14f/f mice. The findings from this work are exciting and novel. The authors demonstrated that a loss of Atg14 led to an excessive pyroptosis in the oviductal epithelial cells that compromises cellular integrity and structure, impeding the transport function of the oviduct. In addition, the authors use both genetic and pharmacological approaches to test the hypothesis. Therefore, the findings from this study are high-impact and likely reproducible. However, there are multiple major concerns that need to be addressed to improve the quality of the work.

Major comments:

(1) It is interesting that deletion of Atg14 using PgrCre results in pyroptosis only in the oviduct; the authors should speculate/evaluate why the oviduct, but not the uterus or follicles. Is there any cellular specificity that is sensitive to autophagy/pyroptosis in the oviduct but not in other cell types? This has not been evaluated or discussed in the manuscript. Is it possible to include GSDMD IHC for the uterine section to ensure that there was no pyroptosis event in the cKO uteri?

We performed GSDMD IHC and found that, unlike in the oviduct, the cKO uteri and ovaries do not exhibit detectable pyroptosis (Revised Figure 5F). Additionally, we have added text to the discussion section addressing possible reasons for the differential impact of Atg14 loss on pyroptosis along the reproductive tract continuum (Line number: 532-538)

(2) Please include an explanation of how a loss of Atg14, important for the initiation process of autophagy (as indicated in line 88), can lead to pyroptosis. There was some discussion about inflammation. But the connection is still missing.

We thank the reviewer for noting on this. We have now included a possible explanation of how autophagy could impact pyroptosis in the discussion section (Line number: 532-538)  

(3) No expression data of ATG14 using IHC/IF analysis were included in the manuscript - this is missing. This is needed and important as the authors found that Foxj1Cre/+; Atg14f/f cKO mice had no fertility defect. Is it possible that ATG14 is not present in the ciliated epithelial cells of the oviduct? In addition, the data in Figure 5B also points to this speculation. This is because the GSDMD (the pyroptosis marker) is only observed in the isthmus region but not the ampulla.

We thank the reviewer for this nice suggestion. We performed the immunofluorescence analysis for ATG14 expression in control and Atg14 cKO oviducts and observed the consistent expression of ATG14 in all the cellular compartments of oviducts including ciliary epithelial cells, secretory epithelial cells, and smooth muscle cells (New Supplementary Figure S2A). We also looked for α-tubulin expressions in the oviduct of Foxj1Cre/+; Atg14 f/f mice and control mice and observed that ciliated epithelial cells that were positive for acetylated α-tubulin staining did not appear to be different in Foxj1Cre/+; Atg14 f/f mice oviduct compared to controls (Revised Figure 4C). However, due to the unavailability of reliable fluorescent-labeled antibodies for both Foxj1 and Atg14, we were unable to conduct the co-localization study as intended. This limitation hindered our ability to precisely determine the spatial overlap of these proteins within the tissue.

(4) In line with the previous comment, is ATG14 present in the human Fallopian tube? If so, which cell type? This needs to be addressed.

Author’s Response: We appreciate the reviewer's valuable suggestion. While we currently lack access to human fallopian tube biopsies, the Human Protein Atlas (https://www.proteinatlas.org/ENSG00000126775-ATG14) demonstrates distinct ATG14 expression in various fallopian tube cell types, with localization in the cytoplasm, membrane, and nucleus.

(5) As PgrCre is also expressed in the pituitary, is it possible that the deletion of Atg14 using PgrCre would affect pituitary function – hence a change in the FSH/LH secretion that subsequently affects ovulation? Although the uterine and ovarian histology in the Atg14 cKO looks similar to the controls, is it possible that cyclicity is also affected? The authors should evaluate whether the estrous cycle takes place regularly.

Author’s Response: Thank you for the insightful comment. However, evaluating the estrous cycle requires significant time and effort and is beyond the scope of the current manuscript. Nonetheless, we have now shown that both P4 and E2 levels were not altered in Atg14 cKO mice, indicating that the loss of Atg14 did not adversely impact the HPG axis, and by extension, pituitary function (Revised Figure 2F).

(6) The number of total embryos/oocytes in the cKO compared to the control has not been evaluated - this data must be included. Do the changes in autophagy in Atg14 cKO affect preimplantation embryo development? Please categorize the embryos found in the oviduct/uterus in both genotypes. i.e., % blastocyst, % morula, % developmentally delayed, % non-viable etc. It would be interesting to evaluate if the oviduct with heavy pyroptosis can support preimplantation embryo development.

Author’s Response: We thank the reviewer for this nice suggestion. We categorized the embryos into different categories as suggested and included the data (Revised Figure 3C and Figure 6D).

(7) It is unclear why the superovulation+mating experiment (Figure 3C) was performed. Please provide justification. Why was the data from natural mating (Figure 3A) insufficient?

Author’s Response: In Figure 3C, superovulation was employed to complement the natural mating studies and to provide stronger evidence for the embryo retention phenotype observed in the oviduct.

(8) In lines 297-298, the conclusion that "ATG14 is required for P4-mediated but not for E2-mediated actions during uterine receptivity" is not entirely correct. This is because the authors also observed that the downregulation of MUC1 (E2-target protein) is absent in the PgrCre/+;Atg14f/f cKO female uteri.

We thank the reviewer for noting this. We detected more E2-induced targets in D-4 pregnant uterine samples and found no change in their expression in response to Atg14 depletion in cKO females (Revised Figure 2E).

(9) Figure 3D: Please include an image that also represents the ampulla region. All images are from the isthmus region. It would be informative to see if the loss of cell boundaries also takes place at the ampulla region in the cKO oviduct.

We thank the reviewer for this nice suggestion. We included the ampulla section from the cKO and control female oviducts (Revised Figure 3F). As PR-cre activity is limited to isthmus only [1, 2], we did not see any structural abnormality in ampulla sections of cKO oviducts.

(10) Figure 3E: Please indicate which region the TEM was performed. Isthmus? Ampulla? Were the changes in mitochondrial phenotype observed across all oviductal regions?

The TEM imaging was performed by the WashU Core services. Although we clearly mentioned the core person to look into the isthmus region only, we are not sure if they accurately follow the instructions.

(11) Figure 4B; the evaluation of FOXJ1 IHC. The authors need to include sections that also have an ampulla region-especially in the cKO. In addition, it is misleading to state that there were fewer FOXJ1+ cells (line 361) in the cKO if the region being evaluated is the isthmus (which has a lot fewer ciliated epithelial cells in general) while the control image showed an ampulla where the abundancy of ciliated epithelial cells (FOXJ1+) is higher than that of the isthmus. The authors also need to include a higher resolution image (a zoom-in at the ciliated epithelial cells with FOXJ1+ signal) as well as the quantification of FOXJ1+ cells.

We appreciate the reviewer for the suggestion. In Figure 4A, we have already shown the ampulla region from both control and cKO oviducts, wherein alpha-tubulin staining was evident in both oviducts.  

We agree with the reviewer that the isthmus usually has fewer ciliary epithelial cells than the ampulla, however, as illustrated in Figures 4A and 4B, Atg14 depletion causes a marked disruption of structural integrity with loss of cell boundaries specifically in the isthmus, which is far more pronounced than in the ampulla. One reason for this is the reported Pgr Cre activity, which is much more robust in the isthmus than in the ampulla [1, 2] . This disruption leads to the substantial loss of both ciliated and secretory cells, compromising the epithelial architecture to such an extent that it is impossible to accurately quantify the Foxj1 signal as can be seen in higher resolution images in New Supplementary Figure S3.

For more clarity, we modified the statement in the revised file (Line Number: 393-396)

(12) All IHC/IF and embryo images need to include the scale bars.

We thank the reviewer for this suggestion. We now included the scale bar in all the images.

(13) Figure 5H: although IL1B is being discussed, there was no data in this study to support the figure.

In Figure 5H, IL1B is presented as part of the pyroptosis signaling pathway. As we have already shown other key executioners of this pathway: Caspase 1 and GSDMD, we believe that additional IL1B data would not provide new insights beyond what has already been shown.

Minor comments:

(1) Please include n (sample size) for all data, including the histology image in the figure legends for all studies.

We now included the sample size in figure legends for all data shown in the manuscript.

(2) Line 32, did the authors mean to say, "Self-digestion of..." instead of "Self-digestion for..."?

In Line 32, we meant, “Cellular self-digestion for female reproductive tract functions”. We have now corrected the statement.

Fig. 1A - please include negative control.

We included the negative control (Revised Figure 1)

(3) Figure 1E left panel and Figure 4C - please label "Average no. of pups/female/litter" as each female has more than one litter over her reproductive lifespan. If the authors represent pups/females, then the number should be accumulative in the range of 35-40pups/females in the control group.

We thank the reviewer for noting this. We now corrected the label in both Revised Figure 1E and Revised Figure 4E.

(4) Line 273: please remove "& F" as there is no Figure F in the image.

We removed “&F” from the Line 273.

(5) The presence of CL is not always indicative of normal hormonal levels; therefore, the authors should include the measurement of progesterone levels at 3.5 dpc in the cKO compared to the control group. Hormonal regulation is also crucial for embryo transport.

We thank the reviewer for this suggestion. We measured not only P4 but also E2 levels in D4 pregnant females and found no significant difference in their levels compared to corresponding controls (Revised Figure 2F).

(6) Figure 2A shows that KRT expression is not present in the control uteri. Although the KRT8 levels may have decreased at 4 dpc, they should be present (see Figure S2A).

We observed no decrease in KRT expression in control uteri on 5 dpc. We included better-resolution images for KRT expression (Revised Figure 2A).

(7) The dotted white lines in Figure 2A are too thick. It's difficult to see the Ki67 positive signal in the luminal epithelial cells. Please also add a quantitative analysis of Ki67+ cells in the luminal epithelium vs. stromal cells.

We now corrected the dotted lines in Revised Figure 2B. However, as the Ki-67 proliferation is evident in the representative images, we believe quantification analysis will not add anything new to the existing conclusion.

(8) Figure 2D - the y-axis mentions the weight ratio. However, the figure legend describes the transcript levels of Atg14 - please correct this.

We corrected the label in the revised manuscript.

(9) Line 294 - Please correct Figure 2C to Figure 2B.

We corrected it.

(10) Line 308 - Please correct Figure 2E to Figure 2F.

We corrected it.

(11) Line 310 - Please correct Figure 2F to Figure 2G.

We corrected it.

(12) Line 311 - Please correct Figure 2F to Figure 2G.

We corrected it.

(13) Information in Figure S2A and S2B should be included in the main figure.

We thank the reviewer for this nice suggestion. We now included the figures S2A and S2B in the main figure (Revised Figure 2C & D).

(14) Figure 3C - due to a lot of cellular debris after flushing, it's difficult to see. But it seems like there are secondary follicles in the flushing of control oviducts - this is highly unlikely. This could be due to an artifact of an accidental poking of the ovaries during collection.

We agree with the reviewer. It might be due to the unintentional poking of the ovaries. We will take extra care in future experiments to avoid this and ensure clean flushing to prevent any confusion from debris or artifacts.

(15) Figure 2B and Figure 3D signals from DAPI are missing - it's black with no blue signal. This could be the data loss during file compression for manuscript submission.

We included better-resolution pictures for the DAPI signal in Revised Figure 2B & Figure 3F.

(16) Explain why some embryos in the cKO make it to the uterus when the females are superovulated.

It might be due to the heightened hormonal stimulation provided by the superovulation which could facilitate the movement of some embryos through the oviduct despite any defects or abnormalities caused by the loss of ATG14 in the oviduct.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Popli et al investigated the roles of the autophagy-related gene, Atg14, in the female reproductive tract (FRT) using conditional knockout mouse models. By ablation of Atg14 in both oviduct and uterus with PR-Cre (Atg14 cKO), the authors discovered that such females are completely infertile. They went on to show that Atg14 cKO females have impaired embryo implantation and uterus receptivity due to impaired response to P4 stimulation and stromal decidualization. In addition to the uterus defect, the authors also discovered that early embryos are trapped inside the oviduct and cannot be efficiently transported to the uterus in these females. They went on to show that oviduct epithelium in Atg14 cKO females showed increased pyroptosis, which disrupts oviduct epithelial integrity and leads to obstructive oviduct lumen and impaired embryo transport. Therefore, the authors concluded that autophagy is critical for maintaining the oviduct homeostasis and keeping the inflammation under check to enable proper embryo transport.

Strengths:

This study revealed an important and unexpected role of the autophagy-related gene Atg14 in preventing pyroptosis and maintaining oviduct epithelial integrity, which is poorly studied in the field of reproductive biology. The study is well designed to test the roles ofATG14 in mouse oviduct and uterus. The experimental data in general support the conclusion and the interpretations are mostly accurate. This work should be of interest to reproductive biologists and scientists in the field of autophagy and pyroptosis.

Weaknesses:

Despite the strengths, there are several major weaknesses raising concerns. In addition, the mismatched figure panels, the undefined acronyms, and the poor description/presentation of some of the data significantly hinder the readability of the manuscript.

(1) In the abstract, the authors stated that "autophagy is critical for maintaining the oviduct homeostasis and keeping the inflammation under check to enable embryo transport". This statement is not substantiated. Although Atg14 is an autophagy-related gene and plays a critical role in oviduct homeostasis, the authors did not show a direct link between autophagy and pyroptosis/oviduct integrity. In addition, the authors pointed out in the last paragraph of the introduction that none of the other autophagy-related genes (ATG16L, FIP200, BECN1) exhibited any discernable impact on oviduct function. Therefore, the oviduct defect is caused by Atg14 specifically, not necessarily by autophagy.

We thank the reviewer for noting this. We corrected the statement in the revised manuscript (Line number: 53-54).

(2) In lines 412-414, the authors stated that "Atg14 ablation in the oviduct causes activation of pyroptosis", which is also not supported by the experimental data. The authors did not show that Atg14 is expressed in oviduct cells. PR-Cre is also not specific in oviduct cells. It is possible that Atg14 knockout in other PR-expressing tissues (such as the uterus) indirectly activates pyroptosis in the oviduct. More experiments will be required to support this claim. In line with the no defect when Atg14 has knocked out in oviduct ciliary cells, it will be good to use the secretory cells Cre, such as Pax8-Cre, to demonstrate that Atg14 functions in the secretory cells of the oviduct thus supporting this conclusion.

We now included the ATG14 expression data in the oviduct (New Supplementary Figure S2A). Consistent with previous studies reporting PR-cre activity in the isthmus [1, 2] , we observed that Atg14 depletion was more pronounced in the isthmus compared to the ampulla. However, generating a secretory Pax-8 cell Cre mice model will require a substantial amount of time and effort, and we respectfully note that this is beyond the scope of the current manuscript.

(3) With FOXJ1-Cre, the authors attempted to specifically knockout Atg14 in ciliary cells, but there are no clear fertility and embryo implantation defects in Foxj1/Atg14 cKO mice. The author should provide verification data to show that Atg14 had been effectively depleted in ciliary cells if Atg14 is normally expressed.

We understand the reviewer’s concern. We included new data for ATG14 expression in control and Atg14 cKO mice oviducts (New Supplementary Figure S2A). However, due to the unavailability of reliable fluorescent-labeled antibodies for both Foxj1 and Atg14, we could not conduct the co-localization studies as intended, and this limitation hindered our ability to precisely determine the spatial overlap of these proteins within the oviduct. Nonetheless, Foxj1-cre is a widely used mice model with reported cre-activity in ciliary epithelial cells including oviduct tissues [3]. Given the widespread expression of ATG14 in all the ciliary and secretory cells (New Supplementary Figure S2A) and distinct FOXJ1 expression in the oviduct (New Supplementary Figure S3), we are confident that Atg14 is deleted in the ciliary epithelial cells of Foxj1/Atg14 cKO mice oviducts.

(4) In lines 307-313, the author tested whether ATG14 is required for the decidualization of HESCs. The author stated that "Control siRNA transfected cells when treated with EPC seemed to change their morphological transformation from fibroblastic to epithelioid (Fig. 2E) and had increased expression of the decidualization markers IGFBP1 and PRL by day three only (Fig. 2F)". First, the labels in Figure 2 are not corresponding to the description in the text. Second, the morphology of the HESCs in the control and Atg14 siRNA group showed no obvious difference even at day 3 and day 6. The author should point out the difference in each panel and explain in the text or figure legend.

Decidualization is a post-implantation event, whereas our study primarily focuses on pre-implantation events in the oviduct. Therefore, we have removed all data related to human and mouse decidualization to enhance the clarity and precision of our study.

(5) In lines 332-336, the authors pointed out that the cKO mice oviduct lining shows marked eosinophilic cytoplasmic change, but there's no data to support the claim. In addition, the authors further described that "some of the cells showed degenerative changes with cytoplasmic vacuolization and nuclear pyknosis, loss of nuclear polarity, and loss of distinct cell borders giving an appearance of fusion of cells (Fig. 3D)". First, Figure 3D did not show all these phenotypes, and it is likely a mismatch to Figure 3E. Even in Figure 3E, it is not obvious to notice all the phenotypes described here. The figure legend is overly simple, and there's no explanation of the arrowheads in the panel. More data/images are required to support the claim here and provide a clear indication and explanation in the figure legend.

Dr. Ramya Masand, Chief pathologist in the Pathology Department at the Baylor College of Medicine, and a contributing author, assessed the H&E-stained oviduct sections from control and cKO mice. We have now included a new Supplementary Figure S3 with previous representative H&E images that depict the cellular alterations described in lines 332–336.

(6) In lines 317-325, it is rather confusing about the description of the portion of embryos from the oviduct and uterus. In addition, the total number of embryos was not provided. I would recommend presenting the numerical data to show the average embryos from the oviduct and uterus instead of using the percentage data in Figures 3A and 5G.

We thank the reviewer for this nice suggestion. We calculated the average number of embryos and found no difference in the number of embryos recovered from cKO or polyphyllin-treated pregnant mice at 4 dpc compared to their controls. (New Supplementary Figure S4A & B).

(7) In lines 389-391, authors tested whether Polyphyllin VI treatment led to activated pyroptosis and blocked embryo transport. Although Figures 5F-G showed the expected embryo transport defect, the authors did not show the pyroptosis and oviduct morphology. It will be important to show that the Polyphyllin VI treatment indeed led to oviduct pyroptosis and lumen disruption.

We performed the GSDMD staining IHC in Polyphyllin VI or vehicle-treated mice oviducts and observed elevated GSDMD expression with Polyphyllin V (New Figure 6E). However, no significant lumen disruption was detected, which may be attributed to the short-term exposure of the oviducts to pyroptosis induction, in contrast to the more pleiotropic effects observed in genetically induced models. Nonetheless, this observation clearly indicates that unscheduled or unwarranted activation of pyroptosis impedes embryo transport.

(8) In line 378, it would be better to include a description of pyroptosis and its molecular mechanisms to help readers better understand your experiments. Alternatively, you can add it in the introduction.

We thank the reviewer for this nice suggestion. We included literature on the pyroptosis pathway in the introduction section (Line Number: 105-118).

(9) Please make sure to provide definitions for the acronyms such as FRT, HESCs, GSDMD, etc.

We added definitions for the acronyms such as FRT, HESCs, and GSDMD used in the study.

(10) It is rather confusing to use oviducal cell plasticity in this manuscript. The work illustrated the oviducal epithelial integrity, not the plasticity.

We thank the reviewer for the suggestion. We have revised the manuscript accordingly to ensure clarity and precision in describing the oviductal epithelial structural changes observed in the absence of ATG14.

A few of the additional comments for authors to consider improving the manuscript are listed below.

(1) Some of the figures are missing scale bars, while others have inconsistent scale bars. It would be better to be consistent.

We now included the scale bars in all images.

(2) On a couple of occasions, the DAPI signal cannot be seen, such as in Figure 2B and Figure 3D.

We now included better-resolution images for the DAPI signal in all fluorescent images shown in the revised manuscript.

(3) Overall, the figure legends can be improved to provide more detailed information to help the reader to interpret the data.

We included additional details in all the figure legends in the revised manuscript.

(4) In Figure 2D, the Y-axis showed the stimulated/unstimulated uterine weight ratio, why did the author put "Atg14" at the top of the graph? At the same time, the X-axis title is missing in Figure 2D.

We apologize for the typo error. We removed “Atg14” from the top of the graph and included the X-axis title in the revised manuscript.

(5) In the left panel of Figure 2G, "ATG14" at the top should be "Atg14" to be consistent.

In Figure 2G, we are representing “ATG14” according to human gene annotation.

(6) In line 559, there miss "(A)" in front of Immunofluorescence analysis of GSDMD.

We thank the reviewer for noting this. We corrected it in the revised manuscript.

Reviewer #3 (Public Review):

Summary:

The manuscript by Pooja Popli and co-authors tested the importance of Atg14 in the female reproductive tract by conditionally deleting Atg14 using Pr Cre and also Foxj1cre. The authors showed that loss of Atg14 leads to infertility due to the retention of embryos within the oviduct. The authors further concluded that the retention of embryos within the oviduct is due to pyroptosis in oviduct cells leading to defective cellular integrity. The manuscript has some interesting findings, however there are also areas that could be improved.

Strengths:

The importance of Atg14 and autophagy in the female reproductive tract is incompletely understood. The manuscript also provide spatial evidence about a new mechanism linking Atg14 to pyroptosis.

We thank the reviewer for the positive statements and constructive comments on our manuscript.

Weaknesses:

(1) It is not clear why the loss of Atg14 selectively induces Pyroptosis within oviduct cells but not in other cellular compartments. The authors should demonstrate that these events are not happening in uterine cells.

We thank the reviewer for this nice suggestion. We performed GSDMD IHC and found that, unlike in the oviduct, the cKO uteri and ovaries do not exhibit detectable pyroptosis (Revised Figure 5F). Additionally, we have added text to the discussion section addressing possible reasons for the differential impact of Atg14 loss on pyroptosis along the reproductive tract continuum (Line number: 532-538)

(2) The manuscript never showed any effect on the autophagy upon loss of Atg14. Is there any effect on autophagy upon Atg14 loss? If so, does that contribute to the observation?

We thank the reviewer for the nice suggestion. We found LC3b and p62 protein levels, two well-known markers of autophagic flux are elevated due to Atg14 loss in the oviduct (New Supplementary Figure S2B).  Since, p62 accumulation is an indicative of the reduced autophagic flux [4], we posit loss of Atg14 results in defective autophagy in the oviduct. Importantly, this defective autophagy adversely impacted the structural integrity of oviductal epithelial cells, causing impairment in embryo transport.

(3) It is not clear what the authors meant by cellular plasticity and integrity. There is no evidence provided in that aspect that the plasticity of oviduct cells is lost. Similarly, more experimental evidence is necessary for the conclusion about cellular integrity.

We thank the reviewer for the suggestion. We have revised the text for clarity and precision in describing the oviductal epithelial structural changes observed in the absence of ATG14. To avoid ambiguity, we have removed the term "cellular plasticity." We have already provided extensive evidence, including multiple H&E stains and immunofluorescence analyses for KRT8 and smooth muscle actin to illustrate cellular integrity in both control and cKO oviducts. However, we respectfully believe that performing additional experiments on cellular integrity would not contribute further to the conclusions already drawn.

(4) The mitochondrial phenotype shown in Figure 3 didn't appear as severe as it is described in the results section. The analyses should be more thorough. They should include multiple frames (in supplemental information) showing mitochondrial morphology in multiple cells. The authors should also test that aspect in uterine cells. The authors should measure Feret's diagram. Diff erence in membrane potential etc. for a definitive conclusion.

We appreciate the reviewer’s suggestion. We carried out the TOM20 (mitochondrial structural marker) and cytochrome C (mitochondrial damage and cell death marker) immune-colocalization study and found loss of TOM20 signal with concomitant cytochrome c leakage into the peri-nuclear space (Revised Figure 5B). Additionally, we also observed reduced expression of mitochondrial structural and functional markers by qPCR analysis (Revised Figure 5C). However, we respectfully argue that conducting membrane potential studies on murine oviducts is extremely complex and is beyond the scope of this study.

(5) The comment that the loss of Atg14 and pyroptosis leads to the narrowing of the lumen in the oviduct should be experimentally shown.

We have now included a New Supplementary Figure S3 with representative previous immunofluorescence images that clearly show the narrowing of the lumen with Atg14 loss in the oviduct.

(6) The manuscript never showed the proper mechanism through which Atg14 loss induces pyroptosis. The authors should link the mechanism.

We respectfully disagree with the reviewer on this point. We have provided substantial evidence regarding the cellular mechanisms through which the loss of Atg14 may lead to the activation of pyroptosis as outlined below:

(1) Cellular Changes: Loss of ATG14 in the oviduct results in cellular swelling and the formation of fused membranous structures, which are characteristic features of pyroptosis activation.

(2) Expression of Key Pyroptosis Proteins: We observed an induced expression of GSDMD and Caspase-1, primary executioners of the pyroptotic pathway, in response to Atg14 loss.

(3) Inflammatory Markers: Elevated levels of inflammatory markers such as TNF-α and CXCR3 were detected, both of which are known to promote pyroptosis [5, 6].

(4) Mitochondrial Damage: We have added new data demonstrating disrupted colocalization of TOM20 (a mitochondrial structural marker) and Cytochrome c (a cell death marker), resulting in Cytochrome c leakage into the perinuclear space (Revised Figure 5B). Additionally, qPCR analysis revealed reduced expression of mitochondrial structural and functional markers in cKO oviduct tissues (Revised Figure 5C).

Based on these evidences, we can clearly say that Atg14 has some direct or indirect link to inflammasome activation. However, understanding the complex rheostat between the Atg14-mediated autophagy and inflammation regulatory axis will necessitate future studies employing sophisticated models, such as combined knockout mice where ATG14 is deleted alongside key inflammatory regulators (e.g., NLRP3, GSDMD, or CASPASE-1). These dual knockout models could provide crucial insights into how ATG14 modulates inflammatory pathways.

References:

(1) Herrera, G.G.B., et al., Oviductal Retention of Embryos in Female Mice Lacking Estrogen Receptor alpha in the Isthmus and the Uterus. Endocrinology, 2020. 161(2).

(2) Soyal, S.M., et al., Cre-mediated recombination in cell lineages that express the progesterone receptor. Genesis, 2005. 41(2): p. 58-66.

(3) Zhang, Y., et al., A transgenic FOXJ1-Cre system for gene inactivation in ciliated epithelial cells. Am J Respir Cell Mol Biol, 2007. 36(5): p. 515-9.

(4) Mizushima, N., T. Yoshimori, and B. Levine, Methods in mammalian autophagy research. Cell, 2010. 140(3): p. 313-26.

(5) Vaher, H., Expanding the knowledge of tumour necrosis factor-alpha-induced gasdermin E-mediated pyroptosis in psoriasis. Br J Dermatol, 2024. 191(3): p. 319-320.

(6) Liu, C., et al., CXCR4-BTK axis mediate pyroptosis and lipid peroxidation in early brain injury after subarachnoid hemorrhage via NLRP3 inflammasome and NF-kappaB pathway. Redox Biol, 2023. 68: p. 102960.

eLife Assessment

This important study offers insights into the function and connectivity patterns of a relatively unknown afferent input from the endopiriform to the CA1 subfield of the ventral hippocampus, suggesting a neural mechanism that suppresses the processing of familiar stimuli in favor of detecting memory guided novelty. The strength of evidence is solid, with careful anatomical and electrophysiological circuit characterization. The work will be of broad interest to researchers studying the neural circuitry of behavior.

Reviewer #1 (Public review):

Summary:

The anatomical connectivity of the claustrum and the role of its output projections has, thus far, not been studied in detail. The aim of this study was to map the outputs of the endopiriform (EN) region of the claustrum complex, and understand their functional role. Here the authors have combined sophisticated intersectional viral tracing techniques, and ex vivo electrophysiology to map the neural circuitry of EN outputs to vCA1, and shown that optogenetic inhibition of the EN→vCA1 projection impairs both social and object recognition memory. Interestingly the authors find that the EN neurons target inhibitory interneurons providing a mechanism for feedforward inhibition of vCA1.

Strengths:

The strength of this study was the application of a multilevel analysis approach combining a number of state-of-the-art techniques to dissect the contribution of the EN→vCA1 to memory function.

In addition the authors conducted behavioural analysis of locomotor activity, anxiety and fear memory, and complemented the analysis of discrimination with more detailed description of the patterns of exploratory behaviour.

Reviewer #2 (Public review):

Summary:

Yamawaki et al., conducted a series of neuroanatomical tracing and whole cell recording experiments to elucidate and characterise a relatively unknown pathway between the endopiriform (EN) and CA1 of the ventral hippocampus (vCA1) and to assess its functional role in social and object recognition using fibre photometry and dual vector chemogenetics. The main findings were that the EN sends robust projections to the vCA1 that collateralise to the prefrontal cortex, lateral entorhinal cortex and piriform cortex, and these EN projection neurons terminate in the stratum lacunosum-moleculare (SLM) layer of distal vCA1, synapsing onto GABAergic neurons that span across the Pyramidal-Stratum Radiatum (SR) and SR-SML borders. It was also demonstrated that EN input disynaptically inhibits vCA1 pyramidal neurons. vCA1 projecting EN neurons receive afferent input from piriform cortex, and from within EN. Finally, fibre photometry experiments revealed that vCA1 projecting EN neurons are most active when mice explore novel objects or conspecifics, and pathway-specific chemogenetic inhibition led to an impairment in the ability to discriminate between novel vs. familiar objects and conspecifics.

Revision 1:<br /> The authors have addressed most of my concerns, but a few weaknesses remain :

(1) I expected to see the addition of raw interaction times with objects and conspecifics for each phase of social testing (pre-test, sociability test, social discrimination), as per my comment on including raw data. However, the authors only provided total distance traveled and velocity, and total interaction time in Figure S9, which is less informative.

(2) The authors observed increased activity in vCA1-projecting EN neurons tracking with the preferred object during the pre-test (object-object exploration) phase of the social tests, and the summary schematic (Figure 9A) depicts animals as showing a preference for one object over the other (although they are identical) in both the social and object recognition tests. However, in the chemogenetic experiment, the data (Fig S9B) indicate that animals did not show this preference for one object over another, making the expected baseline for this task unclear. This also raises an important question of whether the lack of effect from chemogenetic inhibition of vCA1-projecting EN neurons could be attributed to the absence of this baseline preference.<br /> Additionally, the finding that vCA1-projecting EN activity is associated with the preferred object exploration appears to counter the authors' argument that novelty engages this circuit (since both objects are novel in this instance). This discrepancy warrants further discussion.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The anatomical connectivity of the claustrum and the role of its output projections has, thus far, not been studied in detail. The aim of this study was to map the outputs of the endopiriform (EN) region of the claustrum complex, and understand their functional role. Here the authors have combined sophisticated intersectional viral tracing techniques, and ex vivo electrophysiology to map the neural circuitry of EN outputs to vCA1, and shown that optogenetic inhibition of the EN→vCA1 projection impairs both social and object recognition memory. Interestingly the authors find that the EN neurons target inhibitory interneurons providing a mechanism for feedforward inhibition of vCA1.

Strengths:

The strength of this study was the application of a multilevel analysis approach combining a number of state-of-the-art techniques to dissect the contribution of the EN→vCA1 to memory function.

Weaknesses:

Some authors would disagree that the vCA1 represents a 'node for recognition of familiarity' especially for object recognition although that is not to say that it might play some role in discrimination, as shown by the authors. I note however that the references provided in the Introduction, concerning the role of vCA1 in memory refer to anxiety, social memory, temporal order memory, and not novel object recognition memory. Given the additional projections to the piriform cortex shown in the results, I wonder to what extent the observations may be explained by odour recognition effects.

We have added references demonstrating that the ventral hippocampus contributes to object recognition memory in rodents (Broadbent NJ et al., Learn Mem 2010; Titulaer J et al., Front Behav Neurosci 2021).

The odor recognition effect is an interesting perspective that we have also considered. However, in our object recognition test, the same odor (70% EtOH) was used for both objects, yet the mice were able to discriminate between the familiar and novel objects. This suggests that the likelihood of the odor cue contributing to their performance in object discrimination test is low.

In addition, I wondered whether the impairments in discrimination following Chemogenetic inhibition of the EN→vCA1 were due to the subject treating the novel and familiar stimuli as either both novel- which might be observed as an increase in exploration, or both stimuli as familiar, with a decrease in overall exploration.

We thank the reviewer for rising this interesting point. We analyzed the total exploration time (i.e., time in interaction zones in familiar and novel) during social discrimination test. The data is added to Fig. S9. Total exploration time was not affected by CNO treatment. This indicates inhibition of ENvCA1-proj. neurons reduced interaction time with the novel conspecific and increased interaction time with the familiar conspecific. The subject mice seem to give even weight on familiar and novel stimuli.

Reviewer #2 (Public Review):

Summary:

Yamawaki et al., conducted a series of neuroanatomical tracing and whole-cell recording experiments to elucidate and characterise a relatively unknown pathway between the endopiriform (EN) and CA1 of the ventral hippocampus (vCA1) and to assess its functional role in social and object recognition using fibre photometry and dual vector chemogenetics. The main findings were that the EN sends robust projections to the vCA1 that colateralise to the prefrontal cortex, lateral entorhinal cortex, and piriform cortex, and these EN projection neurons terminate in the stratum lacunosum-moleculare (SLM) layer of distal vCA1, synapsing onto GABAergic neurons that span across the Pyramidal-Stratum Radiatum (SR) and SR-SML borders. It was also demonstrated that EN input disynaptically inhibits vCA1 pyramidal neurons. vCA1 projecting EN neurons receive afferent input from the piriform cortex, and from within EN. Finally, fibre photometry experiments revealed that vCA1 projecting EN neurons are most active when mice explore novel objects or conspecifics, and pathway-specific chemogenetic inhibition led to an impairment in the ability to discriminate between novel vs. familiar objects and conspecifics.

This is an interesting mechanistic study that provides valuable insights into the function and connectivity patterns of afferent input from the endopiriform to the CA1 subfield of the ventral hippocampus. The authors propose that the EN input to the vCA1 interneurons provides a feedforward inhibition mechanism by which novelty detection could be promoted. The experiments appear to be carefully conducted, and the methodological approaches used are sound. The conclusions of the paper are supported by the data presented on the whole.

We thank the reviewer for their positive comments on our work.

The authors used dual retrograde tracing and observed that the highest percentage (~30%) of vCA1 projecting EN cells also projected to the PFC. They then employed an intersectional approach to show the presence of collaterals in other cortical areas such as the entorhinal cortex and piriform cortex in addition to the PFC. However, they state that 'Projection to prefrontal cortex was sparse relative to other areas, as expected based on the retrograde labeling data' (referring to Figure 2K) and subsequently appear to dismiss the initial data set indicating strong axonal projections to the PFC.

Our interpretation is that 70% of the ENCA1-proj. population does not send collaterals to the PFC, suggesting that the PFC is not a major target for this population (unlike vCA1 where 100% of its population projects). This hypothesis is supported by our axon branching study, which showed lower axon density in the PFC compared to vCA1 (and other regions). We revised the text to 'much sparser relative to that of vCA1' (line 101) to facilitate a direct comparison with the retrograde and anterograde labeling study.

Since this is a relatively unknown connection, it would be helpful if some evidence/discussion is provided for whether the EN projects to other subfields (CA3, DG) of the ventral hippocampus. This is important, as the retrograde tracer injections depicted in Figure 1B clearly show a spread of the tracer to vCA3 and potentially vDG and it is not possible to ascertain the regional specificity of the pathway.

We addressed the potential caveat associated with the retrograde tracer injection, as mentioned by the reviewer, by performing intersectional axon branching analysis. This analysis demonstrated that EN axons are primarily located in the SLM of the distal CA1 subfield (Figs. 2, 3, S2). However, we occasionally observed very weak labeling in the CA3 or dentate gyrus. We modified our text (lines 106-108) and figure (Fig. S2D) to account for this.

The vCA1 projecting EN cells appear to originate from an extensive range along the AP axis. Is there a topographical organization of these neurons within the vCA1? A detailed mapping of this kind would be valuable.

This is an interesting question for future research. Our data show a non-uniform distribution of this cell type, suggesting the potential for topographic organization.

Given this extensive range in the location of vCA1 EN originating cells, how were the targets (along the AP axis) in EP selected for the calcium imaging?

Using our injection coordinates, ENvCA1-proj. neurons were consistently labeled at high density just posterior to the bregma (Fig. 1J). Therefore, we targeted this region for our imaging.

The vCA1 has extensive reciprocal connections with the piriform cortex as well, which is in close proximity to the EN. How certain are the authors that the chemogenetic targeting was specific to the EN-vCA1 connection?

We performed histology on every animal used in the behavioral study to examine the specificity of hM4D expression, and only included those with specific labeling in the EN.

Raw data for the sociability and discrimination indices should be provided so that the readers can gain further insight into the nature of the impairment.

The raw data for total interaction time during the social discrimination test has been added (Fig. S9F).

Line 222: It is unclear how locomotor activity informs anxiety in the behavioral tests.

The degree of exploratory behavior in a novel context is generally considered to infer anxiety levels in rodents. We have added a review paper (Ref 44, Prut, 2003) that discusses this point.

Figure 7 title; It is stated that activity of EN neurons 'predict' social/object discrimination performance. However, caution must be exercised with this interpretation as the correlational data are underpowered (n=5-8). Furthermore, the results show a significant correlation between calcium event ratios and the discrimination index in the social discrimination test but not the object discrimination test.

We added the sample size for EN calcium imaging during the object recognition memory test (Fig. 7G). The updated data indicate a significant correlation between EN activity and the object recognition index (N = 9, Pearson R = 0.8, p = 0.01).

We have changed the title of Figure 7 to 'Activity of ENvCA1-proj. neurons correlates with social/object discrimination performance’.

While both male and female mice were included in the anatomical tracing and recording experiments, only male mice were used for behavioral tests.

The female behavior was highly inconsistent in the control condition of our social recognition memory paradigm; therefore, we decided to conduct the study with males. We will design a new behavioral paradigm for future studies to address this challenge.

Reviewer #1 (Recommendations For The Authors):

(1) It is not clear how the relative number of vCA1 projecting neurons in Figure 1H was acquired, not enough detail is presented in the methods section. To what extent could these data have been affected by differences in the size or anatomical position of the injection site in vCA1, which judging from the example fluorescent image in Figure 1B also appears to include CA3.

We used AMaSiNe (Song et al. 2020) to semi-automatically quantify fluorescently labeled presynaptic neurons. This open-source software identifies the number and location of these cells across different regions based on the Allen Mouse Brain Common Framework. To control for transfection variability (e.g., due to slight differences in injection volume or site), we normalized the presynaptic cell count in each region by the total number of cells in regions of interest. We performed for N = 5 brain and found consistent trend as seen in Fig. 1H (grey lines).

We have added the detailed method of quantification in the Materials and Methods section (line 393).

(2) For a number of the results, the full statistical values are not presented in the Results section or figure legend.

We have included the full statistical values in the figure legends of the revised manuscript.

(3) It is not clear how much virus was injected in the different experiments (tract racing, electrophysiology, behaviour, etc.). The methods state 50-100ul, but there is no further detail in the results or figure legends.

We have included the injected volumes of the virus in the revised manuscript.

(4) Figure 2 mentions the CLA complex (line 702) but this is not defined in the text. Although the introduction does refer to the claustrum complex, there is no acronym.

We have corrected the manuscript accordingly.

(5) Line 131- 'we recorded from 3-4 GABAergic neurons' - presumably this is in each animal?

We recorded 3 to 4 GABAergic neurons sequentially from the same slice to compare input strength. We have edited the text to clarify this (line 134).

Reviewer #2 (Recommendations For The Authors):

Figure 3C: It is not clear what the dashed lines labelled proximal and distal represent.

It is the proximal and distal vCA1 regions where GFP signals were measured for Fig. 3D. We have modified the figure legend to clarify this (line 736).

Figure 5D: what do the different colors represent? Different colors for one brain?

I assume that the reviewer meant to refer to Fig. 4D instead of Fig. 5D. In Fig. 4D, one color indicates starter cells in one brain. To clarify this, we have edited the figure legend (line 748).

Figure S6E: The images are low resolution and it is hard to decipher the exact locations of labeled neurons. Please provide more guidance (e/g/. labeling areas of interest).

We have added reference lines and labels in Figure S6E.

Some details are missing: what was the volume of AAV injected for each site/experiment; how was CNO made, and where was it purchased from?

We have added this information (lines 330-331; 431-434).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This work presents a replicable difference in predictive processing between subjects with and without tinnitus. In two independent MEG studies and using a passive listening paradigm, the authors identify an enhanced prediction score in tinnitus subjects compared to control subjects. In the second study, individuals with and without tinnitus were carefully matched for hearing levels (next to age and sex), increasing the probability that the identified differences could truly be attributed to the presence of tinnitus. Results from the first study could successfully be replicated in the second, although the effect size was notably smaller.

Throughout the manuscript, the authors provide a thoughtful interpretation of their key findings and offer several interesting directions for future studies. Their conclusions are fully supported by their findings. Moreover, the authors are sufficiently aware of the inherent limitations of cross-sectional studies.

Strengths:

The robustness of the identified differences in prediction scores between individuals with and without tinnitus is remarkable, especially as successful replication studies are rare in the tinnitus field. Moreover, the authors provide several plausible explanations for the decline of the effect size observed in the second study.

The rigorous matching for hearing loss, in addition to age and sex, in the second study is an important strength. This ensures that the identified differences cannot be attributed to differences in hearing levels between the groups.

The used methodology is explained clearly and in detail, ensuring that the used paradigms may be employed by other researchers in future studies. Moreover, the registering of the data collection and analysis methods for Study 2 as a Registered Report should be commended, as the authors have clearly adhered to the methods as registered.

Weaknesses:

Although the authors have been careful to match their experimental groups for age, sex, and hearing loss, there are other factors that may confound the current results. For example, subjects with tinnitus might present with psychological comorbidities such as anxiety and depression. The authors' exclusion of distress as a candidate for explaining the found effects is based solely on an assessment of tinnitus-related distress, while it is currently not possible to exclude the effects of elevated anxiety or depression levels on the results. Additionally, as the authors address in the discussion, the presence of hyperacusis may also play a role in predictive processing in this population.

The authors write that sound intensity was individually determined by presenting a short audio sequence to the participants and adjusting the loudness according to an individual pleasant volume. Neural measurements made during listening paradigms might be influenced by sound intensity levels. The intensity levels chosen by the participants might therefore also have an effect on the outcomes. The authors currently do not provide information on the sound intensity levels in the experimental groups, making it impossible to assess whether sound intensity levels might have played a role.

Thank you very much for your favorable and constructive evaluation of our manuscript. We agree with you on various additional confounds that we did not consider and included a section in our discussion. It is also correct that we did not include the sound intensity levels in our analysis, which is also a potential confound. Unfortunately, we do not have the data on the individual sound intensity levels but we included a section regarding this issue in our discussion as well.

Line 937-949:

“In both studies, tinnitus distress was not correlated with the reported prediction effects. Nevertheless, tinnitus can also be characterized by other features such as its loudness, pitch or duration which were not included in the experimental assessment. Additionally, we solely used a short version of the Mini-TQ (Goebel and Hiller, 1992) in Study 2, which did not allow us to relate prediction scores to subscales like sleep disturbances which potentially influence cognitive functioning and thus predictive processing. Next to sleeping disorders and distress, tinnitus is often also accompanied by psychological comorbidities such as depression or anxiety (Langguth, 2011) which are potential confounds of the results. For the work described in this manuscript the replicability of the core finding was of main importance. More studies are needed taking into account to assess relate the prediction patterns in more detail to aspects of tinnitus sensation and distress.”

Reviewer #2 (Public Review):  

Summary:  

This study aimed to test experimentally a theoretical framework that aims to explain the perception of tinnitus, i.e., the perception of a phantom sound in the absence of external stimuli, through differences in auditory predictive coding patterns. To this aim, the researchers compared the neural activity preceding and following the perception of a sound using MEG in two different studies. The sounds could be highly predictable or random, depending on the experimental condition. They revealed that individuals with tinnitus and controls had different anticipatory predictions. This finding is a major step in characterizing the top-down mechanisms underlying sound perception in individuals with tinnitus.

Strengths:  

This article uses an elegant, well-constructed paradigm to assess the neural dynamics underlying auditory prediction. The findings presented in the first experiment were partially replicated in the second experiment, which included 80 participants. This large number of participants for an MEG study ensures very good statistical power and a strong level of evidence. The authors used advanced analysis techniques - Multivariate Pattern Analysis (MVPA) and classifier weights projection - to determine the neural patterns underlying the anticipation and perception of a sound for individuals with or without tinnitus. The authors evidenced different auditory prediction patterns associated with tinnitus. Overall, the conclusions of this paper are well supported, and the limitations of the study are clearly addressed and discussed.  

Weaknesses:  

Even though the authors took care of matching the participants in age and sex, the control could be more precise. Tinnitus is associated with various comorbidities, such as hearing loss, anxiety, depression, or sleep disorders. The authors assessed individuals' hearing thresholds with a pure tone audiogram, but they did not take into account the high frequencies (6 kHz to 16 kHz) in the patient/control matching. Moreover, other hearing dysfunctions, such as speech-in-noise deficits or hyperacusis, could have been taken into account to reinforce their claim that the observed predictive pattern was not linked to hearing deficits. Mental health and sleep disorders could also have been considered more precisely, as they were accounted for only indirectly with the score of the 10-item mini-TQ questionnaire evaluating tinnitus distress. Lastly, testing the links between the individuals' scores in auditory prediction and tinnitus characteristics, such as pitch, loudness, duration, and occurrence (how often it is perceived during the day), would have been highly informative.

Thank you very much for your careful and constructive evaluation. We agree with the weaknesses stated in our manuscript and aimed to highlight these aspects more in our analyses and discussion, so future studies can take them into account (see e.g., line 937949). 

Recommendations for the authors:  

Reviewer #1 (Recommendations For The Authors):

I would strongly recommend the inclusion of data on the used sound intensity levels. It would be very useful to assess whether there are any group differences regarding sound intensity of the stimuli, to exclude any effects of sound intensity on the results.

We agree with you that - next to experimental aspects like the stimulus frequencies and the number of trials - the sound intensity levels potentially influence the effects as well. Unfortunately, this data was not saved during the experimental procedure and we are not able to include this as a variable in our analyses. As we, however, acknowledge this issue and want to provide guidelines for future research, we added a section to our discussion targeting sound intensity levels. 

Line 902-913:

“Thirdly, both studies used individual sound intensity levels to ensure a comfortable listening situation for the participants. These differences in sound intensity levels are, however, a potential confound in the experimental design as well since sound intensity can have an impact on neural responses (Thaerig et al., 2008). Although in this design, we expect the intensity levels balanced equally to the hearing loss of the participants (which did not differ between groups), and basic decoding of sound frequency did not differ in both studies, we are not able to ultimately exclude the sound intensity level as a driver of our effects. Future studies should include a perceived loudness matching for each frequency and should compare the adapted sound intensity values between each group or integrate them into the analysis (e.g., using the logistic regression approach in Fig. 8).”

Reviewer #2 (Recommendations For The Authors):

Major comments

Introduction

• The authors wrote: "Overall, this situation calls for the pursuit of alternative or complementary models that place less emphasis on the hearing status of the individual." They clearly demonstrated that the altered-gain model focuses on hearing loss and does not overcome the three described limitations. However, they mentioned other models focusing on brain activity outside of the auditive pathway (noise cancellation, map reorganization, specific neural networks. The authors should better explain the novelty of their approach compared to the existing ones.

Thank you for your input. The inconclusive results and open questions about the altered-gain framework let us search for a different theoretical foundation for this work. We agree with you, that there are other models such as the map reorganization theory or neural network models next to the altered gain model and recent literature showed results supporting these frameworks (see e.g., a review from our group discussing tinnitus research in MEG over the last 10 years, Reisinger et al. (2023)). Nevertheless, as we focus on prediction processes, the Bayesian inference framework in tinnitus (Sedley et al., 2016) fits best for our approach. As we stated in line 113-116 “The Bayesian inference framework could, therefore, explain the experience of tinnitus in lieu of any increase in neural activity in the auditory system, or indicate an additional alteration, on top of hearing loss, for tinnitus to be perceived”, this framework differs from the other models and demonstrate a novel approach in tinnitus research. The novelty in this work is our methodological approach, which allows for explicit analyses of predictive patterns, irrespective of the exact location in the brain. This is a first step towards our actual underlying question whether aberrant auditory prediction patterns act as a neural correlate of tinnitus or rather as a risk factor or disposition. In our opinion, this question is of crucial relevance for understanding tinnitus processes on a neural level and our robust effects highlight the necessity to investigate these predictive processes in a longitudinal manner. We included a paragraph in our manuscript to make this more apparent for the reader. 

Line 128-137:

“We utilized a powerful, recently established experimental approach (Demarchi et al., 2019) showing anticipatory activations of tonotopically specific auditory templates for regular tone sequences. This method allows us to explicitly investigate predictive patterns in line with the Bayesian inference framework (Sedley et al., 2016), leading towards the overall question whether alterations in predictive coding can be interpreted as a neural correlate of tinnitus or rather as a risk factor. Since this question can solely be targeted in a longitudinal manner, we aimed in a first step to investigate prediction patterns in tinnitus over two independent samples, deriving robust effects that should be considered in future research.”

• "This conceptual model bridges several explanatory gaps: for example, the inconsistent findings in humans regarding the "altered gain" view which states enhanced neural activity in the auditory pathway". What are "the inconsistent findings in humans regarding the 'altered gain'"? It would be helpful if the authors were more explicit about their idea here and added reference(s) to support it.

Thank you for pointing that out. We agree with you that this section lacks clarity and we aimed to be more precise. 

Line 108-116:

“This conceptual model bridges several explanatory gaps: for example, the inconsistent findings in humans regarding the “altered gain” view which states altered neural activity in the auditory pathway. Recent findings vary in both the targeted frequency bands and the direction of the reported power changes which impede consistent conclusions (Eggermont and Roberts, 2015; Elgohyen et al., 2015, Reisinger et al., 2023). The Bayesian inference framework could, therefore, explain the experience of tinnitus in lieu of any increase in neural activity in the auditory system, or indicate an additional alteration, on top of hearing loss, for tinnitus to be perceived.”

• I suggest moving this part to the discussion:

"However, alternative explanations cannot be excluded with certainty, such as tinnitus being the cause of altered prediction tendencies or that there is a third variable being responsible for predictions and tinnitus development. Furthermore, even if altered predictive tendencies were to be found, there could be various possibilities of exactly how they could be altered to contribute to the onset or persistence of tinnitus. Some further clarity might then be gained through longitudinal studies in humans or animals."

Thank you for your suggestion, we moved this part to the corresponding section in the discussion.

Line 742-756:

“Distinct predictive processing patterns could e.g., either develop within an individual in contributing to chronification of tinnitus (e.g., shift of “default prediction” from silence to sound; Sedley, 2019). Alternatively, they could be conceived as sensory processing style, making certain individuals more vulnerable to develop tinnitus under certain conditions (e.g., hearing loss, aging), a notion reminiscent of the “strong prior” hypothesis of hallucinations (Corlett et al., 2019). Hence, the direction of the effect remains unclear and alternative explanations, such as a third variable being responsible for predictions and tinnitus development, cannot be excluded with certainty. Furthermore, even if altered predictive tendencies were to be found, there could be various possibilities of exactly how they could be altered to contribute to the onset or persistence of tinnitus. In any case, any more conclusive claims would require longitudinal data, ideally with a tinnitus-free baseline. As such research is challenging to implement, especially in humans, we first focused in this work on finding cross-sectional group differences between individuals with and without tinnitus.”

Methods

Participants

• "We calculated the individual mean hearing ability based on the values for 500, 1000, 2000, and 4000 Hz, which is a common approach for averaging results of pure-tone audiometry". Even if this method has been used multiple times in the literature, I would not recommend it as it can hide differences. Hearing loss is usually larger at high frequencies (starting at 6 000 Hz). An average threshold calculated with those central frequencies is more relevant for clinical use than in research. I strongly recommend performing a linear model with the factors Frequency (including all tested frequencies), Group, Ear side, and their interactions to precisely test the group differences in hearing thresholds.

Thank you for pointing that out. We agree with you that higher frequencies are of potential interest as well when analyzing hearing loss. We included your suggested linear model in our methods section and the results were in line with our assumption that the groups did not differ substantially. Additionally, we included another logistic regression model in our exploratory analyses when investigating the influence of hearing loss on the prediction scores. Once more, the addition of higher frequencies did not substantially influence the effects.

Line 194-203:

“We calculated the individual mean hearing ability based on the values for 500, 1000, 2000, and 4000 Hz, which is a common approach for averaging results of pure-tone audiometry (i.e., PTA-4, see for example Lin et al. (2011); Ozdek et al. (2010)). Using independent t-tests, we found no differences in hearing status over frequencies between groups for the left(t=-1.19, p=.238) and right ear (t=-1.72, p=.09). An additional linear regression including all frequencies from 125 Hz to 8000 Hz also showed that hearing thresholds did not differ between ears (b=0.311, SE=1.600, p=.846) and groups (b=1.702, SE=1.553, p=.273), but solely between frequencies (b=0.003, SE=0.000, p<.001). Interactions were not significant as well.”

Line 712-725:

“As these logistic regression models were computed using an average hearing score computed over the frequencies 500, 1000, 2000, and 4000 Hz (i.e., PTA-4, see for example Lin et al. (2011); Ozdek et al. (2010)), we questioned whether hearing loss in higher frequencies influenced our effects. We therefore computed an additional logistic regression including also the PTA values of 6000 and 8000 Hz. In this analysis, hearing loss was not a significant predictor of tinnitus but rather showed a trend with b\=0.211, SE\=0.111, p\=.062. Prediction scores, however, remained a significant predictor of tinnitus even after including high-frequency hearing loss (b\=0.232, SE\=0.111, p\=.040). In this analysis, odds ratios indicated an increase of 26% in the odds of having tinnitus with a one standard deviation increase in the prediction score. Overall, this analysis strongly supports the notion that the main effect genuinely reflects a process related to the experience or statistical risk of experiencing tinnitus.”

Stimuli and experimental procedure

• Can you explain the use of movies during sound listening? And not an active listening task with oddball events, for example, to ensure that the subject attention is directed to the sounds?

Thank you for your comment. We agree with you that attention is a relevant factor and with our design we cannot exclude potential attention effects on our findings. We chose this paradigm since previous research in our group including this exact experimental design (Demarchi et al., 2019) impressively demonstrated the formation of feature-specific auditory predictions in the brain and we aimed to investigate to what extent this can be detected in the tinnitus brain.

We acknowledged this issue in our discussion (see line 916-919): “In the current work, we used passive listening tasks including a movie to reduce attentional focus on the presented stimuli. Therefore, we cannot draw conclusions whether differences in attention had an influence on the effects. Future studies should include more manipulations of attention to investigate its relevance”. 

Results

Pre-stimulus effects are not related to hearing loss and tinnitus-related features

• How was the hearing loss calculated for this analysis? I recommend a PCA on the hearing levels, to get individual scores with a data-driven approach. Usually, the first dimension will be an average of all the frequencies. The second should be a difference between low and high frequencies. The same comment applies to study 2.

Thank you for pointing that out. In the first study, participant groups were not controlled for hearing loss and pure-tone audiograms were solely averaged over all frequencies and both ears. As we marked out throughout the manuscript, insufficient control for hearing loss was the key issue in study 1 which led to the implementation of study 2. Further, we do not have data about the hearing status of every participant in study 1 and we do therefore not believe that a more complex approach for calculating hearing loss will increase interpretability in study 1. Nevertheless, we agree with you that it is not apparent how hearing loss was calculated in study 1. The results of the pure-tone audiometry were averaged over all frequencies and both ears, but no cut-off values were defined to characterize hearing loss. We therefore highly appreciate your detailed revision of our manuscript and adjusted the phrasing in the corresponding section. With our approach, it is not justifiable to talk about hearing loss but rather hearing thresholds. As for study 2, the methodological approach was reviewed and accepted as a Registered Report and we therefore do not want to deviate drastically from our pre-registered approach.

Line 162-165:

“Standardized pure-tone audiometric testing for frequencies from 125Hz to 8kHz was performed in 31 out of 34 tinnitus participants using Interacoustic AS608 audiometer.

Averages were computed over all frequencies and both ears.”

Line 356-362:

“In the whole sample of participants with tinnitus (n=34) we performed a Spearman correlation of the β-coefficient values corresponding to the time-point of the maximum and the minimum t-value in intergroup analysis (comprised of positive and negative significant clusters emerging in group comparison for sound trials) with hearing thresholds (averaged audiogram for both ears), tinnitus loudness (10-point scale) and tinnitus distress scores (TQ).”

Line 463-464:

See as well Line 471-481.

Line 491-495:

“Our main findings are: 1) basic processing of carrier frequencies are not altered in tinnitus; 2) with increasing regularity of the sequence, individuals with tinnitus show relatively enhanced predictions of frequency information; 3) the effect is not related to hearing thresholds and tinnitus distress or loudness in this sample.”

• In the methods, the authors indicated that the volume was adjusted individually at a pleasant volume. Can authors test if the volume was related to the individual's accuracy? Did they test that all frequencies were audible for all participants?

Thank you for your feedback. We agree with you that it would be interesting to see whether sound intensity levels were related to the accuracy. Unfortunately, data regarding the volume was not saved during the experimental procedure and we are not able to include this as a variable in our analyses. We acknowledge this issue and added a section to our discussion targeting sound intensity levels. As for the second question, the individual volume adjustment was also meant to guarantee that all frequencies were audible for the participant. We clarified this in the methods section. Overall, it is important to mention that we did not find any differences between groups in the decoding of random tones (see Fig. 2 and Fig. 6C), indicating that the volume did not substantially have an influence on one group compared to the other.

Line 232-234:

“Sound intensity was individually determined by presenting a short audio sequence to the participants and adjusting the loudness according to an individual pleasant volume with all four frequencies audible for the participant.”

Line 902-913:

“Thirdly, both studies used individual sound intensity levels to ensure a comfortable listening situation for the participants. These differences in sound intensity levels are, however, a potential confound in the experimental design as well since sound intensity can have an impact on neural responses (Thaerig et al., 2008). Although in this design, we expect the intensity levels balanced equally to the hearing loss of the participants (which did not differ between groups), and basic decoding of sound frequency did not differ in both studies, we are not able to ultimately exclude the sound intensity level as a driver of our effects. Future studies should include a perceived loudness matching for each frequency and should compare the adapted sound intensity values between each group or integrate them into the analysis (e.g., using the logistic regression approach in Fig. 8).”

Pre-stimulus differences in ordered and random tone sequences are not related to tinnitus distress • Accuracy was not correlated with tinnitus distress. Could the authors test if the accuracy was related to other clinical data, such as tinnitus pitch, duration, and loudness? And at the subscales of the mini-TQ?

We appreciate your constructive feedback and agree with you that other tinnitus features such as pitch, duration, or loudness are also interesting in this regard. Unfortunately, these features were not assessed in study 2 and we are therefore not able to provide this information. Additionally, we solely used a short version of the Mini-TQ in this study and did not assess all subscales but rather used all available items for calculating tinnitus distress. This is a limitation of our study design and we included it in the discussion.

Line 937-949:

“In both studies, tinnitus distress was not correlated with the reported prediction effects. Nevertheless, tinnitus can also be characterized by other features such as its loudness, pitch or duration which were not included in the experimental assessment. Additionally, we solely used a short version of the Mini-TQ (Goebel and Hiller, 1992) in Study 2, which did not allow us to relate prediction scores to subscales like sleep disturbances which potentially influence cognitive functioning and thus predictive processing. [...] More studies are needed taking into account to assess relate the prediction patterns in more detail to aspects of tinnitus sensation and distress.”

The strength of group effects differs between the two studies

• This section should be in the discussion, not the results

Thank you for your valuable input. In this section, we show comparisons between the two studies and report Bayes factors over time for the differences in decoding accuracy (see Figure 7A). We introduce novel results and believe therefore that this section should remain in the results and is discussed later in the manuscript.  

Discussion

• Globally, the discussion is very long and a bit speculative. I recommend the authors shorten the discussion (especially the speculations), and delete the repetition.

Thank you very much for your constructive feedback. We aimed to shorten our discussion and delete repetitions to increase clarity and readability.

• The effect of hearing loss has been tested in this study, evaluated as the mean hearing threshold of 4 central frequencies. However, hearing abilities cannot be limited to a central audiogram. High frequencies, speech-in-noise abilities, or other hidden hearing loss can be impacted, even for individuals without hearing loss on 500Hz- 4000Hz. The conclusion on the prediction effect being independent of hearing loss should include this limitation.

Thank you for pointing that out. We added this limitation to the discussion.

Line 781-794:

“In a complementary analysis, we used our prediction score in addition to hearing loss magnitudes as predictors of tinnitus in a logistic regression. Prediction related pre-activation levels were informative whether participants perceived tinnitus, also when statistically controlling for hearing loss. However, it has to be mentioned that we calculated hearing loss based on the PTA results of the frequencies between 500 and 4000 Hz. This does not reflect hearing impairments like high frequency hearing loss or hidden hearing loss (i.e., hearing difficulties despite a normal audiogram, Liberman (2015)). As for hidden hearing loss, we were not able to draw conclusions regarding our effects since this concept of hearing damage is difficult to measure objectively, especially in humans. However, we included an additional logistic regression expanding the frequency range up to 8000 Hz and again, hearing loss did not substantially impact the prediction score as an informative tinnitus predictor.”

Line 712-723:

“As these logistic regression models were computed using an average hearing score computed over the frequencies 500, 1000, 2000, and 4000 Hz (i.e., PTA-4, see for example Lin et al. (2011); Ozdek et al. (2010)), we questioned whether hearing loss in higher frequencies influenced our effects. We therefore computed an additional logistic regression including also the PTA values of 6000 and 8000 Hz. In this analysis, hearing loss was not a significant predictor of tinnitus but rather showed a trend with b\=0.211, SE\=0.111, p\=.062. Prediction scores, however, remained a significant predictor of tinnitus even after including high-frequency hearing loss (b\=0.232, SE\=0.111, p\=.040). In this analysis, odds ratios indicated an increase of 26% in the odds of having tinnitus with a one standard deviation increase in the prediction score.”

• "An increased focus on hippocampal regions, e.g., in fMRI, patient, or animal studies, could be a worthwhile complement to our MEG work, given the outstanding relevance of medial temporal areas in the formation of associations in statistical learning paradigms (see e.g., Covington et al., (2018); Schapiro et al., (2016)).".

in the opinion of this reviewer, this claim is not well introduced and should be removed.

Thank you for pointing that out. In our opinion, an increased focus on hippocampal regions is an important consideration for future research and we decided to keep this part in the manuscript. However, we added a third reference highlighting the relevance of temporal areas in tinnitus to strengthen our claim. 

Line 866-868:

“... given the outstanding relevance of medial temporal areas in the formation of associations in statistical learning paradigms (see e.g., Covington et al., (2018); Paquette et al., (2017); Schapiro et al., (2016)).”

References:

Paquette, S., Fournier, P., Dupont, S., de Edelenyi, F. S., Galan, P., & Samson, S. (2017). Risk of tinnitus after medial temporal lobe surgery. JAMA neurology, 74(11), 1376-1377. https://doi.org/10.1001/jamaneurol.2017.2718.

• "Overall, our work clearly underlines the true presence of differences, in terms of predictive processing, between individuals with and without tinnitus. At the same time, distinct design choices impact the strength of the effects which is not only apparent in the present work but was also reported recently by Yukhnovich and colleagues (2024). Further to controlling for basic variables (age, sex, hearing loss), future studies using our paradigm and analysis approach should opt for a broad frequency spacing (>2 octaves) and ideally more than 2000 trials per carrier frequency in the random sequence. These recommendations are likely even more important for efforts of testing this paradigm using EEG, which normally comes with inferior data quality as compared to MEG."

This reviewer considers that the entire paragraph should be deleted, as the effects are already covered in the previous paragraph.

Thank you very much for your feedback, however, we believe that this paragraph acts as a brief and accurate summary for our guidelines to improve future research in this field. This section therefore remained in the manuscript.

Minor comments

Introduction

• "The onsets of tinnitus and hearing loss often do not occur at the same time ". This sentence should have a reference.

We appreciate your careful evaluation of our manuscript and included a reference to the sentence pointing out hearing loss as a precursor of tinnitus.

Line 95f.:

“2) The onsets of tinnitus and hearing loss often do not occur at the same time (Roberts et al., 2010).” 

Methods

Participants

• Participants' laterality needs to be mentioned.

Thank you for your input. We agree with you that laterality is an interesting aspect that should be taken into account. Unfortunately, however, we did not assess this in the current design. We mentioned the lack of this information in the methods section.

Line 158:

“Laterality of the participants was not assessed.”

176-177:

“No participants with psychiatric or neurological diseases were included in the sample. Laterality of the participants was not assessed.”

"Four individuals with tinnitus did not show any audiometric abnormality; four of the participants showed unilateral hearing impairments; 26 volunteers had high-frequency hearing loss; and six individuals were hearing impaired over most frequencies (i.e. hearing thresholds higher than 30 dB)."

This part is not precise enough. "Unilateral hearing impairment": is it on one or multiple frequencies? "26 volunteers had high-frequency hearing loss". What is considered as highfrequency here? The precision "(i.e. hearing thresholds higher than 30 dB)" can be dropped as it was defined in the sentence just before.

We appreciate your constructive feedback and added information to clarify the audiometric characteristics of our participants.

Line 186-190:

“Four individuals with tinnitus did not show any audiometric abnormality; four of the participants showed unilateral hearing impairments on at least one frequency; 26 volunteers had high-frequency hearing loss (i.e. hearing thresholds higher than 30 dB); and six individuals were hearing impaired over most frequencies (i.e. hearing thresholds higher than 30 dB).”

Results

• Figure 3C: are those group differences significant? It should be noted on the graphs.

• Figure 6D: I would suggest to remove this figure, as the correlation is not significant.

• Figure 7A: It would be useful to precise the number of trials for each study, in parenthesis.

• Figure 8 is unnecessary.

Thank you for your careful assessment of our figures. We agree with you that significance should be indicated in Figure 3C and that the precise number of trials is relevant information in Figure 7A. We corrected the figures accordingly. However, the Figures 6D and 8 remained in the manuscript since they were already part of our Registered Report and we do not want to remove graphical information that was reviewed and accepted already.

eLife Assessment

This important work presents two studies on predictive processes in subjects with and without tinnitus. The evidence supporting the authors' claims is compelling, as their second study serves as an independent replication of the first. Rigorous matching between study groups was performed, especially in the second study, increasing the probability that the identified differences in predictive processing can truly be attributed to the presence of tinnitus. This work will be of interest to researchers, especially neuroscientists, in the tinnitus field.

Reviewer #2 (Public review):

Summary:

This study aimed to test experimentally a theoretical framework that aims to explain the perception of tinnitus, i.e., the perception of a phantom sound in the absence of external stimuli, through differences in auditory predictive coding patterns. To this aim, the researchers compared the neural activity preceding and following the perception of a sound using MEG in two different studies. The sounds could be highly predictable or random, depending on the experimental condition. They revealed that individuals with tinnitus and controls had different anticipatory predictions. This finding is a major step in characterizing the top-down mechanisms underlying sound perception in individuals with tinnitus.

Strengths:

This article uses an elegant, well-constructed paradigm to assess the neural dynamics underlying auditory prediction. The findings presented in the first experiment were partially replicated in the second experiment, which included 80 participants. This large number of participants for an MEG study ensures very good statistical power and a strong level of evidence. The authors used advanced analysis techniques - Multivariate Pattern Analysis (MVPA) and classifier weights projection - to determine the neural patterns underlying the anticipation and perception of a sound for individuals with or without tinnitus. The authors evidenced different auditory prediction patterns associated with tinnitus. Overall, the conclusions of this paper are well supported, and the limitations of the study are clearly addressed and discussed.

Weaknesses:

Even though the authors took care of matching the participants in age and sex, the control could be more precise. Tinnitus is associated with various comorbidities, such as hearing loss, anxiety, depression, or sleep disorders. The authors assessed individuals' hearing thresholds with a pure tone audiogram, but they did not take into account the high frequencies (6 kHz to 16 kHz) in the patient/control matching. Moreover, other hearing dysfunctions, such as speech-in-noise deficits or hyperacusis, could have been taken into account to reinforce their claim that the observed predictive pattern was not linked to hearing deficits. Mental health and sleep disorders could also have been considered more precisely, as they were accounted for only indirectly with the score of the 10-item mini-TQ questionnaire evaluating tinnitus distress. Lastly, testing the links between the individuals' scores in auditory prediction and tinnitus characteristics, such as pitch, loudness, duration, and occurrence (how often it is perceived during the day), would have been highly informative.

Comments on revisions:

Thank you for your responses. There are a few remaining points that, if addressed, could further enhance the manuscript:

- While the manuscript acknowledges the limitation of not matching groups on hearing thresholds in Study 1, a deeper analysis of participants' hearing abilities and their impact on MEG results, similar to that conducted in Study 2, would be valuable. Specifically, including a linear model that considers all frequencies, group membership, and their interactions could highlight differences across groups. Additionally, examining the effect of high-frequency hearing loss on prediction scores, as performed in Study 2, would strengthen the analysis, particularly given the trend noted (line 719). Such an addition could make a significant contribution to the literature by exploring how hearing abilities may influence prediction patterns.

- The connection with the hippocampal regions (line 864) remains somewhat unclear. While the inclusion of the Paquette reference appropriately links temporal region activity with tinnitus, it does not fully support the statement: "An increased focus on hippocampal regions, e.g., in fMRI, patient, or animal studies, could be a worthwhile complement to our MEG work, given the outstanding relevance of medial temporal areas in the formation of associations in statistical learning paradigms"

- Authors should add a comparison of participants mini-TQ scores on both studies<br /> - Authors should add significant level on Fig 6.B as in Fig 3.C, and a n.s on Fig 6.D

eLife Assessment

This is a potentially important study on interpretation of protein coding genetic variation in CDKN2A. The presentation of the data has improved, revealing that the experimental design is flawed and concerns that the data that are not robust enough to support the major claim of supporting clinical variant interpretation for CDKN2A. This work, while incomplete, will serve as a resource for diagnostic labs as well as cancer geneticists.

Reviewer #1 (Public review):

Summary:

Kimura et al performed a saturation mutagenesis study of CDKN2A to assess functionality of all possible missense variants and compare them to previously identified pathogenic variants. They also compared their assay result with those from in silico predictors.

Strengths:

CDKN2A is an important gene that modulate cell cycle and apoptosis, therefore it is critical to accurately assess functionality of missense variants. Overall, the paper reads well and touches upon major discoveries in a logical manner.

Weaknesses:

The paper lacks proper details for experiments and basic data, leaving the results less convincing. Analyses are superficial and does not provide variant-level resolution. Many of which were addressed during the revision process.

Comments on revisions

The manuscript was improved during the revision process.

Reviewer #2 (Public review):

Summary:

This study describes a deep mutational scan across CDKN2A using suppression of cell proliferation in pancreatic adenocarcinoma cells as a readout for CDKN2A function. The results are also compared to in silico variant predictors currently utilized by the current diagnostic frameworks to gauge these predictors' performance. The authors also functionally classify CDKN2A somatic mutations in cancers across different tissues

Review:

The goal of this paper was to perform functional classification of missense mutations in CDKN2A in order to generate a resource to aid in clinical interpretation of CDKN2A genetic variants identified in clinical sequencing. In our initial review, we concluded that this paper was difficult to review because there was a lack of primary data and experimental detail. The authors have significantly improved the clarity, methodological detail and data exposition in this revision, facilitating a fuller scientific review. Based on the data provided we do not think the functional characterization of CDKN2A variants is robust or complete enough to meet the stated goal of aiding clinical variant interpretation. We think the underlying assay could be used for this purpose but different experimental design choices and more replication would be required for these data to be useful. Alternatively, the authors could also focus on novel CDKN2A variants as there seems to be potential gain of function mutations that are simply lumped into "neutral" that may have important biological implications.

Major concerns:

Low experimental concordance. The p-value scatter plot (Figure 2 Figure Supplement 3A) across 560 variants shows low collinearity indicating poor replicability. These data should be shown in log2fold changes, but even after model fitting with the gamma GLM still show low concordance which casts strong doubt on the function scores.<br /> The more detailed methods provided indicate that the growth suppression experiment is done in 156 pools with each pool consisting of the 20 variants corresponding to one of the 156 aa positions in CKDN2A. There are several serious problems with this design.

Batch effects in each of the pools preventing comparison across different residues. We think this is a serious design flaw and not standard for how these deep mutational scans are done. The standard would be to combine all 156 pools in a single experiment. Given the sequencing strategy of dividing up CDKN2A into 3 segments, the 156 pools could easily have been collapsed into 3 (1 to 53, 54 to 110, 111 to 156). This would significantly minimize variation in handling between variants at each residue and would be more manageable for performance of further replicates of the screen for reproducibility purposes. The huge variation in confluency time 16-40 days for each pool suggest that this batch effect is a strong source of variation in the experiment

Lack of experimental/biological replication: The functional assay was only performed once on all 156 CDKN2A residues and was repeated for only 28 out of 156 residues, with only ~80% concordance in functional classification between the first and second screens. This is not sufficiently robust for variant interpretation. Why was the experiment not performed more than once for most aa sites?

For the screen, the methods section states that PANC-1 cells were infected at MOI=1 while the standard is an MOI of 0.3-0.5 to minimize multiple variants integrating into a single cell. At an MOI =1 under a Poisson process which captures viral integration, ~25% of cells would have more than 1 lentiviral integrant. So in 25% of the cells the effect of a variant would be confounded by one or more other variants adding noise to the assay.

While the authors provide more explanation of the gamma GLM, we strongly advise that the heatmap and replicate correlations be shown with the log2 fold changes rather than the fit output of the p-values.

In this study, the authors only classify variants into the categories "neutral", "indeterminate", or "deleterious" but they do not address CDKN2A gain-of-function variants that may lead to decreased proliferation. For example, there is no discussion on variants at residue 104, whose proliferation values mostly consist of higher magnitude negative log2fold change values. These variants are defined as neutral but from the one replicate of the experiment performed, they appear to be potential gain-of-function variants.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Kimura et al performed a saturation mutagenesis study of CDKN2A to assess the functionality of all possible missense variants and compare them to previously identified pathogenic variants. They also compared their assay result with those from in silico predictors.

Strengths:

CDKN2A is an important gene that modulates cell cycle and apoptosis, therefore it is critical to accurately assess the functionality of missense variants. Overall, the paper reads well and touches upon major discoveries in a logical manner.

Weaknesses:

The paper lacks proper details for experiments and basic data, leaving the results less convincing. Analyses are superficial and do not provide variant-level resolution.

We thank the reviewer for their comments. We have updated the manuscript to include additional detail of experimental methods and variant level resolution of data and analyses. We have also conducted additional analyses to compare variant classifications using a gamma generalized linear model and log2 normalized fold change, establish the effect of low variant coverage on variant functional classifications, determine the performance of combining multiple in silico predictions, and determine the prevalence of functionally deleterious variants in gnomAD and functionally deleterious variants of uncertain significance in ClinVar compared all CDKN2A missense variants.

Reviewer #2 (Public Review):

This study describes a deep mutational scan across CDKN2A using suppression of cell proliferation in pancreatic adenocarcinoma cells as a readout for CDKN2A function. The results are also compared to in silico variant predictors currently utilized by the current diagnostic frameworks to gauge these predictors' performance. The authors also functionally classify CDKN2A somatic mutations in cancers across different tissues.

This study is a potentially important contribution to the field of cancer variant interpretation for CDKN2A, but is almost impossible to review because of the severe lack of details regarding the methods and incompleteness of the data provided with the paper. We do believe that the cell proliferation suppression assay is robust and works, but when it comes to the screening of the library of CDKN2A variants the lack of primary data and experimental detail prevents assessment of the scientific merit and experimental rigor.

We are grateful for the opportunity to clarify our experimental methods and to provide additional data in the revised manuscript. The manuscript has been updated to include, among other changes, additional information on assay design, analysis of variant representation in the library, inclusion of primary data with variant level resolution, and a comparison of variant classifications using a gamma generalized linear model and log2 normalized fold change.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major issues:

(1) Can the pathogenicity values of individual amino acid changes be opened to the public? It would serve as a valuable asset to the community.

Thank you for your suggestion. We are happy to provide this information. Individual variant data and functional classifications from the functional assay are given in Appendix 1-table 4.

(2) In the method section, it is not clear (at least to the reviewer) whether the protocol describing the construction of the CDKN2A missense library was provided.

Thank you for your comment. We have included additional information in the manuscript describing construction of the CDKN2A missense library.

“CDKN2A expression plasmid libraries

Codon-optimized CDKN2A cDNA using p16INK4A amino acid sequence (NP_000068.1), was designed (Appendix 1-table 12) and pLJM1 containing codon optimized CDKN2A (pLJM1-CDKN2A) generated by Twist Bioscience (South San Francisco, CA). 156 plasmid libraries were then synthesized by using pLJM1-CDKN2A, such that each library contained all possible 20 amino acids variants (19 missense and 1 synonymous) at a given position, generating 500 ng of each plasmid library (Twist Bioscience, South San Francisco, CA). The proportion of variant in each library was shown in Appendix 1-table 2. Variants with a representation of less than 1% in a plasmid library were individually generated using the Q5 Site-Directed Mutagenesis kit (New England Biolabs, Ipswich, MA; catalog no. E0552), and added to each library to a calculated proportion of 5%. Primers used for site-directed mutagenesis are given in Appendix 1-table 13. Each library was then amplified to generate at least 5 ug of plasmid DNA using QIAGEN Plasmid Midi Kit (QIAGEN, Germantown, MD; catalog no. 12143).”

(3) The paper lacks basic experimental results. The results cover almost all possible missense variants, but it would be clearer if actual coverage values used for calculating relative enrichment were shown. Are all variants well covered? Isn't there any spurious signal due to low coverage? How many times were the experiments performed? Also, how many cells were used, what was the expected MOI, and what proportion of harvested cells is thought to have a single variant? How can you distinguish the effect of a single variant from a multiple variants effect?

We thank the reviewer for their comment. We have provided additional information in the manuscript to address these issues. Briefly, in response to each issue:

(1) We have provided read count data for all variants, used to determine functional classifications based on either gamma generalized linear model or normalized fold change, in Appendix 1-table 4.

(2) To assess if low variant coverage resulted in spurious signals, we compared prevalence of functionally deleterious classifications among variants binned by coverage in the Day 9 cell pool. We did not identify any statistically significant differences based on variant coverage.

“We also determined whether underrepresentation in the cell pool at Day 9 affected variant functional classifications. Fifty-three of 2,964 missense variants (1.8%) were present in the cell pool at Day 9 of the first assay replicate (experiment 1) at < 2%, as determined by the number of sequence reads supporting the variant (Figure 2 -figure supplement 4A, Appendix 1-table 4). There was no statistically significant difference in the proportion of variants classified as functionally deleterious for variants present in less than 2% of the cell pool at Day 9 (12 of 53 variants; 22.6%), and variants present in more than 2% of the cell pool (496 of 2,911 variants; 17.0%) (P value = 0.28) (Figure 2 -figure supplement 4B). We also found no significant differences in the proportion of variants classified as functionally deleterious for variants present in more than 2% of the cell pool at Day 9 when variants were binned in 1% intervals (Figure 2 -figure supplement 4B).”

(3) The assay was repeated in duplicate for 28 CDKN2A residues. For the remaining 128 residues of CDKN2A, the assay was completed once. We found good agreement between variant classifications in assay repeats. We have added to the text as follows:

“To confirm the reproducibility of our variant classifications, 28 amino acid residues were assayed in duplicate, and variants classified using the gamma GLM. The majority of missense variants, 452 of 560 (80.7%), had the same functional classification in each of the two replicates (Figure 2 -figure supplement 3A and B, Appendix 1-table 4).”

We have also added discussion of this study limitation to the manuscript:

“We repeated our functional assay twice for 28 CDKN2A residues. For the remaining 128 residues of CDKN2A, the functional assay was completed once. While we found general agreement between functional classifications from each replicate for the 28 residues assayed in duplicate, additional repeats for each residue are necessary to determine variability in variant functional classifications.”

(4) We have added additional information about the number of cells used for transduction and MOI to the method section:

“Lentiviral transduction

PANC-1 cells were used for CDKN2A plasmid library and single variant CDKN2A expression plasmid transductions. PANC-1 cells previously transduced with pLJM1-CDKN2A (PANC-1CDKN2A) and selected with puromycin were used for CellTag library transductions. Briefly, 1 x 105 cells were cultured in media supplemented with 10 ug/ml polybrene and transduced with 4 x 107 transducing units per mL of lentivirus particles. Cells were then centrifuged at 1,200 x g for 1 hour. After 48 hours of culture at 37oC and 5% CO2, transduced cells were selected using 3 µg/ml puromycin (CDKN2A plasmid libraries and single variant CDKN2A expression plasmids) or 5 µg/ml blasticidin (CellTag plasmid library) for 7 days. Expected MOI was one. After selection, cells were trypsinized and 5 x 105 cells were seeded into T150 flasks. DNA was collected from remaining cells and this sample was named as (Day 9). T150 flasks were cultured until confluent and then DNA was collected. The time for cells to become confluent varied for each amino acid residue (Day 16 – 40, Appendix 1-table 5).”

(5) Our assay was not designed to distinguish multiple variant effects. However, we do not anticipate multiple transductions to significantly impact variant classifications in our assay. We found that our functional classifications were consistent with previously reported classifications:

“In general, our results were consistent with previously reported classifications. Of variants identified in patients with cancer and previously reported to be functionally deleterious in published literature and/or reported in ClinVar as pathogenic or likely pathogenic (benchmark pathogenic variants), 27 of 32 (84.4%) were functionally deleterious in our assay (Figure 2B, Figure 2 -figure supplement 1B and 1C, Appendix 1-table 4) (Chaffee et al., 2018; Chang et al., 2016; Horn et al., 2021; Hu et al., 2018; Kimura et al., 2022; McWilliams et al., 2018; Roberts et al., 2016; Zhen et al., 2015). Five benchmark pathogenic variants were characterized as indeterminate function, with log2 P values from -19.3 to -33.2. Of 156 synonymous variants and six missense variants previously reported to be functionally neutral in published literature and/or reported in ClinVar as benign or likely benign (benchmark benign variants), all were characterized as functionally neutral in our assay (Figure 2B, Figure 2 -figure supplement 1B and 1C, Appendix 1-table 4) (Kimura et al., 2022; McWilliams et al., 2018; Roberts et al., 2016). Of 31 VUSs previously reported to be functionally deleterious, 28 (90.3%) were functionally deleterious and 3 (9.7%) were of indeterminate function in our assay. Similarly, of 18 VUSs previously reported to be functionally neutral, 16 (88.9%) were functionally neutral and 2 (11.1%) were of indeterminate function in our assay, (Figure 2B, Figure 2 -figure supplement 1B and 1C, Appendix 1-table 4).”

(4) Comparison of functional classifications (shown in Figure 3) from this study and other in silico tools is superficial. The analysis is based on the presumption that their result is gold-standard, thereby calculating the sensitivity, accuracy, and PPV of individual predictors. But apparently, this won't be true, so it would be more reasonable to check the "correlation" of the study results and other predictors: e.g. which variants show consistent results between this study and other predictors? Are there any indicators of consistent vs inconsistent results? How does the consistency change by protein sequences or domains? Etc

Thank you for your comment. We have added additional analysis to our manuscript comparing our functional classifications with in silico variant effect predictions. Specifically, we have included analysis combining multiple predictors:

“We also tested the effect of combining multiple in silico predictors. 904 missense variants had in silico predictions from all 7 algorithms. The remaining 2,060 missense variants had in silico predictions from 5 algorithms. Of variants with in silico predictions from all 7 algorithms, 378 (41.8%) had predictions of deleterious or pathogenic effect from a majority of algorithms (≥ 4), and of these, 137 (36.2%) were functionally deleterious in our assay. Similarly, of 2,060 missense variants that had in silico predictions from 5 algorithms, 1107 (53.7%) had predictions of deleterious or pathogenic effect from a majority of algorithms (≥ 3), of which, 361 (32.6%) were functionally deleterious in our assay (Appendix 1-table 7).”

(5) Similarly, Figure 4 does not deliver much information, either. Rather than delivering a simple summary, it would be more informative if deeper analyses were conducted. e.g., do pathogenic variants show higher frequency among patients, or higher variant frequency in tumors (if data were available).

We have included additional analysis of somatic alterations in the manuscript. We found pathogenic/likely pathogenic somatic mutations were enriched in patients. This was also the case for somatic mutations that were classified as functionally deleterious in our assay. We also found statistically significant depletion of functionally deleterious mutations in colorectal adenocarcinoma. Interestingly, no patients with a somatic mutation in a mismatch repair gene had a functionally deleterious CDKN2A missense somatic mutation. However, this observation was not statistically significant. Future studies will determine whether CDKN2A and MMR gene somatic mutations are mutually exclusive in colorectal adenocarcinoma.

“We found that 34.2% - 53.4% of unique missense somatic mutations classified as functionally deleterious, with 61.4% - 67.6% of patients having a functionally deleterious somatic mutation (Figure 4A, Appendix 1-table 9). As with functionally deleterious variants, functionally deleterious missense somatic mutations were also not distributed evenly across CDKN2A, being enriched within the ankyrin repeat 3 (Figure 4B, Appendix 1-table 9). We found that 32.4% - 50.0% of all functionally deleterious missense somatic mutations occurred within ankyrin repeat 3, with 48.0% - 58.0% of patients in each cohort having a functionally deleterious missense somatic mutation in this domain. Notably, 65.7% - 76.0% of functionally deleterious missense somatic mutations in this domain were in residues 80-89 (Appendix 1-table 9).”

“We were also able to determine the functional classification of CDKN2A missense somatic mutations in COSMIC, TCGA, JHU, and MSK-IMAPCT by cancer type. We found that 22.2% - 100% of CDKN2A missense somatic mutations were functionally deleterious depending on cancer type (Figure 4-figure supplement 2A-D). When considering missense somatic mutation reported in any database, there was a statistically significant depletion of functionally deleterious mutations in colorectal adenocarcinoma (20.4%; adjusted P value = 5.4 x 10-9) (Figure 4C). As the proportion of missense somatic mutations that were functionally deleterious was less in colorectal carcinoma compared to other types of cancer, we assessed whether somatic mutations in mismatch repair genes (MLH1, MLH3, MSH2, MSH6, PMS1, and PMS2) were associated with the functional status of CDKN2A missense somatic mutations. Thirty-five patients in COSMIC had a CDKN2A missense somatic mutation, of which 12 (34.3%) had a somatic mutation in a mismatch repair gene. We found that no patients with a somatic mutation in a mismatch repair gene had a functionally deleterious CDKN2A missense somatic mutation compared to 6 of 23 samples (26.1%) without a somatic mutation in a mismatch repair gene (P value = 0.062).”

(6) It would be helpful to validate the neutral variants set. Are variants of UK biobank or gnomAD enriched on neutral population? Are synonymous variants exclusively found in neutral populations?

Thank you for the suggestion. All synonymous variants were found to functionally neutral in our assay. We also assessed VUSs from gnomAD and found a lower prevalence of functionally deleterious variants compared to all CDKN2A variants and CDKN2A missense somatic mutations:

“The Genome Aggregation Database (gnomAD) v4.1.0 reports 287 missense variants in CDKN2A, including the 13 pathogenic, 4 likely pathogenic, 3 likely benign, 3 benign, and 264 VUSs classified using ACMG variant interpretation guidelines (Figure 5A, Figure 5B, and Appendix 1-table 10). Of the 264 missense VUSs, 177 were functionally neutral (67.0%), 56 (21.2%) were indeterminate function, and 31 (11.7%) were functionally deleterious in our assay using the gamma GLM for classification (Figure 5C).”

(7) They used a pancreatic cancer cell line and assayed for cell proliferation. The limitations of this method and the possibility of complementing the limitations should be discussed.

Thank you for the suggestion. We have added discussion of this limitation to our manuscript:

“We characterized variants based upon a broad cellular phenotype, cell proliferation, in a single PDAC cell line. It is possible that CDKN2A variant functional classifications are cell-specific and assay-specific. Our assay may not encompass all cellular functions of CDKN2A and an alternative assay of a specific CDKN2A function, such as CDK4 binding, may result in different variant functional classifications. Furthermore, CDKN2A variants may have different effects if alternative cell lines are used for the functional assay. However, cell-specific effects appear to be limited. In our previous study, we characterized 29 CDKN2A VUSs in three PDAC cell lines, using cell proliferation and cell cycle assays, and found agreement between all functional classifications (Kimura et al., 2022).”

Minor issues:

(1) Figures 2B, C: it would be more intuitive to plot significance by logging p-values than raw p-values.

We used log2 P value (or log2 normalized fold change) for figures in the manuscript as appropriate.

(2) Figure 2D: annotate protein domain information at the side. Supplementary Figure 2 shows the domains but it would be more informative to show it in Figure 2D heatmap.

Thank you for the suggestion, we have annotated protein domain information on the left side of the heatmap in (the now) Figure 2C.

Reviewer #2 (Recommendations For The Authors):

Major Concerns:

(1) How many replicates of the screen were performed? It seems like only one library infection/ proliferation assay was done. If so this is insufficient to obtain any idea of the uncertainty of measurement for each variant.

The assay was repeated in duplicate for 28 CDKN2A residues. For the remaining 128 residues of CDKN2A, the assay was completed once. We found good agreement between variant classifications in assay repeats. We have added to the text as follows:

“To confirm the reproducibility of our variant classifications, 28 amino acid residues were assayed in duplicate, and variants classified using the gamma GLM. The majority of missense variants, 452 of 560 (80.7%), had the same functional classification in each of the two replicates (Figure 2 -figure supplement 3A and B, Appendix 1-table 4).”

We have also added discussion of this study limitation to the manuscript:

“We repeated our functional assay twice for 28 CDKN2A residues. For the remaining 128 residues of CDKN2A, the functional assay was completed once. While we found general agreement between functional classifications from each replicate for the 28 residues assayed in duplicate, additional repeats for each residue are necessary to determine variability in variant functional classifications.”

(2) The count data from the experiment and NGS pipeline to call variants need to be provided for each replication (i.e. the counts that were fed into the gamma model)

Accompanying this should be information about the depth of sequencing of the cells, the number of cells infected with the library, and standard metrics for pooled screens.

Quality metrics regarding the representation and completeness of the TWIST library need to be provided. See Brenan et al. Cell Reports (2016) Supplemental Figure 1

Thank you for your suggestion. We are happy to provide this additional information. Sequence read counts for each variant are given in Appendix 1-table 4. We have provided addition detail in the methods section on functional assay, including number of cells infected with each library:

“Lentiviral transduction

PANC-1 cells were used for CDKN2A plasmid library and single variant CDKN2A expression plasmid transductions. PANC-1 cells previously transduced with pLJM1-CDKN2A (PANC-1CDKN2A) and selected with puromycin were used for CellTag library transductions. Briefly, 1 x 105 cells were cultured in media supplemented with 10 ug/ml polybrene and transduced with 4 x 107 transducing units per mL of lentivirus particles. Cells were then centrifuged at 1,200 x g for 1 hour. After 48 hours of culture at 37oC and 5% CO2, transduced cells were selected using 3 µg/ml puromycin (CDKN2A plasmid libraries and single variant CDKN2A expression plasmids) or 5 µg/ml blasticidin (CellTag plasmid library) for 7 days. Expected MOI was one. After selection, cells were trypsinized and 5 x 105 cells were seeded into T150 flasks. DNA was collected from remaining cells and this sample was named as (Day 9). T150 flasks were cultured until confluent and then DNA was collected. The time for cells to become confluent varied for each amino acid residue (Day 16 – 40, Appendix 1-table 5). DNA was extracted from PANC-1 cells using the PureLink Genomic DNA Mini Kit (Invitrogen, Carlsbad, CA; catalog no. K1820-01). The assay for CellTag library was repeated in triplicate. We repeated our CDKN2A assay in duplicate for 28 residues. For the remaining 128 CDKN2A residues the assay was completed once.”

We have also provided additional information on the TWIST library:

“CDKN2A expression plasmid libraries

Codon-optimized CDKN2A cDNA using p16INK4A amino acid sequence (NP_000068.1), was designed (Appendix 1-table 12) and pLJM1 containing codon optimized CDKN2A (pLJM1-CDKN2A) generated by Twist Bioscience (South San Francisco, CA). 156 plasmid libraries were then synthesized by using pLJM1-CDKN2A, such that each library contained all possible 20 amino acids variants (19 missense and 1 synonymous) at a given position, generating 500 ng of each plasmid library (Twist Bioscience, South San Francisco, CA). The proportion of variant in each library was shown in Appendix 1-table 2. Variants with a representation of less than 1% in a plasmid library were individually generated using the Q5 Site-Directed Mutagenesis kit (New England Biolabs, Ipswich, MA; catalog no. E0552), and added to each library to a calculated proportion of 5%. Primers used for site-directed mutagenesis are given in Appendix 1-table 13. Each library was then amplified to generate at least 5 ug of plasmid DNA using QIAGEN Plasmid Midi Kit (QIAGEN, Germantown, MD; catalog no. 12143).”

(3) It is unclear when barcode abundance is assessed in the cell proliferation assay/in the screen. The exact timepoints of "before and after in vitro culture" (line 91) need to be clarified in the text.

We are happy to clarify. We collected DNA on Day 9 post transfection and at confluency. Day of confluency for each residue is detailed in Appendix 1-table 5. The text of the manuscript has been updated appropriately.

(4) Is "before" day 9, as detailed in Figure 1 source data 1? If so, it is misleading to state that the experiment is in culture for 14 days but call day 9 "before... in vitro culture."

The "before" sample should be obtained immediately after viral infection and selection with the library to provide a representation of library representation.

We apologize for your confusion. We have clarified in the text and figures that our baseline measurement was at Day 9 post transfection. We also determined whether the proportion of each variant is maintained in the Day 9 cell pool compared to the amplified plasmid library for three CDKN2A amino acid residues (p.R24, p.H66, and p.A127) and updated the manuscript text:

“To confirm that the representation of each variant was maintained after transduction, we transduced three lentiviral libraries (amino acid residues p.R24, p.H66, and p.A127) individually into PANC-1 cells and determined the proportion of each variant in the amplified plasmid library and in the cell pool at Day 9 post-transduction. The proportion of each variant in the amplified plasmid library and in the cell pool at Day 9 were highly correlated (Figure 1 -figure supplement 2C and D, Appendix 1-table 3).”

(5) There is no information regarding the function of each variant, aside from just a p-value resulting from the final analysis with the gamma model. Some variants may cause loss of function, others may be neutral while others may be gain of function. Simply providing a p-value is not sufficient. The standard in the field is to provide a function score/ test-statistic giving the sign and magnitude of the effect. For proliferation assays at least a ratio of fold-change of (mut/ synonymous)[day 14] vs (mut/synonymous)[baseline] should be provided.

Thank you for your comment. We have provided read counts, P values, and functional classifications for each variant using the gamma GLM in Appendix 1-table 4. We have also analyzed variants using log2 normalized fold change. This data is presented in the text and compared to our classifications with the gamma GLM. We have provided normalized fold change and resulting classification for each variant in Appendix 1-table 6.

(6) A plot of the distribution of function scores for all variants is needed. This will serve as an effective visual to distinguish the control variants from those that are functionally deleterious or benign/neutral (see Findlay et al. Nature (2018) Figure 3A for an example visual).

Thank you for your suggestion. We have provided additional figures to visualize distribution of assay outputs using the gamma GLM in Figure 2 -figure supplement 1.

(7) Synonymous variants are used as a proxy for WT per variant library, but do all the synonymous variants truly behave like WT CDKN2A in their ability to suppress cell proliferation? A plot of the distribution of synonymous variant function relative to WT CDKN2A function would be effective here.

All 156 synonymous variants suppressed cell proliferation and were classified as functionally neutral in our assay using the gamma GLM. The manuscript has been updated to reflect this:

“Of 156 synonymous variants and six missense variants previously reported to be functionally neutral in published literature and/or reported in ClinVar as benign or likely benign (benchmark benign variants), all were characterized as functionally neutral in our assay (Figure 2B, Figure 2 -figure supplement 1B and 1C, Appendix 1-table 4)”

(8) The gamma generalized linear model is not commonly used to analyze the results of saturation mutagenesis screens. Please provide a justification for the use of this analysis method vs using log fold change as other dms scan studies have done (PMID: 27760319, PMID: 30224644).

Thank you for this important suggestion. We are happy to provide additional information. We used a gamma GLM to functionally characterize CDKN2A variants as it does not rely on an annotated set of pathogenic and benign variants to determine classification thresholds. Instead, classification thresholds are determined using the change in representation of 20 non-functional barcodes in a pool of PANC-1 cells stably expressing CDKN2A after a period of in vitro growth. As a gamma GLM is not commonly used for saturation mutagenesis screens, as noted by the reviewer, we also classified variants using log2 normalized fold change. We compared variant functional classifications using the gamma GLM and log2 normalized fold change and in general we found agreement between both methods with 98.5% of missense variants classified as functionally deleterious using a gamma GLM, similarly classified using log2 normalized fold change. We have updated the text to reflect this reasoning and additional analysis.

(9) The statistical methods used to calculate enrichment of deleterious variants per region of CDKN2A (Figure 2 supplement 1B; lines 163-168) are not described anywhere in the paper. Additionally, the same statistical analysis is not applied to the variants in the subregions near the ankyrin repeats (lines 168-172).

We are happy to clarify and have added text to the methods section:

“Z-tests with multiple test correction performed with the Bonferroni method was used in the following comparisons: 1) proportion of functionally deleterious variants present in < 2% of the cell pool and ≥ 2% of the cell pool at Day 9 binned in 1% intervals, 2) proportion of variants in each domain predicted to have deleterious or pathogenic effect by the majority of algorithms, 3) proportion of functionally deleterious variants in each domain, and 4) proportion of functionally deleterious missense variants and somatic mutations.”

Minor:

(1) Please review the manuscript for spelling and grammatical errors.

Sure.

eLife Assessment

This work presents a valuable exploration of AI-assisted protein engineering, particularly in designing a VHH antibody with enhanced resistance and stability to extreme environments. However, the approach is weakened by incomplete support, with computational methods and experimental design appearing somewhat arbitrary and lacking clear justification. Further justification of the chosen methods and clearer exposition would strengthen the study's support and conclusions.

Reviewer #1 (Public review):

Summary:

In this manuscript, the model's capacity to capture epistatic interactions through multi-point mutations and its success in finding the global optimum within the protein fitness landscape highlights the strength of deep learning methods over traditional approaches.

Strengths:

It is impressive that the authors used AI combined with limited experimental validation to achieve such significant enhancements in protein performance. Besides, the successful application of the designed antibody in industrial settings demonstrates the practical and economic relevance of the study. Overall, this work has broad implications for future AI-guided protein engineering efforts.

Weaknesses:

However, the authors should conduct a more thorough computational analysis to complement their manuscript. While the identification of improved multi-point mutants is commendable, the manuscript lacks a detailed investigation into the mechanisms by which these mutations enhance protein properties. The authors briefly mention that some physicochemical characteristics of the mutants are unusual, but they do not delve into why these mutations result in improved performance. Could computational techniques, such as molecular dynamics simulations, be employed to explore the effects of these mutations? Additionally, the authors claim that their method is efficient. However, the selected VHH is relatively short (<150 AA), resulting in lower computational costs. It remains unclear whether the computational cost of this approach would still be acceptable when designing larger proteins (>1000 AA). Besides, the design process involves a large number of prediction tasks, including the properties of both single-site saturation and multi-point mutants. The computational load is closely tied to the protein length and the number of mutation sites. Could the authors analyze the model's capability boundaries in this regard and discuss how scalable their approach is when dealing with larger proteins or more complex mutation tasks?

Reviewer #2 (Public review):

In this paper, the authors aim to explore whether an AI model trained on natural protein data can aid in designing proteins that are resistant to extreme environments. While this is an interesting attempt, the study's computational contributions are weak, and the design of the computational experiments appears arbitrary.

(1) The writing throughout the paper is poor. This leaves the reader confused.

(2) The main technical issue the authors address is whether AI can identify protein mutations that adapt to extreme environments based solely on natural protein data. However, the introduction could be more concise and focused on the key points to better clarify the significance of this question.

(3) The authors did not develop a new model but instead used their previously developed Pro-PRIME model. This significantly weakens the novelty and contribution of this work.

(4) The computational experiments are not well-justified. For instance, the authors used a zero-shot setting for single-point mutation experiments but opted for fine-tuning in multiple-point mutation experiments. There is no clear explanation for this discrepancy. How does the model perform in zero-shot settings for multiple-point mutations? How would fine-tuning affect single-point mutation results? The choice of these strategies seems arbitrary and lacks sufficient discussion.

Author response:

Reviewer #1:

Weaknesses：

However, the authors should conduct a more thorough computational analysis to complement their manuscript. While the identification of improved multi-point mutants is commendable, the manuscript lacks a detailed investigation into the mechanisms by which these mutations enhance protein properties. The authors briefly mention that some physicochemical characteristics of the mutants are unusual, but they do not delve into why these mutations result in improved performance. Could computational techniques, such as molecular dynamics simulations, be employed to explore the effects of these mutations?  Additionally, the authors claim that their method is efficient. However, the selected VHH is relatively short (<150 AA), resulting in lower computational costs. It remains unclear whether the computational cost of this approach would still be acceptable when designing larger proteins (>1000 AA). Besides, the design process involves a large number of prediction tasks, including the properties of both single-site saturation and multi-point mutants. The computational load is closely tied to the protein length and the number of mutation sites. Could the authors analyze the model's capability boundaries in this regard and discuss how scalable their approach is when dealing with larger proteins or more complex mutation tasks?

We agree that further analysis of the mechanisms by which the identified mutations enhance protein performance would strengthen our study. In the revised manuscript, we plan to conduct molecular dynamics simulations to explore the physicochemical effects of these mutations in more details. This analysis will help elucidate how the observed structural and dynamic changes contribute to the improved resistance and stability of the designed VHH antibody.

We acknowledge the need to assess the scalability of our method to larger proteins. To address this, we will include an analysis of the method’s performance when applied to longer proteins, including an estimation of computational cost and potential bottlenecks.

Reviewer #2:

(1) The writing throughout the paper is poor. This leaves the reader confused.

(2) The main technical issue the authors address is whether AI can identify protein mutations that adapt to extreme environments based solely on natural protein data. However, the introduction could be more concise and focused on the key points to better clarify the significance of this question.

(3) The authors did not develop a new model but instead used their previously developed Pro-PRIME model. This significantly weakens the novelty and contribution of this work.

(4) The computational experiments are not well-justified. For instance, the authors used a zero-shot setting for single-point mutation experiments but opted for fine-tuning in multiple-point mutation experiments. There is no clear explanation for this discrepancy. How does the model perform in zero-shot settings for multiple-point mutations? How would fine-tuning affect single-point mutation results? The choice of these strategies seems arbitrary and lacks sufficient discussion.

(1&2) We will revise the manuscript to improve the overall clarity and readability. Specifically, we will restructure the introduction to focus more concisely on the key scientific questions and contributions of our study.

(3) While the Pro-PRIME model was previously developed, this work focuses on designing proteins with properties that do not naturally exist and are scarce in the natural world. To address the concern about novelty, we will expand the discussion to highlight this unique contribution and its implications for advancing protein design.

(4) We appreciate the comment regarding the discrepancy between the zero-shot and fine-tuning strategies. In the revised manuscript, we will provide a detailed explanation for the choice of these settings, including an analysis of the trade-offs between zero-shot and fine-tuning approaches in multi-point mutation tasks. We will also explore the model’s performance in zero-shot settings for multi-point mutations and report these results in the supplementary materials to ensure completeness.

eLife Assessment

This study follows up on Arimura et al's powerful new method MagIC-Cryo-EM for imaging native complexes at high resolution. Using a clever design embedding protein spacers between the antibody and the nucleosomes purified, thereby minimizing interference from the beads, the authors concentrate linker histone variant H1.8 containing nucleosomes. From these samples, the authors obtain convincing atomic structures of the H1.8 bound chromatosome purified from interphase and metaphase cells, finding a NPM2 chaperone bound form exists as well. Caveats include the use of formaldehyde crosslinking and tagged H1.8 which might affect the structures obtained; and the NPM2 work could be better incorporated into the main findings. Overall this is an important new tool in the arsenal of single molecule biologists, permitting a deep dive into structure of native complexes. This work will be of high interest to a broad swathe of scientists studying native macromolecules present at low concentrations in cells.

Reviewer #1 (Public review):

Summary:

In this manuscript, Arimura et al describe MagIC-Cryo-EM, an innovative method for immune-selective concentrating of native molecules and macromolecular complexes for Cryo-EM imaging and single-particle analysis. Typically, Cryo-EM imaging requires much larger concentrations of biomolecules than that are feasible to achieve by conventional biochemical fractionation. Overall, this manuscript is meticulously and clearly written and may become a great asset to other electron microscopists and chromatin researchers.

Strengths:

Previously, Arimura et al. (Mol. Cell 2021) isolated from Xenopus extract and resolved by Cryo-EM a sub-class of native nucleosomes conjugated containing histone H1.8 at the on-dyad position, similar to that previously observed by other researchers with reconstituted nucleosomes. Here they sought to analyze immuno-selected nucleosomes aiming to observe specific modes of H1.8 positioning (e.g. on-dyad and off-dyad) and potentially reveal structural motifs responsible for the decreased affinity of H1.8 for the interphase chromatin compared to metaphase chromosomes. The main strength of this work is a clever and novel methodological design, in particular the engineered protein spacers to separate captured nucleosomes from streptavidin beads for a clear imaging. The authors provide a detailed step-by-step description of MagIC-Cryo-EM procedure including nucleosome isolation, preparation of GFP nanobody attached magnetic beads, optimization of the spacer length, concentration of the nucleosomes on graphene grids, data collection and analysis, including their new DUSTER method to filter-out low signal particles. This tour de force methodology should facilitate considering of MagIC-Cryo-EM by other electron microscopists especially for analysis of native nucleosome complexes.<br /> In pursue of biologically important new structures, the immune-selected H1.8-containing nucleosomes were solved at about 4A resolution; their structure appears to be very similar to the previously determined structure of H1.8-reconstituted nucleosomes. There were no apparent differences between the metaphase and interphase complexes suggesting that the on-dyad and off-dyad positioning does not explain the differences in H1.8 - nucleosome binding. However, they were able to identify and solve complexes of H1.8-GFP with histone chaperone NPM2 in a closed and open conformation providing mechanistic insights for H1-NPM2 binding and the reduced affinity of H1.8 to interphase chromatin as compared to metaphase chromosomes.

Weaknesses:

Still, I feel that there are certain limitations and potential artifacts resulting from formaldehyde fixation, use of bacterial-expressed recombinant H1.8-GFP, and potential effects of magnetic beads and/or spacer on protein structure, that should be more explicitly discussed. Also, the GFP-pulled down H1.8 nucleosomes should be better characterized biochemically to determine the actual linker DNA lengths (which are known to have a strong effect of linker histone affinity) and presence or absence of other factors such as HMG proteins that may compete with linker histones and cause the multiplicity of nucleosome structural classes (such as shown on Fig. 3F) for which the association with H1.8 is uncertain.

Reviewer #2 (Public review):

Summary:

The authors present a straightforward and convincing demonstration of a reagent and workflow that they collectively term "MagIC-cryo-EM", in which magnetic nanobeads combined with affinity linkers are used to specifically immobilize and locally concentrate complexes that contain a protein-of-interest. As a proof of concept, they localize, image, and reconstruct H1.8-bound nucleosomes reconstructed from frog egg extracts. The authors additionally devised an image-processing workflow termed "DuSTER", which increases the true positive detections of the partially ordered NPM2 complex. The analysis of the NPM2 complex {plus minus} H1.8 was challenging because only ~60 kDa of protein mass was ordered. Overall, single-particle cryo-EM practitioners should find this study useful.

Strengths:

The rationale is very logical and the data are convincing.

Weaknesses: I have seen an earlier version of this study at a conference. The conference presentation was much easier to follow than the current manuscript. It is as if this manuscript had undergone review at another journal and includes additional experiments to satisfy previous reviewers. Specifically, the NPM2 results don't seem to add much to the main story (MagIC-cryo-EM), and read more like an addendum. The authors could probably publish the NPM2 results separately, which would make the core MagIC results (sans DusTER) easier to read.

Reviewer #3 (Public review):

Summary:

In this paper, Arimura et al report a new method, termed MagIC-Cryo-EM, which refers to the method of using magnetic beads to capture specific proteins out of a lysate via, followed immunoprecipitation and deposition on EM grids. The so-enriched proteins can be analzyed structurally. Importantly, the nanoparticles are further functionalized with protein-based spacers, to avoid a distorted halo around the particles. This is a very elegant approach and allows the resolution of the stucture of small amounts of native proteins at atomistic resolution.<br /> Here, the authors apply this method to study the chromatosome formation from nucleosomes and the oocyte-specific linker histone H1.8. This allows them to resolve H1.8-containing chromatomosomes from oocyte extract in both interphase and metaphase conditions at 4.3 A resolution, which reveal a common structure with H1 placed right at the dyad and contacting both entry-and exit linker DNA.<br /> They then investigate the origin of H1.8 loss during interphase. They identify a non-nucleosomal H1.8-containing complex from interphase preparations. To resolve its structure, the authors develop a protocol (DuSTER) to exclude particles with ambiguous center, revealing particles with five-fold symmetry, that matches the chaperone NPM2. MS and WB confirms that the protein is present in interphase samples but not metaphase. The authors further separate two isoforms, an open and closed form that coexist. Additional densities in the open form suggest that this might be bound H1.8.

Strengths:

Together this is an important addition to the suite of cryoEM methods, with broad applications. The authors demonstrate the method using interesting applications, showing that the methods work and they can get high resolution structures from nucleosomes in complex with H1 from native environments.

Weaknesses:

The structures of the NPM2 chaperone is less well resolved, and some of the interpretation in this part seems only weakly justified.

eLife Assessment

This interesting study presents valuable information on how human cytomegalovirus (HCMV) infection disrupts the activity of the TEAD1 transcription factor, leading to widespread chromatin alterations. However, the precise mechanisms underlying this disruption and the extent to which these chromatin changes influence HCMV replication remain unclear. The study is supported by solid evidence, which would be made stronger by including functional analyses. This work will be of interest to virology, chromosome biology and transcriptional co-regulation fields.

Reviewer #1 (Public review):

The manuscript by Sayeed et al. uses a comprehensive series of multi-omics approaches to demonstrate that late-stage human cytomegalovirus (HCMV) infection leads to a marked disruption of TEAD1 activity, a concomitant loss of TEAD1-DNA interactions, and extensive chromatin remodeling. The data are thoroughly presented and provide evidence for the role of TEAD1 in the cellular response to HCMV infection. However, a key question remains unresolved: is the observed disruption of TEAD1 activity a direct consequence of HCMV infection, or could it be secondary to the broader innate antiviral response? In this respect, the study would benefit from experiments that assess the effect of TEAD1 overexpression or knockdown/deletion on HCMV replication dynamics. Such functional assays could help delineate whether TEAD1 perturbation directly influences viral replication or is part of a downstream/indirect cellular response, providing deeper mechanistic insights.

Reviewer #2 (Public review):

Summary:

This work uses genomic and biochemical approaches for HCMV infection in human fibroblasts and retinal epithelial cell lines, followed by comparisons and some validations using strategies such as immunoblots. Based on these analyses, they propose several mechanisms that could contribute to the HCMV-induced diseases, including closing of TEAD1-occupying domains and reduced TEAD1 transcript and protein levels, decreased YAP1 and phospho-YAP1 levels, and exclusion of TEAD1 exon 6.

Strengths:

The genomics experiments were done in duplicates and data analyses show good technical reproducibility. Data analyses are performed to show changes at the transcript and chromatin level changes, followed by some Western blot validations.

Weaknesses:

This work, at the current stage, is quite correlative since no functional studies are done to show any causal links. For readers who are outside the field, some clarifications of the system and design need to be stated.

eLife Assessment

This study examines the impact of DNA methylation on CTCF binding in two cancer cell lines. Increased CTCF binding sites are enriched in gene bodies, and associate with nuclear speckles, indicating a potential role in increased transcription. However, the association with nuclear speckles needs to be more diligently demonstrated. Thus the strength of the evidence is considered incomplete. This work would be made more valuable to the community if these claims were buttressed by additional evidence and a deeper discussion of new findings in the light of previous relevant literature. This work will be of interest to the chromosome biology/epigenetics field.

Reviewer #1 (Public review):

Summary<br /> Roseman et al. use a new inhibitor of the maintenance DNA methyltransferase DNMT1 to probe the role of methylation on binding of the CTCF protein, which is known to be involved chromatin loop formation. As previous reported, and as expected based on our knowledge that CTCF binding is methylation-sensitive, the authors find that loss of methylation leads to additional CTCF binding sites and increased loop formation. By comparing novel loops with the binding of the pre-mRNA splicing factor SON, which localizes to the nuclear speckle compartment, they propose that these reactivated loops localize to near speckles. This behavior is dependent on CTCF whereas degradation of two speckle proteins does not affect CTCF binding or loop formation. The authors propose a model in which DNA methylation controls the association of genome regions with speckles via CTCF-mediated insulation.

Strengths<br /> The strengths of the study are 1) the use of a new, specific DNMT1 inhibitor and 2) the observation that genes whose expression is sensitive to DNMT1 inhibition and dependent on CTCF (cluster 2) show higher association with SON than genes which are sensitive to DNMT1 inhibition but are CTCF insensitive, is in line with the authors' general model.

Weaknesses<br /> There are a number of significant weaknesses that as a whole undermine many of the key conclusions, including the overall mechanistic model of a direct regulatory role of DNA methylation on CTCF-mediated speckle association of chromatin loops.

(1) The authors frequently make quasi-quantitative statements but do not actually provide the quantitative data, which they actually all have in hand. To give a few examples: "reactivated CTCF sites were largely methylated (p. 4/5), "many CTCF binding motifs enriched..." (p.5), "a large subset of reactivated peaks..."(p.5), "increase in strength upon DNMT1 inhibition" (p.5); "a greater total number....." (p.7). These statements are all made based on actual numbers and the authors should mention the numbers in the text to give an impression of the extent of these changes (see below) and to clarify what the qualitative terms like "largely", "many", "large", and "increase" mean. This is an issue throughout the manuscript and not limited to the above examples.<br /> Related to this issue, many of the comparisons which the authors interpret to show differences in behavior seem quite minor. For example, visual inspection suggests that the difference in loop strength shown in figure 1E is something like from 0 to 0.1 for K562 cells and a little less for KCT116 cells. What is a positive control here to give a sense of whether these minor changes are relevant. Another example is on p. 7, where the authors claim that CTCF partners of reactivated peaks tend to engage in a "greater number" of looping partners, but inspection of Figure 2A shows a very minor difference from maybe 7 to 7.5 partners. While a Mann-Whitney test may call this difference significant and give a significant P value, likely due to high sample number, it is questionable that this is a biologically relevant difference.

(2) The data to support the central claim of localization of reactivated loops to speckles is not overly convincing. The overlap with SON Cut&Tag (figure 2F) is partial at best and although it is better with the publicly available TSA-seq data, the latter is less sensitive than Cut&Tag and more difficult to interpret. It would be helpful to validate these data with FISH experiments to directly demonstrate and measure the association of loops with speckles (see below).

(3) It is not clear that the authors have indeed disrupted speckles from cells by degrading SON and SRRM2. Speckles contain a large number of proteins and considering their phase separated nature stronger evidence for their complete removal is needed. Note that the data published in ref 58 suffers from the same caveat.

(4) The authors ascribe a direct regulatory role to DNA methylation in controlling the association of some CTCF-mediated loops to speckles (p. 20). However, an active regulatory role of speckle association has not been demonstrated and the observed data are equally explainable by a more parsimonious model in which DNA methylation regulates gene expression via looping and that the association with speckles is merely an indirect bystander effect of the activated genes because we know that active genes are generally associated with speckles. The proposed mechanism of a regulatory role of DNA methylation in controlling speckle association is not convincingly demonstrated by the data. As a consequence, the title of the paper is also misleading.

(5) As a minor point, the authors imply on p. 15 that ablation of speckles leads to misregulation of genes by altering transcription. This is not shown as the authors only measure RNA abundance, which may be affected by depletion of constitutive splicing factors, but not transcription. The authors would need to show direct effects on transcription.

Reviewer #2 (Public review):

Summary:<br /> CTCF is one of the most well-characterized regulators of chromatin architecture in mammals. Given that CTCF is an essential protein, understanding how its binding is regulated is a very active area of research. It has been known for decades that CTCF is sensitive to 5-cystosine DNA methylation (5meC) in certain contexts. Moreover, at genomic imprints and in certain oncogenes, 5meC-mediated CTCF antagonism has very important gene regulatory implications. A number of labs (eg, Schubeler and Stamatoyannopoulos) have assessed the impact of DNA methylation on CTCF binding, but it is important to also interrogate the effect on chromatin organization (ie, looping). Here, Roseman and colleagues used a DNMT1 inhibitor in two established human cancer lines (HCT116 [colon] and K562 [leukemia]), and performed CTCF ChIPseq and HiChIP. They showed that "reactivated" CTCF sites-that is, bound in the absence of 5meC-are enriched in gene bodies, participate in many looping events, and intriguingly, appear associated with nuclear speckles. This last aspect suggests that these reactivated loops might play an important role in increased gene transcription. They showed a number of genes that are upregulated in the DNA hypomethylated state actually require CTCF binding, which is an important result.

Strengths:<br /> Overall, I found the paper to be succinctly written and the data presented clearly. The relationship between CTCF binding in gene bodies and association with nuclear speckles is an interesting result. Another strong point of the paper was combining DNMT1 inhibition with CTCF degradation.

Weaknesses:<br /> The most problematic aspect of this paper in my view is the insufficient evidence for the association of "reactivated" CTCF binding sites with nuclear speckles needs to be more diligently demonstrated (see Major Comment). One unfortunate aspect was that this paper neglected to discuss findings from our recent paper, wherein we also performed CTCF HiChIP in a DNA methylation mutant (Monteagudo-Sanchez et al., 2024 PMID: 39180406). It is true, this is a relatively recent publication, although the BioRxiv version has been available since fall 2023. I do not wish to accuse the authors of actively disregarding our study, but I do insist that they refer to it in a revised version. Moreover, there are a number of differences between the studies such that I find them more complementary rather than overlapping. To wit, the species (mouse vs human), the cell type (pluripotent vs human cancer), the use of a CTCF degron, and the conclusions of the paper (we did not make a link with nuclear speckles). Furthermore, we used a constitutive DNMT knockout which is not viable in most cell types (HCT116 cells being an exception), and in the discussion mentioned the advantage of using degron technology:

"With high-resolution techniques, such as HiChIP or Micro-C (119-121), a degron system can be coupled with an assessment of the cis-regulatory interactome (118). Such techniques could be adapted for DNA methylation degrons (eg, DNMT1) in differentiated cell types in order to gauge the impact of 5meC on the 3D genome."

The authors here used a DNMT1 inhibitor, which for intents and purposes, is akin to a DNMT1 degron, thus I was happy to see a study employ such a technique. A comparison between the findings from the two studies would strengthen the current manuscript, in addition to being more ethically responsible.

eLife Assessment

This is an important study that reports the mechanism by which Ankle2 (LEM4 in humans) interacts with and recruits PP2A and the ER protein Vap33 to promote BAF dephosphorylation and mediate nuclear membrane reformation, using Drosophila as their model. Using Ankle2 mutants, they find that the ER protein Vap33 is key for the normal interphase localisation of Ankle2/LEM4 and also impacts on the function of Ankle2/LEM4 during mitosis. The authors use a variety of complementary techniques and provide convincing evidence to support the claims. The conclusions about the subcellular localization of Ankle2 might be incomplete since they are drawn from overexpression experiments.

Reviewer #1 (Public review):

Summary:

In organisms with open mitosis, nuclear envelope breakdown at mitotic entry and re-assembly of the nuclear envelope at the end of mitosis are important, highly regulated processes. One key regulator of nuclear envelope re-assembly is the BAF (Barrier-to-Autointegration) protein, which contributes to cross-linking of chromosomes to the nuclear envelope. Crucially, BAF has to be in a dephosphorylated form to carry out this function, and PP2A has been shown to be the phosphatase that dephosphorylates BAF. The Ankle2/LEM4 protein has previously been identified as an important regulator of PP2A in the dephosphorylation of BAF but its precise function is not fully understood, and Li and colleagues set out to investigate the function of Ankle2/LEM4 in both Drosophila flies and Drosophila cell lines.

Strengths:

The authors use a combination of biochemical and imaging techniques to understand the biology of Ankle2/LEM4. On the whole, the experiments are well conducted and the results look convincing. A particular strength of this manuscript is that the authors are able to study both cellular phenotypes and organismal effects of their mutants by studying both Drosophila D-mel cells and whole flies.

The work presented in this manuscript significantly enhances our understanding of how Ankle2/LEM4 supports BAF dephosphorylation at the end of mitosis. Particularly interesting is the finding that Ankle2/LEM4 appears to be a bona fide PP2A regulatory protein in Drosophila, as well as the localisation of Ankle2/LEM4 and how this is influenced by the interaction between Ankle2 and the ER protein Vap33. It would be interesting to see, though, whether these insights are conserved in mammalian cells, e.g. does mammalian Vap33 also interact with LEM4? Is LEM4 also a part of the PP2A holoenzyme complex in mammalian cells?

Weaknesses:

This work is certainly impactful but more discussion and comparison of the Drosophila versus mammalian cell system would be helpful. Also, to attract the largest possible readership, the Ankle2 protein should be referred to as Ankle2/LEM4 throughout the paper to make it clear that this is the same molecule.

A schematic model at the end of the final figure would be very useful to summarise the findings.

Reviewer #2 (Public review):

The authors first identify Ankle2 as a regulatory subunit and direct interactor of PP2A, showing they interact both in vitro and in vivo to promote BAF dephosphorylation. The Ankyrin domain of Ankle2 is important for the interaction with PP2A. They then show Ankle2 also interacts with the ER protein Vap33 through FFAT motifs and they particularly co-localize during mitosis. The recruitment of Ankle2 to Vap33 is essential to ER and nuclear envelop membrane in telophase while earlier in mitosis, it relies on the C terminus but not the FFAT motifs for recruitments to the nuclear membrane and spindle envelop in early mitosis. The molecular determinants and receptors are currently not known. The authors check the function of the PP2A recruitment to Ankle2/Vap33 in the context of embryos and show this recruitment pathway is functionally important. While the Ankle2/Vap33 interaction is dispensable in adult flies -looking at wing development, the PP2A/Ankle2 interaction is essential for correct wing and fly development. Overall, this is a very complete paper that reveals the molecular mechanism of PP2A recruitment to Ankle2 and studies both the cellular and the physiological effect of this interaction in the context of fly development.

Strengths:

The paper is well written and the narrative is well-developed. The figures are of high quality, well-controlled, clearly labelled, and easy to understand. They support the claims made by the authors.

Weaknesses:

The study would benefit from being discussed in the context of what is already known on Ankle2 biology in C.elegans and human cells. It is important to highlight the structures shown in the paper are alphafold models, rather than validated structures.

Reviewer #3 (Public review):

Summary:

The authors were interested in how Ankle2 regulates nuclear envelope reformation after cell division. Other published manuscripts, including those from the authors, show without a doubt that Ankle2 plays a role in this critical process. However, the mechanism by which Ankle2 functions was unclear. Previous work using worms and humans (Asencio et al., 2012) established that human ANKLE2 could bind endogenous PP2A subunits. The binding was direct and was mediated through a region before and including the first ankyrin repeat in human ANKLE2. In addition to its interaction with PP2A, Asencio et al., 2012 also show that ANKLE2 regulates VRK1 kinase activity. Together PP2A and VRK1 regulate BAF phosphorylation for proper nuclear envelope reformation. Here, the authors provide more evidence for interaction with PP2A by also mapping the domain of interaction to the ankyrin repeat in Drosophila. In addition, the ankyrin repeat is essential for nuclear envelope reformation after division. They show that Ankle2 can bind in a PP2A complex without other known regulatory subunits of PP2A. The authors also identify a novel interaction with ER protein Vap33, but functional relevance for this interaction in nuclear envelope reformation is not provided in the manuscript, which the authors explicitly state. This manuscript does not comment on the activity of Ballchen/VRK1 in relation to Ankle2 loss and BAF phosphorylation or nuclear envelope reformation, even though links were previously shown by multiple studies (Asencio et al., Link et al., Apridita Sebastian et al.,). Nuclear envelope defects were rescued by the reduction of VRK1 in two of these manuscripts. It is possible that BAF phosphorylation phenotypes can be contributed by both PP2A inactivity and VRK1 overactivity due to the loss of Ankle2.

Strengths:

This manuscript is a useful finding linking Ankle2 function during nuclear envelope reformation to the PP2A complex. The authors present solid data showing that Ankle2 can form a complex with PP2A-29B and Mts and generate a phosphoproteomic resource that is fundamentally important to understanding Ankle2 biology.

Weaknesses:

However, the main findings/conclusions about subcellular localization might be incomplete since they are drawn from overexpression experiments. In addition, throughout the text, some conclusions are overstated or are not supported by data.

eLife Assessment

This valuable study reports the first characterization of the CG14545 gene in Drosophila melanogaster, which the authors name "Sakura." Acting during germline stem cell fate and differentiation, Sakura is required for both oogenesis and female fertility. The evidence supporting the claims of the authors is solid, but the manuscript would be strengthened by a more in-depth investigation into the cause-and-effect relationships for the different defects observed.

Reviewer #1 (Public review):

Summary:

In this manuscript, Azlan et al. identified a novel maternal factor called Sakura that is required for proper oogenesis in Drosophila. They showed that Sakura is specifically expressed in the female germline cells. Consistent with its expression pattern, Sakura functioned autonomously in germline cells to ensure proper oogenesis. In Sakura KO flies, germline cells were lost during early oogenesis and often became tumorous before degenerating by apoptosis. In these tumorous germ cells, piRNA production was defective and many transposons were derepressed. Interestingly, Smad signaling, a critical signaling pathway for GSC maintenance, was abolished in sakura KO germline stem cells, resulting in ectopic expression of Bam in whole germline cells in the tumorous germline. A recent study reported that Bam acts together with the deubiquitinase Otu to stabilize Cyc A. In the absence of sakura, Cyc A was upregulated in tumorous germline cells in the germarium. Furthermore, the authors showed that Sakura co-immunoprecipitated Otu in ovarian extracts. A series of in vitro assays suggested that the Otu (1-339 aa) and Sakura (1-49 aa) are sufficient for their direct interaction. Finally, the authors demonstrated that the loss of otu phenocopies the loss of sakura, supporting their idea that Sakura plays a role in germ cell maintenance and differentiation through interaction with Otu during oogenesis.

Strengths:

To my knowledge, this is the first characterization of the role of CG14545 genes. Each experiment seems to be well-designed and adequately controlled.

Weaknesses:

However, the conclusions from each experiment are somewhat separate, and the functional relationships between Sakura's functions are not well established. In other words, although the loss of Sakura in the germline causes pleiotropic effects, the cause-and-effect relationships between the individual defects remain unclear.

Reviewer #2 (Public review):

In this study, the authors identified CG14545 (and named it Sakura), as a key gene essential for Drosophila oogenesis. Genetic analyses revealed that Sakura is vital for both oogenesis progression and ultimate female fertility, playing a central role in the renewal and differentiation of germ stem cells (GSC).

The absence of Sakura disrupts the Dpp/BMP signaling pathway, resulting in abnormal bam gene expression, which impairs GSC differentiation and leads to GSC loss. Additionally, Sakura is critical for maintaining normal levels of piRNAs. Also, the authors convincingly demonstrate that Sakura physically interacts with Otu, identifying the specific domains necessary for this interaction, suggesting a cooperative role in germline regulation. Importantly, the loss of otu produces similar defects to those observed in Sakura mutants, highlighting their functional collaboration.

The authors provide compelling evidence that Sakura is a critical regulator of germ cell fate, maintenance, and differentiation in Drosophila. This regulatory role is mediated through the modulation of pMad and Bam expression. However, the phenotypes observed in the germarium appear to stem from reduced pMad levels, which subsequently trigger premature and ectopic expression of Bam. This aberrant Bam expression could lead to increased CycA levels and altered transcriptional regulation, impacting piRNA expression. Given Sakura's role in pMad expression, it would be insightful to investigate whether overexpression of Mad or pMad could mitigate these phenotypic defects (UAS-Mad line is available at Bloomington Drosophila Stock Center).

A major concern is the overstated role of Sakura in regulating Orb. The data does not reveal mislocalized Orb; rather, a mislocalized oocyte and cytoskeletal breakdown, which may be secondary consequences of defects in oocyte polarity and structure rather than direct misregulation of Orb. The conclusion that Sakura is necessary for Orb localization is not supported by the data. Orb still localizes to the oocyte until about stage 6. In the later stage, it looks like the cytoskeleton is broken down and the oocyte is not positioned properly, however, there is still Orb localization in the ~8-stage egg chamber in the oocyte. This phenotype points towards a defect in the transport of Orb and possibly all other factors that need to localize to the oocyte due to cytoskeletal breakdown, not Orb regulation directly. While this result is very interesting it needs further evaluation on the underlying mechanism. For example, the decrease in E-cadherin levels leads to a similar phenotype and Bam is known to regulate E-cadherin expression. Is Bam expressed in these later knockdowns?

The manuscript would benefit from a more balanced interpretation of the data concerning Sakura's role in Orb regulation. Furthermore, a more expanded discussion on Sakura's potential role in pMad regulation is needed. For example, since Otu and Bam are involved in translational regulation, do the authors think that Mad is not translated and therefore it is the reason for less pMad? Currently the discussion presents just a summary of the results and not an extension of possible interpretation discussed in context of present literature.

Reviewer #3 (Public review):

In this very thorough study, the authors characterize the function of a novel Drosophila gene, which they name Sakura. They start with the observation that sakura expression is predicted to be highly enriched in the ovary and they generate an anti-sakura antibody, a line with a GFP-tagged sakura transgene, and a sakura null allele to investigate sakura localization and function directly. They confirm the prediction that it is primarily expressed in the ovary and, specifically, that it is expressed in germ cells, and find that about 2/3 of the mutants lack germ cells completely and the remaining have tumorous ovaries. Further investigation reveals that Sakura is required for piRNA-mediated repression of transposons in germ cells. They also find evidence that sakura is important for germ cell specification during development and germline stem cell maintenance during adulthood. However, despite the role of sakura in maintaining germline stem cells, they find that sakura mutant germ cells also fail to differentiate properly such that mutant germline stem cell clones have an increased number of "GSC-like" cells. They attribute this phenotype to a failure in the repression of Bam by dpp signaling. Lastly, they demonstrate that sakura physically interacts with otu and that sakura and otu mutants have similar germ cell phenotypes. Overall, this study helps to advance the field by providing a characterization of a novel gene that is required for oogenesis. The data are generally high-quality and the new lines and reagents they generated will be useful for the field. However, there are some weaknesses and I would recommend that they address the comments in the Recommendations for the authors section below.

eLife Assessment

This useful study presents findings on how some antibiotics, which inhibit protein synthesis in bacteria, affect the translation in mitochondrial ribosomes. The authors provide solid evidence that most tested antibiotics act similarly on bacterial and mitochondrial translation. Additionally, this work shows that alternative translation initiation events might exist in two specific mt-mRNAs (MT-ND1 and MT-ND5). The conclusions of this manuscript are of broad interest to the antibiotic and the mitochondrial fields.

Reviewer #1 (Public review):

Summary:

This study aimed to determine whether bacterial translation inhibitors affect mitochondria through the same mechanisms. Using mitoribosome profiling, the authors found that most antibiotics, except telithromycin, act similarly in both systems. These insights could help in the development of antibiotics with reduced mitochondrial toxicity.<br /> They also identified potential novel mitochondrial translation events, proposing new initiation sites for MT-ND1 and MT-ND5. These insights not only challenge existing annotations but also open new avenues for research on mitochondrial function.

Strengths:

Ribosome profiling is a state-of-the-art method for monitoring the translatome at very high resolution. Using mitoribosome profiling, the authors convincingly demonstrate that most of the analyzed antibiotics act in the same way on both bacterial and mitochondrial ribosomes, except for telithromycin. Additionally, the authors report possible alternative translation events, raising new questions about the mechanisms behind mitochondrial initiation and start codon recognition in mammals.

Weaknesses:

The main weaknesses of this study are:<br /> - While the authors highlight an interesting difference in the inhibitory mechanism of telithromycin on bacterial and mitochondrial ribosomes, mechanistic explanations or hypotheses are lacking.<br /> - The assignment of alternative start codons in MT-ND1 and MT-ND5 is very interesting but does not seem to fully align with structural data.<br /> - The newly proposed translation events in the ncRNAs are preliminary and should be further substantiated with additional evidence or interpreted with more caution.

Reviewer #2 (Public review):

In this study, the authors set out to explore how antibiotics known to inhibit bacterial protein synthesis also affect mitoribosomes in HEK cells. They achieved this through mitoribosome profiling, where RNase I and Mnase were used to generate mitoribosome-protected fragments, followed by sequencing to map the regions where translation arrest occurs. This profiling identified the codon-specific impact of antibiotics on mitochondrial translation.

The study finds that most antibiotics tested inhibit mitochondrial translation similarly to their bacterial counterparts, except telithromycin, which exhibited distinct stalling patterns. Specifically, chloramphenicol and linezolid selectively inhibited translation when certain amino acids were in the penultimate position of the nascent peptide, which aligns with their known bacterial mechanism. Telithromycin stalls translation at an R/K-X-R/K motif in bacteria, and the study demonstrated a preference for arresting at an R/K/A-X-K motif in mitochondria. Additionally, alternative translation initiation sites were identified in MT-ND1 and MT-ND5, with non-canonical start codons. Overall, the paper presents a comprehensive analysis of antibiotics in the context of mitochondrial translation toxicity, and the identification of alternative translation initiation sites will provide valuable insights for researchers in the mitochondrial translation field.

From my perspective as a structural biologist working on the human mitoribosome, I appreciate the use of mitoribosome profiling to explore off-target antibiotic effects and the discovery of alternative mitochondrial translation initiation sites. However, the description is somewhat limited by a focus on this single methodology. The authors could strengthen their discussion by incorporating structural approaches, which have contributed significantly to the field. For example, antibiotics such as paromomycin and linezolid have been modeled in the human mitoribosome (PMID: 25838379), while streptomycin has been resolved (10.7554/eLife.77460), and erythromycin was previously discussed (PMID: 24675956). The reason we can now describe off-target effects more meaningfully is due to the availability of fully modified human mitoribosome structures, including mitochondria-specific modifications and their roles in stabilizing the decoding center and binding ligands, mRNA, and tRNAs (10.1038/s41467-024-48163-x).<br /> These and other relevant studies should be acknowledged throughout the paper to provide additional context.

Reviewer #3 (Public review):

Summary:

Recently, the off-target activity of antibiotics on human mitoribosome has been paid more attention in the mitochondrial field. Hafner et al applied mitoribosome profilling to study the effect of antibiotics on protein translation in mitochondria as there are similarities between bacterial ribosome and mitoribosome. The authors conclude that some antibiotics act on mitochondrial translation initiation by the same mechanism as in bacteria. On the other hand, the authors showed that chloramphenicol, linezolid and telithromycin trap mitochondrial translation in a context-dependent manner. More interesting, during deep analysis of 5' end of ORF, the authors reported the alternative start codon for ND1 and ND5 proteins instead of previously known one. This is a novel finding in the field and it also provides another application of the technique to further study on mitochondrial translation.

Strengths:

This is the first study which applied mitoribosome profiling method to analyze mutiple antibiotics treatment cells.<br /> The mitoribosome profiling method had been optimized carefully and has been suggested to be a novel method to study translation events in mitochondria. The manuscript is constructive and written well.

Weaknesses:

This is a novel and interesting study, however, most of the conclusion comes from mitoribosome profiling analysis, as a result, the manuscript lacks the cellular biochemical data to provide more evidence and support the findings.

eLife Assessment

The authors studied the relationship between structural and functional lateralization in the planum temporale region of the brain, whilst also considering the morphological presentation of a single or duplicated Heschl's gyrus. The analyses are compelling due to a large sample size, inter-rater reliability, and corrections for multiple comparisons. The associations in this important work might serve as a reference for future targeted-studies on brain lateralization.

Reviewer #1 (Public review):

Summary:

Qin and colleagues analysed data from the Human Connectome Project on four right-handed subgroups with different gyrification patterns in Heschl's gyrus. Based on these groups, the authors highlight the structure-function relationship of planum temporale asymmetry in lateralised language processing at the group level and next at the individual level. In particular, the authors propose that especially microstructural asymmetries are related to functional auditory language asymmetries in the planum temporale.

Strengths:

The study is interesting because of an ongoing and long-standing debate about the relationship between structural and functional brain asymmetries, and in particular whether structural brain asymmetries can be seen as markers of functional language brain lateralisation.

In this debate, the relationship between Heschl's gyrus asymmetry and planum temporale asymmetry is rare and therefore valuable here. A large sample size and inter-rater reliability support the findings.

Weaknesses:

The authors highlight the microstructural results, but could also emphasise on their interesting macrostructural results.

Author response:

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

Reviewer #1 (Public Review):

Summary:

Qin and colleagues analysed data from the Human Connectome Project on four right-handed subgroups with different gyrification patterns in Heschl's gyrus. Based on these groups, the authors highlight the structure-function relationship of planum temporale asymmetry in lateralised language processing at the group level and next at the individual level. In particular, the authors propose that especially microstructural asymmetries are related to functional auditory language asymmetries in the planum temporale.

Strengths:

The study is interesting because of an ongoing and long-standing debate about the relationship between structural and functional brain asymmetries, and in particular whether structural brain asymmetries can be seen as markers of functional language brain lateralisation.

In this debate, the relationship between Heschl's gyrus asymmetry and planum temporale asymmetry is rare and therefore valuable here. A large sample size and inter-rater reliability support the findings.

Weaknesses:

In this case of multiple brain measures, it would be important to provide the reader with some sort of effect size (e.g. Cohen's d) to help interpret the results.

Thank you for pointing this out. In the revised version, the effect size, i.e., Cohen's d, has been incorporated into the results (page 8, line 159-160; page 9, line 181-186, supplementary page 14, Table S14).

In addition, the authors highlight the microstructural results in spite of the macrostructural results. However, the macrostructural surface results are also strong. I would suggest either reducing the emphasis on micro vs macrostructural results or adding information to justify the microstructural importance.

In the original manuscript, we highlighted the results of microstructural measures because the correlations between PT microstructural and functional measures were more pronounced both within the hemispheres and in terms of asymmetry, compared with the significant results of surface area. Following your comments here, we now lowered the tone of microstructure results (page 2, line 40; page 14, line 267), and added relevant discussion regarding the macrostructural results in the revised version (page 18, line 363-370; as copied below):

“As for macrostructural measures, the asymmetric PT surface area was also associated with speech comprehension AI. Given that the within-hemispheric coupling tendency between surface and speech comprehension existed only in the left PT, it was possible that the larger surface area of the left PT led to a less recruitment of its right homologous, and therefore the lateralization of functional activity would be more pronounced. Additionally, an opposite tendency was found between the correlation of speech perception and comprehension with surface area, potentially implying the segregation of the different speech processing in the PT area.”

Recommendations for the authors:

I have only some comments that I wish to be addressed by the authors:

(1) Please always specify "structural" or "functional" asymmetry or lateralisation, as the reader may be confused.

This has been done in relevant places.

(2) Please state that the scale is not the same between the results in Figure 3.

This have been specified, as suggested (see below).

“Notably, we did not standardize these structural measures, so the scales differed between indicators.”

(3) It may be of interest to the reader to learn more about interpretations of how Heschl's gyrus and planum temporale asymmetries are related.

Thank you for this comment. Given that the asymmetry of Heschl's gyrus was not analyzed in the present study, we do not have direct data/results for such an interpretation. Also, we reviewed the literature but found no relevant results on how Heschl's gyrus and planum temporale asymmetries are related. To address this, specific investigation targeting on this topic is needed. This has now been added in the discussion (page 20, line 415-417).

(4) As this manuscript builds somewhat on the Science Advances article by Ocklenburg et al. (2018), it would be important to discuss how this more liberal planum temporale definition might (or might not) affect the results compared to the more conservative planum temporale definition described here.

Yes, the definition of planum temporale varies across studies. Our current manual one is relatively more conservative than the Ocklenburg et al. (2018), in which the planum temporale was automatically derived from the Destrieux atlas. We believe that the definition of the planum temporale likely have non-trivial impact on the results, and our current manual definition with the consideration of the HG duplication should be more reliable and accurate, therefore favored, relative to the other ones. This has been briefly discussed in the revision (page 15-16, line 300-304).

(5) I would like the authors to briefly but critically discuss what exactly the MRI NODDI model measures and how this is interpreted as measuring microstructural properties of tissue.

We now provided relevant information regarding the NODDI measures (page 26, line 552-558; as copied below).

“NODDI is a highly effective method for detecting key features of neurite morphology, which employs a tissue model that detects three microstructural environments: the intracellular, extracellular and cerebrospinal fluid compartments (Zhang et al., 2012). In the grey matter of the cerebral cortex, the neurite density index (NDI) is an estimated volume fraction of the intracellular microstructural environment, with higher NDIs indicating greater neurite density (Jespersen et al., 2010; Zhang et al., 2012). The orientation dispersion index (ODI) is a measure of the alignment or dispersion of neurite, with higher ODIs indicating more dispersed neurite and lower ODIs indicating more aligned neurite (Jespersen et al., 2012; Zhang et al., 2012).”

(6) While not mandatory, I would be interested to read the authors' thoughts on the evolution of such a functional/(micro)structural lateralisation link of the planum temporale, in light of the literature on planum temporale asymmetries in (newborn) non-human primate species.

Thank you for this inspiring suggestion. We have incorporated relevant discussion into the revised version (page 15, line 281-288; as copied below).

“Moreover, there exist evolutionary evidence supporting the role of the PT as an anatomical substrate for language lateralization. For example, the leftward structural asymmetry of the PT have been observed in multiple non-human primates, including chimpanzees, macaques, and baboons (Becker et al., 2024; Gannon et al., 1998; Xia et al., 2019). Particularly, recent studies on baboons further demonstrated that PT structural leftward asymmetry in newborn baboons could predict future development of communicative gestures, implying a key role of PT structural asymmetry in the lateralized communication system for human and non-human brain evolution (Becker et al., 2024, 2021).”

Reference

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Gannon PJ, Holloway RL, Broadfield DC, Braun AR. 1998. Asymmetry of Chimpanzee Planum Temporale: Humanlike Pattern of Wernicke’s Brain Language Area Homolog. Science 279:220–222. doi:10.1126/science.279.5348.220

Jespersen SN, Bjarkam CR, Nyengaard JR, Chakravarty MM, Hansen B, Vosegaard T, Østergaard L, Yablonskiy D, Nielsen NChr, Vestergaard-Poulsen P. 2010. Neurite density from magnetic resonance diffusion measurements at ultrahigh field: Comparison with light microscopy and electron microscopy. NeuroImage 49:205–216. doi:10.1016/j.neuroimage.2009.08.053

Jespersen SN, Leigland LA, Cornea A, Kroenke CD. 2012. Determination of Axonal and Dendritic Orientation Distributions Within the Developing Cerebral Cortex by Diffusion Tensor Imaging. IEEE Trans Med Imaging 31:16–32. doi:10.1109/TMI.2011.2162099

Xia J, Wang F, Wu Z, Wang L, Zhang C, Shen D, Li G. 2019. Mapping hemispheric asymmetries of the macaque cerebral cortex during early brain development. Hum Brain Mapp. doi:10.1002/hbm.24789

Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. 2012. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61:1000–1016. doi:10.1016/j.neuroimage.2012.03.072

Reviewer #2 (Public Review):

Summary:

The authors assessed the link between structural and functional lateralization in area PT, one of the brain areas that is known to exhibit strong structural lateralization, and which is known to be implicated in speech processing. Importantly, they included the sulcal configuration of Heschl's gyrus (HG), presenting either as a single or duplicated HG, in their analysis. They found several significant associations between microstructural indices and task-based functional lateralization, some of which depended on the sulcal configuration.

Strengths:

A clear strength is the large sample size (n=907), an openly available database, and the fact that HG morphology was manually classified in each individual. This allows for robust statistical testing of the effects across morphological categories, which is not often seen in the literature.

Weaknesses:

- Unfortunately, no left-handers were included in the study. It would have been a valuable addition to the literature, to study the effect of handedness on the observed associations, as many previous studies on this topic were not adequately powered. The fact that only right-handers were studied should be pointed out clearly in the introduction or even the abstract.

Thank for pointing this out. We have explicitly specified this in the Abstract and Introduction.

- The tasks to quantify functional lateralization were not specifically designed to pick up lateralization. In the interest of the sample size, it is understandable that the authors used the available HCP-task-battery results, however, it would have been feasible to access another dataset for validation. A targeted subset of results, concerning for example the relationship between sulcal morphology and task-based functional lateralization, could be re-assessed using other open-access fMRI datasets.

Yes, the fMRI task was not specifically designed to evaluate PT functional lateralization, which has been acknowledged in the discussion (page 17, line 330-342). Given the observed small effect size of our current structural-functional relationship, reproducing similar results with other datasets would require a cohort with a large sample size. This would induce a quite labor-intensive work given our current manual protocol for outlining PT and HG for everyone. The lack of validation with independent dataset has been discussed as a limitation in the revised version. We will try to conduct such a validation in future work, likely after developing an automatic pipeline for accurately extracting the PT and HG in the individual space (like the manual outlining protocol).

- The study is mainly descriptive and the general discussion of the findings in the larger context of brain lateralization comes a bit short. For example, are the observed effects in line with what we know from other 'language-relevant' areas? What could be the putative mechanisms that give rise to functional lateralization based on the microstructural markers observed? And which mechanisms might be underlying the formation of a duplicated HG?

Thank you for these insightful comments. As suggested, we strengthened the discussion as below:

“Another possible explanation could be that higher myelin content and larger surface area in left PT potentially indicated more white matter connection with other language-related regions such as Broca’s area, and therefore is more involved in language tasks than its right homolog (Allendorfer et al., 2016; Catani et al., 2005; Giampiccolo and Duffau, 2022).

The distinct roles of left and right PT in speech processing have been well-documented. A number of studies substantiated that PT of the left hemisphere responded more strongly to lexical-semantic and syntactic aspects of sentence processing, whereas the right hemisphere demonstrated a greater involvement in the speech melody (Albouy et al., 2020; Meyer et al., 2002).

These findings are consistent with those reported for the arcuate fasciculus (AF). The left AF has been identified as a crucial structure for language function (Giampiccolo and Duffau, 2022; Zhang et al., 2021). Disruption to this pathway has been linked to multimodal phonological and semantic deficits (Agosta et al., 2010), while injuries in the right AF did not affect language function (Zeineh et al., 2015).”

Regarding the mechanism underlying the formation of a duplicated HG, we did not come up with good thoughts after careful literature review. Also, we feel that this is kind of out of the scope of the present study and therefore did not add more discussion on this topic.

Recommendations for the authors:

(1) The data availability statement makes no explicit mention of the manual labels of HG configuration. Would the authors consider making available a list of HCP-subject-ID with a morphological group (L1/R1, L1/R2, etc.) for replicability and for re-use by other researchers?

The list of HCP-subject-ID with a morphological group (L1/R1, L1/R2, etc.) is now available in the supplementary material 2. We have specified this in the revised version.

(2) It would be helpful to state again the statistical tests associated with the p-value in the figure/table caption, e.g. Table 2.

As suggested, we now specified the statistical method in the figure/table caption.

(3) Sometimes, the y-axis labels are missing or not clear, for example in Figure S2.

Sorry about these. We double-checked all the figures, and corrected the missing or unclear labels for Figure S2 and S3 in the revised version.

(4) In a few instances the font sizes vary within a figure caption.

This has been corrected in the revision.

Reference

Agosta F, Henry RG, Migliaccio R, Neuhaus J, Miller BL, Dronkers NF, Brambati SM, Filippi M, Ogar JM, Wilson SM, Gorno-Tempini ML. 2010. Language networks in semantic dementia. Brain J Neurol 133:286–299. doi:10.1093/brain/awp233

Albouy P, Benjamin L, Morillon B, Zatorre RJ. 2020. Distinct sensitivity to spectrotemporal modulation supports brain asymmetry for speech and melody. Science 367:1043–1047. doi:10.1126/science.aaz3468

Allendorfer JB, Hernando KA, Hossain S, Nenert R, Holland SK, Szaflarski JP. 2016. Arcuate fasciculus asymmetry has a hand in language function but not handedness. Hum Brain Mapp 37:3297–3309. doi:10.1002/hbm.23241

Catani M, Jones DK, Ffytche DH. 2005. Perisylvian language networks of the human brain. Ann Neurol 57:8–16. doi:10.1002/ana.20319

Giampiccolo D, Duffau H. 2022. Controversy over the temporal cortical terminations of the left arcuate fasciculus: a reappraisal. Brain J Neurol 145:1242–1256. doi:10.1093/brain/awac057

Meyer M, Alter K, Friederici AD, Lohmann G, von Cramon DY. 2002. FMRI reveals brain regions mediating slow prosodic modulations in spoken sentences. Hum Brain Mapp 17:73–88. doi:10.1002/hbm.10042

Zeineh MM, Kang J, Atlas SW, Raman MM, Reiss AL, Norris JL, Valencia I, Montoya JG. 2015. Right arcuate fasciculus abnormality in chronic fatigue syndrome. Radiology 274:517–526. doi:10.1148/radiol.14141079

Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. 2012. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61:1000–1016. doi:10.1016/j.neuroimage.2012.03.072

Zhang J, Zhong S, Zhou L, Yu Yamei, Tan X, Wu M, Sun P, Zhang W, Li J, Cheng R, Wu Y, Yu Yanmei, Ye X, Luo B. 2021. Correlations between Dual-Pathway White Matter Alterations and Language Impairment in Patients with Aphasia: A Systematic Review and Meta-analysis. Neuropsychol Rev 31:402–418. doi:10.1007/s11065-021-09482-8

Reviewing Editor:

I encourage the authors to incorporate the suggestions of the reviewers, such as:

(1) to provide more in-depth interpretations about how and why structural and functional lateralization relate,

Done.

(2) to provide statistical effect sizes,

Done.

(3) to make their sulcal-morphology classification openly available,

Done.

(4) to provide statistical effect sizes,

Done

(5) to discuss the possible impact of diverging PT definitions with regard to previous studies,

Done.

(6) to provide more in-depth interpretations about how and why structural and functional lateralization relate.

Done.

Detailed comments:

In an impressive cohort of 907 human participants, the present paper presents a very interesting set of data on PT asymmetries not only at the macro-structural but also at the microstructural levels in order to investigate their potential correlates with PT functional asymmetry in relation to perceptual acoustic language tasks.

I believe this is a key paper for the following reasons:

(1) it provides critical data and results for addressing a controversial but important question: the relevance of measures of anatomical asymmetry for inferring its language-related functional hemispheric specialization;

(2) to do so, the authors made a very impressive effort to manually trace the anatomical delineation of the planum temporale at different levels in every participant, the best (but crazy time-consuming) approach so far to document interindividual variability of the PT and to address such a question;

(3) the contribution is particularly relevant regarding the statistical power of the study, the study and measures having been done in 907 participants!

(4) I also found the study well designed and well written with great relevance of the findings for the field.

As the results, the authors reported asymmetric measures of microstructural asymmetry (including intracortical myelin content, neurite density, and neurite orientation) but also of macrostructural asymmetries in relation to functional lateralization for language.

Comments：

I have only 2 additional minor comments of my own:

(1) In agreement with reviewer 2, I don't understand why the authors seem to downplay the links they found between gross PT asymmetry and functional lateralization. I recommend the authors to highlight and discuss this important result, just as the microstructural PT asymmetries and their functional links.

This has been done (page 18, line 363-370).

(2) PT structural asymmetry (both micro & macro) has been well documented in nonhuman primates (and their functional link with manual lateralization for gestural communication). Without detailing this literature, I recommend the authors at least mention this literature as a comparative perspective in the introduction and/or discussion in order to make the question of PT asymmetry less anthropocentric.

This has been done (page 15, line 281-288).

eLife Assessment

This study investigates the molecular mechanisms underlying chronic pain-related memory impairment by focusing on S1P/S1PR1 signaling in the dentate gyrus (DG) of the hippocampus. Through behavioral tests (Y-maze and Morris water maze) and RNA-seq analysis, the researchers discovered that S1P/S1PR1 signaling is crucial for determining susceptibility to memory impairment, with decreased S1PR1 expression linked to structural plasticity changes and memory deficits. This work has important significance and a convincing level of evidence, thus offering new insights into the mechanisms underlying chronic pain-related memory impairment.

Reviewer #1 (Public review):

This work from Cui, Pan, Fan et al explores memory impairment in chronic pain mouse models, a topic of great interest for the neurobiology field. In particular, the work starts from a very interesting observation, that WT mice can be divided in susceptible and unsusceptible to memory impairment upon modelling chronic pain with CCI. This observation represents the basis of the work where the authors identify the sphingosine receptor S1PR1 as down-regulated in the dentate gyrus of susceptible animals and demonstrate through an elegant range of experiments involving AAV mediated knockdown or overexpression of S1PR1 that this receptor is involved in the memory impairment observed with chronic pain. Importantly for translational purposes, they also show that activation of S1PR1 through a pharmacological paradigm is able to rescue the memory impairment phenotype.

The authors also link these defects to reduced dendritic branching and reduced number of mature excitatory synapses in the DG to the memory phenotype.

They then proceed to explore possible mechanisms downstream of S1PR1 that could explain this reduction in dendritic spines. They identify integrin α2 as an interactor of S1PR1 and show a reduction in several proteins involved in actin dynamic, which is crucial for dendritic spine formation and plasticity.

They thus hypothesize that the interaction between S1PR1 and Integrin α2 is fundamental for the activation of Rac1 and Cdc42 and consequently for the polymerisation of actin; a reduction in this pathway upon chronic pain would thus lead to impaired actin polymerisation, synapse formation and thus impaired memory.

The work is of great interest and the experiments are of very good quality with results of great importance.

Comments on revisions:

The authors have replied satisfactorily to my previous concerns.

Reviewer #2 (Public review):

Summary:

The study investigates the molecular mechanisms underlying chronic pain-related memory impairment by focusing on S1P/S1PR1 signaling in the dentate gyrus (DG) of the hippocampus. Through behavioural tests (Y-maze and Morris water maze) and RNA-seq analysis, the researchers segregated chronic pain mice into memory impairment-susceptible and -unsusceptible subpopulations. They discovered that S1P/S1PR1 signaling is crucial for determining susceptibility to memory impairment, with decreased S1PR1 expression linked to structural plasticity changes and memory deficits.

Knockdown of S1PR1 in the DG induced a susceptible phenotype, while overexpression or pharmacological activation of S1PR1 promoted resistance to memory impairment and restored normal synaptic structure. The study identifies actin cytoskeleton-related pathways, including ITGA2 and its downstream Rac1/Cdc42 signaling, as key mediators of S1PR1's effects, offering new insights and potential therapeutic targets for chronic pain-related cognitive dysfunction.

This manuscript consists of a comprehensive investigation and significant findings. The study provides novel insights into the molecular mechanisms of chronic pain-related memory impairment, highlighting the critical role of S1P/S1PR1 signaling in the hippocampal dentate gyrus. The clear identification of S1P/S1PR1 as a potential therapeutic target offers promising avenues for future research and treatment strategies. The manuscript is well-structured, methodologically sound, and presents valuable contributions to the field.

Strengths:

(1) The manuscript is well-structured and written in clear, concise language. The flow of information is logical and easy to follow.

(2) The segregation of mice into memory impairment-susceptible and -unsusceptible subpopulations is innovative and well-justified. The statistical analyses are robust and appropriate for the data.

(3) The detailed examination of S1PR1 expression and its impact on synaptic plasticity and actin cytoskeleton reorganization is impressive. The findings are significant and contribute to the understanding of chronic pain-related memory impairment.

Comments on revisions:

The authors have satisfactorily addressed all the issues raised.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This work from Cui, Pan, Fan, et al explores memory impairment in chronic pain mouse models, a topic of great interest in the neurobiology field. In particular, the work starts from a very interesting observation, that WT mice can be divided into susceptible and unsusceptible to memory impairment upon modelling chronic pain with CCI. This observation represents the basis of the work where the authors identify the sphingosine receptor S1PR1 as down-regulated in the dentate gyrus of susceptible animals and demonstrate through an elegant range of experiments involving AAV-mediated knockdown or overexpression of S1PR1 that this receptor is involved in the memory impairment observed with chronic pain. Importantly for translational purposes, they also show that activation of S1PR1 through a pharmacological paradigm is able to rescue the memory impairment phenotype.

The authors also link these defects to reduced dendritic branching and a reduced number of mature excitatory synapses in the DG to the memory phenotype.

They then proceed to explore possible mechanisms downstream of S1PR1 that could explain this reduction in dendritic spines. They identify integrin α2 as an interactor of S1PR1 and show a reduction in several proteins involved in actin dynamic, which is crucial for dendritic spine formation and plasticity.

They thus hypothesize that the interaction between S1PR1 and Integrin α2 is fundamental for the activation of Rac1 and Cdc42 and consequently for the polymerisation of actin; a reduction in this pathway upon chronic pain would thus lead to impaired actin polymerisation, synapse formation, and thus impaired memory.

The work is of great interest and the experiments are of very good quality with results of great importance. I have however some concerns. The main concern I have relates to the last part of the work, namely Figures 8 and 9, which I feel are not at the same level as the results presented in the previous 7 Figures, which are instead outstanding.

In particular:

- In Figure 8, given the reduction in all the proteins tested, the authors need to check some additional proteins as controls. One good candidate could be RhoA, considering the authors say it is activated by S1PR2 and not by S1PR1;

Thanks for your suggestion. We tested the expression level of RhoA in mice 7 days and 21 days post CCI as negative controls (Supplemental Figure 9).

- In addition to the previous point, could the authors also show that the number of neurons is not grossly different between susceptible and unsusceptible mice? This could be done by simply staining for NeuN or performing a western blot for a neuronal-specific protein (e.g. Map2 or beta3-tubulin);

As suggested, we performed immunofluorescence using NeuN antibody to detect the number of neurons in susceptible and unsusceptible mice. The number is not significantly different between the two populations (Supplementary Figure 7).

- In Figure 8, the authors should also evaluate the levels of activated RAC1 and activated Cdc42, which are much more important than just basal levels of the proteins to infer an effect on actin dynamics. This is possible through kits that use specific adaptors to pulldown GTP-Rac1 and GTP-Cdc42;

Thanks for your constructive suggestion. An elevated level and hyperactivation of Rac1 protein are both associated with actin dynamics and dendritic development [1]. We agree that showing the levels of activated RAC1 is better to infer its effect on actin dynamics. Here in Figure 8, the purpose of this experiment is to prove the levels of actin organization related proteins are altered according to the expression level of S1PR1, thus drawing a conclusion that the actin organization was disrupted, but not to specifically emphasize that S1PR1 activated these proteins. We apologize for the confusion made but we think the current data is enough to support the conclusion.

Thanks again for your advice. Your understanding is greatly appreciated.

- In Figure 9C, the experiment is performed in an immortalised cell line. I feel this needs to be performed at least in primary hippocampal neurons;

Thanks for your suggestion. As suggested, we performed the experiment in primary hippocampal neurons. Knockdown of S1pr1 in primary hippocampal neurons induced reduction in the number of branches and filamentous actin. Please refer to the updated Figure 9C.

- In Figure 9D, the authors use a Yeast two-hybrid system to demonstrate the interaction between S1PR1 and Integrin α2. However, as the yeast two-hybrid system is based on the proximity of the GAL4 activating domain and the GAL4 binding domain, which are used to activate the transcription of reporter genes, the system is not often used when probing the interaction between transmembrane proteins. Could the authors use other transmembrane proteins as negative controls?;

Thanks for your question. We apologize for the unclear description in the method part. Traditional yeast two-hybrid system can only detect protein interactions that occur in the nucleus, but cannot detect ones between membrane proteins. Here, we utilized the split-ubiquitin membrane-based Yeast two-hybrid system. Briefly, in the ubiquitin system, ubiquitin, a protein composed of 76 amino acid residues that can mediate the ubiquitination degradation of target proteins by proteasomes, is split into two domains, namely Cub at the C-terminus and NbuG at the N-terminus, which are fused and expressed with the bait protein “Bait” and the prey protein “Prey”, respectively. At the same time, Cub is also fused with transcription factors. If Bait and Prey proteins could bind, Cub and NbuG would be brought together and a complete ubiquitin would be formed, which would be recognized by the proteasome and the fused transcription factor would be cut off and enter the cell nucleus to activate the expression of the reporter gene. We then determine whether the Bait and Prey proteins interact with each other through the growth of the yeast.

Thanks again for pointing this out. We reworded the method in M&M (Line 678-696).

- In Figure 9E, the immunoblot is very unconvincing. The bands in the inputs are very weak for both ITGA2 and S1PR1, the authors do not show the enrichment of S1PR1 upon its immunoprecipitation and the band for ITGA2 in the IP fraction has a weird appearance. Were these experiments performed on DG lysates only? If so, I suggest the authors repeat the experiment using the whole brain (or at least the whole hippocampus) so as to have more starting material. Alternatively, if this doesn't work, or in addition, they could also perform the immunoprecipitation in heterologous cells overexpressing the two proteins;

Thanks for the question and suggestion. We used DG lysates from both the dentate gyrus of a single mouse as the starting material. We updated the result which showed clearer bands (Figure 9E).

- About the point above, even if the results were convincing, the authors can't say that they demonstrate an interaction in vivo. In co-IP experiments, the interaction is much more likely to occur in the lysate during the incubation period rather than being conserved from the in vivo state. These co-IPs demonstrate the ability of proteins to interact, not necessarily that they do it in vivo. If the authors wanted to demonstrate this, they could perform a Proximity ligation assay in primary hippocampal neurons, using antibodies against S1PR1 and ITGA2.

Thanks for your concern. Co-immunoprecipitation (Co-IP) is the gold standard to identify protein-protein interactions [2], and it is one of the most efficient techniques to study these protein-protein interactions in vivo [3]. We repeated the experiment and followed the experimental procedure exactly to avoid the protein interaction due to over-incubation. Over-incubation, particularly at room temperature, may result in non-specific binding and therefore high background, thus we performed Co-IPs at 4°C to preserve protein interactions. We agree that Proximity ligation assay is better suited for studies of endogenously expressed proteins in primary cells [4]. Since we optimized the experiment procedure to avoid non-specific binding and particularly, Co-IP utilized proteins from DG lysates which could validate the specificity of the protein interaction in native tissue, we prefer to keep the Co-IP result in Figure 9E.

Thanks again for your suggestion. We appreciate your understanding on this matter.

- In Figure 9H, could the authors increase the N to see if shItga2 causes further KD in the CCI?

As suggested, we repeated the experiment and increased the N to 6. As shown in the following picture, shItga2 did not cause further KD in the CCI.

Author response image 1.

- To conclusively demonstrate that S1PR1 and ITGA2 participate in the same pathway, they could show that knocking down the two proteins at the same time does not have additive effects on behavioral tests compared to the knockdown of each one of them in isolation.

Thanks for your suggestion. As suggested, we knocked down the two proteins at the same and did not observe additive effects on behavioral tests compared to the knockdown of each one of them in isolation. Please refer to Figure 9L-O.

Other major concerns:

- Supplementary Figure 5: the image showing colocalisation between S1PR1 and CamKII is not very convincing. Is the S1PR1 antibody validated on Knockout or knockdown in immunostaining?;

S1PR1 is a membrane receptor and the S1P1 antibody (PA1-1040, Invitrogen) shows membranous staining with diffuse dot-like signals (Please refer to the image “A” provided by ThermoFisher Scientific). Here, we utilized the antibody to detect the expression of S1PR1 in DG granule cells. We can see the diffuse dot-like signals aggregated in each single granule cell. CaMKII shows intense staining around the border of the granule cell soma (Image “B”) [5]. According to the images shown in Supplementary Figure 5B, we concluded that S1PR1 is expressed in CaMKII+ cells.

Besides, as suggested, we validated the S1PR1 antibody on knockdown in immunostaining (Image “C” and “D”). The expression of S1PR1 is significantly decreased compared with the control.

Author response image 2.

- It would be interesting to check S1PR2 levels as a control in CCI-chronic animals;

As suggested, we quantified the S1PR2 levels in Sham and CCI animals, and there is no significant difference between groups (Supplementary Figure 9).

- Figure 1: I am a bit concerned about the Ns in these experiments. In the chronic pain experiments, the N for Sham is around 8 whereas is around 20 for CCI animals. Although I understand higher numbers are necessary to see the susceptible and unsusceptible populations, I feel that then the same number of Sham animals should be used;

Thanks for your concern. In the preliminary experiment, we noticed that the ratio of susceptible and unsusceptible populations is around 1:1. After the behavioral tests, we need to further take samples to investigate molecular and cellular changes of each group. Thus, we set sham around 8 and CCI around 20 to ensure that after characterization into susceptible and unsusceptible groups, each group has relatively equal numbers for further investigations.

- Figures 1E and 1G have much higher Ns than the other panels. Why is that? If they have performed this high number of animals why not show them in all panels?;

Thanks for your concern. For Figure 1B, C, D and F, we showed the data for each batch of experiment, while for Figure 1E and 1G, we used data collected from all batches of experiment. To show the data from a single batch, we would like to demonstrate the ratio of susceptible to unsusceptible is relatively stable, but not only based on a big sample size.

- In the experiments where viral injection is performed, the authors should show a zoomed-out image of the brain to show the precision of the injection and how spread the expression of the different viruses was;

As suggested, we showed the zoomed-out image in Supplementary Figure 6. The viruses are mainly expressed in the hippocampal DG.

- The authors should check if there is brain inflammation in CCI chronic animals. This would be interesting to explain if this could be the trigger for the effects seen in neurons. In particular, the authors should check astrocytes and microglia. This is of interest also because the pathways altered in Figure 8A are related to viral infection.

- If the previous point shows increased brain inflammation, it would be interesting for the authors to check whether a prolonged anti-inflammatory treatment in CCI animals administered before the insurgence of memory impairment could stop it from happening;

- In addition, the authors should speculate on what could be the signal that can induce these molecular changes starting from the site of injury;

- Also, as the animals are all WT, the authors should speculate on what could render some animals prone to have memory impairments and others resistant.<br />

Thanks for the above four suggestions. We have observed inflammation including T cell infiltration and microglia activation in the hippocampal DG in CCI chronic animals and also used S1PR1 modulator which has anti-lymphocyte mediated inflammatory effect to prevent the insurgence of memory impairment from happening. We also examined the alteration in the numbers of peripheral T-lymphocyte subsets and the serum levels of cytokines. Furthermore, we found a neuron-microglia dialogue in the DG which may promote the resilience to memory impairment in CCI animals. Since these are unpublished results, we apologize that we would not give much detailed information to the public at the current stage. We will publish these data as soon as possible. Thanks for your understanding.

Reviewer #2 (Public Review):

Summary:

The study investigates the molecular mechanisms underlying chronic pain-related memory impairment by focusing on S1P/S1PR1 signaling in the dentate gyrus (DG) of the hippocampus. Through behavioural tests (Y-maze and Morris water maze) and RNA-seq analysis, the researchers segregated chronic pain mice into memory impairment-susceptible and -unsusceptible subpopulations. They discovered that S1P/S1PR1 signaling is crucial for determining susceptibility to memory impairment, with decreased S1PR1 expression linked to structural plasticity changes and memory deficits.

Knockdown of S1PR1 in the DG induced a susceptible phenotype, while overexpression or pharmacological activation of S1PR1 promoted resistance to memory impairment and restored normal synaptic structure. The study identifies actin cytoskeleton-related pathways, including ITGA2 and its downstream Rac1/Cdc42 signaling, as key mediators of S1PR1's effects, offering new insights and potential therapeutic targets for chronic pain-related cognitive dysfunction.

This manuscript consists of a comprehensive investigation and significant findings. The study provides novel insights into the molecular mechanisms of chronic pain-related memory impairment, highlighting the critical role of S1P/S1PR1 signaling in the hippocampal dentate gyrus. The clear identification of S1P/S1PR1 as a potential therapeutic target offers promising avenues for future research and treatment strategies. The manuscript is well-structured, methodologically sound, and presents valuable contributions to the field.

Strengths:

(1) The manuscript is well-structured and written in clear, concise language. The flow of information is logical and easy to follow.

(2) The segregation of mice into memory impairment-susceptible and -unsusceptible subpopulations is innovative and well-justified. The statistical analyses are robust and appropriate for the data.

(3) The detailed examination of S1PR1 expression and its impact on synaptic plasticity and actin cytoskeleton reorganization is impressive. The findings are significant and contribute to the understanding of chronic pain-related memory impairment.

Weaknesses:

(1) Results: While the results are comprehensive, some sections are data-heavy and could be more reader-friendly with summarized key points before diving into detailed data.

Thanks for the suggestion. For the first sentence in each part/paragraph, we used statement that summarises what will be investigating in the following experiments to make it more reader-friendly. They are labeled as blue in the main text.

(2) Discussion: There is a need for a more balanced discussion regarding the limitations of the study. For example, addressing potential biases in the animal model or limitations in the generalizability of the findings to humans would strengthen the discussion. Also, providing specific suggestions for follow-up studies would be beneficial.

As suggested, we discussed more on the limitations of this study and outlined some directions for future research (Line 481-498).

(3) Conclusion: The conclusion, while concise, could better highlight the study's broader impact on the field and potential clinical implications.

Thanks. We reworded the conclusion to better highlight the impacts of this study (Line 501-505).

Reviewer #3 (Public Review):

Summary of the Authors' Objectives:

The authors aimed to delineate the role of S1P/S1PR1 signaling in the dentate gyrus in the context of memory impairment associated with chronic pain. They sought to understand the molecular mechanisms contributing to the variability in memory impairment susceptibility and to identify potential therapeutic targets.

Major Strengths and Weaknesses of the Study:

The study is methodologically robust, employing a combination of RNA-seq analysis, viral-mediated gene manipulation, and pharmacological interventions to investigate the S1P/S1PR1 pathway. The use of both knockdown and overexpression approaches to modulate S1PR1 levels provides compelling evidence for its role in memory impairment. The research also benefits from a comprehensive assessment of behavioral changes associated with chronic pain.

However, the study has some weaknesses. The categorization of mice into 'susceptible' and 'unsusceptible' groups based on memory performance requires further validation. Additionally, the reliance on a single animal model may limit the generalizability of the findings. The study could also benefit from a more detailed exploration of the impact of different types of pain on memory impairment.

Assessment of the Authors' Achievements:

The authors successfully identified S1P/S1PR1 signaling as a key factor in chronic pain-related memory impairment and demonstrated its potential as a therapeutic target. The findings are supported by rigorous experimental evidence, including biochemical, histological, and behavioral data. However, the study's impact could be enhanced by further exploration of the molecular pathways downstream of S1PR1 and by assessing the long-term effects of S1PR1 manipulation.

Impact on the Field and Utility to the Community:

This study is likely to have a significant impact on pain research by providing a novel perspective on the mechanisms underlying memory impairment in chronic pain conditions. The identification of the S1P/S1PR1 pathway as a potential therapeutic target could guide the development of new treatments.

Additional Context for Readers:

The study's approach to categorizing susceptibility to memory impairment could inspire new methods for stratifying patient populations in clinical settings.

Recommendations:

(1) A more detailed explanation of the k-means clustering algorithm and its application in categorizing mice should be provided.

As suggested, we explained the k-means clustering algorithm in details (Line 697-711).

(2) The discussion on the potential influence of different pain types or sensitivities on memory impairment should be expanded.

Thanks for your suggestion. We discussed this point in the limitations of this study (Line 484-491).

(3) The protocol for behavioral testing should be clarified and the potential for learning or stress effects should be addressed.

Thanks for your suggestion. We clarified the order of the battery of behavioral tests in this study (Line 537-542). We start with the least stressful test (Y-maze) and leave the most stressful of all for last (Morris Water maze) [6]. Besides, we also conducted behavioral assays to prove that a one-day rest is enough to decrease carryover effects from prior test (Y-maze). We examined the stress related behaviors one day after Y-maze (23d post CCI) using open field test (OFT) and elevated plus maze (EPM). As shown in Author response image 3, the tests did not reflect the mice were under stressful circumstances. Thus, the order in which the tests were performed are appropriate in this study.

Author response image 3.

(4) Conduct additional behavioral assays for other molecular targets implicated in the study.

We agree that other molecular targets on susceptibility to memory impairment would be interesting to know. Our study was designed to focus specifically on ITGA2 this time and we'd like to keep the focus intact, but we have included your point as a consideration for future study (Lines 496-498). Thank you for the suggestion.

(5) The effective drug thresholds and potential non-specific effects of pharmacological interventions should be discussed in more detail.

As suggested, we emphasized this point of drug SEW2871 in Line 242-245.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor concerns:

- In Figure 6E the lines of the different groups are not visible. Showing the errors as error bars for each point would probably be better;

We apologize for the mistake of using mean±SD here instead of mean±SEM. After changing to mean±SEM, the lines of Figure 6E, Figure 7E and 7L become much clearer. It looks a little bit messy to show the error bars since there are numerous points, so we prefer to keep the line style.

- Do the authors have any speculation on why the % time in the quadrant is not further affected in the KD Itga2 in CCI animals (Figure 9K)?;

In CCI animals, the level of S1PR1 expression is decreased. ITGA2 may participate in the same pathway with S1PR1. Thus, knocking down ITGA2 in CCI animals will not further affect the animal behaviors. This has been proved by knocking down the two proteins at the same time and no additive effects were observed on behavioral tests compared to the knockdown of each one of them in isolation (Figure 9L-O).

- In the methods, it's unclear if in the multiple infusion, the animals were anaesthetised or kept awake;

We have clarified this point in the method. mice were deeply anesthetized by 1% pentobarbital sodium (40 mg/kg, i.p.). (Line 649-650)

- As the DG is quite small, could the authors clarify if, when performing western blots, they used the two DGs from one animal for each sample or if they pulled together the DGs of several animals?;

We used the two DGs from one animal for each sample. The amount of protein extracted from each sample is enough for 20-30 times of Western Blot assays. We have now added this to the method for clarity (Line 612).

- Is it possible to check the correlation between performance in the YM and MWM with S1PR1 levels?;

We would also be interested in this point. The data that we have cannot reveal this for it is difficult to manipulate the S1PR1 levels by using KD and overexpression viruses.

- EM images have a poor resolution in the figures, could the authors show higher-resolution images?;

We have inserted 300 DPI images for high resolution output.

- In line 268 there is a mention of an "ShLamb1"?

We apologize for the mistake and it was revised.

Reviewer #3 (Recommendations For The Authors):

This study explored the role of S1P/S1PR1 signaling within the dentate gyrus (DG) in chronic pain-related memory impairment using a murine model. The authors identified decreased expression of S1PR1 in the DG of mice susceptible to memory deficits. They demonstrated that S1PR1 knockdown increased susceptibility to memory deficits, whereas its overexpression or pharmacological activation mitigated these effects. Further biochemical and immunofluorescence analyses indicated that disruptions in S1P/S1PR1 signaling were related to disruptions in actin cytoskeleton dynamics, influenced by molecular pathways involving ITGA2, Rac1/Cdc42 signaling, and the Arp2/3 complex. These findings offer intriguing insights and suggest a potential therapeutic target for treating memory impairment in chronic pain.

Major Concerns:

The following five major concerns are the same with the five recommendations from Reviewer 3 on Page 9-10. Please refer to the answers above.

(1) The division of subjects into 'susceptible' and 'unsusceptible' categories requires further clarification regarding the methodologies and rationale employed, particularly concerning the use of the k-means clustering algorithm in data analysis. This explanation will strengthen the scientific grounding of the categorization process.

(2) The categorization of 'susceptible' and 'unsusceptible' groups might also benefit from a more detailed analysis or discussion concerning the influence of different pain sensitivities or types of pain assessments. Although the study mentions that memory impairment stands independent of pain thresholds, a more nuanced exploration could provide deeper insights.

(3) The article could benefit from more clarity on the protocol of behavioral testing, especially regarding the potential effects of repeated testing on performance outcomes due to learning or stress.

(4) While the connection between S1P/S1PR1 signaling and the molecular pathways highlighted (ITGA2, Rac1/Cdc42, Arp2/3) is intriguing, only ITGA2 underwent further behavioral validation in vivo. Conducting additional behavioral assays for one or more of the molecular targets could substantially strengthen these findings.

(5) Discussions regarding effective drug thresholds and the potential for non-specific effects are essential to fully evaluate the implications of pharmacological interventions utilized in the study.

Minor Concerns:

(1) Clarification of evidence of the specific infusion sites in pharmacological experiments would enhance the transparency and replicability of these methods.

For the infusion of S1PR1 agonist, guide cannula (internal diameter 0.34 mm, RWD) was unilaterally implanted into DG of hippocampus (-1.3 A/P, -1.95 M/L, and -2.02 D/V) as evidenced by Figure 5B.

(2) It would be beneficial if the manuscript provided details regarding the efficiency and reach of viral transfection within the neuronal population. This information would help in assessing the impact of genetic manipulations.

S1PR1 immunostaining showed that the efficiency is quite high and the reach of viral transfection is sufficient.

Author response image 4.

(3) The manuscript should make explicit the normalization techniques used in quantitative assessments such as Western blotting, including the housekeeping genes or proteins used for this purpose.

Here, we used housekeeping protein normalization for normalizing Western blot data. GAPDH was used as the internal control. First, the stained blot is imaged, a rectangle is drawn around the target protein in each lane, and the signal intensity inside the rectangle is measured by using ImageJ. The signal intensity obtained can then be normalized by being divided by the signal intensity of the loading internal control (GAPDH) detected on the same blot. The average of the ratios from the control group is calculated, and all individual ratios are divided by this average to obtain a new set of values, which represent the normalized values (Line 619-625).

(4) Details about the control groups in behavioral assessments were subjected to comparable handling and experimental conditions as the chronic pain groups are crucial, barring nerve injury, for maintaining the integrity of the comparative analysis.

We agree that a control group and an experimental group is identical in all respects except for one difference-nerve injury. We have added this point in the method (Line 520-522).

Minor Recommendations:

The following four minor recommendations are the same with the four minor concerns from Reviewer 3 on Page 12-13. Please refer to the answers above.

(1) Clarify the specifics of infusion site verification in pharmacological experiments.

(2) Provide details on the efficiency and neuronal reach of viral transfections.

(3) Explicitly describe the normalization techniques used in quantitative assessments.

(4) Ensure that control groups in behavioral assessments undergo comparable handling to maintain analysis integrity.

References

(1) Gualdoni, S., et al., Normal levels of Rac1 are important for dendritic but not axonal development in hippocampal neurons. Biology of the Cell, 2007. 99(8): p. 455-464.

(2) Alam, M.S., Proximity Ligation Assay (PLA). Curr Protoc Immunol, 2018. 123(1): p. e58.

(3) Song, P., S. Zhang, and J. Li, Co-immunoprecipitation Assays to Detect In Vivo Association of Phytochromes with Their Interacting Partners. Methods Mol Biol, 2021. 2297: p. 75-82.

(4) Krieger, C.C., et al., Proximity ligation assay to study TSH receptor homodimerization and crosstalk with IGF-1 receptors in human thyroid cells. Frontiers in Endocrinology, 2022. 13.

(5) Arruda-Carvalho, M., et al., Conditional Deletion of α-CaMKII Impairs Integration of Adult-Generated Granule Cells into Dentate Gyrus Circuits and Hippocampus-Dependent Learning. The Journal of Neuroscience, 2014. 34(36): p. 11919-11928.

(6) Wolf, A., et al., A Comprehensive Behavioral Test Battery to Assess Learning and Memory in 129S6/Tg2576 Mice. PLoS One, 2016. 11(1): p. e0147733.

eLife Assessment

This important work investigates how orientation signals detected in higher brain areas may be transformed into motor responses in behaving animals. The authors characterize two types of descending neurons (DNs) that connect the brain to motor units and are involved in different aspects of turning control. They further show that orientation signals act by preferentially increasing relative stimulation onto left- or right-turn-inducing DNs. These convincing results, together with the independent work that they have inspired, represent significant progress in our understanding of mechanisms of animal navigation.

Reviewer #1 (Public review):

Summary:

The paper addresses the knowledge gap between the representation of goal direction in the central complex and how motor systems stabilize movement toward that goal. The authors focused on two descending neurons, DNa01 and 02, and showed that they play different roles in steering the fly toward a goal. They also explored the connectome data to propose a model to explain how these DNs could mediate response to lateralized sensory inputs. They finally used lateralized optogenetic activation/inactivation experiments to test the roles of these neurons in mediating turnings in freely walking flies.

Strengths:

The experiments are well-designed and controlled. The experiment in Figure 4 is elegant, and the authors put a lot of effort into ensuring that ATP puffs do not accidentally activate the DNs. They also have explained complex experiments well. I only have minor comments for the authors.

Weaknesses:

(1) I do not fully understand how the authors extracted the correlation functions from the population data in Figure 1. Since the ipsilateral DNs are anti-correlated with the contralateral ones, I expected that the average will drop to zero when they are pooled together (e.g., 1E-G). Of course, this will not be the case if all the data in Figure 1 are collected from the same brain hemisphere. It would be helpful if the authors could explain this.

(2) What constitutes the goal directions in Figures 1-3 and 8, as the authors could not use EPG activity as a proxy for goal directions? If these experiments were done in the dark, without landmarks, one would expect the fly's heading to drift randomly at times, and they would not engage the DNa01/02 for turning. Do the walking trajectories in these experiments qualify as menotactic bouts?

(3) In Figure 2B, the authors mentioned that DNa02 overpredicts and 01 underpredicts rapid turning and provided single examples. It would be nice to see more population-level quantification to support this claim.

Reviewer #2 (Public review):

The data is largely electrophysiological recordings coupled with behavioral measurements (technically impressive) and some gain-of-function experiments in freely walking flies. Loss-of-function was tested but had minimal effect, which is not surprising in a system with partially redundant control mechanisms. The data is also consistent with/complementary to subsequent manuscripts (Yang 2023, Feng 2024, and Ros 2024) showing additional descending neurons with contributions to steering in walking and flying.

The experiments are well executed, the results interesting, and the description clear. Some hypotheses based on connectome anatomy are tested: the insights on the pre-synaptic side - how sensory and central complex heading circuits converge onto these DNs are stronger than the suggestions about biomechanical mechanisms for how turning happens on the motor side.

Of particular interest is the idea that different sensory cues can converge on a common motor program. The turn-toward or turn-away mechanism is initiated by valence rather than whether the stimulus was odor or temperature or memory of heading. The idea that animals choose a direction based on external sensory information and then maintain that direction as a heading through a more internal, goal-based memory mechanism, is interesting but it is hard to separate conclusively.

The "see-saw", where left-right symmetry is broken to allow a turn, presumably by excitation on one side and inhibition of the other leg motor modules, is interesting but not well explained here. How hyperpolarization affects motor outputs is not clear.

The statement near Figure 5B that "DNa02 activity was higher on the side ipsilateral to the attractive stimulus, but contralateral to the aversive stimulus" is really important - and only possible to see because of the dual recordings.

Reviewer #3 (Public review):

Summary:

Rayshubskiy et al. performed whole-cell recordings from descending neurons (DNs) of fruit flies to characterize their role in steering. Two DNs implicated in "walking control" and "steering control" by previous studies (Namiki et al., 2018, Cande et al., 2018, Chen et al., 2018) were chosen by the authors for further characterization. In-vivo whole-cell recordings from DNa01 and DNa02 showed that their activity predicts spontaneous ipsilateral turning events. The recordings also showed that while DNa02 predicts transient turns DNa01 predicts slow sustained turns. However, optogenetic activation or inactivation showed relatively subtle phenotypes for both neurons (consistent with data in other recent preprints, Yang et al 2023 and Feng et al 2024). The authors also further characterized DNa02 with respect to its inputs and showed a functional connection with olfactory and thermosensory inputs as well as with the head-direction system. DNa01 is not characterized to this extent.

Strengths:

(1) In-vivo recordings and especially dual recordings are extremely challenging in Drosophila and provide a much higher resolution DN characterization than other recent studies that have relied on behavior or calcium imaging. Especially impressive are the simultaneous recordings from bilateral DNs (Figure 3). These bilateral recordings show clearly that DNa02 cells not only fire more during ipsilateral turning events but that they get inhibited during contralateral turns. In line with this observation, the difference between left and right DNa02 neuronal activity is a much better predictor of turning events compared to individual DNa02 activity.

(2) Another technical feat in this work is driving local excitation in the head-direction neuronal ensemble (PEN-1 neurons), while simultaneously imaging its activity and performing whole-cell recordings from DNa02 (Figure 4). This impressive approach provided a way to causally relate changes in the head-direction system to DNa02 activity. Indeed, DNa02 activity could predict the rate at which an artificially triggered bump in the PEN-1 ring attractor returns to its previous stable point.

(3) The authors also support the above observations with connectomics analysis and provide circuit motifs that can explain how the head direction system (as well as external olfactory/thermal stimuli) communicated with DNa02. All these results unequivocally put DNa02 as an essential DN in steering control, both during exploratory navigation as well as stimulus-directed turns.

Weaknesses:

(1) I understand that the first version of this preprint was already on biorxiv in 2020, and some of the "weaknesses" I list are likely a reflection of the fact that I'm tasked to review this manuscript in late 2024 (more than 4 years later). But given this is a 2024 updated version it suffers from laying out the results in contemporary terms. For instance, the manuscript lacks any reference to the DNp09 circuit implicated in object-directed turning and upstream to DNa02 even though the authors cite one of the papers where this was analyzed (Braun et al, 2024). More importantly, these studies (both Braun et al 2024 and Sapkal et al 2024) along with recent work from the authors' lab (Yang et al 2023) and other labs (Feng et al 2024) provide a view that the entire suite of leg kinematics changes required for turning are orchestrated by populations of heterogeneous interconnected DNs. Moreover, these studies also show that this DN-DN network has some degree of hierarchy with some DNs being upstream to other DNs. In this contemporary view of steering control, DNa02 (like DNg13 from Yang et al 2023) is a downstream DN that is recruited by hierarchically upstream DNs like DNa03, DNp09, etc. In this view, DNa02 is likely to be involved in most turning events, but by itself unable to drive all the motor outputs required for the said events. This reasoning could be used while discussing the lack of major phenotypes with DNa02 activation or inactivation observed in the current study, which is in stark contrast to strong phenotypes observed in the case of hierarchically upstream DNs like DNp09 or DNa03. In the section, "Contributions of single descending neuron types to steering behavior": the authors start off by asking if individual DNs can make measurable contributions to steering behavior. Once more, any citations to DNp09 or DNa03 - two DNs that are clearly shown to drive strong turning-on activation (Bidaye et al, 2020, Feng et al 2024) - are lacking. Besides misleading the reader, such statements also digress the results away from contemporary knowledge in the field. I appreciate that the brief discussion in the section titled "Ensemble codes for steering" tries to cover these recent updates. However, I think this would serve a better purpose in the introduction and help guide the results.

(2) The second major weakness is the lack of any immunohistochemistry (IHC) images quantifying the expression of the genetic tools used in these studies. Even though the main split-Gal4 tools for DNa01 and DNa02 were previously reported by Namiki et al, 2018, it is important to document the expression with the effectors used in this work and explicitly mention the expression in any ectopic neurons. Similarly, for any experiments where drivers were combined together (double recordings, functional connectivity) or modified for stochastic expression (Figure 8), IHC images are absolutely necessary. Without this evidence, it is difficult to trust many of the results (especially in the case of behavioral experiments in Figure 8). For example, the DNa01 genetic driver used by the authors is also expressed in some neurons in the nerve cord (as shown on the Flylight webpage of Janelia Research Campus). One wonders if all or part of the results described in Figure 8 are due to DNa01 manipulation or manipulation of the nerve cord neurons. The same applies for optic lobe neurons in the DNa02 driver.

(3) The paper starts off with a comparative analysis of the roles of DNa01 and DNa02 during steering. Unfortunately, after this initial analysis, DNa01 is largely ignored for further characterization (e.g. with respect to inputs, connectomics, etc.), only to return in the final figure for behavioral characterization where DNa01 seems to have a stronger silencing phenotype compared to DNa02. I couldn't find an explanation for this imbalance in the characterization of DNa01 versus DNa02. Is this due to technical reasons? Or was it an informed decision due to some results? In addition to being a biased characterization, this also results in the manuscript lacking a coherent thread, which in turn makes it a bit inaccessible to the non-specialist.

(4) There seems to be a discrepancy with regard to what is emphasized in the main text and what is shown in Figures S3/S4 in relation to the role of these DNs in backward walking. There are only two sentences in the main text where these figures are cited.<br /> a) "DNa01 and DNa02 firing rate increases were not consistently followed by large changes in forward velocity (Figs. 1G and S3)."<br /> b) "We found that rotational velocity was consistently related to the difference in right-left firing rates (Fig. 3B). This relationship was essentially linear through its entire dynamic range, and was consistent across paired recordings (Fig. 3C). It was also consistent during backward walking, as well as forward walking (Fig. S4)."<br /> These main text sentences imply the role of the difference between left and right DNa02 in turning. However, the actual plots in the Figures S3 and S4 and their respective legends seem to imply a role in "backward walking". For instance, see this sentence from the legend of Figure S3 "When (ΔvoltageDNa02>>ΔvoltageDNa01), the fly is typically moving backward. When (firing rateDNa02>>firing rateDNa01), the fly is also often moving backward, but forward movement is still more common overall, and so the net effect is that forward velocity is small but still positive when (firing rateDNa02>>firing rateDNa01). Note that when we condition our analysis on behavior rather than neural activity, we do see that backward walking is associated with a large firing rate differential (Fig. S4)." This sort of discrepancy in what is emphasized in the text, versus what is emphasized in the figures, ends up confusing the reader. More importantly, I do not agree with any of these conclusions regarding the implication of backward walking. Both Figures S3 and S4 are riddled with caveats, misinterpretations, and small sample sizes. As a result, I actually support the authors' decision to not infer too much from these figures in the "main text". In fact, I would recommend going one step further and removing/modifying these figures to focus on the role of "rotational velocity". Please find my concerns about these two figures below:<br /> a) In Figures S3 and S4, every heat map has a different scale for the same parameter: forward velocity. S3A is -10 to +10mm/s. S3B is -6 to +6 S4B (left) is -12 to +12 and S4B (right) is -4 to +4. Since the authors are trying to depict results based on the color-coding this is highly problematic.<br /> b) Figure S3A legend "When (ΔvoltageDNa02>>ΔvoltageDNa01), the fly is typically moving backward." There are also several instances when ΔvoltageDNa02= ΔvoltageDNa01 and both are low (lower left quadrant) when the fly is typically moving backwards. So in my opinion, this figure in fact suggests DNa02 has no role in backward velocity control.<br /> c) Based on the example traces in S4A, every time the fly walks backwards it is also turning. Based on this it is important to show absolute rotational velocity in Figure S4C. It could be that the fly is turning around the backward peak which would change the interpretation from Figure S4C. Also, it is important to note that the backward velocities in S4A are unprecedentedly high. No previous reports show flies walking backwards at such high velocities (for example see Chen et al 2018, Nat Comm. for backward walking velocities on a similar setup).<br /> d) In my opinion, Figure S4D showing that right-left DNa02 correlates with rotational velocity, regardless of whether the fly is in a forward or backward walking state, is the only important and conclusive result in Figures S3/S4. These figures should be rearranged to only emphasize this panel.

(5) Figure 3 shows a really nice analysis of the bilateral DNa02 recordings data. While Figure S5 shows that authors have a similar dataset for DNa01, a similar level analysis (Figures 3D, E) is not done for DNa01 data. Is there a reason why this is not done?

(6) In Figure 4 since the authors have trials where bump-jump led to turning in the opposite direction to the DNa02 being recorded, I wonder if the authors could quantify hyperpolarization in DNa02 as is predicted from connectomics data in Figure 7.

(7) Figure 6 suggests that DNa02 contains information about latent steering drives. This is really interesting. However, in order to unequivocally claim this, a higher-resolution postural analysis might be needed. Especially given that DNa02 activation does not reliably evoke ipsilateral turning, these "latent" steering events could actually contain significant postural changes driven by DNa02 (making them "not latent"). Without this information, at least the authors need to explicitly mention this caveat.

(8) Figure 7 would really benefit from connectome data with synapse numbers (or weighted arrows) and a corresponding analysis of DNa01.

(9) In Figure 8E, the most obvious neuronal silencing phenotype is decreased sideways velocity in the case of DNa01 optogenetic silencing. In Figure S2, the inverse filter for sideways velocity for DNa01 had a higher amplitude than the rotational velocity filter. Taken together, does this point at some role for DNa01 in sideways velocity specifically?

(10) In Figure 8G, the effect on inner hind leg stance prolongation is very weak, and given the huge sample size, hard to interpret. Also, it is not clear how this fits with the role of DNa01 in slow sustained turning based on recordings.

Author response:

We thank the reviewers for their feedback. We are currently revising the manuscript to address their questions and concerns. Here we briefly summarize our planned revisions.

Reviewer 1 requested clarification on three points. We will clarify all these points with text edits. One point is brief enough to be addressed here: in cases when we pooled data from the left and right hemispheres, the reviewer wants to know how this was done. Simply put, we defined the “ipsi” side of the body as the side where the recorded DN resided, and we defined “contra” as the other side.

Reviewer 2 requested clarification on two minor points. We will clarify these points with text edits and with an additional analysis.

Reviewer 3 had a number of substantive concerns. Briefly:

(1) The reviewer asks us to improve its discussion of some relevant literature. We will provide updated information on the DN steering network, and in particular, we will cite Bidaye et al. 2020 and Sapkal et al. 2024. We apologize for the oversight.

(2) The reviewer asks us for immunofluorescent images documenting the expression patterns of our effector transgenes. With regard to GtACR1::eYPF expression, we will include these images in our resubmission. With regard to ReachR expression, we expressed this reagent stochastically under hs-FLP control, and so different brains had different expression patterns; however, we carefully documented the number of DNa02 cells that expressed ReachR in each brain. With regard to GFP expression, these expression patterns are available online from the FlyLight documentation associated with Namiki et al. eLife 2018 (https://splitgal4.janelia.org/precomputed/Descending%20Neurons%202018.html). The UAS-GFP transgene used by Namiki et al. 2018 (pJFRC200-10XUASIVS-myr::smGFP-HA in attP18) is different from the UAS-GFP transgene we used (10XUAS-IVS-mCD8::GFP(su(Hw)attP8), and so there may be minor differences in expression pattern. However, it should be noted that we only used GFP expression to target somata for patch clamp recording, and DNa01 and DNa02 somata have a distinctive location and a distinctive size; when we performed these recordings, we only targeted a soma in this location, and we verified that there were no “distractor” somata in this vicinity with similar size and appearance. The same applies to patch clamp recordings targeted via Halo7 expression (SiR110-HaloTag fluorescence). In paired recordings from both DNa02 and DN01, we verified the identity of each cell as described in Fig. S1.

(3) The reviewer asks why we focused on DNa02 in the latter part of the manuscript, rather than DNa01. We made this decision because DNa02 is more highly predictive of steering behavior, as compared to DNa01 (Fig. 1H). Also, an impulse of DNa02 activity is followed by a relatively large turning maneuver, on average, whereas an impulse of DNa01 activity is followed by a relatively small turning maneuver (Fig. 1E-F). Moreover, DNa02 has many more synaptic inputs in the brain (Fig. 7A), and it has many more direct synaptic connections onto motor neurons (Fig. 1B).

(4) The reviewer highlights difficulties in interpreting DN activity during backward movement (Figs. S3/S4). We included this material in the spirit of completeness, but we agree with the reviewer that it is difficult to interpret. In our revision, we will omit Fig. S3C and Fig. S4A-B, and we will revise these legends to improve clarity.

(5) The reviewer asks why do a systematic analysis of paired DNa01 recordings, as we did for DNa02. It is difficult to get paired right/left recordings from two DNs of the same type in the same fly, while the fly is walking vigorously, and we were only able to get two such paired recordings from DNa01. We did not feel this was a sufficiently large sample size to support a systematic analysis. We chose not to invest more time in getting more paired DNa01 recordings because we thought that DNa02 was more important, for the reasons noted above.

(6) The reviewer asks for an analysis of trials where bump-jump led to turning in the opposite direction to the DNa02 being recorded. We will provide this analysis in the revision.

(7) The reviewer points out that “latent” steering drives might not be latent, as they might produce small postural changes we are not capturing. This is a fair point, and we will note this in our revision.

(8) The reviewer asks for a systematic analysis of DNa01 inputs in Figure 7, similar to our analysis of DNa02 inputs. Here we would prefer to focus on DNa02, for three reasons. First, we think DNa02 is likely more important, for the reasons noted above. Second, there has been some uncertainty as to the identity of DNa01 in connectome data; indeed, in the hemibrain data set, the cell recently identified as DNa01 was annotated as VES006 (Schlegel et al. Nature 634: 139-152). Third, the cell now identified as DNa01 does not receive direct input from either the central complex or the mushroom body, and for this reason, we felt that the inputs to DNa01 might be less interesting to a general audience.

(9) The reviewer wonders whether DNa01 is more involved in sideways movement, rather than rotational movement. Our data do not support this conclusion: rather, our data show that DNa01 is only weakly correlated with sideways movement. Thus, the forward filter (Fig. 1F) shows that an impulse of DNa01 activity is (on average) followed by a relatively small amount of sideways movement. Conversely, the reverse filter (in Fig. S2I) shows that an impulse of sideways movement is (on average) preceded by a relatively large amount of DNa01 activity.

(10) The reviewer points out that the phenotype associated with optogenetic suppression in Fig. 8G is weak. We will highlight this point and discuss potential reasons for this weak phenotype in the revision.

eLife Assessment

This study presents an important finding on sperm flagellum and HTCA stabilization. The evidence supporting the authors' claims is convincing. The work will be of broad interest to cell and reproductive biologists working on cilium and sperm biology.

Reviewer #1 (Public review):

In this paper, Wu et al. investigated the physiological roles of CCDC113 in sperm flagellum and HTCA stabilization by using CRISPR/Cas knockouts mouse models, co-IP and single sperm imaging. They find that CCDC113 localizes in the linker region among radial spokes, the nexin-dynein regulatory complex (N-DRC), and doublet microtubules (DMTs) RS, N-DRC and DMTs and interacts with axoneme-associated proteins CFAP57 and CFAP91, acting as an adaptor protein that facilitates the linkage between RS, N-DRC and DMTs within the sperm axoneme. They show the disruption of CCDC113 produced spermatozoa with disorganized sperm flagella and CFAP91, DRC2 could not colocalize with DMTs in Ccdc113-/- spermatozoa. Interestingly, the data also indicate that CCDC113 could localize on the HTCA region, and interact with HTCA-associated proteins. The knockout of Ccdc113 could also produce acephalic spermatozoa. By using Sun5 and Centlein knockout mouse models, the authors further find SUN5 and CENTLEIN are indispensable for the docking of CCDC113 to the implantation site on the sperm head. Overall, the experiments were designed properly and performed well to support the authors' observation in each part. Furthermore, the study's findings offer valuable insights into the physiological and developmental roles of CCDC113 in the male germ line, which can provide insight into impaired sperm development and male infertility. The conclusions of this paper are mostly well supported by data, but some points need to be clarified and discussed.

(1) In Fig. 1, a sperm flagellum protein, which is far way from CCDC113, should be selected as a negative control to exclude artificial effects in co-IP experiments.<br /> (2) Whether the detachment of sperm head and tail in Ccdc113-/- mice is a secondary effect of the sperm flagellum defects? The author should discuss this point.<br /> (3) Given that some cytoplasm materials could be observed in Ccdc113-/- spermatozoa (Fig. 5A), whether CCDC113 is also essential for cytoplasmic removal?<br /> (4) Although CCDC113 could not bind to PMFBP1, the localization of CCDC113 in Pmfbp1-/- spermatozoa should be also detected to clarify the relationship between CCDC113 and SUN5-CENTLEIN-PMFBP1.

Comments on revisions:

The authors addressed all my concerns. The manuscript was greatly improved.

Reviewer #2 (Public review):

Summary:

In the present study, the authors select the coiled-coil protein CCDC113 and revealed its expression in the stages of spermatogenesis in the testis as well as in the different steps of spermiogenesis with expression also mapped in the different parts of the epididymis. Gene deletion led to male infertility in CRISPR-Cas9 KO mice and PAS staining showed defects mapped in the different stages of the seminiferous cycle and through the different steps of spermiogenesis. EM and IF with several markers of testis germ cells and spermatozoa in the epididymis indicated defects in flagella and head-to-tail coupling for flagella as well as acephaly. The authors' co-IP experiments of expressed CCDC113 in HEK293T cells indicated an association with CFAP91 and DRC2 as well as SUN5 and CENTLEIN.

The authors propose that CCDC113 connects CFAP91 and DRC2 to doublet microtubules of the axoneme and CCDC113's association with SUN5 and CENTLEIN to stabilize the sperm flagellum head-to-tail coupling apparatus. Extensive experiments mapping CCDC13 during postnatal development are reported as well as negative co-IP experiments and studies with SUN5 KO mice as well as CENTLEIN KO mice.

Strengths:

The authors provide compelling observations to indicate the relevance of CCDC113 to flagellum formation with potential protein partners. The data are relevant to sperm flagella formation and its coupling to the sperm head.

Weaknesses:

The authors' observations are consistent with the model proposed but the authors' conclusions for the mechanism may require direct demonstration in sperm flagella. The Walton et al paper shows human CCDC96/113 in cilia of human respiratory epithelia. An application of such methodology to the proteins indicated by Wu et al for the sperm axoneme and head-tail coupling apparatus is eagerly awaited as a follow-up study.

Author response:

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

This study presents a valuable finding on sperm flagellum and HTCA stabilization. The evidence supporting the authors' claims is incomplete. The work will be of broad interest to cell and reproductive biologists working on cilium and sperm biology.

We thank the Editor and the two reviewers for their time and thorough evaluation of our manuscript. We greatly appreciate their valuable guidance on improving our study. In the revised manuscript, we have conducted additional experiments and provided quantitative data in response to the reviewers' comments. Furthermore, we have refined the manuscript and added further context to elucidate the significance of our findings for the readers.

Public Reviews:

Reviewer #1 (Public Review):

In this paper, Wu et al. investigated the physiological roles of CCDC113 in sperm flagellum and HTCA stabilization by using CRISPR/Cas knockouts mouse models, co-IP, and single sperm imaging. They find that CCDC113 localizes in the linker region among radial spokes, the nexin-dynein regulatory complex (N-DRC), and doublet microtubules (DMTs) RS, N-DRC, and DMTs and interacts with axoneme-associated proteins CFAP57 and CFAP91, acting as an adaptor protein that facilitates the linkage between RS, N-DRC, and DMTs within the sperm axoneme. They show the disruption of CCDC113 produced spermatozoa with disorganized sperm flagella and CFAP91, DRC2 could not colocalize with DMTs in Ccdc113-/- spermatozoa. Interestingly, the data also indicate that CCDC113 could localize on the HTCA region, and interact with HTCA-associated proteins. The knockout of Ccdc113 could also produce acephalic spermatozoa. By using Sun5 and Centlein knockout mouse models, the authors further find SUN5 and CENTLEIN are indispensable for the docking of CCDC113 to the implantation site on the sperm head. Overall, the experiments were designed properly and performed well to support the authors' observation in each part. Furthermore, the study's findings offer valuable insights into the physiological and developmental roles of CCDC113 in the male germ line, which can provide insight into impaired sperm development and male infertility. The conclusions of this paper are mostly well supported by data, but some points need to be clarified and discussed.

We thank Reviewer #1 for his or her critical reading and the positive assessment.

(1) In Figure 1, a sperm flagellum protein, which is far away from CCDC113, should be selected as a negative control to exclude artificial effects in co-IP experiments.

We greatly appreciate Reviewer #1’s insightful suggestion. In response, we selected two sperm outer dense fiber proteins, ODF1 and ODF2, which are located distant from the sperm axoneme, as negative controls in the co-IP experiments. As shown in Figure 1- figure supplement 1A and B, neither ODF1 nor ODF2 bound to CCDC113, indicating the interaction observed in Figure 1 is not an artifact.

(2) Whether the detachment of sperm head and tail in Ccdc113-/- mice is a secondary effect of the sperm flagellum defects? The author should discuss this point.

Good question. Considering that CCDC113 is localized in the sperm neck region and interacts with SUN5 and CENTLEIN, it may play a direct role in connecting the sperm head and tail. Indeed, PAS staining revealed that Ccdc113–/– sperm heads exhibit abnormal orientation in stages V–VIII of the seminiferous epithelia (Figure 6C-D). Furthermore, transmission electron microscopy (TEM) analysis indicated that the absence of CCDC113 caused detachment of the damaged coupling apparatus from the sperm head in step 9–11 spermatids (Figure 6E). These results suggest that the detachment of the sperm head and tail in Ccdc113–/– mice may not be a secondary effect of sperm flagellum defects. We have discussed this point further below:

“CCDC113 can interact with SUN5 and CENTLEIN, but not PMFBP1 (Figure 7A-C), and left on the tip of the decapitated tail in Sun5–/– and Centlein–/– spermatozoa (Figure 7K and L). Furthermore, CCDC113 colocalizes with SUN5 in the HTCA region, and immunofluorescence staining in spermatozoa shows that SUN5 is positioned closer to the sperm nucleus than CCDC113 (Figure 7G and H). Therefore, SUN5 and CENTLEIN may be closer to the sperm nucleus than CCDC113. PAS staining revealed that Ccdc113–/– sperm heads are abnormally oriented in stages V–VIII seminiferous epithelia (Figure6 C and D), and TEM analysis further demonstrated that the disruption of CCDC113 causes the detachment of the destroyed coupling apparatus from the sperm head in step 9–11 spermatids (Figure 6E). All these results suggest that the detachment of sperm head and tail in Ccdc113–/– mice may not be a secondary effect of sperm flagellum defects.”

(3) Given that some cytoplasm materials could be observed in Ccdc113-/- spermatozoa (Fig. 5A), whether CCDC113 is also essential for cytoplasmic removal?

Good question. Unremoved cytoplasm could be detected in spermatozoa by using transmission electron microscopy (TEM) analysis, including disrupted mitochondria, damaged axonemes, and large vacuoles. These observations indicate defects in cytoplasmic removal in Ccdc113–/– mice. We have discussed this point as below:

“Moreover, TEM analysis detected excess residual cytoplasm in spermatozoa, including disrupted mitochondria, damaged axonemes, and large vacuoles, indicating defects in cytoplasmic removal in Ccdc113–/– mice (Figure 5A).”

(4) Although CCDC113 could not bind to PMFBP1, the localization of CCDC113 in Pmfbp1-/- spermatozoa should be also detected to clarify the relationship between CCDC113 and SUN5-CENTLEIN-PMFBP1.

We appreciate Reviewer #1’s suggestion. We have analyzed the localization of CCDC113 in Pmfbp1-/- spermatozoa and found that CCDC113 was located at the tip of the decapitated tail in Pmfbp1-/- spermatozoa (Figure 7K and L). This finding has been incorporated into the revised manuscript as below:

“To further elucidate the functional relationships among CCDC113, SUN5, CENTLEIN, and PMFBP1 at the sperm HTCA, we examined the localization of CCDC113 in Sun5-/-, Centlein–/–, and Pmfbp1–/– spermatozoa. Compared to the control group, CCDC113 was predominantly localized on the decapitated flagellum in Sun5-/-, Centlein–/–, and Pmfnp1–/– spermatozoa (Figure 7K and L), indicating SUN5, CENTLEIN, and PMFBP1 are crucial for the proper docking of CCDC113 to the implantation site on the sperm head. Taken together, these data demonstrate that CCDC113 cooperates with SUN5 and CENTLEIN to stabilize the sperm HTCA and anchor the sperm head to the tail.”

Reviewer #2 (Public Review):

Summary:

In the present study, the authors select the coiled-coil protein CCDC113 and revealed its expression in the stages of spermatogenesis in the testis as well as in the different steps of spermiogenesis with expression also mapped in the different parts of the epididymis. Gene deletion led to male infertility in CRISPR-Cas9 KO mice and PAS staining showed defects mapped in the different stages of the seminiferous cycle and through the different steps of spermiogenesis. EM and IF with several markers of testis germ cells and spermatozoa in the epididymis indicated defects in flagella and head-to-tail coupling for flagella as well as acephaly. The authors' co-IP experiments of expressed CCDC113 in HEK293T cells indicated an association with CFAP91 and DRC2 as well as SUN5 and CENTLEIN.

The authors propose that CCDC113 connects CFAP91 and DRC2 to doublet microtubules of the axoneme and CCDC113's association with SUN5 and CENTLEIN to stabilize the sperm flagellum head-to-tail coupling apparatus. Extensive experiments mapping CCDC13 during postnatal development are reported as well as negative co-IP experiments and studies with SUN5 KO mice as well as CENTLEIN KO mice.

Strengths:

The authors provide compelling observations to indicate the relevance of CCDC113 to flagellum formation with potential protein partners. The data are relevant to sperm flagella formation and its coupling to the sperm head.

We are grateful to Reviewer #2 for his or her recognition of the strength of this study.

Weaknesses:

The authors' observations are consistent with the model proposed but the authors' conclusions for the mechanism may require direct demonstration in sperm flagella. The Walton et al paper shows human CCDC96/113 in cilia of human respiratory epithelia. An application of such methodology to the proteins indicated by Wu et al for the sperm axoneme and head-tail coupling apparatus is eagerly awaited as a follow-up study.

We thank Reviewer 2 for his/her kindly help in improving the manuscript.  We now understand that directly detection of CCDC113 precise localization in sperm axoneme and head-tail coupling apparatus (HTCA) using cryo-electron microscopy (cryo-EM) could powerfully strengthen our model. Recent advances in cryo-EM have indeed advanced our understanding of axonemal structures analysis of axonemal structures and determined the structures of native axonemal DMTs from mouse, bovine, and human sperm (Leung et al., 2023; Zhou et al., 2023). However, high-resolution structures of sperm axoneme and HTCA regions, including those involving CCDC113, have yet to be fully characterized. Thus, we would like to discuss this point and consider it a valuable direction for future research.

“Given that the cryo-EM of sperm axoneme and HTCA could powerfully strengthen the role of CCDC113 in stabilizing sperm axoneme and head-tail coupling apparatus, it a valuable direction for future research.”

References:

Bazan, R., Schröfel, A., Joachimiak, E., Poprzeczko, M., Pigino, G., & Wloga, D. (2021). Ccdc113/Ccdc96 complex, a novel regulator of ciliary beating that connects radial spoke 3 to dynein g and the nexin link. PLoS Genet, 17(3), e1009388.

Ghanaeian, A., Majhi, S., McCafferty, C. L., Nami, B., Black, C. S., Yang, S. K., Legal, T., Papoulas, O., Janowska, M., Valente-Paterno, M., Marcotte, E. M., Wloga, D., & Bui, K. H. (2023). Integrated modeling of the Nexin-dynein regulatory complex reveals its regulatory mechanism. Nat Commun, 14(1), 5741.

Leung, M. R., Zeng, J., Wang, X., Roelofs, M. C., Huang, W., Zenezini Chiozzi, R., Hevler, J. F., Heck, A. J. R., Dutcher, S. K., Brown, A., Zhang, R., & Zeev-Ben-Mordehai, T.  (2023). Structural specializations of the sperm tail. Cell, 186(13), 2880-2896.e2817

Walton, T., Gui, M., Velkova, S., Fassad, M. R., Hirst, R. A., Haarman, E., O'Callaghan, C., Bottier, M., Burgoyne, T., Mitchison, H. M., & Brown, A. (2023). Axonemal structures reveal mechanoregulatory and disease mechanisms. Nature, 618(7965), 625-633.

Zhou, L., Liu, H., Liu, S., Yang, X., Dong, Y., Pan, Y., Xiao, Z., Zheng, B., Sun, Y., Huang, P., Zhang, X., Hu, J., Sun, R., Feng, S., Zhu, Y., Liu, M., Gui, M., & Wu, J. (2023). Structures of sperm flagellar doublet microtubules expand the genetic spectrum of male infertility. Cell, 186(13), 2897-2910.e2819.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Please provide full gel for the Figure 2C experiment (could be as a supplementary file).

Thanks for your insightful suggestions. We have replaced Figure 2C and provided the full gel in Figure 2-figure supplement 1A.

(2) The authors write on Line 163 "In contrast, the flagellum staining appeared reduced in Ccdc113-/- seminiferous tubules (Fig. 2J, red asterisk)." However, the magnification of the pictures is not sufficient to distinguish anything in the panel mentioned, please provide others.

Many thanks for pointing this out. We have provided the iconic figure to show the flagella defect in seminiferous tubules.

(3) Please add statistical p-values for figures.

Thanks for your valuable advice. We have added statistical p-values to the figures in the revised manuscript.

(4) Line 128: Should "speculate" be "speculated"?

Thank you for pointing out this problem. We have corrected it in the revised manuscript, as shown below:

“Given that CFAP91 has been reported to stabilize RS on the DMTs (Bicka et al., 2022; Dymek et al., 2011; Gui et al., 2021) and cryo-EM analysis shows that CCDC113 is closed to DMTs, we speculated that CCDC113 may connect RS to DMTs by binding to CFAP91 and microtubules.”

(5) In lines 384-385, more "-" is typed.

Thank you for pointing out this problem. We have corrected it in the revised manuscript, as shown below:

“Furthermore, CCDC113 colocalizes with SUN5 in the HTCA region, and immunofluorescence staining in spermatozoa shows that SUN5 is closer to the sperm nucleus than CCDC113 (Figure 7G and H). Therefore, SUN5 and CENTLEIN may be closer to the sperm nucleus than CCDC113.”

(6) In general, the article has many typos and should be professionally proofread.

Many thanks for pointing this out. We have thoroughly revised the manuscript with the assistance professional proofreading.

Reviewer #2 (Recommendations For The Authors):

Can the authors indicate in the Materials and Methods if n=3 biological replicates were done for all co-IP, EM, LM, and IF studies? The statistical analysis section indicates this but quantification is missing for most figures including co-IP, most IF, PAS staining, EM, etc.

We thank Reviewer 2 for the insightful comments and guidance to improve our data quality. All the experiments in this study were repeated at least three times to ensure reproducibility. We have quantified the co-IP experiments in Figures 1C-H and 7A-F, the IF data in Figures 2K, 5C, and 5D, as well as the PAS staining in Figure 6C. Since electron microscopy samples require very little testicular tissue and the sections obtained are very thin, the likelihood of capturing sections specifically at the sperm head-tail junction is considerably low. This challenge makes it difficult to perform quantitative analysis and statistical evaluation in the TEM experiment. To address this limitation, we have quantified the percentage of _Ccdc113-/-_sperm heads with abnormal orientation in stages V–VIII of the seminiferous epithelium to indicate impaired head-to-tail anchorage.

Figure S2 is compelling and might be indicated as a major figure instead of a supplementary figure.

We appreciate the positive comment. We have included it as a major figure in Figure 3F.

Figure 4A may be incomplete. Data sets for RNA expression suggest high expression in the ovary and other organs in males and females including the brain and are not indicated by the authors. Figure 4A may be considered for removal with a more complete study for another paper.

Thank you for pointing out this issue. We reviewed RNA expression data from various tissues using RNA-Seq data from Mouse ENCODE (https://www.ncbi.nlm.nih.gov/gene/244608) and found that CCDC113 is highly expressed in the testis, but not significantly in the ovary and brain (Figure 4- figure supplement 1A). Additionally, we re-evaluated CCDC113 protein levels in the spleen, lung, kidney, testis, intestine, stomach, brain, and ovary, confirming that it is highly expressed in the testes, with negligible expression in the ovary and brain (Figure 4- figure supplement 1B). In line with Reviewer 2's suggestion, we have removed Figure 4A in the revised manuscript.

There are grammatical errors throughout the manuscript and Figure 7 is truncated.

Thank you for pointing out this problem. We have thoroughly revised the manuscript with the assistance professional proofreading.

The Introduction and Discussion parts of the paper may need some clarification for the general reader. The material in the "Additional Context " section of the critique below may be a helpful place to introduce what a stage is, and the steps in germ cell development in the testis with the latter of course where and when the flagellum develops.

We appreciate your valuable suggestions. We have referred to the material in the “Additional Context” section to introduce the stages of spermatogenesis and the steps in germ cell development in the testis in the introduction and results.

“Male fertility relies on the continuous production of spermatozoa through a complex developmental process known as spermatogenesis. Spermatogenesis involves three primary stages: spermatogonia mitosis, spermatocyte meiosis, and spermiogenesis. During spermiogenesis, spermatids undergo complex differentiation processes to develop into spermatozoa, which includes nuclear elongation, chromatin remodeling, acrosome formation, cytoplasm elimination, and flagellum development (Hermo et al., 2010).”

Hermo, L., Pelletier, R. M., Cyr, D. G., & Smith, C. E. (2010). Surfing the wave, cycle, life history, and genes/proteins expressed by testicular germ cells. Part 1: background to spermatogenesis, spermatogonia, and spermatocytes. Microscopy research and technique, 73(4), 241–278. https://doi.org/10.1002/jemt.20783

“Pioneering work in the mid-1950s used the PAS stain in histologic sections of mouse testis to visualize glycoproteins of the acrosome and Golgi in seminiferous tubules (Oakberg, 1956). The pioneers discovered in cross-sectioned seminiferous tubules the association of differentiating germ cells with successive layers to define different stages that in mice are twelve, indicated as Roman numerals (XII). For each stage, different associations of maturing germ cells were always the same with early cells in differentiation at the periphery and more mature cells near the lumen. In this way, progressive differentiation from stem cells to mitotic, meiotic, acrosome-forming, and post-acrosome maturing spermatocytes was mapped to define spermatogenesis with the XII stages in mice representing the seminiferous cycle. The maturation process from acrosome-forming cells to mature spermatocytes is defined as spermiogenesis with 16 different steps that are morphologically distinct spermatids (O'Donnell L, 2015).”

Oakberg, E. F. (1956). A description of spermiogenesis in the mouse and its use in analysis of the cycle of the seminiferous epithelium and germ cell renewal. The American journal of anatomy, 99(3), 391-413. https://doi.org/10.1002/aja.1000990303

O'Donnell L. (2015). Mechanisms of spermiogenesis and spermiation and how they are disturbed. Spermatogenesis, 4(2), e979623. https://doi.org/10.4161/21565562.2014.979623

For the Discussion, the authors indicate that the function of CCDC113 in mammals is unknown yet the authors point to the work of Walton et al on human respiratory epithelia that points to a function for CCDC96/113. The work in the manuscript here does indicate a role in sperm flagella and the head-to-tail coupling apparatus but remains descriptive until the methodology of Walton et al is applied. Hopefully, the authors will consider it for a follow-up study.

Thank you for pointing out this problem. We have revised this part and highlighted the Walton et al’s work in the Discussion.

“CCDC113 is a highly evolutionarily conserved component of motile cilia/flagella. Studies in the model organism, Tetrahymena thermophila, have revealed that CCDC113 connects RS3 to dynein g and the N-DRC, which plays essential role in cilia motility (Bazan et al., 2021; Ghanaeian et al., 2023). Recent studies have also identified the localization of CCDC113 within the 96-nm repeat structure of the human respiratory epithelial axoneme, and localizes to the linker region among RS, N-DRC and DMTs (Walton et al., 2023). In this study, we reveal that CCDC113 is indispensable for male fertility, as Ccdc113 knockout mice produce spermatozoa with flagellar defects and head-tail linkage detachment (Figure 3D).”

“Overall, we identified CCDC113 as a structural component of both the flagellar axoneme and the HTCA, where it performs dual roles in stabilizing the sperm axonemal structure and maintaining the structural integrity of HTCA. Given that the cryo-EM of sperm axoneme and HTCA could powerfully strengthen the role of CCDC113 in stabilizing sperm axoneme and head-tail coupling apparatus, it a valuable direction for future research.”

The Discussion may be focused on the key aspects of CCDC113 related to sperm flagella and the head-to-tail coupling apparatus that represent a genuine advance. The more speculative parts of the Discussion that have not been addressed by experimentation in the Results section may be considered for removal in the Discussion section.

Thank you for pointing out this. We have removed the speculative parts of the Discussion that have not been addressed by experimentation in the Results section.

Additional Context to help readers understand the significance of the work:

Pioneering work in the mid-1950s used the periodic acid Schiff (PAS) stain in histologic sections of rodent testis to visualize glycoproteins of the acrosome and Golgi in seminiferous tubules. The pioneers discovered in cross-sectioned seminiferous tubules the association of differentiating germ cells with successive layers to define different stages that in mice are twelve, indicated as Roman numerals (XII). For each stage, different associations of maturing germ cells were always the same with early cells in differentiation at the periphery and more mature cells near the lumen. In this way, progressive differentiation from stem cells to mitotic, meiotic, acrosome-forming, and post-acrosome maturing spermatocytes was mapped to define spermatogenesis with the XII stages in mice representing the seminiferous cycle. The maturation process from acrosome-forming cells to mature spermatocytes is defined as spermiogenesis with 19 different steps that are morphologically distinct spermatids. It is from steps 8-19 of spermiogenesis that the formation of the flagellum takes place. Final maturation occurs in the epididymis as sperm move through the caput, corpus, and cauda of the organ with motile spermatozoa generated.

Thank you very much!

eLife Assessment

This valuable study investigates the oscillatory activity of gonadotropin-releasing hormone (GnRH) neurones in mice using GCaMP fiber photometry. It demonstrates three distinct patterns of oscillatory activity that occur in GnRH neurons comprising low-level rapid baseline activity, abrupt short-duration oscillations that drive pulsatile gonadotropin secretion, and, in females, a gradual and prolonged oscillating increase in activity responsible for the relatively short-lived preovulatory LH surge. The evidence presented in the study is solid, offering theoretical implications for understanding the behaviour of GnRH neurones in the context of reproductive physiology, and will be of interest to researchers in neuroendocrinology and reproductive biology.

Reviewer #1 (Public review):

Summary:

The authors aimed to investigate the oscillatory activity of GnRH neurones in freely behaving mice. By utilising GCaMP fiber photometry, they sought to record real-time neuronal activity to understand the patterns and dynamics of GnRH neuron firing and their implications for reproductive physiology.

Strengths:

- The use of GCaMP fiber photometry allows for high temporal resolution recordings of neuronal activity, providing real-time data on the dynamics of GnRH neurones.<br /> - Recording in freely behaving animals ensures that the findings are physiologically relevant and not artifacts of a controlled laboratory environment.<br /> - The authors used statistical methods to characterise the oscillatory patterns, ensuring the reliability of their findings.

Weaknesses:

- While the study identifies distinct oscillatory patterns in GnRH neurones' calcium dynamics, it falls short in exploring the functional implications of these patterns for GnRH pulsatility and overall reproductive physiology.<br /> - The study lacks broader discussion to include comparisons with existing studies on GnRH neurone activity and pulsatility and highlight how the findings of this study align with or differ from previous research and what novel contributions are made.<br /> - The authors aimed to characterise the oscillatory activity of GnRH neurons and successfully identified distinct oscillatory patterns. The results support the conclusion that GnRH neurons exhibit complex oscillatory behaviours, which are critical for understanding their role in reproductive physiology. However, it has not been made clear what exactly do the authors mean by "multi-dimensional oscillatory patterns" and how has this been shown.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors report GCaMP fiber-photometry recordings from the GnRH neuron distal projections in the ventral arcuate nucleus. The recording are taken from intact, male and female, freely behaving mice. The report three patterns of neuronal activity:

1) abrupt increases in the Ca2+ signals that are perfectly correlated with LH pulses.

2) a gradual, yet fluctuating (with a slow ultradian frequency), increase in activity, which is associated with the onset of the LH surge in female animals.

3) clustered (high frequency) baseline activity in both female and male animals.

Strengths:

The GCaMP fiber-photometry recordings reported here are the first direct recordings from GnRH neurones in free behaving mice. These recordings suggest a rich repertoire of activity, including the integration of distinct "surge" and "pulse" generation signals, and an ultradian rhythm during the onset of the surge.

Weaknesses:

The data analysis methods used for the characterisation of the oscillatory behaviour could be complemented with more advanced wavelet methods to quantify and analyse how the frequency content of the observed Ca2+ signal changes over the cycle.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors aimed to investigate the oscillatory activity of GnRH neurones in freely behaving mice. By utilising GCaMP fiber photometry, they sought to record real-time neuronal activity to understand the patterns and dynamics of GnRH neuron firing and their implications for reproductive physiology.

Strengths:

(1) The use of GCaMP fiber photometry allows for high temporal resolution recordings of neuronal activity, providing real-time data on the dynamics of GnRH neurones.

(2) Recording in freely behaving animals ensures that the findings are physiologically relevant and not artifacts of a controlled laboratory environment.

(3) The authors used statistical methods to characterise the oscillatory patterns, ensuring the reliability of their findings.

Weaknesses:

(1) While the study identifies distinct oscillatory patterns in GnRH neurones' calcium dynamics, it falls short in exploring the functional implications of these patterns for GnRH pulsatility and overall reproductive physiology.

The functional roles of pulsatile and surge patterns of GnRH release are extremely well established. We have found perfect correlations between GnRH neuron dendron GCaMP activity and LH pulses as well as the LH surge clearly indicating the function of these activity patterns. We do not know the functional role of the clustered high-frequency basal activity that we have discovered and, as noted in the Discussion, are unsure of its physiological importance. Although it may be minor, it will require future investigation.

(2) The study lacks a broader discussion to include comparisons with existing studies on GnRH neurone activity and pulsatility and highlight how the findings of this study align with or differ from previous research and what novel contributions are made.

The Reviewer fails to recognise that these are first recordings of GnRH neurons in vivo. There are no prior studies for comparison. We have noted the only other in vivo study (undertaken by ourselves) many years ago in anaesthetized mice. It was never expected that electrophysiological recordings of GnRH neurons in acute brain slices (by ourselves and others) would reflect their activity in vivo. Now that we know this to be the case, it would be churlish to point this out explicitly. We have made some modifications to the Discussion by comparing the present data more thoroughly with other in vivo GnRH secretion and kisspeptin neuron activity studies.

(3) The authors aimed to characterise the oscillatory activity of GnRH neurons and successfully identified distinct oscillatory patterns. The results support the conclusion that GnRH neurons exhibit complex oscillatory behaviours, which are critical for understanding their role in reproductive physiology. However, it has not been made clear what exactly the authors mean by "multi-dimensional oscillatory patterns" and how has this been shown.

The study shows three types of GnRH neuron activity; two of which would be classified as oscillatory in nature and these show different temporal dimensions.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors report GCaMP fiber-photometry recordings from the GnRH neuron distal projections in the ventral arcuate nucleus. The recordings are taken from intact, male and female, freely behaving mice. The report three patterns of neuronal activity:

(1) Abrupt increases in the Ca2+ signals that are perfectly correlated with LH pulses.

(2) A gradual, yet fluctuating (with a slow ultradian frequency), increase in activity, which is associated with the onset of the LH surge in female animals.

(3) Clustered (high frequency) baseline activity in both female and male animals.

Strengths:

The GCaMP fiber-photometry recordings reported here are the first direct recordings from GnRH neurones in vivo. These recordings have uncovered a rich repertoire of activity suggesting the integration of distinct "surge" and "pulse" generation signals, and an ultradian rhythm during the onset of the surge.

Weaknesses:

The data analysis method used for the characterisation of the ultradian rhythm observed during the onset of the surge is not detailed enough. Hence, I'm left wondering whether this rhythm is in any way correlated with the clusters of activity observed during the rest of the cycle and which have similar duration.

We have provided further information on the characterisation of the ultradian rhythm observed at the time of the surge. Whether this is related to the clustered basal activity is an interesting point but very difficult to resolve. We note that the “basal” and “surge” ultradian oscillations have very different durations of ~30 and ~80 min suggesting that they may be independent phenomenon. However, the only way to really exclude a similar genesis will be to establish the origin of each type of oscillatory activity. Preliminary data in the lab show that the RP3V kisspeptin neurons exhibit an identical pattern of ultradian oscillation at the time of the surge leading us to suspect that the surge oscillation is driven by this input. As noted in the Discussion it is presently difficult to determine where the high basal activity originates.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Evidence of Multi-Dimensional Oscillatory Patterns: The manuscript presents data showing the oscillatory activity of GnRH neurones with distinct frequency and amplitude characteristics. The analysis includes statistical tests that illustrate the variability in neuronal firing patterns. However, the multi-dimensional nature of this activity has not been demonstrated. It is not clear what is meant by "dimension" with regard to the calcium recordings (oscillatory activity). If the authors refer to the frequency content of the calcium signal then a proper Fourier or Wavelet analysis should be carried out to characterise the multiple frequencies present in the calcium dynamics in male mice and during various stages of the cycle in female mice

The study shows three types of GnRH neuron activity; two of which would be classified as oscillatory in nature. One occurs for ~10 min every hour or so and the other occurs for ~ 12 hours once every 4-5 days. This does not require any analysis to distinguish between the two or claim that they are different i.e. multidimensional. 

(2) Data Interpretation: Expand the discussion on the physiological relevance of the identified oscillatory patterns. Specifically, explore how these patterns might influence GnRH pulsatility, hormone secretion dynamics, and reproductive cycles.

The functional roles of pulsatile and surge patterns of GnRH release are extremely well established. We have found perfect correlations between GnRH neuron dendron GCaMP activity and LH pulses as well as the LH surge clearly indicating the function of these activity patterns. We do not know the functional role of the clustered high-frequency basal activity that we have discovered and, as noted in the Discussion, are unsure of its physiological importance. Although it may be minor, it will require future investigation.

(3) Literature Contextualisation: Broaden the discussion to include comparisons with existing studies on GnRH neuron activity and pulsatility. Highlight how the findings of this study align with or differ from previous research and what novel contributions are made.

The Reviewer fails to recognise that these are first recordings of GnRH neurons in vivo. There are no prior studies for comparison. We have noted the only other in vivo study (undertaken by ourselves) many years ago in anaesthetized mice. It would be naive to expect that electrophysiological recordings of GnRH neurons in acute brain slices (by ourselves and others) would reflect their activity in vivo. Now that we know this to be the case, it would be churlish to point this out explicitly. We have made some modifications to the Discussion by comparing the present data more thoroughly with other in vivo GnRH secretion and kisspeptin neuron activity studies.

(4) Future Directions: Suggest potential follow-up experiments to explore the regulatory mechanisms underlying the observed oscillatory patterns. This could include investigating the role of neurotransmitters, hormonal feedback mechanisms, and other factors that might influence GnRH neuron activity.

By addressing these recommendations, the authors can further strengthen their manuscript and enhance its impact on the field.

Reviewer #2 (Recommendations For The Authors):

Suggestions:

(1) The authors might want to analyse their inter-peak interval data by fitting them to a simple parametric statistical model (the gamma distribution would be a good choice to capture the skewness of these data). This way they would be able to describe the observed variability, and if the fits are not good back up to their claims "The dSEs occurred on average ... and showed no clear modal distribution pattern (Fig. 2D)".

Thank you for the suggestion. We have carried out Shapiro-Wilk tests for male inter-peak interval distribution and found a W value of 0.87 and P value <0.0001****, providing strong evidence that the data is not normally distributed. Skewness and Kurtosis values are 1.39 and 1.81 respectively, indicating that the distribution is right-skewed with a platykurtic distribution, indicating that the data is less peaked and more spread out than the normal distribution (with a kurtosis of 3). This has now been added to the manuscript.

(2) If I understand correctly, in Figure 3D, inter-peak intervals from all 4 stages of the estrus cycle are pooled together. It would also be interesting if the authors gave the interval histograms for the different stages of the cycle separately.

We have now plotted the inter-peak interval distribution histograms for each individual cycle next to the example traces in Figure 3. The descriptions of the distribution pattern are also updated in the figure legends.

(3) In Figure 3C, one can see the mean interval for different animals (as open circles), is that right? Is the statistical test run on these animals mean, or is the entire dSEs dataset used? In any case, it's not clear to the reader how variable intervals are in individual recordings from each animal. Could the authors add this information (could be easily added in the figure caption)?

The reviewer is correct, that each open circle is the mean interval for each animal. The statistical test was run on the animals mean. Now this information is added to the figure legend.

(4) The authors should explain how they identify the regions (clusters) of high-frequency baseline activity, which they present in Figure 4.

The relevant information is now added to the methods section under the heading ‘GCaMP6 fiber photometry and blood sampling’.

(5) The authors should detail how to identify and characterise the ultradian rhythm they observe at the onset of the surge.

The relevant information is now added to the methods section under the heading ‘GCaMP6 fiber photometry and blood sampling’.

(6) The author could perform some kind of wavelet-type analysis to quantify and analyse how the frequency content of the observed Ca2+ signal changes over the cycle. From their current analysis, I am not sure whether the ultradian oscillations they observe during the surge are related to the low-activity cluster events they observe during the other stages of the cycle.

This is an interesting point but very difficult to resolve. We note that the “basal” and “surge” ultradian oscillations have very different durations of ~30 and ~80 min suggesting that they may be independent phenomenon. However, the only way to really exclude a similar genesis will be to establish the origin of each type of oscillatory activity. Preliminary data in the lab show that the RP3V kisspeptin neurons exhibit an identical pattern of ultradian oscillation at the time of the surge leading us to suspect that the surge oscillation is driven by this input. As noted in the Discussion it is presently difficult to determine where the high basal activity originates.

Author response:

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

Response to Reviewer’s comments

We are most grateful for the opportunity to address the reviewer comments. Point-by-point responses are presented below.

Overall, the paper has several strengths, including leveraging large-scale, multi-modal datasets, using computational reasonable tools, and having an in-depth discussion of the significant results.

We thank the reviewer for the very supportive comments.

Based on the comments and questions, we have grouped the concerns and corresponding responses into three categories.

(1) The scope and data selection

The results are somewhat inconclusive or not validated.

The overall results are carefully designed, but most of the results are descriptive. While the authors are able to find additional evidence either from the literature or explain the results with their existing knowledge, none of the results have been biologically validated. Especially, the last three result sections (signaling pathways, eQTLs, and TF binding) further extended their findings, but the authors did not put the major results into any of the figures in the main text.”

The goal of this manuscript is to provide a list of putative childhood obesity target genes to yield new insights and help drive further experimentation. Moreover, the outputs from signaling pathways, eQTLs, and TF binding, although noteworthy and supportive of our method, were not particularly novel. In our manuscript we placed our focus on the novel findings from the analyses. We did, however, report the part of the eQTLs analysis concerning ADCY3, which brought new insight to the pathology of obesity, in Figure 4C.

The manuscript would benefit from an explanation regarding the rationale behind the selection of the 57 human cell types analyzed. it is essential to clarify whether these cell types have unique functions or relevance to childhood development and obesity.

We elected to comprehensively investigate the GWAS-informed cellular underpinnings of childhood development and obesity. By including a diverse range of cell types from different tissues and organs, we sought to capture the multifaceted nature of cellular contributions to obesity-related mechanisms, and open new avenues for targeted therapeutic interventions.

There are clearly cell types that are already established as being key to the pathogenesis of obesity when dysregulated: adipocytes for energy storage, immune cell types regulating inflammation and metabolic homeostasis, hepatocytes regulating lipid metabolism, pancreatic cell types intricately involved in glucose and lipid metabolism, skeletal muscle for glucose uptake and metabolism, and brain cell types in the regulation of appetite, energy expenditure, and metabolic homeostasis.

While it is practical to focus on cell types already proven to be associated with or relevant to obesity, this approach has its limitations. It confines our understanding to established knowledge and rules out the potential for discovering novel insights from new cellular mechanisms or pathways that could play significant roles in the pathogenesis if obesity. Therefore, it was essential to reflect known biology against the unexplored cell types to expand our overall understanding and potentially identify innovative targets for treatment or prevention.

I wonder whether the used epigenome datasets are all from children. Although the authors use literature to support that body weight and obesity remain stable from infancy to adulthood, it remains uncertain whether epigenomic data from other life stages might overlook significant genetic variants that uniquely contribute to childhood obesity.

The datasets utilized in our study were derived from a combination of sources, both pediatric and adult. We recognize that epigenetic profiles can vary across different life stages but our principal effort was to characterize susceptibility BEFORE disease onset.

Given that the GTEx tissue samples are derived from adult donors, there appears to be a mismatch with the study's focus on childhood obesity. If possible, identifying alternative validation strategies or datasets more closely related to the pediatric population could strengthen the study's findings.

We thank the reviewer for raising this important point. We acknowledge that the GTEx tissue samples are derived from adult donors, which might not perfectly align with the study's focus on childhood obesity. The ideal strategy would be a longitudinal design that follows individuals from childhood into adulthood to bridge the gap between pediatric and adult data, offering systematic insights into how early-life epigenetic markers influencing obesity later in life. In future work, we aim to carry out such efforts, which will represent substantial time and financial commitment.

Along the same lines, the Developmental Genotype-Tissue Expression (dGTEx) Project is a new effort to study development-specific genetic effects on gene expression at 4 developmental windows spanning from infant to post-puberty (0-18 years). Donor recruitment began in August 2023 and remains ongoing. Tissue characterization and data production are underway. We hope that with the establishment of this resource, our future research in the field of pediatric health will be further enhanced.

Figure 1B: in subplots c and d, the results are either from Hi-C or capture-C. Although the authors use different colors to denote them, I cannot help wondering how much difference between Hi-C and capture-C brings in. Did the authors explore the difference between the Hi-C and capture-C?

Thank you for your comment. It is not within the scope of our paper to explore the differences between the Hi-C and Capture-C methods. In the context of our study, both methods serve the same purpose of detecting chromatin loops that bring putative enhancers to sometimes genomically distant gene promoters. Consequently, our focus was on utilizing these methods to identify relevant chromatin interactions rather than comparing their technical differences.

(2) Details on defining different categories of the regions of interest

Some technical details are missing.

While the authors described all of their analysis steps, a lot of the time, they did not mention the motivation. Sometimes, the details were also omitted.”

We have added a section to the revision to address the rationale behind different OCRs categories.

Line 129: should "-1,500/+500bp" be "-500/+500bp"?

A gene promoter was defined as a region 1,500 bases upstream to 500 bases downstream of the TSS. Most transcription factor binding sites are distributes upstream (5’) from TSS, and the assembly of transcription machinery occurs up to 1000 bases 5’ from TSS. Given our interest in SNPs that can potentially disrupt transcription factor binding, this defined promoter length allowed us to capture such SNPs in our analyses.

How did the authors define a contact region?

Chromatin contact regions identified by Hi-C or Capture-C assays are always reported as pairs of chromatin regions. The Supplementary eMethods provide details on the method of processing and interaction calling from the Hi-C and Capture-C data.

The manuscript would benefit from a detailed explanation of the methods used to define cREs, particularly the process of intersecting OCRs with chromatin conformation data. The current description does not fully clarify how the cREs are defined.

In the result section titled "Consistency and diversity of childhood obesity proxy variants mapped to cREs", the authors introduced the different types of cREs in the context of open chromatin regions and chromatin contact regions, and TSS. Figure 2A is helpful in some way, but more explanation is definitely needed. For example, it seems that the authors introduced three chromatin contacts on purpose, but I did not quite get the overall motivation.

We apologize for the confusion. Our definition of cREs is consistent throughout the study. Figure 2A will be the first Figure 1A in the revision in order to aid the reader.

The 3 representative chromatin loops illustrate different ways the chromatin contact regions (pairs of blue regions under blue arcs) can overlap with OCRs (yellow regions under yellow triangles – ATAC peaks) and gene promoters.

(1) The first chromatin loop has one contact region that overlaps with OCRs at one end and with the gene promoter at the other. This satisfies the formation of cREs; thus, the area under the yellow ATAC-peak triangle is green.

(2) The second loop only overlapped with OCR at one end, and there was no gene promoter nearby, so it is unqualified as cREs formation.

(3) The third chromatin loop has OCR and promoter overlapping at one end. We defined this as a special cRE formation; thus, the area under the yellow ATAC-peak triangle is green.

To avoid further confusion for the reader, we have eliminated this variation in the new illustration for the revised manuscript.

Figure 2A: The authors used triangles filled differently to denote different types of cREs but I wonder what the height of the triangles implies. Please specify.

The triangles are illustrations for ATAC-seq peaks, and the yellow chromatin regions under them are OCRs. The different heights of ATAC-seq peaks are usually quantified as intensity values for OCRs. However, in our study, when an ATAC-seq peak passed the significance threshold from the data pipeline, we only considered their locations, regardless of their intensities. To avoid further confusion for the reader, we have eliminated this variation in the new illustration for the revised manuscript.

Figure 1B-c. the title should be "OCRs at putative cREs". Similarly in Figure 1B-d.

cREs are a subset of OCRs.

- In the section "Cell type specific partitioned heritability", the authors used "4 defined sets of input genomic regions". Are you corresponding to the four types of regions in Figure 2A? 

Figure 2A is the first Figure 1A in the revision and is modified to showcase how we define OCRs and cREs.

It seems that the authors described the 771 proxies in "Genetic loci included in variant-to-genes mapping" (ln 154), and then somehow narrowed down from 771 to 94 (according to ln 199) because they are cREs. It would be great if the authors could describe the selection procedure together, rather than isolated, which made it quite difficult to understand.

In the Methods section entitled “Genetic loci included in variant-to-genes mapping," we described the process of LD expansion to include 771 proxies from 19 sentinel obesity-significantly associated signals. Not all of these proxies are located within our defined cREs. Figure 2B, now Figure 2A in the revision, illustrates different proportions of these proxies located within different types of regions, reducing the proxy list to 94 located within our defined cREs.

Figure 2. What's the difference between the 771 and 758 proxies?

13 out of 771 proxies did not fall within any defined regions. The remaining 758 were located within contact regions of at least one cell type regardless of chromatin state.

(3) Typos

In the paragraph "Childhood obesity GWAS summary statistics", the authors may want to describe the case/control numbers in two stages differently. "in stage 1" and "921 cases" together made me think "1,921" is one number.

This has been amended in the revision.

Hi-C technology should be spelled as Hi-C. There are many places, it is miss-spelled as "hi-C". In Figure 1, the author used "hiC" in the legend. Similarly, Capture-C sometime was spelled as "capture-C" in the manuscript.

At the end of the fifth row in the second paragraph of the Introduction section: "exisit" should be "exist".

In Figure 2A: "Within open chromatin contract region" should be "Within open chromatin contact region”

These typos and terminology inconsistencies have been amended in the revision.

eLife Assessment

This important study presents genome-wide high-resolution chromatin-based 3D genomic interaction maps for over 50 diverse human cell types and integrates these data with pediatric obesity GWAS. The work provides convincing evidence that multiple pancreatic islet cell types are key effector cell types. The authors also perform variant-to-gene mapping to nominate genes underlying several GWAS hits. Overall, the results will be of interest to both the fields of 3D genome architecture and pediatric obesity.

Joint Public Reviews:

Summary:

This paper studies the genetic factors contributing to childhood obesity. Through a comprehensive analysis integrating genome-wide association study (GWAS) data with 3D genomic datasets across 57 human cell types, consisting of Capture-C/Hi-C, ATAC-seq, and RNA-seq, the study identifies significant genetic contributions to obesity using stratified LD score regression, emphasizing the enrichment of genetic signals in pancreatic alpha cells and identification of significant effector genes at obesity-associated loci such as BDNF, ADCY3, TMEM18, and FTO. Additionally, the study implicated ALKAL2, a gene responsive to inflammation in nerve nociceptors, as a novel effector gene at the TMEM18 locus, suggesting a role for inflammatory and neurological pathways in obesity's pathogenesis which was supported through colocalization analysis using eQTL derived from the GTEx dataset. This comprehensive genomic analysis sheds light on the complex genetic architecture of childhood obesity, highlighting the importance of cellular context for future research and the development of more effective strategies.

Strengths:

Overall, the paper has several strengths, including leveraging large-scale, multi-modal datasets, using appropriate computational tools, and in-depth discussion of their significant results.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Zhang et al. report a genetic screen to identify novel transcriptional regulators that could coordinate mitochondrial biogenesis. They performed an RNAi-based modifier screen wherein they systematically knocked down all known transcription factors in the developing Drosophila eye, which was already sensitised and had decreased mitochondrial DNA content. Through this screen, they identify CG1603 as a potential regulator of mitochondrial content. They show that protein levels of mitochondrial proteins like TFAM, SDHA, and other mitochondrial proteins and mtDNA content are downregulated in CG1603 mutants. RNA-Seq and ChIP-Seq further show that CG1603 binds to the promoter regions of several known nuclear-encoded mitochondrial genes and regulates their expression. Finally, they also identified YL-1 as an upstream regulator of CG1603. Overall, it is a very important study as our understanding of the regulation of mitochondrial biogenesis remains limited across metazoans. Most studies have focused on PGC-1α as a master regulator of mitochondrial biogeneis, which seems a context-dependent regulator. Also, PGC-1α mediated regulation could not explain the regulation of 1100 genes that are required for mitochondrial biogenesis. Therefore, identifying a new regulator is crucial for understanding the overall regulation of mitochondrial biogenesis.

Reviewer #2 (Public Review):

Summary:

In this study, the authors aim to identify the nuclear genome-encoded transcription factors that regulate mtDNA maintenance and mitochondrial biogenesis. They started with an RNAi screening in developing Drosophila eyes with reduced mtDNA content and identified a number of putative candidate genes. Subsequently, using ChIP-seq data, they built a potential regulatory network that could govern mitochondrial biogenesis. Next, they focused on a candidate gene, CG1603, for further characterization. Based on the expression of different markers, such as TFAM and SDHA, in the RNAi and OE clones in the midgut cells, they argue that CG1603 promotes mitochondrial biogenesis and the expression of ETC complex genes. Then, they used a mutant of CG1603 and showed that both mtDNA levels and mitochondrial protein levels were reduced. Using clonal analyses, they further show a reduction in mitochondrial biogenesis and membrane potential upon loss of CG1603. They made a reporter line of CG1603, showed that the protein is localized to the mitochondria, and binds to polytene chromosomes in the salivary gland. Based on the RNA-seq results from the mutants and the ChIP data, the authors argue that the nucleus-encoded mitochondrial genes that are downregulated >2 folds in the CG1603 mutants and that are bound by CG1603 are related to ETC biogenesis. Finally, they show that YL-1, another candidate in the network, is an upstream regulator of CG1603.

Strengths:

This is a valuable study, which identifies a potential regulator and a network of nucleus-encoded transcription factors that regulate mitochondrial biogenesis. Through in-vivo and in-vitro experimental evidence, the authors identify the role of CG1603 in this process. The screening strategy was smart, and the follow-up experiments were nicely executed.

Weaknesses:

Some additional experiments showing the effects of CG1603 loss on ETC integrity and functionality would strengthen the work.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Fig 3F: SDHA levels are severely downregulated in CG1603 RNAi clones. Therefore, estimating mitochondrial volume based on the SDHA reporter might be misleading. I suggest the authors perform this experiment with an independent marker of mitochondria, like mitoTracker Green or other dyes. I also suggest checking for mitochondrial number/quantity/size by electron microscopy.

Even though being downregulated, the SDHA-mNeon signal in EC clones clearly outlined mitochondria and the overall mitochondrial network, allowing us to quantify the total mitochondrial volume. Examining mitochondrial number/quantity/size by electron microscopy would further strengthen this statement, and we will consider it in future studies.

(2) The authors might comment on whether there was any decrease in the volume of CG1603i clone cells. And whether this was taken into account while normalising the mitochondrial volume.

The size/volume of CG1603i clone cells were indeed decreased, which was considered while normalizing the mitochondrial volume. We clarified this point in methods section (page 18, line 511-512 (revised version page 18, line 515-517)).

(3) Line 230-234: Collectively, these results demonstrate that CG1603 promotes the expression of both nuclear and mtDNA-encoded ETC genes and boosts mitochondrial biogenesis. CG1603 RNAi produced very few EC clones, consistent with the notion that mitochondrial respiration is necessary for ISCs differentiation.

(4) Quantifying the number of EC clone cells observed might help support this statement.

This is a great point. We quantified the number of EC clone cells, and the data was included in the revised Figure 3—figure supplement.

(5) Figure 5: The intensity of MTGreen in CH1603 clones seems comparable to that in control cells, at least visually. Since the authors claim a reduction in mitochondrial volume in CG1603 mutants, it is crucial to estimate mitochondrial volume based on MTGreen intensity in mutant and control cells.

There are two types of clones shown in Figure 5:  germ cell clones including all 16 germ cells in the same egg chamber and follicle cell clones. We highlight these two types of clones in the revised Figure 5, to emphasize this point. The total MT Green intensity in both germ cell and follicle cell CG1603PBac clones were reduced, compared to germ cells in adjacent egg chambers and adjacent follicle cells in the same egg chamber, respectively. We included the quantification of MTGreen intensity in the revised Figure 5—figure supplement C. Examining mitochondrial number/quantity/size by electron microscopy would further strengthen this statement, and we will consider it in future studies.

(6) Figure 8: It would be interesting to know what happens to steady-state mtDNA levels during YL-1 knockdown. If decreased, could overexpressing CG1603 in YL-1 knockdown cells rescue the phenotype?

YL-1 knockdown reduced steady-state mtDNA levels in eyes, and overexpressing CG1603 restored mtDNA level in YL-1 knockdown cells. These results are included in the revised Figure 8-figure supplement C.

Minor comments:

(7) The paper is lucidly written, but there are minor typos in several places. The authors might proofread it to remove these errors.

We corrected typos and other minor errors in the manuscript.

(8) Quantification for Figure 8 - Supplementary needs to be included.

We performed the quantification, and the result is shown in Figure 8—figure supplement B.

Reviewer #2 (Recommendations For The Authors):

(1) In lines 275-276 and Figure 6E, the authors mention that more than 800 nuclear-encoded mitochondrial genes were reduced by >2-folds in CG1603 mutants. One gene related to mitochondrial replication and three genes related to mtDNA transcription were among them. Was TFAM one of these candidates? What were the reduction levels of TFAM mRNA in RNA seq results? Can the author confirm it via RT-PCR?

In RNAseq analyses, TFAM was differentially expressed with a log2 Fold-Change of “ -0.74”, corresponding to ~1.6-fold decrease, and hence was not one of these candidates that were down-regulated more than two folds in CG1603 mutant. Per reviewer’s suggestion, we carried out RT-PCR and found TFAM was downregulated about 2-fold in CG1603 mutant. We included this result in the revised Figure 6F and listed all differentially expressed genes in Supplementary file 5a.

(2) In many places, the authors argued about the role of CG1603 in ETC biogenesis. Also, the RNA-seq data shows that 64 genes related to the ETC complex were reduced by > 2-fold in CG1603 mutant. Therefore, it would be critical to expand a little on this aspect. For example, what are these genes and related to which of the ETC complex? Can the authors show the reduced levels of some of the candidate genes from each complex via RT-PCR?

We listed all ETC genes that were down-regulated more than two folds in CG1603 mutant in a separate sheet in Supplementary file 5b. We further validated the reduced expression of ETC genes by RT-PCR on three randomly selected candidate genes from each complex. The result is included in the revised Figure 6F.

(3) To make their argument solid on the role of CG1603 on ETC biogenesis, it is important to show the assembly/integrity of ETC complexes as well as the functionality/activity of the ETC complexes in CG1603 mutants.

We purified mitochondria, and assayed assembly/integrity of three ETC complexes (Complex I, II and IV) and their activities, using blue native PAGE analysis and in gel activity analysis, respectively.  The amount of these three complexes, and accordingly, their activities were all markedly reduced in CG1603 mutant compared to wt.  The result is included as Figure 4—figure supplement A.

(4) CG1603 has already been named as cliff. Why do the authors not use this name, or alternatively propose one?

We thank the reviewer for the note. The CG1603 has not been named as cliff when we were preparing this manuscript.

(5) In lines 230-231, based on the TFAM-GFP and SDHA-mNG levels, the authors claim that "these results demonstrate that CG1603 promotes the expression of both nuclear and mtDNA-encoded ETC genes..." The authors may tone down this statement since it sounds overstating. It would be prudent to claim that a subset of genes are regulated by CG1603.

We appreciate the reviewer’s suggestion. We revised the text to tone down this statement (page 8, line 201; page 9, line 229-230).

eLife Assessment

This study's findings substantially advance our understanding of an important aspect of mitochondrial metabolism. The data are compelling and the study is well executed. The work is relevant to all who are interested in the biogenesis of mitochondria.

Reviewer #1 (Public review):

In this manuscript, Zhang et al. report a genetic screen to identify novel transcriptional regulators that coordinate mitochondrial biogenesis. They performed an RNAi-based modifier screen wherein they systematically knocked down all known transcription factors in the developing Drosophila eye, which was sensitized and had decreased mitochondrial DNA content. Through this screen, they identify CG1603 as a potential regulator of mitochondrial volume. They show that protein levels of mitochondrial proteins like TFAM, SDHA, and other mitochondrial proteins and mtDNA content are downregulated in CG1603 mutants. RNA-Seq and ChIP-Seq further show that CG1603 binds to the promoter regions of several known nuclear-encoded mitochondrial genes and regulates their expression. Finally, they also identified YL-1 as an upstream regulator of CG1603. Most studies have focused on PGC-1α as a master regulator of mitochondrial biogenesis. which seems to be a context-dependent regulator. Also, PGC-1α mediated regulation does not explain the regulation of 1100 genes that are required for mitochondrial biogenesis. Therefore, identifying new regulators in this work is crucial for the advancement of our understanding of mitochondrial biogenesis.

Reviewer #2 (Public review):

Summary:

In this study, the authors identified nuclear genome-encoded transcription factors that regulate mtDNA maintenance and mitochondrial biogenesis. They started with an RNAi screening in developing Drosophila eyes with reduced mtDNA content and identified several putative candidate genes. Subsequently, using ChIP-seq data, they built a potential regulatory network that seems to govern mitochondrial biogenesis. Next, they focused on a candidate gene, CG1603 /clifford, for further characterization. Based on the expression of different markers, such as TFAM and SDHA, in RNAi and overexpression clones in the midgut, they argued that CG1603 promotes mitochondrial biogenesis and the expression of ETC complex genes. They used a CG1603 mutant to show reduced mtDNA and mitochondrial protein levels. Clonal analyses showed a reduction in mitochondrial biogenesis and membrane potential upon loss of CG1603. They further showed that the protein is localized to the mitochondria, and binds to polytene chromosomes in the salivary gland. Based on the RNA-seq results from the mutants and the ChIP data, the authors argued that the nucleus-encoded mitochondrial genes are downregulated >2 folds in the CG1603 mutants and that the regulatory elements bound by CG1603 are related to ETC biogenesis. Finally, they showed that YL-1, another candidate in the network, is an upstream regulator of CG1603. The screening strategy was well-designed, and the follow-up experiments were nicely executed.

Comments on revisions:

The authors have addressed my previous comments satisfactorily.

Author response:

Reviewer #1:

Weaknesses:

However, given that S1P is upstream NF-κB signaling, it is unclear if it offers conceptual innovations as compared to previous studies from the same team (Palazzo et al. 2020; 2022, 2023)

We find distinct differences between the impacts of S1P- and NFkB-signaling on glial activation, neuronal differentiation of the progeny of MGPCs and neuronal survival in damaged retinas. In the current study we demonstrate that 2 consecutive daily intravitreal injections of S1P selectively activated mTor (pS6) and Jak/Stat3 (pStat3), but not MAPK (pERK1/2) signaling in Müller glia.  Further, inhibition of S1P synthesis (SPHK1 inhibitor) decreased ATF3, mTor (pS6) and pSmad1/5/9 levels in activated Müller glia in damaged retinas. Inhibition of NFkB-signaling in damaged chick retinas did not impact the above-mentioned cell signaling pathways (Palazzo et al., 2020). Thus, S1P-signaling impacts cell signaling pathways in MG that are distinct from NFκB, but we cannot exclude the possibility of cross-talk between NFkB and these pathways. Further, inhibition of NFκB-signaling potently decreases numbers of dying cells and increases numbers of surviving ganglion cells (Palazzo et al 2020). Consistent with these findings, a TNF orthologue, which presumably activates NFκB-signaling, exacerbates cell death in damage retinas (Palazzo et al., 2020). By contrast, 5 different drugs targeting S1P-signaling had no effect on numbers of dying cells and only one S1PR1 inhibitor modestly decreased numbers of dying cells (current study). In addition, inhibition of NFκB does not influence the neurogenic potential of MGPCs in damaged chick retinas (Palazzo et al., 2020), whereas inhibition of S1P receptors (S1PR1 and S1PR3) and inhibition of S1P synthesis (SPHK1) significantly increased the differentiation of amacrine-like neurons in damaged retinas (current study). Collectively, in comparison to the effects of pro-inflammatory cytokines and NFκB-signaling, our current findings indicate that S1P-signaling through S1PR1 and S1PR3 in Müller glia has distinct effects upon cell signaling pathways, neuronal regeneration and cell survival in damaged retinas. We will revise text in the Discussion to better highlight these important distinctions between NFκB- and S1P-signaling.

Reviewer #2:

Weaknesses:

The methodology is not very clean. A number of drugs (inhibitors/ antagonists/agonists signal modulators) are used to modulate S1P expression or signaling in the retina without evidence that these drugs are reaching the target cells. No alternative evaluation if the drugs, in fact, are effective. The drug solubility in the vehicle and in the vitreous is not provided, and how did they decide on using a single dose of each drug to have the optimal expected effect on the S1P pathway?

Müller glia are the predominant retinal cell type that expresses S1P receptors. Consistent with these patterns of expression, we report Müller glia-specific effects of different agonists and antagonists that increase or decrease S1P-signaling. Since we compare cell-level changes within contralateral eyes wherein one retina is exposed to vehicle and the other is exposed to vehicle plus drug, it seems highly probable that the drugs are eliciting effects upon the Müller glia. It is possible, but very unlikely, that the responses we observed could have resulted from drugs acting on extra-retinal tissues, which might secondarily release factors that elicit cellular responses in Müller glia. However, this seems unlikely given the distinct patterns of expression for different S1P receptors in Müller glia, and the outcomes of inhibiting Sphk1 or S1P lyase on retinal levels of S1P.

For example, we provide evidence that S1PR1 and S1PR3 expression is predominant in Müller glia in the chick retina using single cell-RNA sequencing and fluorescence in situ hybridization (FISH). Thus, we expect that S1PR1/3-targeting small molecule inhibitors to directly act on Müller glia, which is consistent with our read-outs of cell signaling with injections of S1P in undamaged retinas. We show that SPHK1 and SGPL1, which encode the enzymes that synthesize or degrade S1P, are expressed by different retinal cell types, including the Müller glia. The efficacy of the drugs that target SPHK1 and SGPL1 was assessed by measuring levels of S1P in the retina. By using liquid chromatography and tandem mass spectroscopy (LC-MS/MS), we provide data that inhibition of S1P synthesis (inhibition of SPHK1) significantly decreased levels of S1P in normal retinas, whereas inhibition of S1P degradation (inhibition of SGPL1) increased levels of S1P in damaged retinas (Fig. 5).  These data suggest that the SPHK1 inhibitor and the SGPL1 inhibitor specifically act at the intended target to influence retinal levels of S1P.  Further, inhibition of SPHK1 (to decrease levels S1P) results in decreased levels of ATF3, pS6 (mTor) and pSMAD1/5/9 in Müller glia, consistent with the notion that reduced levels of S1P in the retina impacts signaling at Müller glia. Finally, we find similar cellular responses to chemically different agonists or antagonists, and we find opposite cellular responses to agonists and antagonists, which are expected to be complimentary if the drugs are specifically acting at the intended targets in the retina. We will revise the Discussion to better address caveats and concerns regarding the actions and specificity of different drugs within the retina following intravitreal delivery.

We will provide the drug solubility specifications and estimates of the initial maximum dose per eye for each drug. For chick eyes between P7 and P14, these estimates will assume a volume of about 100 µl of liquid vitreous, 800 µl gel vitreous and an average eye weight of 0.9 grams. We will revise Table 1 (pharmacological compounds) with ranges of reported in vivo ED50’s (mg/kg) for drugs and we will list the calculated initial maximum dose (mg/kg equivalent per eye). Doses were chosen based on estimates of the initial maximum ocular dose that were within the range of reported ED50’s. However, as is the case for any in vivo model system, it is difficult to predict rates of drug diffusion out of the vitreous, how quickly the drugs are cleared from the entire eye, how much of the compound enters the retina, and how quickly the drug is cleared from the retina. Accordingly, we assessed drug specificity and sites of activation by relying upon readouts of cell signaling pathways, parsed with S1P receptor expression patterns, together with measurements of retinal levels of S1P following exposure to drugs targeting enzymes that catalyze synthesis or degradation of S1P, as described above.

eLife Assessment

This valuable study investigates the signaling pathways regulating retina regeneration. Solid evidence shows that the sphingosine-1-phosphate (S1P) signaling pathway is inhibited following retinal injury. Small-molecule activators and inhibitors support a model in which S1P signaling must be inhibited to generate Müller glia progenitor cells-a key step in retinal regeneration. The presented results support the major conclusions. However, the methodology concerning drug treatments is unclear, and the conceptual innovation is, to some extent, incremental.

Reviewer #1 (Public review):

Summary:

This study shows that the pro-inflammatory S1P signaling regulates the responses of muller glial cells to damage. The authors describe the expression of S1P signaling components. Using agonist and antagonist of the pathways they also investigate their effect on the de-differentiation and proliferation of Muller glial cells in damaged retina of postnatal chicks. They show that S1PR1 is highly expressed in resting MG and non-neurogenic MGPCs. This receptor suppresses the proliferation and neuronal activity promotes MGPC cell cycle re-entry and enhanced the number of regenerated amacrine-like cells after retinal damage. The formation of MGPCs in damaged retinas is impaired in the absence of microglial cells. This study further shows that ablation of microglial cells from the retina increases the expression of S1P-related genes in MG, whereas inhibition of S1PR1 and SPHK1 partially rescues the formation of MGPCs in damaged retinas depleted of microglia. The studies also show that expression of S1P-related genes is conserved in fish and human retinas.

Strengths:

This is well-conducted study, with convincing images and statistically relevant data

Weaknesses:

However, given that S1P is upstream N NF-κB signaling, it is unclear if it offers conceptual innovations as compared to previous studies from the same team (Palazzo et al. 2020; 2022, 2023)

Reviewer #2 (Public review):

Summary:

Sphingosine-1-phosphate (S1P) metabolic and signaling genes are expressed highly in retinal Müller glia (MG) cells. This study tested how S1P signaling regulates glial phenotype, dedifferentiation of, reprogramming into proliferating MG-derived progenitor cells (MGPCs), and neuronal differentiation of the progeny of MGPCs using in vivo chick retina. Major techniques used are Sc-RNASeq and immunohistochemistry to determine the gene expression and proliferation of MG cells that co-label with signaling antibodies or mRNA FISH following treating the in vivo eyes with various S1P signaling antagonists, agonists, and signal modulators. The major conclusions drawn are supported by the results presented. However, the methodology they have used to modulate the S1P pathway using various chemical drugs raises questions about the outcomes and whether those are the real effects of S1P receptor modulation or S1P synthesis inhibition.

Strengths:

- Use of elaborated single-cell RNAseq expression data.<br /> - Use of FISH for S1P receptors and kinase as a good quality antibody is not available.<br /> - Use of EdU assay in combination with IHC<br /> - Comparison with human and Zebrafish Sc-RNA data

Weaknesses:

The methodology is not very clean. A number of drugs (inhibitors/ antagonists/agonists signal modulators) are used to modulate S1P expression or signaling in the retina without evidence that these drugs are reaching the target cells. No alternative evaluation if the drugs, in fact, are effective. The drug solubility in the vehicle and in the vitreous is not provided, and how did they decide on using a single dose of each drug to have the optimal expected effect on the S1P pathway?

eLife Assessment

This is a useful contribution to our understanding of taste perception. The idea that specific receptors function in the pharynx to mediate responses to carboxylic acids is interesting, although the expression analysis is incomplete. Reviewers also have a number of other suggestions for improvement, including the request that authors provide more details about the methodology used. In general, the claims are supported by solid evidence and add to a growing body of literature on this topic.

Reviewer #1 (Public review):

Summary:

Shrestha et al report an investigation of mechanisms underlying gustatory preference for carboxylic acids in Drosophila. They begin with a screen of selected IR mutants, identifying 5 candidates - 2 IR co-receptors and 3 other IRs - whose loss of function causes defects in feeding preference for one or more of the three tested carboxylic acids. The requirement for IR51b, IR94a, and IR94h in carboxylic acid responses is evaluated in more detail using behavior, electrophysiology (labellar sensilla), and calcium imaging (pharyngeal neurons). The behavioral valence of IR94a and IR94h neurons is assessed using optogenetics. Overall the study uses a variety of approaches to test and validate the requirement of IRs in pharyngeal carboxylic acid taste.

Strengths:

The involvement of the identified IRs in gustatory responses to carboxylic acids is very clear from this study. The authors use mutants and transgenic rescue experiments and evaluate outcomes using electrophysiology, behavior, and imaging. Complementary approaches of loss-of-function and artificial activation support the main conclusion that the identified pharyngeal neurons sense carboxylic acids and convey a positive behavioral valence.

Weaknesses:

Some aspects of expression analysis and calcium imaging need to be clarified to better support the conclusions.

(1) The conclusion of two parallel IR-mediated pathways rests on expression analysis of Ir94a-GAL4 and Ir94h-GAL4 lines and the observation that Ir51b expression driven by either can rescue the Ir51b mutant phenotype. However, the expression analysis is not as rigorous as it needs to be for such a conclusion. Prior work found co-expression of Ir94a and Ir94h in the LSO. Here, the co-expression of the two drivers has not been examined, and Ir94a-GAL4 does not appear to be expressed in the LSO. Given the challenges in validating expression patterns in pharyngeal organs, the possibility that the drivers do not entirely capture endogenous expression cannot be ruled out. Rescue experiments using feeding preference or single-cell imaging don't suffice as validation. Plus, the expression of Ir51b could not be defined.

(2) The description of methods and results for the ex vivo calcium imaging is not satisfactory. Details about which cells are being analyzed, and in which organs are not included. No solvent stimulus is tested. The temporal dynamics of the responses are not presented. Movies of the imaging are not included as supplementary information - it would be important to visualize those with what was considered modest movement.

(3) The observed differences in phenotypes of Ir25a and Ir76b mutants are intriguing, as are those between the co-receptor mutants and Ir51b, Ir94a, and Ir94h, but have not been sufficiently considered. Prior studies have also found roles for other response modes (OFF response), other IRs and GRs, and other organs (labellum, tarsi) in behavioral responses to carboxylic acids. Overall, the authors' model may be overly simplistic, and the discussion does not do justice to how their model reconciles with the body of work that already exists.

Reviewer #2 (Public review):

Shrestha et al investigated the role of IR receptors in the detection of 3 carboxylic acids in adult Drosophila. A low concentration of either of these carboxylic acids added to 2 mM sucrose (1% lactic acid (LA), citric acid (CA), or glycolic acid (GA)) stimulates the consumption of adult flies in choice conditions. The authors use this behavioral test to screen the impact of mutations within 33 receptors belonging to the IR family, a large family of receptors derived from glutamate receptors and expressed both in the olfactory and gustatory sensilla of insects. Within the panel of mutants tested, they observed that 3 receptors (IR25a, IR51b, and IR76b) impaired the detection of LA, CA, and GA, and that 2 others impacted the detection of CA and GA (IR94a and IR94h). Interestingly, impairing IR51b, IR94a, and IR94h did not affect the electrophysiological responses of external gustatory sensilla to LA, CA, and GA. Thanks to the use of GAL4 strains associated with these receptors and thanks to the use of poxn mutants (which do not develop external gustatory sensilla but still have functional internal receptors), they show evidence that IR94a and IR94h are only expressed in two clusters of gustatory neurons of the pharynx, respectively in the VCSO (ventral cibarial sense organ) and in the VCSO + LSO (labral sense organ). As for IR51b, the GAL4 approach was not successful but RT-PCR made on different parts of the insect showed an expression both in the pharyngeal organs and in peripheral receptors. These main findings are then complemented by a host of additional experiments meant to better understand the respective roles of IR94a and IR94h, by using optogenetics and brain calcium imaging using GCamp6. They also report a failed attempt to co-express IR51b, IR94a, and IR94h into external receptors, a co-expression which did not confer the capability of bitter-sensitive cells (expressing GR33a-GAL4) to detect either of the carboxylic acids. These data complete and expand previous observations made on this group and others, and dot to 2 new IR receptors which show an unsuspected specific expression, into organs that still remain difficult to study.

The conclusions of this paper are supported by the data presented, but it remains difficult to make general conclusions as concerns the mechanisms by which carboxylic acids are detected.

(1) All experiments were done with 1% of carboxylic acids. What is the dose dependency of the behavioral responses to these acids, and is it conceivable that other receptors are involved at other concentrations?

(2) One result needs to be better discussed and hypotheses proposed - which is why the mutations of most receptors lead to a loss of detection (mutant flies become incapable of detecting the acid) while mutations in IR94a and IR94h make CA and GA potent deterrents. Does it mean that CA and GA are detected by another set of receptors that, when activated, make flies actively avoid CA and GA? In that case, do the authors think that testing receptors one by one is enough to uncover all the receptors participating in the detection of these substances?

(3) The paper needs to be updated with a recent paper published by Guillemin et al (2024), indicating that LA is detected externally by a combination of IR94e, IR76b and IR25a. IR25a might help to form a fully functional receptor in GR33a neurons (a former study from Chen et al (2017) indicate that IR25a is expressed in all gustatory neurons of the pharynx).

(4) Although it was not the main focus of the paper, it would have been most interesting if the cells expressing IR94a and IR94h were identified, and placed on the functional map proposed by the group of Dahanukar (Chen et al 2017 Cell Reports, Chen et al 2019 Cell Reports).

Reviewer #3 (Public review):

Summary:

In this work, the authors investigated the molecular and cellular basis of sour taste perception in Drosophila melanogaster, focusing on identifying receptors that mediate attractive responses to certain carboxylic acids. It builds on previous work from the same group that had identified the IR co-receptors IR25a and IR76b for this sensory process, screening a set of mutants in IRs to identify three, IR51b, IR94a, and IR94h, required for feeding preference responses to some or all of the tested acids.

Strengths:

The work is of interest because it assigns sensory roles to IRs of previously unknown function, in particular IR94a and IR94h, and points to pharyngeal neurons in which these receptors are expressed as the relevant sensory neurons (potentially with different roles for IR94a- and IR94h-expressing neurons). The work combines elegant genetics, simple but effective feeding and taste assays, chemo-/opto-genetic activation, and some calcium imaging. Overall the presented data look solid and well-controlled.

Weaknesses:

The in situ expression analysis relies entirely on transgenic driver lines for IR94a and IR94h (which had been previously described, though not fully cited in this work). Importantly, given that many of the behavioral experiments (genetic rescue, physiology, artificial activation) use the IR94a and IR94h GAL4 driver lines, it would be helpful to validate that these faithfully reflect IR94a and IR94h expression (as far as I can tell, such validation wasn't done in the original papers describing these lines as part of a large collection of IR drivers). For IR51b, pharyngeal expression is concluded indirectly from non-quantitative RT-PCR analysis (genetic reporters did not work). The lack of direct detection of gene/protein expression (for example, through RNA FISH, immunofluorescence, or protein tagging) would have made for a more complete characterization of these receptors (for example, there is no direct evidence that they also express IR25a and IR76b, as one might expect). Finally, the relationship of IR94a and IR94h neurons to other types of pharyngeal neurons remains unclear, as are their projection patterns in the SEZ.

Conceptually, the work is of interest mostly to those in the immediate field; there have been a very large number of studies in the past decade (several from this lab) characterizing the contributions of different IRs to various chemosensory processes. The current work doesn't lend much insight into the nature of the minimal functional unit of gustatory IRs (reconstitution of a functional IR in a heterologous neuron/cell has not been achieved here, but this is a limitation of many other previous studies), nor to how different pharyngeal sensory pathways might collaborate to control behavior. Nevertheless, the findings provide a useful contribution to the literature.

Author response:

Reviewer #1 (Public review):

Summary:

Shrestha et al report an investigation of mechanisms underlying gustatory preference for carboxylic acids in Drosophila. They begin with a screen of selected IR mutants, identifying 5 candidates - 2 IR co-receptors and 3 other IRs - whose loss of function causes defects in feeding preference for one or more of the three tested carboxylic acids. The requirement for IR51b, IR94a, and IR94h in carboxylic acid responses is evaluated in more detail using behavior, electrophysiology (labellar sensilla), and calcium imaging (pharyngeal neurons). The behavioral valence of IR94a and IR94h neurons is assessed using optogenetics. Overall the study uses a variety of approaches to test and validate the requirement of IRs in pharyngeal carboxylic acid taste.

Strengths:

The involvement of the identified IRs in gustatory responses to carboxylic acids is very clear from this study. The authors use mutants and transgenic rescue experiments and evaluate outcomes using electrophysiology, behavior, and imaging. Complementary approaches of loss-of-function and artificial activation support the main conclusion that the identified pharyngeal neurons sense carboxylic acids and convey a positive behavioral valence.

Weaknesses:

Some aspects of expression analysis and calcium imaging need to be clarified to better support the conclusions.

(1) The conclusion of two parallel IR-mediated pathways rests on expression analysis of Ir94a-GAL4 and Ir94h-GAL4 lines and the observation that Ir51b expression driven by either can rescue the Ir51b mutant phenotype. However, the expression analysis is not as rigorous as it needs to be for such a conclusion. Prior work found co-expression of Ir94a and Ir94h in the LSO. Here, the co-expression of the two drivers has not been examined, and Ir94a-GAL4 does not appear to be expressed in the LSO. Given the challenges in validating expression patterns in pharyngeal organs, the possibility that the drivers do not entirely capture endogenous expression cannot be ruled out. Rescue experiments using feeding preference or single-cell imaging don't suffice as validation. Plus, the expression of Ir51b could not be defined.

Based on current literature, Ir94a and Ir94h exhibit distinct expression patterns localized to different sensory regions. Specifically, Ir94a is primarily expressed in the V5 region of the VCSO, where it co-localizes with Ir94c-GAL4 (Chen et al., 2017). Conversely, Ir94h is found in the L7-7 sensilla of the LSO, where it co-expresses with Ir94f, and also within the V2 cells of the VCSO. Notably, the projections of Ir94a and Ir94h into the dorso-anterior subesophageal ganglion suggest divergent expression patterns rather than co-expression in the pharyngeal regions (Koh et al., 2014). Regarding co-expression of Ir94a and Ir94h in the LSO, we did not find any evidence to support this claim. Our data reinforce this view, showing that Ir94a-GAL4 expression is limited to the VCSO, while Ir94h-GAL4 is present in both the LSO and VCSO. Thus, the notion of co-expression of Ir94a and Ir94h in the LSO is not substantiated by current evidence.

As a reviewer suggested, it is possible that the GAL4 drivers utilized may not fully reflect the endogenous expression of these receptors. Despite this limitation, our behavioral, expression, and physiological analyses strongly suggest that Ir94a and Ir94h are located in distinct regions, supporting a model of two parallel IR-mediated pathways operating within the sensory system.

In addition, RT-PCR analysis confirmed the presence of Ir51b. However, due to methodological constraints, we were unable to conduct cell-type-specific expression studies using Ir51b-GAL4. This limitation, which we have acknowledged in the manuscript, does not detract from our core findings but highlights an area for future research. Further studies utilizing cell-specific expression analysis and co-expression studies with additional drivers could offer more definitive insights into IR51b’s functional role and its interactions within broader IR-mediated pathways.

(2) The description of methods and results for the ex vivo calcium imaging is not satisfactory. Details about which cells are being analyzed, and in which organs are not included. No solvent stimulus is tested. The temporal dynamics of the responses are not presented. Movies of the imaging are not included as supplementary information - it would be important to visualize those with what was considered modest movement.

We appreciate this valuable feedback. As discussed above, Ir94h is specifically expressed in the L7-7 sensilla of the LSO, while Ir94a is expressed in the V2 cells of the VCSO. This evidence led us to focus specifically on these cells in our calcium imaging study to ensure accuracy and relevance. In our experiments, Adult hemolymph solution (AHL) (108 mM NaCl, 5 mM KCl, 8.2 mM MgCl2, 2 mM CaCl2, 4 mM NaHCO3, 1 mM NaH2PO4, 5 mM HEPES, pH 7.5) was used as the solvent and employed as a pre-stimulus (as mentioned in the Methods section). During this phase, we observed no changes in fluorescence, indicating that AHL itself did not influence the responses. Fluorescence changes occurred only when the test chemical, dissolved in AHL, was introduced. To further confirm that AHL had no impact on the results, we conducted continuous recordings with AHL alone before beginning our main experiments, and these trials confirmed the absence of fluorescence alterations. We have included the temporal dynamics and supplementary video recordings to provide a more comprehensive understanding of our findings.

(3) The observed differences in phenotypes of Ir25a and Ir76b mutants are intriguing, as are those between the co-receptor mutants and Ir51b, Ir94a, and Ir94h, but have not been sufficiently considered. Prior studies have also found roles for other response modes (OFF response), other IRs and GRs, and other organs (labellum, tarsi) in behavioral responses to carboxylic acids. Overall, the authors' model may be overly simplistic, and the discussion does not do justice to how their model reconciles with the body of work that already exists.

Stanley et al. (2021) reported that the gustatory detection of lactic acid requires both IRs and GRs functioning together. Specifically, they found that IR25a mediates the onset peak response (ON response) to lactic acid, while GRs dampen this response and contribute to a removal peak (OFF response). Interestingly, in Ir25a mutants, a small onset peak still occurred, while Gr64a-f mutants showed an enhanced onset, suggesting that IRs and GRs interact dynamically to modulate taste responses.

In our previous work, we also observed the role of sweet GRs, in addition to Ir25a and Ir76b, in detecting carboxylic acids in the labellum (Shrestha et al., 2021). This raises the possibility of a similar interplay with carboxylic acids in our current study, where different IRs may contribute to distinct aspects of sensory responses in the pharynx, leading to the phenotypic differences we observed. Moreover, Chen et al. (2017) demonstrated that sour-sensing neurons in the tarsi express both IR76b and IR25a and specifically respond to carboxylic and inorganic acids without reacting to sweet or bitter compounds. This finding points to a specialized role for these receptors in sour detection and suggests a coordinated response involving multiple sensory organs—such as the labellum, tarsi, and pharynx.

The phenotypic differences observed in our mutants align with a more integrated model of carboxylic acid detection, in which multiple receptors and sensory organs contribute to the overall behavioral response. This supports the idea that our current model offers a more detailed understanding of how different carboxylic acids are detected and processed by the gustatory system.

Reviewer #2 (Public review):

Shrestha et al investigated the role of IR receptors in the detection of 3 carboxylic acids in adult Drosophila. A low concentration of either of these carboxylic acids added to 2 mM sucrose (1% lactic acid (LA), citric acid (CA), or glycolic acid (GA)) stimulates the consumption of adult flies in choice conditions. The authors use this behavioral test to screen the impact of mutations within 33 receptors belonging to the IR family, a large family of receptors derived from glutamate receptors and expressed both in the olfactory and gustatory sensilla of insects. Within the panel of mutants tested, they observed that 3 receptors (IR25a, IR51b, and IR76b) impaired the detection of LA, CA, and GA, and that 2 others impacted the detection of CA and GA (IR94a and IR94h). Interestingly, impairing IR51b, IR94a, and IR94h did not affect the electrophysiological responses of external gustatory sensilla to LA, CA, and GA. Thanks to the use of GAL4 strains associated with these receptors and thanks to the use of poxn mutants (which do not develop external gustatory sensilla but still have functional internal receptors), they show evidence that IR94a and IR94h are only expressed in two clusters of gustatory neurons of the pharynx, respectively in the VCSO (ventral cibarial sense organ) and in the VCSO + LSO (labral sense organ). As for IR51b, the GAL4 approach was not successful but RT-PCR made on different parts of the insect showed an expression both in the pharyngeal organs and in peripheral receptors. These main findings are then complemented by a host of additional experiments meant to better understand the respective roles of IR94a and IR94h, by using optogenetics and brain calcium imaging using GCamp6. They also report a failed attempt to co-express IR51b, IR94a, and IR94h into external receptors, a co-expression which did not confer the capability of bitter-sensitive cells (expressing GR33a-GAL4) to detect either of the carboxylic acids. These data complete and expand previous observations made on this group and others, and dot to 2 new IR receptors which show an unsuspected specific expression, into organs that still remain difficult to study.

The conclusions of this paper are supported by the data presented, but it remains difficult to make general conclusions as concerns the mechanisms by which carboxylic acids are detected.

(1) All experiments were done with 1% of carboxylic acids. What is the dose dependency of the behavioral responses to these acids, and is it conceivable that other receptors are involved at other concentrations?

In our study, we conducted experiments to examine the dose dependency of behavioral responses to carboxylic acids, with results presented in Supplementary Figure 1. We found that lower concentrations of carboxylic acids are perceived as attractive, while higher concentrations are aversive. This differential response suggests that the receptors identified in our study are primarily tuned to detect low concentrations of these acids. Since higher concentrations elicited aversive responses, it is plausible that additional receptors, beyond the scope of our study, may be involved in sensing these higher concentrations. These receptors could be part of other gustatory receptor neurons that respond specifically to increased acid levels, as fruit flies tend to avoid higher concentrations. We propose that future research could investigate these alternative pathways to gain a complete understanding of the behavioral responses to carboxylic acids. In summary, our findings suggest that specific receptors are involved in detecting low concentrations, while distinct receptor pathways—possibly mediated by other GRNs—may regulate responses to higher concentrations.

(2) One result needs to be better discussed and hypotheses proposed - which is why the mutations of most receptors lead to a loss of detection (mutant flies become incapable of detecting the acid) while mutations in IR94a and IR94h make CA and GA potent deterrents. Does it mean that CA and GA are detected by another set of receptors that, when activated, make flies actively avoid CA and GA? In that case, do the authors think that testing receptors one by one is enough to uncover all the receptors participating in the detection of these substances?

As we mentioned above, it is possible that distinct receptor pathways mediate avoidance of GA and CA. This suggests that CA and GA might activate different sets of receptors that trigger avoidance behavior, pointing to a more complex interplay of receptor activity than we initially considered. Certain acids may indeed be detected by multiple receptors, with each receptor contributing uniquely to the behavioral response. Regarding the sufficiency of testing receptors individually, we recognize the limitations of this approach. Examining receptors one by one may not reveal the full spectrum of receptors involved, especially due to potential interactions or compensatory mechanisms that only emerge when certain receptors are inactive. Therefore, a more holistic approach—such as genetic screens for behavioral responses or using complex genetic models to disrupt multiple receptors simultaneously—could provide deeper insights. Moving forward, incorporating receptor interactions that modulate each other, along with more comprehensive assays, could help explain these discrepancies by uncovering previously overlooked receptor functions.

(3) The paper needs to be updated with a recent paper published by Guillemin et al (2024), indicating that LA is detected externally by a combination of IR94e, IR76b and IR25a. IR25a might help to form a fully functional receptor in GR33a neurons (a former study from Chen et al (2017) indicate that IR25a is expressed in all gustatory neurons of the pharynx).

According to Guillemin et al. (2024), the combination of IR94e, IR76b, and IR25a is required for amino acid detection but not for detecting lactic acid (LA). In their calcium imaging experiments, 100 mM LA elicited a response similar to the vehicle control, suggesting that these receptors do not play a role in LA detection.

(4) Although it was not the main focus of the paper, it would have been most interesting if the cells expressing IR94a and IR94h were identified, and placed on the functional map proposed by the group of Dahanukar (Chen et al 2017 Cell Reports, Chen et al 2019 Cell Reports).

The expression patterns of IR94a and IR94h were previously detailed by Chen et al. (2017), showing that IR94h is expressed in the labial sense organ (LSO, specifically in L7-7) and the ventral cibarial sense organ (VCSO, V2), while IR94a is expressed in the VCSO (V5). Given this established information, we referenced these known expression patterns without replicating the mapping in our study. Our primary focus was to investigate the functional role of these neurons within the pharynx, and we believe we have successfully highlighted their specific contributions. However, we recognize that integrating the functional mapping of these neurons in alignment with the work of Dahanukar’s group would have strengthened our findings and provided a more comprehensive understanding. We acknowledge this as a limitation of our study and appreciate your suggestion, as it points to a valuable direction for future research.

Reviewer #3 (Public review):

Summary:

In this work, the authors investigated the molecular and cellular basis of sour taste perception in Drosophila melanogaster, focusing on identifying receptors that mediate attractive responses to certain carboxylic acids. It builds on previous work from the same group that had identified the IR co-receptors IR25a and IR76b for this sensory process, screening a set of mutants in IRs to identify three, IR51b, IR94a, and IR94h, required for feeding preference responses to some or all of the tested acids.

Strengths:

The work is of interest because it assigns sensory roles to IRs of previously unknown function, in particular IR94a and IR94h, and points to pharyngeal neurons in which these receptors are expressed as the relevant sensory neurons (potentially with different roles for IR94a- and IR94h-expressing neurons). The work combines elegant genetics, simple but effective feeding and taste assays, chemo-/opto-genetic activation, and some calcium imaging. Overall the presented data look solid and well-controlled.

Weaknesses:

The in situ expression analysis relies entirely on transgenic driver lines for IR94a and IR94h (which had been previously described, though not fully cited in this work). Importantly, given that many of the behavioral experiments (genetic rescue, physiology, artificial activation) use the IR94a and IR94h GAL4 driver lines, it would be helpful to validate that these faithfully reflect IR94a and IR94h expression (as far as I can tell, such validation wasn't done in the original papers describing these lines as part of a large collection of IR drivers). For IR51b, pharyngeal expression is concluded indirectly from non-quantitative RT-PCR analysis (genetic reporters did not work). The lack of direct detection of gene/protein expression (for example, through RNA FISH, immunofluorescence, or protein tagging) would have made for a more complete characterization of these receptors (for example, there is no direct evidence that they also express IR25a and IR76b, as one might expect). Finally, the relationship of IR94a and IR94h neurons to other types of pharyngeal neurons remains unclear, as are their projection patterns in the SEZ.

Conceptually, the work is of interest mostly to those in the immediate field; there have been a very large number of studies in the past decade (several from this lab) characterizing the contributions of different IRs to various chemosensory processes. The current work doesn't lend much insight into the nature of the minimal functional unit of gustatory IRs (reconstitution of a functional IR in a heterologous neuron/cell has not been achieved here, but this is a limitation of many other previous studies), nor to how different pharyngeal sensory pathways might collaborate to control behavior. Nevertheless, the findings provide a useful contribution to the literature.

We appreciate your thoughtful feedback. As noted in our response, our primary objective was to investigate the sensory functions of IR94a and IR94h. To this end, we conducted behavioral assays, which we validated with additional approaches including genetic rescue, physiological tests, and artificial activation. Throughout these experiments, we extensively utilized Ir94a- and Ir94h-GAL4 driver lines. To ensure these lines accurately reflect the expression of IR94a and IR94h, we verified their expression patterns using immunohistochemistry across various body parts. Our results align with previous findings that show both receptors are exclusively expressed in the pharynx. Regarding IR51b, we employed RT-PCR due to its high sensitivity and specificity, which supported our hypothesis. Nonetheless, we agree that more direct detection methods would have provided a stronger validation of IR51b expression. Our previous study (Sang et al., 2024) also demonstrated the pharyngeal expression of co-expressed receptors, specifically IR25a and IR76b. However, we recognize that the lack of direct evidence for their co-expression with IR51b remains a significant gap. This limitation primarily stems from the unavailability of specific reagents needed for direct assays targeting IR51b, which restricted our experimental approach.

You also raised the potential relationship between IR94a and IR94h neurons and other pharyngeal neuron types, including their projection patterns in the subesophageal zone. This is indeed an important area for future research that could clarify neural connectivity and further our understanding of sensory mechanisms. However, our study was focused on exploring sensory mechanisms in peripheral regions rather than detailed neural mapping in the SEZ. Investigating these connections would undoubtedly provide valuable insights into the neural circuitry involved and represents an intriguing direction for future research.

Reviewer #2 (Public review):

Summary:

Salt stress is a significant and growing concern for agriculture in some parts of the world. While the effects of sodium excess have been studied in Arabidopsis and (many) crop species, most studies have focused on Na uptake, toxicity and overall effects on yield, rather than on developmental responses to excess Na, per se. The work by Ishka and colleagues aims to fill this gap.

Working from an existing dataset that exposed a diverse panel of A. thaliana accessions to control, moderate, and severe salt stress, the authors identify candidate loci associated with altering the root:shoot ratio under salt stress. Following a series of molecular assays, they characterize a DUF247 protein which they dub SR3G, which appears to be a negative regulator of root growth under salt stress.

Overall, this is a well-executed study which demonstrates the functional role played by a single gene in plant response to salt stress in Arabidopsis.

Review of revised manuscript:

The authors have addressed my point-by-point comments to my satisfaction. In the cases where they have changed their manuscript language, clarified figures, or added analyses I have no further comment. In some cases, there is a fruitful back-and-forth discussion of methodology which I think will be of interest to readers.

I have nothing to add during this round of review. I think that the paper and associated discussion will make a nice contribution to the field

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors aim to assess the effect of salt stress on root:shoot ratio, identify the underlying genetic mechanisms, and evaluate their contribution to salt tolerance. To this end, the authors systematically quantified natural variations in salt-induced changes in root:shoot ratio. This innovative approach considers the coordination of root and shoot growth rather than exploring biomass and the development of each organ separately. Using this approach, the authors identified a gene cluster encoding eight paralog genes with a domain-of-unknown-function 247 (DUF247), with the majority of SNPs clustering into SR3G (At3g50160). In the manuscript, the authors utilized an integrative approach that includes genomic, genetic, evolutionary, histological, and physiological assays to functionally assess the contribution of their genes of interest to salt tolerance and root development.

Strengths:

The holistic approach and integrative methodologies presented in the manuscript are essential for gaining a mechanistic understanding of a complex trait such as salt tolerance. The authors focused on At3g50160 but included in their analyses additional DUF247 paralogs, which further contributes to the strength of their approach. In addition, the authors considered the developmental stage (young seedlings, early or late vegetative stages) and growth conditions of the plants (agar plates or soil) when investigating the role of SR3G in salt tolerance and root or shoot development.

Weaknesses:

The authors' claims and interpretation of the results are not fully supported by the data and analyses. In several cases, the authors report differences that are not statistically significant (e.g., Figures 4A, 7C, 8B, S14, S16B, S17C), use inappropriate statistical tests (e.g., t-test instead of Dunnett Test/ANOVA as in Figures 10B-C, S19-23), present standard errors that do not seem to be consistent with the post-hoc Tukey HSD Test (e.g., Figures 4, 9B-C, S16B), or lack controls (e.g., Figure 5C-E, staining of the truncated versions with FM4-64 is missing).

We thank the reviewer for their critical thoughts on the presented data. We have revised our data interpretation in the main text to more accurately reflect the results. Given the nature of our experimental setup, where we trace the roots of individual Arabidopsis seedlings grown on plates, there is considerable biological variation, which makes achieving strong statistical significance between samples or genotypes challenging. However, we think that the representation of the data as transparently as possible is necessary to provide the readers and reviewers a true picture of the variability that we are observing.  Consequently, we have centered our data interpretation around observable trends that facilitate drawing conclusions.

The choice of statistical test is closely tied to the specific biological question being addressed. In Figures 10A-C, as in Figures 6A-B, we compared all genotypes to the wild-type Col-0 within each condition, and thus ANOVA analysis, testing the general effect of the genotype across both mutants and Col-0 wild-type is not appropriate. Similarly, in Figures S19-S23, we compared each mutant line to the wild-type Col-0 under each condition.

We repeated the post-hoc Tukey HSD Test for Figures 4, 9B-C, and S16B and made adjustments where necessary (see tracked changes manuscript).

The truncated versions do not localize to the plasma membrane; instead, they are targeted to the nucleus and cytosol, mimicking the localization pattern of free GFP, which was used as a control in Panel F. Therefore, we believe that having FM4-64 as a control for these specific images is not informative, but instead using free GFP is serving as a better control in that particular construct.

In other cases, traits of root system architecture and expression patterns are inconsistent between different assays despite similar growth conditions (e.g., Figures S17A-B vs. 10A-C vs. 6A, and Figures S16B vs. 4A/9B), or T-DNA insertion alleles of WRKY75 that are claimed to be loss-of-function show comparable expression of WRKY75 as WT plants. Additionally, several supplemental figures are mislabeled (Figures S6-9), and some figure panels are missing (e.g., Figures S16C and S17E).

We thank the reviewer for raising these points and noticing the inconsistency between different assays (e.g., Figures S17A-B vs. 10A-C vs. 6A, and Figures S16B vs. 4A/9B). As mentioned above, considerable biological variation makes achieving strong statistical significance between samples, genotypes, or experiments challenging. Thus, we have centered our data interpretation around observable “trends” between experiments to facilitate drawing conclusions. Considering Figures S17A-B, 10A-C, and 6A, we acknowledge the reviewer's concern about inconsistencies in root system architecture across experiments. Initially, we observed that the sr3g mutant had reduced lateral root length compared to Col-0 under salt stress. This led us to focus on this specific phenotypic trait rather than the overall root system architecture. Despite some variation, the sr3g mutant consistently showed a similar trend/phenotype when compared to Col-0 under salt stress. We believe the variation in main root length and lateral root number between experiments is due to inherent differences between biological replicates.

Regarding gene expression patterns between Figures S16B and 4A/9B, we included part of Figure 9B (SR3G gene expression in Col-0) in Figure 4A. Figure S16B represents a completely different assay. Despite variations between assays, the overall message remains consistent: SR3G gene expression is induced under salt stress in the root but not in the shoot.

Both SR3G and WRKY75 are expressed at very low levels, even under the 75 mM salt stress condition we tested. When gene expression is so low, detecting changes is challenging due to inherent variations. Nonetheless, we observed a reduction in WRKY75 expression in the mutant lines compared to wild-type Col-0, though this reduction was not statistically significant. More importantly, we observed a similar phenotype in the wrky75 mutant, specifically reduced main root length under salt stress, consistent with the findings of the published paper in The Plant Cell by Lu et al. (2023) “Lu, K.K., Song, R.F., Guo, J.X., Zhang, Y., Zuo, J.X., Chen, H.H., Liao, C.Y., Hu, X.Y., Ren, F., Lu, Y.T. and Liu, W.C., 2023. CycC1; 1–WRKY75 complex-mediated transcriptional regulation of SOS1 controls salt stress tolerance in Arabidopsis. The Plant Cell, 35(7), pp.2570-2591”.

We appreciate the reviewer for spotting the missing labels for Figures S6-9. We corrected them at the main text, figures, and legends. We added panel C to Figure S16 and removed panel E from Figure S17 legend,  now they match to actual figures and legends.

Consequently, the authors' decisions regarding subsequent functional assays, as well as major conclusions about gene function, including SR3G function in root system architecture, involvement in root suberization, and regulation of cellular damage are incomplete.

We greatly appreciate the reviewer's thorough review of our manuscript and their critical comments. We have carefully addressed all comments and concerns.

Reviewer #2 (Public Review):

Salt stress is a significant and growing concern for agriculture in some parts of the world. While the effects of sodium excess have been studied in Arabidopsis and (many) crop species, most studies have focused on Na uptake, toxicity, and overall effects on yield, rather than on developmental responses to excess Na, per se. The work by Ishka and colleagues aims to fill this gap.

Working from an existing dataset that exposed a diverse panel of A. thaliana accessions to control, moderate, and severe salt stress, the authors identify candidate loci associated with altering the root:shoot ratio under salt stress. Following a series of molecular assays, they characterize a DUF247 protein which they dub SR3G, which appears to be a negative regulator of root growth under salt stress.

Overall, this is a well-executed study that demonstrates the functional role played by a single gene in plant response to salt stress in Arabidopsis.

The abstract and beginning of the Discussion section highlight the "new tool" developed here for measuring biomass accumulation. I feel that this distracts from the central aims of the study, which is really about the role of a specific gene in root development under salt stress. I would suggest moving the tool description to less prominent parts of the manuscript.

We appreciate the reviewer's suggestion. We believe that the innovative tool used to extract shoot-to-root ratio data from previous experiments underscores the value of reutilizing previously acquired data for new discoveries and demonstrates how reanalyzing the same data can provide fresh insights, such as identification of new allelic variation. Therefore, we decided to retain this section, as our discovery of the SR3G gene originated from this innovative tool.

Recommendations for the authors:

Reviewer #3 (Recommendations For The Authors):

Line 58 (opening sentence) - salt accumulation in the soil is not caused by evaporation exceeding input; that scenario results in soil water deficit. The issue is when the input water has dissolved ions.

We thank the reviewer for raising this important point. While this point is theoretically true, all of the water that is found in natural environments contains some dissolved ions. Therefore, drought conditions will lead, over time, to increased soil salinization. We have amended this sentence to represent our point better.

“Salt stress is predominant in the dryland areas where evaporation rate exceeds water input. As all water contains dissolved ions, the prolonged exposure to drought stress results in increased accumulation of salts in the upper soil layers 1–3.”

I feel that it would be helpful, for replication and for interpretation, if the authors could provide water potentials for the growing media used throughout. What water potentials are the plants experiencing when grown in 1/2 MS + agar at 0, 75, and 150mM NaCl? Juenger and Verslues present a great recent discussion of the importance of reporting these values (Juenger, T. E. and P. E. Verslues (2023). "Time for a drought experiment: Do you know your plants' water status?" Plant Cell 35(1): 10-23.)

Critically, how do the water potentials experienced by agar-grown plants compare to those experienced in soil-grown plants? As a stated aim of this study is to allow translation to crops these data are very important to convince physiologists of the relevance of the results.

We thank the reviewer for raising this important point. We completely agree that growing plants on agar plates is an artificial setup and knowing the water potential of the plants within this setup would be highly informative. However, as indicated in review by Juenger and Verslues 2023, the agar plate setup is much more reproducible compared to various soil conditions, and we report the media composition in sufficient detail for it to be reproduced in other laboratory conditions.

Furthermore, while investigating the water status of plants and soil is indeed intriguing, it is beyond the scope of this study and would require us to redo the experiments with specific tools listed within the Juennger and Verslues review, which are currently not within our laboratory equipment list.

Importantly, any changes reported in this manuscript apply equally to both wild-type and mutant lines under all conditions. We provide extensive report on the soil type used, as well as soil quantity. We are using the gravimetric method to determine the water content, and salt stress application, as described in previous works from our lab (Yu and Sussman et al., 2024 Plant Physiology and Awlia et al., 2016 Frontiers in Plant Science).