10,000 Matching Annotations
  1. Jan 2025
    1. eLife Assessment

      This study presents a new quantitative method, CROWN-seq, to map the cap-adjacent RNA modification N6,2'-O-dimethyladenosine (m6Am) with single nucleotide resolution. Using thoughtful controls and well-validated reagents, the authors provide compelling evidence that the method is reliable and reproducible. Additionally, the study provides important evidence that m6Am may increase transcription in modified mRNAs. However, the data only demonstrates a correlation between m6Am and transcriptional regulation rather than causality. Overall, this study is poised to advance m6Am research, being of broad interest to the RNA biology and gene regulation fields.

    2. Reviewer #1 (Public review):

      Summary:

      In this manuscript, Liu et al. present CROWN-seq, a technique that simultaneously identifies transcription-start nucleotides and quantifies N6,2'-O-dimethyladenosine (m6Am) stoichiometry. This method is derived from ReCappable-seq and GLORI, a chemical deamination approach that differentiates A and N6-methylated A. Using ReCappable-seq and CROWN-seq, the authors found that genes frequently utilize multiple transcription start sites, and isoforms beginning with an Am are almost always N6-methylated. These findings are consistently observed across nine cell lines. Unlike prior reports that associated m6Am with mRNA stability and expression, the authors suggest here that m6Am may increase transcription when combined with specific promoter sequences and initiation mechanisms. Additionally, they report intriguing insights on m6Am in snRNA and snoRNA and its regulation by FTO. Overall, the manuscript presents a strong body of work that will significantly advance m6Am research.

      Strengths:

      The technology development part of the work is exceptionally strong, with thoughtful controls and well-supported conclusions.

      Weaknesses:

      Given the high stoichiometry of m6Am, further association with upstream and downstream sequences (or promoter sequences) does not appear to yield strong signals. As such, transcription initiation regulation by m6Am, suggested by the current work, warrants further investigation.

    3. Reviewer #2 (Public review):

      Summary:

      In the manuscript "Decoding m6Am by simultaneous transcription-start mapping and methylation quantification" Liu and co-workers describe the development and application of CROWN-Seq, a new specialized library preparation and sequencing technique designed to detect the presence of cap-adjacent N6,2'-O-dimethyladenosine (m6Am) with single nucleotide resolution. Such a technique was a key need in the field since prior attempts to get accurate positional or quantitative measurements of m6Am positioning yielded starkly different results and failed to generate a consistent set of targets. As noted in the strengths section below the authors have developed a robust assay that moves the field forward.

      Furthermore, their results show that most mRNAs whose transcription start nucleotide (TSN) is an 'A' are in fact m6Am (85%+ for most cell lines). They also show that snRNAs and snoRNAs have a substantially lower prevalence of m6Am TSNs.

      Strengths:

      Critically, the authors spent substantial time and effort to validate and benchmark the new technique with spike-in standards during development, cross-comparison with prior techniques, and validation of the technique's performance using a genetic PCIF1 knockout. Finally, they assayed nine different cell lines to cross-validate their results. The outcome of their work (a reliable and accurate method to catalog cap-adjacent m6Am) is a particularly notable achievement and is a needed advance for the field.

      Weaknesses:

      No major concerns were identified by this reviewer.

      Mid-level Concerns: All previous concerns were addressed in the revised version

    4. Reviewer #3 (Public review):

      Summary:

      m6Am is an abundant mRNA modification present on the TSN. Unlike the structurally similar and abundant internal mRNA modification m6A, m6Am's function has been controversial. One way to resolve controversies surrounding mRNA modification functions has been to develop new ways to better profile said mRNA modification. Here, Liu et al. developed a new method (based on GLORI-seq for m6A-sequencing), for antibody-independent sequencing of m6Am (CROWN-seq). Using appropriate spike-in controls and knockout cell lines, Liu et al. clearly demonstrated CROWN-seq's precision and quantitative accuracy for profiling transcriptome-wide m6Am. Subsequently, the authors used CROWN-seq to greatly expand the number of known m6Am sites in various cell lines and also determine m6Am stoichiometry to generally be high for most genes. CROWN-seq identified gene promoter motifs that correlate best with high stoichiometry m6Am sites, thereby identifying new determinants of m6Am stoichiometry. CROWN-seq also helped reveal that m6Am does not regulate mRNA stability or translation (as opposed to past reported functions). Rather, m6Am stoichiometry correlates well with transcription levels. Finally, Liu et al. reaffirmed that FTO mainly demethylates m6Am, not of mRNA but of snRNAs and snoRNAs.

      Strengths:

      This is a well-written manuscript that describes and validates a new m6Am-sequencing method: CROWN-seq as the first m6Am-sequencing method that can both quantify m6Am stoichiometry and profile m6Am at single-base resolution. These advantages facilitated Liu et al. to uncover new potential findings related to m6Am regulation and function. I am confident that CROWN-seq will likely be the gold standard for m6Am-sequencing henceforth.

      Weaknesses:

      Though the authors have uncovered a potentially new function for m6Am, they need to be clear that without identifying a mechanism, their data might only be demonstrating a correlation between the presence of m6Am and transcriptional regulation rather than causality.

    5. Author response:

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

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, Liu et al. present CROWN-seq, a technique that simultaneously identifies transcription-start nucleotides and quantifies N6,2'-O-dimethyladenosine (m6Am) stoichiometry. This method is derived from ReCappable-seq and GLORI, a chemical deamination approach that differentiates A and N6-methylated A. Using ReCappable-seq and CROWN-seq, the authors found that genes frequently utilize multiple transcription start sites, and isoforms beginning with an Am are almost always N6-methylated. These findings are consistently observed across nine cell lines. Unlike prior reports that associated m6Am with mRNA stability and expression, the authors suggest here that m6Am may increase transcription when combined with specific promoter sequences and initiation mechanisms. Additionally, they report intriguing insights on m6Am in snRNA and snoRNA and its regulation by FTO. Overall, the manuscript presents a strong body of work that will significantly advance m6Am research.

      Strengths:

      The technology development part of the work is exceptionally strong, with thoughtful controls and well-supported conclusions.

      We appreciate the reviewer for the very positive assessment of the study. We have addressed the concerns below.

      Weaknesses:

      Given the high stoichiometry of m6Am, further association with upstream and downstream sequences (or promoter sequences) does not appear to yield strong signals. As such, transcription initiation regulation by m6Am, suggested by the current work, warrants further investigation.

      We thank the reviewer for the insightful comments. We have softened the language related to m<sup>6</sup>Am and transcription regulation. We totally agree with the reviewer that future investigation is required to determine the molecular mechanism behind m<sup>6</sup>Am and transcription regulation.

      Reviewer #2 (Public review):

      Summary:

      In the manuscript "Decoding m6Am by simultaneous transcription-start mapping and methylation quantification" Liu and co-workers describe the development and application of CROWN-Seq, a new specialized library preparation and sequencing technique designed to detect the presence of cap-adjacent N6,2'-O-dimethyladenosine (m6Am) with single nucleotide resolution. Such a technique was a key need in the field since prior attempts to get accurate positional or quantitative measurements of m6Am positioning yielded starkly different results and failed to generate a consistent set of targets. As noted in the strengths section below the authors have developed a robust assay that moves the field forward.

      Furthermore, their results show that most mRNAs whose transcription start nucleotide (TSN) is an 'A' are in fact m6Am (85%+ for most cell lines). They also show that snRNAs and snoRNAs have a substantially lower prevalence of m6Am TSNs.

      Strengths:

      Critically, the authors spent substantial time and effort to validate and benchmark the new technique with spike-in standards during development, cross-comparison with prior techniques, and validation of the technique's performance using a genetic PCIF1 knockout. Finally, they assayed nine different cell lines to cross-validate their results. The outcome of their work (a reliable and accurate method to catalog cap-adjacent m6Am) is a particularly notable achievement and is a needed advance for the field.

      Weaknesses:

      No major concerns were identified by this reviewer.

      We thank the reviewer for the positive assessment of the method and dataset. We have addressed the concerns below.

      Mid-level Concerns:

      (1) In Lines 625 and 626, the authors state that “our data suggest that mRNAs initate (mis-spelled by authors) with either Gm, Cm, Um, or m6Am.” This reviewer took those words to mean that for A-initiated mRNAs, m6Am was the ‘default’ TSN. This contradicts their later premise that promoter sequences play a role in whether m6Am is deposited.

      We thank the reviewer for the comment. We have changed this sentence into “Instead, our data suggest that mRNAs initiate with either Gm, Cm, Um, or Am, where Am are mostly m<sup>6</sup>Am modified.” The revised sentence separates the processes of transcription initiation and m<sup>6</sup>Am deposition, which will not confuse the reader.

      (2) Further, the following paragraph (lines 633-641) uses fairly definitive language that is unsupported by their data. For example in lines 637 and 638 they state “We found that these differences are often due to the specific TSS motif.” Simply, using ‘due to’ implies a causative relationship between the promoter sequences and m6Am has been demonstrated. The authors do not show causation, rather they demonstrate a correlation between the promoter sequences and an m6Am TSN. Finally, despite claiming a causal relationship, the authors do not put forth any conceptual framework or possible mechanism to explain the link between the promoter sequences and transcripts initiating with an m6Am.

      (3) The authors need to soften the language concerning these data and their interpretation to reflect the correlative nature of the data presented to link m6Am and transcription initiation.

      For (2) and (3). We have softened the language in the revised manuscript. Specifically, for lines 633-641 in the original manuscript, we have changed “are often due to” into “are often related to” in the revised manuscript, which claims a correlation rather than a causation.

      Reviewer #3 (Public review):

      Summary:

      m6Am is an abundant mRNA modification present on the TSN. Unlike the structurally similar and abundant internal mRNA modification m6A, m6Am’s function has been controversial. One way to resolve controversies surrounding mRNA modification functions has been to develop new ways to better profile said mRNA modification. Here, Liu et al. developed a new method (based on GLORI-seq for m6A-sequencing), for antibody-independent sequencing of m6Am (CROWN-seq). Using appropriate spike-in controls and knockout cell lines, Liu et al. clearly demonstrated CROWN-seq’s precision and quantitative accuracy for profiling transcriptome-wide m6Am. Subsequently, the authors used CROWN-seq to greatly expand the number of known m6Am sites in various cell lines and also determine m6Am stoichiometry to generally be high for most genes. CROWN-seq identified gene promoter motifs that correlate best with high stoichiometry m6Am sites, thereby identifying new determinants of m6Am stoichiometry. CROWN-seq also helped reveal that m6Am does not regulate mRNA stability or translation (as opposed to past reported functions). Rather, m6Am stoichiometry correlates well with transcription levels. Finally, Liu et al. reaffirmed that FTO mainly demethylates m6Am, not of mRNA but of snRNAs and snoRNAs.

      Strengths:

      This is a well-written manuscript that describes and validates a new m6Am-sequencing method: CROWN-seq as the first m6Am-sequencing method that can both quantify m6Am stoichiometry and profile m6Am at single-base resolution. These advantages facilitated Liu et al. to uncover new potential findings related to m6Am regulation and function. I am confident that CROWN-seq will likely be the gold standard for m6Am-sequencing henceforth.

      Weaknesses:

      Though the authors have uncovered a potentially new function for m6Am, they need to be clear that without identifying a mechanism, their data might only be demonstrating a correlation between the presence of m6Am and transcriptional regulation rather than causality.

      We thank the reviewer for the very positive assessment of the CROWN-seq method. We have softened the language which is related to the correlation between m<sup>6</sup>Am and transcription regulation.

      Reviewer recommendations:

      We thank the reviewers for their constructive suggestions. In the revised manuscript, we have corrected the errors and updated the requested discussions and figures.

      Reviewer #1 (Recommendations for the authors):

      (1) The prior work from the research group, "Reversible methylation of m6Am in the 5′ cap controls mRNA stability" (PMID: 28002401), should be cited, even if the current findings differ from earlier conclusions-particularly in line 58 and the section titled "m6Am does not substantially influence mRNA stability or translation".

      We thank the reviewer for this comment. We have added the citation.

      (2) I wonder why the authors chose to convert A to I before capping and recapping, as RNA fragmentation caused by chemical treatment may introduce noise into these processes.

      We thank the reviewer for this comment. This is a very good point. We have indeed considered this alternative protocol. There are two concerns in performing decapping-and-recapping before A-to-I conversion: (1) it is unclear whether the 3’-desthiobiotin, which is essential for the 5’ end enrichment, is stable or not during the harsh A-to-I conversion; (2) performing decapping-and-recapping first requires more enzyme and 3’-desthiobiotin-GTP, which are the major cost of the library preparation. This is because the input of CROWN-seq (~1 μg mRNA) is much higher than that in ReCappable-seq (~5 μg total RNA or ~250 ng mRNA). In the current protocol, many 5’ ends are highly fragmented and therefore are lost during the A-to-I conversion. As a result, less enzyme and 3’-desthiobiotin-GTP are needed.

      (3) During CROWN-seq benchmarking, the authors found that 93% of reads mapped to transcription start sites, implying a 7% noise level with a spike-in probe. This noise could lead to false positives in TSN assignments in real samples. It appears that additional filters (e.g., a known TSS within 100 nt) were applied to mitigate false positives. If so, I recommend that the authors clarify these filters in the main text.

      We thank the reviewer for this comment. We think that the spike-in probes might lead to an underestimation of the accuracy of TSN mapping. The spike-in probes are made by in vitro transcription with m<sup>7</sup>Gpppm<sup>6</sup>AmG or m<sup>7</sup>GpppAmG analogs. We found that the in vitro transcription exhibits a small amount of non-specific initiation, which leads to spike-in probes with 5’ ends that are not precisely aligned with the desired TSS. To better illustrate the mapping accuracy of CROWN-seq, we provided Figure 2H, which compares the non-conversion rates of newly found A-TSNs between wild-type and PCIF1 knock cells. If the newly found A-TSNs are real, they should show high non-conversion rates in wild-type cells (i.e., high m<sup>6</sup>Am) and almost zero non-conversion rates (i.e., Am) in PCIF1 knockout cells. As expected, most of the newly found A-TSNs are true A-TSNs since they are m6Am in wild-type and Am in PCIF1 knockout. Thus, we think that CROWN-seq is very precise in TSS mapping. We have clarified this in the Discussion.

      (4) I wonder if PCIF1 knockout affects TSN choice and abundance. If not, this data should be presented. If so, how are these changes accounted for in Figure 2H and Figure S5?

      We thank the reviewer for this comment.  PCIF1 KO does not really affect TSN choice. Here we calculate the correlation of relative TSN expression within genes between wild-type and PCIF1 KO cells (shown using Pearson’s r). It shows that most of the genes have similar TSN choices (with higher Pearson’s r) in both wild-type and PCIF1 KO cells. Thus, PCIF1 KO does not alter global TSN expressions.

      Author response image 1.

      (5) The manuscript refers to Am as a rare modification in mRNA (e.g., introduction lines 101-102; discussion lines 574, 608; and possibly other locations) without specifying this only applies to transcription start sites. As this study does not cover entire mRNA sequences, these statements may not be misleading.

      We thank the reviewer for this comment.  We have clarified it.

      Reviewer #2 (Recommendations for the authors):

      (1) On line 122, the authors state that: "On average, a gene uses 9.5{plus minus}9 (mean and s.d., hereafter) TSNs (Figure 1A)." However, they do not discuss the dispersion apparent in the TSNs they observed. Figure panels 1A, B, and S1A, B show a range of 120 bases or less. What is the predominant range of distances between annotated TSNs and the newly identified ones?

      1a) For example, what percentage of new TSNs fall within 20? 50? 75? bases of the annotated sites? Additional text describing the distribution of these TSNs would help readers better understand the diversity inherent in these novel 5' RNA ends. Notably, this additional text likely is best placed in the CROWN-Seq section related to Figure 2 or S2.

      We thank the reviewer for this comment. We have updated Figure S2 to describe the newly found TSSs. Depending on the coverage in CROWN-seq, the TSSs with higher coverage tend to overlap with or locate proximally to known TSSs. In contrast, the TSSs with low coverage tend to be located further away from annotated TSSs.

      1b) The alternate TSNs can have effects on splicing patterns and isoform identity. Providing a few sentences to explain how regularly this occurs would be helpful.

      We thank the reviewer for this comment. It is a very interesting point. Different TSNs can indeed have different splicing patterns. Although the discovery of splicing patterns regulated by TSNs is out of the scope of this study, we have discussed this possibility in the revised Discussion section.

      (2) On Lines 241 and 242, the authors mentioned that 1284 sites were excluded from the analysis based on low (under 20-explained in the figure legend) read count, distance from TSS, or false negatives (which are not explained). Although I agree that the authors are justified in setting these reads aside, the information could be useful to readers willing to perform follow-up work if their mRNAs of interest were included in these 1284 sites.

      2a) An annotation of all of these sites (broken down by category, i.e. the 811, the 343, and the 130) as a supplementary table should be provided.

      We thank the reviewer for this comment. We have added the categories to the revised Table S1.

      (3) Although I have marked several typos/grammar mistakes in several parts of this review, others exist elsewhere in the text and should be corrected.

      We thank the reviewer for this comment. We have corrected them.

      (4) In lines 122 and 123 the authors say "Only ~9% of genes contain a single TSN (Figure 1A)." However, their figure shows 81% with a single TSN. Why is there a 10% discrepancy?

      We thank the reviewer for this comment. We have corrected the plot in Figure 1A, to match the description.

      (5) The first Tab of Table S2 is labeled 'Legend', but is blank. Is this intentional?

      We thank the reviewer for this comment. We have updated the table legends.

      (6) On lines 70 and 76 of the supplementary figure file pertaining to Figure S2, the legend labels for Figure S2E and S2F are not accurate, they need to be changed to G and H.

      (7) In Figure 4A 'percentile' is misspelled.

      (8) The color-coding legend for the 4 bases is missing from (and should be added to) Figure S4A.

      (9) On Lines 984, 1163, and 1194 the '2s' should be properly sub-scripted where appropriate.

      For (6) to (9). We thank the reviewer for finding these issues. We have now corrected them.

      Reviewer #3 (Recommendations for the authors):

      (1) The authors should discuss if their results can definitively distinguish between the SSCA+1GC motif promoting m6Am that, in turn, promotes transcription, versus the SCA+1GC motif promoting m6Am but also separately promoting transcription in a m6Am-independent manner. The authors should also discuss this in light of recent findings by An et al. (2024 Mol. Cell), which support the former conclusion.

      We thank the reviewer for the suggestion. We now have updated the Discussion to address that our paper and An et al. can support each other.

      (2) Given that the authors showed m6Am promotes gene expression (Figure 5) but does not affect mRNA stability (Fig. S5), logic dictates that m6Am must regulate mRNA transcription. However, the authors should explain why this regulation focuses on the initiation aspect of transcription rather than other aspects of transcriptional e.g. premature termination, pause release, and elongation.

      We thank the reviewer for this comment. In this study, we did not profile the 3’ ends of nascent RNAs and thus we can only make conclusions about the overall transcription process but not a specific aspect. We have updated the revised Discussion section to mention that An et al. discovered that m<sup>6</sup>Am can sequester PCF11 and thus promote transcription, and therefore some of the effects we see could be related to differential premature termination.

      (3) Authors should add alternative versions of Figure 1D but with 3 colours corresponding to Am vs. m6Am vs. Cm/Gm/Um for all the cells, they performed CROWN-seq on.

      We thank the reviewer for this comment. We have updated Figure S5 as the corresponding figure showing the fraction of Am vs. m6Am vs. Cm/Gm/Um.

      (4) Figure 2H (left): Please comment on the few outliers that still show high non-conversion even in PCIF1-KO cells.

      We thank the reviewer for this comment. We have discussed the outliers in the main text. These outliers can be found in the revised Table S3.

      (5) Line 254: "Second, if these sites were RNA fragments they would not contain m6Am." is missing a comma.

      (6) S2G and S2H labelling in Figure S2 legends is wrong.

      For (5) and (6). We thank the reviewer for these comments. We have corrected them.

      (7) Figure 3D: Many gene names are printed multiple times (e.g. ACTB is printed 5 times). Is this correct; is each dot representing 1 cell line?

      We thank the reviewer for this comment. These gene names represent different transcription-start nucleotides. We now clarify that each instance refers to a different start site.

      (8) S5A-C: Even if there's no substantial difference, authors should still display the Student's T-test P-values as they did for S5D-G.

      We thank the reviewer for this comment. We have updated the P-values.

      (9) Figure 5C and S5E: Why are the authors not showing the respective analysis for C-TSN and U-TSN genes?

      We thank the reviewer for this comment. Most mRNAs start with A or G. We therefore selected G-TSN as the control. Unlike G-TSNs which occur in diverse sequence and promoter contexts, C-TSNs and U-TSNs are unusual. Genes that mainly use C-TSNs and U-TSNs are the so-called “5’ TOP (Terminal OligoPyrimidine)” genes. The 5’ TOP genes are mostly genes related to translation and metabolism, and thus their expressions reflect the homeostasis of cell metabolism. Thus, we were concerned that any differential expression of the C-TSN and U-TSN genes between wild-type and PCIF1 knockout cells might reflect specific effects on TOP transcriptional regulation rather than the general effects of PCIF1 on transcription.

      (10) Line 82, 470, 506, 676: The authors should also cite Koh et al (2019 Nat. Comm.) in these lines that describe how snRNAs can also be m6Am-methylated and how FTO targets these same snRNAs for demethylation.

      We thank the reviewer for this comment. We have updated the citation.

    1. eLife Assessment

      In this article, Cheng et al present an important finding that advances the understanding of mitochondrial stress response(s). The authors employed mass spectrometry-based methods in conjunction with standard molecular and cellular biology techniques to provide compelling evidence that phosphatidylethanolamine-binding protein 1 (PEBP1) acts as a pivotal regulator of the mitochondrial component of integrated stress response. Notwithstanding that this discovery is likely to be of significant interest to researchers across a broad spectrum of disciplines ranging from cell biology to neuroscience, it was thought that further mechanistic dissection of the role of PEBP1 in modulating integrated stress response may further strengthen this study.

    2. Reviewer #1 (Public review):

      Summary:

      In this study, the authors use thermal proteome profiling to capture changes in protein stability following a brief (30 min) treatment of cells with various mitochondrial stressors. This approach identified PEBP1 as a potentiator of Integrated Stress Response (ISR) induction by various mitochondrial stressors, although the specific dynamics vary by stressor. PEBP1 deletion attenuates DELE1-HRI-mediated activation of the ISR, independent of its known role in the RAF/MEK/ERK pathway. These effects can be bypassed by HRI overexpression and do not affect DELE1 processing. Interestingly, in cells, PEBP1 physically interacts with eIF2alpha, but not its phosphorylated form (eIF2alpha-P), leading the authors to suggest that PEBP1 functions as a scaffold to promote eIF2alpha phosphorylation by HRI.

      Strengths:

      The authors present a clear and well-structured study, beginning with an original and unbiased approach that effectively addresses a novel question. The investigation of PEBP1 as a specific regulator of the DELE1-HRI signaling axis is particularly compelling, supported by extensive data from both genetic and pharmacological manipulations. Including careful titrations, time-course experiments, and orthogonal approaches strengthens the robustness of their findings and bolsters their central claims.

      Moreover, the authors skillfully integrate publicly available datasets with their original experiments, reinforcing their conclusions' generality and broader relevance. This comprehensive combination of methodologies underscores the reliability and significance of the study's contributions to our understanding of stress signaling.

      Weaknesses:

      While the study presents exciting findings, there are a few areas that could benefit from further exploration. The HRI-DELE1 pathway was only recently discovered, leaving many unanswered questions. The observation that PEBP1 interacts with eIF2alpha, but not with its phosphorylated form, suggests a novel mechanism for regulating the Integrated Stress Response (ISR). However, as they note themselves, the authors do not delve into the biochemical or molecular mechanisms through which PEBP1 promotes HRI signaling. Given the availability of antibodies against phosphorylated HRI, it would have been interesting to explore whether PEBP1 influences HRI phosphorylation. Furthermore, since the authors already have recombinant PEBP1 protein (as shown in Figure 1D), additional in vitro experiments such as in vitro immunoprecipitation, FRET, or surface plasmon resonance (SPR) could have confirmed the interaction with eIF2alpha. Future studies might investigate whether PEBP1 directly interacts with HRI, stimulates its auto-phosphorylation or kinase activity, or serves as a template for oligomerization, potentially supported by structural characterization of the complex and mutational validation.

      Another point of weakness is the unclear significance of the 1.5-2x enhanced interaction with eIF2alpha upon PEBP1 phosphorylation, as there is little evidence to show that this increase has any downstream effects. The ATF4-luciferase reporter experiments, comparing WT and S153D overexpression, may have reached saturation with WT, making it difficult to detect further stimulation by S153D. Additionally, expression levels for WT and mutant forms are not provided, making it challenging to interpret the results. It would also be interesting to explore whether combined mitochondrial stress and PMA treatment further enhance the ISR.

      Lastly, while the authors claim that oligomycin does not significantly alter the melting temperature of recombinant PEBP1 in vitro, the data in Figure S1D suggest a small shift. Without variance measures across replicates or background subtraction, this claim is less convincing. The inclusion of statistical analyses would strengthen the interpretation of these results.

      Impact on the field:

      The study's relevance is underscored by the fact that overactive ISR is linked to a broad range of neurodegenerative diseases and cognitive disorders, a field actively being explored for therapeutic interventions, with several drugs currently in clinical trials. Similarly, mitochondrial dysfunction plays a well-established role in brain health and other diseases. Identifying new targets within these pathways, like PEBP1, could provide alternative therapeutic strategies for treating such conditions. Therefore, gaining a deeper understanding of the mechanisms through which PEBP1 influences ISR regulation is highly pertinent and could have far-reaching implications for the development of future therapies.

    3. Reviewer #2 (Public review):

      Summary:

      In this work, Cheng et al use the TPP/MS-CETSA strategy to discover new components for the mitochondria arm of the Integrated Stress Response. By using short exposures of several drugs that potentially induce mitochondrial stress, they find significant CETSA shifts for the scaffold protein PEBP1 both for antimycinA and oligomycin, making PEBP1 a candidate for mitochondrial-induced ISR signaling. After extensive follow-up work, they provide good support that PEBP1 is likely involved in ISR, and possibly act through an interaction with the key ISR effector node EIF2a.

      Strengths:

      The work adds an important understanding of ISR signaling where PEBP1 might also constitute a druggable node to attenuate cellular stress. Although CETSA has great potential for dissecting cellular pathways, there are few studies where this has been explored, particularly with such an extensive follow-up, also giving the work methodological implications. Together I therefore think this study could have a significant impact.

      Weaknesses:

      The TPP/MS-CETSA experiment is quite briefly described and might have a too relaxed cut-off. The assays confirming interactions between PEBP1 and EIF2a might not be fully conclusive.

    4. Reviewer #3 (Public review):

      Summary:

      In this paper, Chang and Meliala et al. demonstrate that PEBP1 is a modulator of the ISR, specifically through the induction of mitochondrial stress. The authors utilize thermal proteome profiling (TPP) by which they identify PEPB1 as a thermally stabilized protein upon oligomycin treatment, indicating its role in mitochondrial stress. Moreover, RNA-sequencing analysis indicated that PEBP1 may be specifically modulating the mitochondrial stress-induced ISR, as PEBP1 knock-out reduces phosphorylation of eIF2α. They also show that PEBP1 function is independent of ER stress specifically tunicamycin treatment and loss of PEBP1 does affect mitochondrial ISR but in an OMA1, DELE1 independent manner. Thus, the authors hypothesized that PEBP1 interacts directly with eIF2α, functioning as a scaffolding protein. However, direct co-immunoprecipitation failed to demonstrate PEBP1 and eIF2α potential interaction. The authors then used a NanoBiT luminescence complementation assay to show the PEBP1-eIF2a interaction and its disruption by S51 phosphorylation.

      Strengths:

      Taken together, this work is novel, and the data presented suggests PEBP1 has a role as a modulator of the mitochondrial ISR, enhancing the signal to elicit the necessary response.

      Weaknesses:

      The one major issue of this work is the lack of a mechanism showing precisely how PEBP1 amplifies the mitochondrial integrated stress response. The work, as it is described, presents data suggesting PEBP1's role in the ISR but fails to present a more conclusive mechanism.

    5. Author response:

      We thank all the reviewers for their insightful comments on this work.

      Response to Reviewer #1:

      We greatly appreciate your comments on the general reliability and significance of our work. We fully agree that it would have been ideal to have additional evidence related to the role of PEBP1 in HRI activation. Unfortunately, we have not been able to find phospho-HRI antibodies that work reliably. The literature seems to agree with this as a band shift using total-HRI antibodies is usually used to study HRI activation. However, with the cell lines showing the most robust effect with PEBP1 knockout or knockdown, we are yet to convince ourselves with the band shifts we see. This could be addressed by optimizing phos-tag gels although these gels can be a bit tricky with complex samples such as cell lysates which contain many phosphoproteins.

      To address the interaction between PEBP1 and eIF2alpha more rigorously we were inspired by the insights you and reviewer #2 provided. While we are unable to do further experiments, we now think it would indeed be possible to do this with either using the purified proteins and/or CETSA WB. These experiments could also provide further evidence for the role of PEBP1 phosphorylation. Although phosphorylation of PEBP1 at S153 has been implicated as being important for other functions of PEBP1, we are not sure about its role here. It may indeed have little relevance for ISR signalling.

      For the in vitro thermal shift assay, we have performed two independent experiments. While it appears that there is a slight destabilization of PEBP1 by oligomycin, the ultimate conclusion of this experiment remains incomplete as there could be alternative explanations despite the apparent simplicity of the assay due the fluorescence background by oligomycin only. We now provide a lysate based CETSA analysis which does not display the same PEBP1 stabilization as the intact cell experiment. As for the signal saturation in ATF4-luciferase reporter assay, this is a valid point.

      Response to Reviewer #2:

      We strongly agree that CETSA has a lot of potential to inform us about cellular state changes and this was indeed the starting point for this project. We apologize for being (too) brief with the explanations of the TPP/MS-CETSA approach and we have now added a bit more detail. With regard to the cut-offs used for the mass spectrometry analysis, you are absolutely right that we did not establish a stringent cut-off that would show the specificity of each drug treatment. Our take on the data was that using the p values (and ignoring the fold-changes) of individual protein changes as in Fig 1D, we can see that mitochondrial perturbations display a coordinated response. We now realize that the downside of this representation is that it obscures the largest and specific drug effects. As mentioned in the response to Reviewer #1, we now also think that it would be possible to obtain more evidence for the potential interaction between PEBP1 and eIF2alpha using CETSA-based assays.

      Response to Reviewer #3:

      Thank you for your assessment, we agree that this manuscript would have been made much stronger by having clearer mechanistic insights. As mentioned in the responses to other reviewers above, we aim to address this limitation in part by looking at the putative interaction between PEBP1 and eIF2alpha with orthogonal approaches. However, we do realize that analysis of protein-protein interactions can be notoriously challenging due to false negative and false positive findings. As with any scientific endeavor, we will keep in mind alternative explanations to the observations, which could eventually provide that cohesive model explaining how precisely PEBP1, directly or indirectly, influences ISR signalling.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors): 

      The data overall are very solid, and I would only recommend the following minor changes: 

      (1) Line 187 and line 268: there is perhaps a trend towards slightly increased ATF4-luc reporter with PEBP1-S153D, but it is not statistically significant, so I would tone down the wording here. 

      We now modified this part to "This data is consistent with the modest increase…" .

      (2) The recently discovered SIFI complex (Haakonsen 2024, https://doi.org/10.1038/s41586023-06985-7) regulates both HRI and DELE1 through bifunctional localization/degron motifs. It seems like PEBP1 also contains such a motif, which suggests a potential mechanism for enrichment near mitochondria, perhaps even in response to stress. Maybe the authors could further speculate on this in the discussion. 

      While working on the manuscript, we considered the possibility that PEBP1 function could be related to SIFI complex and concluded that here is a critical difference: while  SIFI specifically acts to turn off stress response signalling, loss of PEBP1 prevents eIF2alpha phosphorylation. We did not however consider that PEBP1 could have a localization/degron motif. Motif analysis by deepmito (busca.biocomp.unibo.it) and similar tools did not identify any conventional mitochondrial targeting signal although we acknowledge that PEBP1 has a terminal alpha-helix which was identified for SIFI complex recognition. We are not sure why you think PEBP1 contains such a motif and therefore are hesitant to speculate on this further in the manuscript.

      (3) Line 358: references 50 and 45 are identical. 

      Thank you for spotting this. Corrected now. 

      (4) Figure S1D: it looks like Oligomycin has a significant background fluorescence, which makes interpretation of these graphs difficult - do you have measurements of the compound alone that can be used to subtract this background from the data? Based on the Tm I would say it does stabilize recombinant PEBP1, and there is no quantification of the variance across the 3 replicates to say there is no difference. 

      You are right, this assay is problematic due to the background fluorescence. The measurements with oligomycin only and subtracting this background results in slightly negative values and nonsensical thermal shift curves. We now additionally show quantification from two different experiments (unfortunately we ran out of reagents for further experiments), and this quantification shows that if anything, oligomycin causes mild destabilization of recombinant PEBP1. We also used lysate CETSA assay which does not show thermal stabilization of PEBP1 by oligomycin, ruling out a direct effect. We attempted to use ferrostatin1 as a positive control as it may bind PEBP1-ALOX protein complex, and it appeared to show marginal stabilization of PEBP1. 

      Reviewer #2 (Recommendations for the authors): 

      I have a few comments for the authors to address: 

      (1) The MS-CETSA experiment is quite briefly described and this could be expanded somewhat. Not clear if multiple biological replicates are used. Is there any cutoff in data analysis based on fold change size (which correlated to the significance of cellular effects), etc? As expected from only one early timepoint (see eg PMID: 38328090), there appear to be a limited number of significant shifts over the background (as judged from Figure S1A). In the Excel result file, however (if I read it right) there are large numbers of proteins that are assigned as stabilized or destabilized. This might be to mark the direction of potential shifts, but considering that most of these are likely not hits, this labeling could give a false impression. Could be good to revisit this and have a column for what could be considered significant hits, where a fold change cutoff could help in selecting the most biologically relevant hits. This would allow Figure 1D to be made crisper when it likely dramatically overestimates the overlap between significant CETSA shifts for these drugs.  

      Fair point, while we focused more on PEBP1, it is important to have sufficient description of the methods. We used duplicate samples for the MS, which is probably the most important point which was absent from the original submission as is now added to the methods. We also added slightly more description on the data analysis. While the AID method does not explicitly use log2 fold changes, it does consider the relative abundance of proteins under different temperature fractions. Since the Tm (melting temperature) for each protein can be at any temperature, we felt that if would be complicated to compare fractions where the protein stability is changed the most and even more so if we consider both significance and log2FC. Therefore, we used this multivariate approach which indicates the proteins with most likely changes across the range of temperatures. To acknowledge that most of the statistically significant changes are not the much over the background as you correctly pointed out, we now add to the main text that “However, most of these changes are relatively small. To focus our analysis on the most significant and biologically relevant changes…” We also agree that it may be confusing that the AID output reports de/stabilization direction for all proteins. In general, we are not big fans of cutoffs as these are always arbitrary, but with multivariate p value of 0.1 it becomes clear that there are only a relatively small number of hits with larger changes. We have now added to the guide in the data sheet that "Primarily, use the adjusted p value of the log10 Multivariate normal pvalue for selecting the overall statistically significant hits (p<0.05 equals  -1.30 or smaller; p<0.01 equals  -2 or smaller)". We have also added to the guide part of the table that “Note that this prediction does not consider whether the change is significant or not, it only shows the direction of change”

      (2) On page 4 the authors state "We reasoned that thermal stability of proteins might be particularly interesting in the context of mitochondrial metabolism as temperature-sensitive fluorescent probes suggest that mitochondrial temperature in metabolically active cells is close to 50{degree sign}C". I don't see the relevance of this statement as an argument for using TPP/CETSA. When this is also not further addressed in the work, it could be deleted.

      Deleted. We agree, while this is an interesting point, it is not that relevant in this paper. 

      (3) To exclude direct drug binding to PEBP1, a thermofluor experiment is performed (Fig S1D). However, the experiment gives a high background at the lower temperatures and it could be argued that this is due to the flouroprobe binding to a hydrophobic pocket of the protein, and that oligomycin at higher concentrations competes with this binding, attenuating fluorescence. These are complex experiments and there could be other explanations, but the authors should address this. An alternative means to provide support for non-binding would be a lysate CETSA experiment, with very short (1-3 minutes) drug exposure before heating. This would typically give a shift when the protein is indicated to be CETSA responsive as in this case. 

      Agree. However, we don't have good means to perform the thermofluor experiments to rule out alternative explanations. What we can say is (as discussed above for reviewer #1, point 4) that quantification from two different experiments shows that oligomycin is does not thermally stabilizing recombinant PEBP1. To complement this conclusion, we used lysate CETSA assay which does not show thermal stabilization of PEBP1 by oligomycin. In this assay we attempted to use ferrostatin1 as a positive control as it may bind PEBP1-ALOX protein complex, and it appeared to show marginal stabilization of PEBP1. But since we lack a robust positive control for these assays, some doubt will inevitably remain.

      (4) The authors appear to have missed that there is already a MS-CETSA study in the literature on oligomycin, from Sun et al (PMID: 30925293). Although this data is from a different cell line and at a slightly longer drug treatment and is primarily used to access intracellular effects of decreased ATP levels induced by oligomycin, the authors should refer to this data and maybe address similarities if any.  

      Apologies for the oversight, the oligomycin data from this paper eluded us at it was mainly presented in the supplementary data. We compared the two datasets and find found some overlap despite the differences in the experimental details. Both datasets share translational components (e.g. EIF6 and ribosomal proteins), but most notably our other top hit BANF1 which we mentioned in the main text was also identified by Sun et al. We have updated the manuscript text as "Other proteins affected by oligomycin included BANF1, which binds DNA in an ATP dependent manner [16], and has also identified as an oligomycin stabilized protein in a previous MS-CETA experiment [23]", citing the Sun et al paper.   

      (5) The confirmation of protein-protein interaction is notoriously prone to false positives. The authors need to use overexpression and a sensitive reporter to get positive data but collect additional data using mutants which provide further support. Typically, this would be enough to confirm an interaction in the literature, although some doubt easily lingers. When the authors already have a stringent in-cell interaction assay for PEBP1 in the CETSA thermal shift, it would be very elegant to also apply the CETSA WB assay to the overexpressed constructs and demonstrate differences in the response of oligomycin, including the mutants. I am not sure this is feasible but it should be straightforward to test. 

      This is a very good suggestion. Unfortunately, due to the time constraints of the graduate students (who must write up their thesis very soon), we are not able to perform and repeat such experiments to the level of confidence that we would like.

      (6) At places the story could be hard to follow, partly due to the frequent introduction of new compounds, with not always well-stated rationale. It could be useful to have a table also in the main manuscript with all the compounds used, with the rationale for their use stated. Although some of the cellular pathways addressed are shown in miniatures in figures, it could be useful to have an introduction figure for the known ISR pathways, at least in the supplement. There are also a number of typos to correct. 

      We agree that there are many compounds used. We have attempted to clarify their use by adding this information into the table of used compounds in the methods and adding an overall schematic to Fig S1G and a note on line 132 "(see Figure 1-figure supplement 1G for summary of drugs used to target PEBP1 and ISR in this manuscript). We have also attempted to remove typos as far as possible.

      (7) EIF2a phosphorylation in S1E does not appear to be more significant for Sodium Arsenite argued to be a positive control, than CCCP, which is argued to be negative. Maybe enough with one positive control in this figure? 

      This experiment was used as a justification for our 30 min time point for the proteomics. By showing the 30 min and 4 h time points as Fig 1G and Figure 1-figure supplement 1F, our point was to demonstrate that the kinetics of phosphorylation and dephosphorylation are relevant. As you correctly pointed out, the stress response induced by sodium arsenite, but also tunicamycin is already attenuated at the 4h time point. We prefer to keep all samples to facilitate comparisons.

      (8) Page 7 reference to Figure S2H, which doesn't exist. Should be S3H.  

      Apologies for the mistake, now corrected to Figure 2-figure supplement 1B.

      (9) Finally, although the TPP labeling of the method is used widely in the literature this is CETSA with MS detection and MS-CETSA is a better term. This is about thermal shifts of individual proteins which is a very well-established biophysical concept. In contrast, the term Thermal Proteome Profiling does not relate to any biophysical concept, or real cell biology concept, as far as I can see, and is a partly misguided term. 

      We changed the term TPP into MS-CETSA, but also include the term TPP in the introduction to facilitate finding this paper by people using the TPP term.

      Reviewer #3 (Recommendations for the authors): 

      Major Issues 

      (1) The one major issue of this work is the lack of a mechanism showing precisely how PEBP1 amplifies the mitochondrial integrated stress response. The work, as it is described, presents data suggesting PEBP1's role in the ISR but fails to present a more conclusive mechanism. The idea of mitochondrial stress causing PEBP1 to bind to eIF2a, amplifying ISR is somewhat vague. Thus, the lack of a more defined model considerably weakens the argument, as the data is largely corollary, showing KO and modulation of PEBP1 definitely has a unique effect on the ISR, however, it is not conclusive proof of what the authors claim. While KO of PEBP1 diminishes the phosphorylation of eIF2a, taken together with the binding to eIF2a, different pathways could be simultaneously activated, and it seems premature to surmise that PEBP1 is specific to mitochondrial stress. Could PEBP1 be reacting to decreased ATP? Release of a protein from the mitochondria in response to stress? Is PEBP1's primary role as a modulator of the ISR, or does it have a role in non-stress-related translation? A cohesive model would tie together these separate indirect findings and constitute a considerable discovery for the ISR field, and the mitochondrial stress field.  

      Thank you for your assessment, we agree that this manuscript would have been much stronger by having clearer mechanistic insights. As with any scientific endeavor, we will keep in mind alternative explanations to the observations, which could eventually provide that cohesive model explaining how precisely PEBP1, directly or indirectly, influences ISR signalling.

      (2) The data relies on the initial identification of PEBP1 thermal stabilization concomitant with mitochondrial ISR induction post-treatment of several small molecules. However, the experiment was performed using a single timepoint of 30 minutes. There was no specific rationale for the choice of this time point for the thermal proteome profiling. 

      The reasoning for this was explicitly stated:  "We reasoned that treating intact cells with the drugs for only 30 min would allow us to observe rapid and direct effects related to metabolic flux and/or signaling related to mitochondrial dysfunction in the absence of major changes in protein expression levels.”

      Minor Issues 

      (1) In Lines 163-166 the authors state "The cells from Pebp1 KO animals displayed reduced expression of common ISR genes (Figure 2F), despite upregulation of unfolded protein response genes Ern1 (Ire1α) and Atf6 genes. This gene expression data therefore suggests that Pebp1 knockout in vivo suppresses induction of the ISR". This statement should be reassessed. While an arm of the UPR does stimulate ISR, this arm is controlled by PERK, and canonically IRE1 and ATF6 do not typically activate the ISR, thus their upregulation is likely unrelated to ISR activation and does not contribute the evidence necessary for this statement. 

      Apologies for the confusion, we aimed to highlight that as there is an increase in the two UPR arms, it is more likely that ISR instead of UPR is reduced. We have now changed the statement to the following:

      "The cells from Pebp1 PEBP1 KO animals displayed reduced expression of common ISR genes (Figure 2F), while there was mild upregulation of the unfolded protein response genes Ern1 (Ire1α) and Atf6 genes. This gene expression data therefore suggests that the reduced expression of common ISR genes is less likely to be mediated by changes in PERK, the third UPR arm, and more likely due to suppression of ISR by Pebp1 knockout in vivo."

      (2) In Lines 169 and 170 the authors state "Western blotting indicated reduced phosphorylation of eIF2α in RPE1 cells lacking PEBP1, suggesting that PEBP1 is involved in regulating ISR signaling between mitochondria and eIF2α". This conclusion is not supported by evidence. A number of pathways could be activated in these knockout cells, and simply observing an increase in p-eIF2α after knocking out PEBP1 does not constitute an interaction, as correlation doesn't mean causation. This KO could indirectly affect the ISR, with PEBP1 having no role in the ISR. While taken together there is enough circumstantial evidence in the manuscript to suggest a role for PEBP1 in the ISR, statements such as these have to be revised so as not to overreach the conclusions that can be achieved from the data, especially with no discernible mechanism.  

      We have now revised this statement by removing the conclusion and stating only the observation:  "Western blotting indicated reduced phosphorylation of eIF2α in RPE1 cells lacking PEBP1 (Fig. 3A)."

    1. eLife Assessment

      This manuscript provides fundamental studies to gain insight into the mutations in the presenilin-1 (PSEN1) gene on proteolytic processing of the amyloid precursor protein (APP). The authors provide compelling evidence using mutations in PSEN to understand what drives alternative substrate turnover with convincing data and rigorous analysis. This deep mechanistic study provides a framework towards the development of small molecule inhibitors to treat AD.

    2. Reviewer #1 (Public review):

      Summary:

      Arafi et al. present results of studies designed to better understand the effects of mutations in the presenilin-1 (PSEN1) gene on proteolytic processing of the amyloid precursor protein (APP). This is important because APP processing can result in the production of the amyloid β-protein (Aβ), a key pathologic protein in Alzheimer's disease (AD). Aβ exists in various forms that differ in amino acid sequence and assembly state. The predominant forms of Aβ are Aβ40 and Aβ42, which are 40 and 42 amino acids in length, respectively. Shorter and longer forms derive from processive proteolysis of the Aβ region of APP by the heterotetramer β-secretase, within which presenilin 1 possesses the active site of the enzyme. Each form may become toxic if it assembles into non-natively folded, oligomeric, or fibrillar structures. A deep mechanistic understanding of enzyme-substrate interactions is a first step toward the design and successful use of small-molecule therapeutics for AD.

      The key finding of Arafi et al. is that PSEN1 amino acid sequence is a major determinant of enzyme turnover number and the diversity of products. For the biochemist, this may not be surprising, but in the context of understanding and treating AD, it is immense because it shifts the paradigm from targeting the results of γ-secretase action, viz., Aβ oligomers and fibrils, to targeting initial Aβ production at the molecular level. It is the equivalent of taking cancer treatment from simple removal of tumorous tissue to the prevention of tumor formation and growth. Arafi et al. have provided us with a blueprint for the design of small-molecule inhibitors of γ-secretase. The significance of this achievement cannot be overstated.

      Strengths and weaknesses:

      The comprehensiveness and rigor of the study are notable. Rarely have I reviewed a manuscript reporting results of so many orthogonal experiments, all of which support the authors' hypotheses, and of so many excellent controls. In addition, as found in clinical trial reports, the limitations of the study were discussed explicitly. None of these significantly affected the conclusions of the study.

      Some minor concerns were expressed during the review process. The authors have revised the manuscript, and in doing so, dealt appropriately with the concerns and strengthened the manuscript.

    3. Reviewer #2 (Public review):

      Summary:

      The work by Arafi et al. show the effect of Familial Alzheimer's Disease presenilin-1 mutants on endoproteinase and carboxylase activity. They have elegantly demonstrated how some of mutants alter each step of processing. Together with FLIM experiments, this study provides additional evidence to support their 'stalled complex hypotheses'.

      Strengths:

      This is a beautiful biochemical work. The approach is comprehensive.

      Weaknesses:

      However, the novelty of this manuscript is questionable since this group has published similar work with different mutants (Ref 11) .

    4. Author response:

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

      comprehensiveness and rigor of the study are notable. Rarely have I reviewed a manuscript reporting the results of so many orthogonal experiments, all of which support the authors' hypotheses, and of so many excellent controls.” Reviewer 2 commented: “They have elegantly demonstrated how some mutants alter each step of processing. Together with FLIM experiments, this study provides additional evidence to support their 'stalled complex hypotheses'….This is a beautiful biochemical work. The approach is comprehensive.”

      Below we respond to the relatively minor concerns of Reviewer 2, which may be included with the first version of the Reviewed Preprint.

      Reviewer 2:

      (1) It appears that the purified γ-secretase complex generates the same amount of Aβ40 and Aβ42, which is quite different in cellular and biochemical studies. Is there any explanation for this?  

      Roughly equal production of Aβ40 and Aβ42 is a phenomenon seen with purified enzyme assays, and the reason for this has not been identified. However, we suggest that what is meaningful in our studies is the relative difference between the effects of FAD-mutant vs. WT PSEN1 on each proteolytic processing step. All FAD mutations are deficient in multiple cleavage steps in γsecretase processing of APP substrate, and these deficiencies correlate with stabilization of E-S complexes.

      (2) It has been reported the Aβ production lines from Aβ49 and Aβ48 can be crossed with various combinations (PMID: 23291095 and PMID: 38843321). How does the production line crossing impact the interpretation of this work?  

      In the cited reports, such crossover was observed when using synthetic Aβ intermediates as substrate. In PMID 2391095 (Okochi M et al, Cell Rep, 2013), Aβ43 is primarily converted to Aβ40, but also to some extent to Aβ38. In PMID: 38843321 (Guo X et al, Science, 2024), Aβ48 is ultimately converted to Aβ42, but also to a minor degree to Aβ40. We have likewise reported such product line “crossover” with synthetic Aβ intermediates (PMID: 25239621; Fernandez MA et al, JBC, 2014). However, when using APP C99-based substrate, we did not detect any noncanonical tri- and tetrapeptide co-products of Aβ trimming events in the LC-MS/MS analyses (PMID: 33450230; Devkota S et al, JBC, 2021). In the original report on identification of the small peptide coproducts for C99 processing by γ-secretase using LC-MS/MS (PMID: 19828817; Takami M et al, J Neurosci, 2009), only very low levels of noncanonical peptides were observed. In the present study, we did not search for such noncanonical trimming coproducts, so we cannot rule out some degree of product line crossover.

      (3) In Figure 5, did the authors look at the protein levels of PS1 mutations and C99-720, as well as secreted Aβ species? Do the different amounts of PS1 full-length and PS1-NTF/CTF influence FILM results?  

      FLIM results depend on the degree that C99 and long Aβ intermediates are bound to γ-secretase compared to unbound C99 and Aβ. The 6E10-Alexa 488 lifetime is significantly decreased by FAD mutations compared to WT PSEN1 (Fig. 5). However, the observed decrease in lifetime with the PSEN1 FAD mutants might also be due to lower levels of C99-720 expression or higher levels of PSEN1 CTF (i.e., mature γ-secretase complexes). We checked the C99-720 fluorescence intensities in the FLIM experiments and found that C99-720 intensities are not significantly different between cells transfected with WT and those with FAD PSEN1. Furthermore, Western blot analysis shows that levels of C99-720 are not significantly low and those of PSEN1 CTF are not high in FAD PSEN1 compared to WT PSEN1 expressing cells. Although PSEN1 CTF levels trend low for PSEN1 F386S, this mutant resulted in decreased FLIM only in Aβ-rich regions. Thus, the reduced FLIM apparently reflects effects of FAD mutation on E-S complex stability. Levels of full-length PSEN1 were also determined and found not to correlated with FLIM effects, although full-length PSEN1 represents protein not incorporated into full active γ-secretase complexes and therefore does not interact with C99-720.

      (4) It is interesting that both Aβ40 and Aβ42 Elisa kits detect Aβ43. Have the authors tested other kits in the market? It might change the interpretation of some published work.  

      We have not tested other ELISA kits. Considering our findings, it would be a good idea for other investigators to test whatever ELISAs they use for specificity vis-à-vis Aβ43.

    1. eLife assessment

      The identification of NCS1 as a distal appendage protein that captures preciliary vesicles has important implications for understanding the early steps of ciliary assembly. Furthermore, the work has important implications for the broader understanding of NCS1, which prior to this work was focused on roles in neurotransmission, but now must be considered in a broader context. The investigators used a variety of state-of-the-art methodologies, and the conclusions are convincingly supported by the experimental data. This work will be of interest to cell biologists studying ciliary assembly, human geneticists exploring the pathology of cilia as well as neurobiologists studying NCS1.

    2. Reviewer #1 (Public Review):

      In this work, Kanie and colleagues explored the role of NCS1 in capturing the ciliary vesicle. The microscopy was well executed and appropriately quantified. The authors convincingly show that while NCS1 is important for capturing the ciliary vesicle, another unknown distal appendage component is partially redundant in that ciliary vesicle capture and ciliary assembly are not fully dependent on NCS1. Overall, I am convinced by the data, and my only concern is that the discussion of the mouse phenotypes does not do a good job of putting this gene into the greater context of the complexity of mouse mutations.

      Interestingly NCS1 has been previously studied in the context of neurotransmission and the new findings raise questions about whether prior findings are actually due to neuronal cilia defects.

    3. Reviewer #2 (Public Review):

      Kanie et al have recently characterized DAP protein CEP89 as important for the recruitment of the ciliary vesicle. Here, they describe a novel interacting partner for CEP89 that can bind membranes and therefore mediates its role in ciliary vesicle recruitment. An initial LAP tag pull-down and mass spectrometry experiment finds NCS-1 and C3ORF14 as CEP89 interactors. This interaction is mapped in the context of the ciliary vesicle formation. From the data presented, it is clear that, upon knockout, the function of these proteins might be compensated by others, as the phenotype can eventually recover over time.

      In terms of the biological significance of this interaction, it would be good to examine (via co-immunoprecipitation) whether the CEP89/NCS-1/C3ORF14 interaction takes place upon serum starvation. Does the complex change?

      Also, for the subdistal appendage localization of NCS-1 and C3ORF14, would this also change upon serum starvation?

      For the ciliation results and the recruitment of IFT88 in CEP89 knockout cell lines, this contradicts previous work from Tanos et al (PMID: 23348840), as well as Hou et al (PMID: 36669498). A parallel comparison using siRNA, a transient knockout system, or a degron system would help understand this. A similar point goes for Figure 4, where the effect on ciliogenesis is minimal in knockout cells, but acute siRNA has been shown to have a stronger phenotype.

      An elegant phenotype rescue is shown in Figure 5. An interesting question would be, how does this mutant and/or the myristoylation affect the recruitment of C3ORF14?

      For the EF-hand mutants, it would be good to use control mutants, from known Ca2+ binding proteins as a control for the experiment shown.

    4. Reviewer #3 (Public Review):

      This work addresses an important question aimed at understanding how membrane docking to the distal appendages is regulated during ciliogenesis. In this study, Tomoharu and colleagues identified interactions between CEP89 (important for RAB34-positive membrane localization to the mother centriole) and NCS1 and C3ORF14. Both these CEP89 interacting proteins were characterized as distal appendage localized proteins between CEP89 and RAB34 based on super-resolution microscopy. Ciliogenesis investigations using knockout cells indicated that NCS1 and CEP89 have similar impaired ciliation due to disruption in vesicle recruitment/RAB34 to the mother centriole, while C3ORF14 had less effect on ciliogenesis. The authors refer to the ciliogenesis requirement for CEP89/NCS1 as ciliary vesicles, which has been previously referred to as preciliary vesicle or distal appendage vesicles. NCS1 distal appendage localization was dependent on CEP89 and TTBK2, but it is not clear how TTBK2 affects NCS1. The authors subsequently performed double knockouts with NCS1 and other distal appendage proteins and showed stronger effects on mother centriole RAB34 levels, suggesting efficient membrane docking during ciliogenesis requires several distal appendage proteins. This is consistent with NCS1 knockout mice which do not display typical ciliopathy phenotypes. These mice do display obesity, which is associated with cilia dysfunction, and show reduced ciliary protein levels. As noted by the authors, the in vivo results for NCS1 knockouts could be affected by the mouse background which was not evaluated. The authors demonstrate the NCS1 myristoylation motif is required for RAB34 localization to the mother centrioles, providing a mechanistic explanation for how distal appendage proteins could interact with membranes during ciliogenesis. Overall the authors' findings support an important role for NCS1 in regulating ciliogenesis via myristoylation-dependent interaction with RAB34-positive membranes docked at the mother centriole.

    5. Author response:

      Reviewer #2 (Public Review): 

      Comment 1: In terms of the biological significance of this interaction, it would be good to examine (via co-immunoprecipitation) whether the CEP89/NCS-1/C3ORF14 interaction takes place upon serum starvation. Does the complex change? 

      NCS1 centriolar localization requires CEP89 as no NCS1 localization was observed in CEP89 knockout cells (Figure 2L; Figure 2-figure supplement 2B). Both CEP89 and NCS1 centriolar localization were observed (Figure 2C; Figure 1D of the PMID: 36711481) in cells grown in serum containing media, although their localization was further enhanced in serum starved cells. From these results, we predict that CEP89 and NCS1 can interact and colocalize in both serum-fed and serum-depleted condition. We think it may not be easy to assess the change in interaction with the co-immunoprecipitation assay, as interactions occur in a test tube, which may not reflect the binding condition inside the cells.

      Comment 2: Also, for the subdistal appendage localization of NCS-1 and C3ORF14, would this also change upon serum starvation? 

      We agree that it would be interesting to see whether the subdistal appendage localization changes upon serum starvation, as NCS1 may capture the ciliary vesicle at the subdistal appendages as we discussed. However, the loss of the subdistal appendage protein, CEP128, blocks subdistal appendage localization of CEP89 [PMID: 32242819] without affecting cilium formation [PMID: 27818179]. This suggests that the subdistal appendage localization of NCS1 or C3ORF14 is likely dispensable for cilium formation.

      Comment 3: For the ciliation results and the recruitment of IFT88 in CEP89 knockout cell lines, this contradicts previous work from Tanos et al (PMID: 23348840), as well as Hou et al (PMID: 36669498). A parallel comparison using siRNA, a transient knockout system, or a degron system would help understand this. A similar point goes for Figure 4, where the effect on ciliogenesis is minimal in knockout cells, but acute siRNA has been shown to have a stronger phenotype. 

      Hou et al. [PMID: 36669498] investigated the role of distal appendage proteins, CEP164, CEP89, and FBF1 in the ciliated chordotonal organ of Drosophila melanogaster by generating knockout Drosophila strains. The results were markedly different from what was observed in mammalian cells. Notably, CEP164 is not required for cilium formation, and CEP89 is required for FBF1 localization in the animal. CEP89 was required for cilium formation in the cells in the ciliated chordotonal organ, of which cilium formation is dependent on IFT machinery. They did not show if IFT centriolar recruitment is affected in the CEP89 mutant cells. These differences likely reflect the divergence of the organization of distal appendage during evolution.

      The ciliation phenotype of our CEP89 knockout cells are milder than what was shown in Tanos et al [PMID: 23348840], but largely consistent with the results from Bornens group, which used siRNA to deplete CEP89 [PMID: 23789104]. Besides, NCS1 knockout cells showed very similar phenotype to the CEP89 knockout cells, and relatively acute deletion of NCS1 (14 days after infection of the lenti-virus containing sgNCS1 without single-cell cloning) displayed an almost identical ciliation defect (Figure 4B-C). Thus, we believe CEP89 is only partially required for cilium formation in RPE-hTERT cells and that the differences are more technical than definitive.

      Comment 4: An elegant phenotype rescue is shown in Figure 5. An interesting question would be, how does this mutant and/or the myristoylation affect the recruitment of C3ORF14? 

      NCS1 is not required for the localization of C3ORF14 (Figure 2M; Figure 2- figure supplement 2C), so we can assume that the myristoylation defective mutant does not affect C3ORF14 recruitment.

      Comment 5: For the EF-hand mutants, it would be good to use control mutants, from known Ca2+ binding proteins as a control for the experiment shown. 

      In the Figure 5-figure supplement 1A-C, we generated a series of EF-hand mutant of NCS1 to see if the calcium binding affects the CEP89 interaction, NCS1 localization, and cilium formation. NCS1 is only protein among the calcium binding NCS family proteins that was found as a positive hit in the mass spec data of CEP89 tandem affinity purification. Therefore, we cannot use other NCS1 family proteins as a control for CEP89 binding, NCS1 localization, and cilium formation.

    1. eLife Assessment

      This fundamental work substantially advances our understanding of how the glycocalyx of cells provide a non-specific barrier for the interaction of viruses with cell-surface receptors. Using both in vitro experiments and in vivo manipulations they provide solid evidence for the properties of the glycocalyx to serve as an energy barrier as a main attribute of its mode of action. The work will be of broad interest to virologists and the cell biology community that studies host-pathogen interactions.

    2. Joint Public Review:

      This manuscript tests the notion that bulky membrane glycoproteins suppress viral infection through non-specific interactions. Using a suite of biochemical, biophysical, and computational methods in multiple contexts (ex vivo, in vitro, and in silico), the authors collect evidence supporting the notion that (1) a wide range of surface glycoproteins erect an energy barrier for the virus to form stable adhesive interface needed for fusion and uptake and (2) the total amount of glycan, independent of their molecular identity, additively enhanced the suppression.

      As a functional assay the authors focus on viral infection starting from the assumption that a physical boundary modulated by overexpressing a protein-of-interest could prevent viral entry and subsequent infection. Here they find that glycan content (measured using the PNA lectin) of the overexpressed protein and total molecular weight, that includes amino acid weight and the glycan weight, is negatively correlated with viral infection. They continue to demonstrate that it is in effect the total glycan content, using a variety of lectin labelling, that is responsible for reduced infection in cells. Because the authors do not find a loss in virus binding this allows them to hypothesize that the glycan content presents a barrier for the stable membrane-membrane contact between virus and cell. They subsequently set out to determine the effective radius of the proteins at the membrane and demonstrate that on a supported lipid bilayer the glycosylated proteins do not transition from the mushroom to the brush regime at the densities used. Finally, using Super Resolution microscopy they find that above an effective radius of 5 nm proteins are excluded from the virus-cell interface.

      The experimental design does not present major concerns and the results provide insight on a biophysical mechanism according to which, repulsion forces between branched glycan chains of highly glycosylated proteins exert a kinetic energy barrier that limits the formation of a membrane/viral interface required for infection.

      However several general and specific concerns remain that the author is recommended to address before their claims as above are compelling.

      GENERAL QUESTIONS:

      (1) For many enveloped viruses, the attachment factors - paradoxically - are also surface glycoproteins, often complexed with a distinct fusion protein. The authors note here that the glycoportiens do not inhibit the initial binding, but only limit the stability of the adhesive interface needed for subsequent membrane fusion and viral uptake. How these antagonistic tendencies might play out should be discussed.

      (2) Unlike polymers tethered to solid surface undergoing mushroom-to-brush transition in density-dependent manner, the glycoproteins at the cell surface are of course mobile (presumably in a density-dependent manner). They can thus redistribute in spatial patterns, which serve to minimize the free energy. I suggest the authors explicitly address how these considerations influence the in vitro reconstitution assays seeking to assess the glycosylation-dependent protein packing.

      (3) The discussion of the role of excluded volume in steric repulsion between glycoprotein needs clarification. As presented, it's unclear what the role of "excluded volume" effects is in driving steric repulsion? Do the authors imply depletion forces? Or the volume unavailable due to stochastic configurations of gaussian chains? How does the formalism apply to branched membrane glycoproteins is not immediately obvious.

      (4) The authors showed that glycoprotein expression inversely correlated with viral infection and link viral entry inhibition to steric hindrance caused by the glycoprotein. Alternative explanations would be that the glycoprotein expression (a) reroutes endocytosed viral particles or (b) lowers cellular endocytic rates and via either mechanism reduce viral infection. The authors should provide evidence that these alternatives are not occurring in their system. They could for example experimentally test whether non-specific endocytosis is still operational at similar levels, measured with fluid-phase markers such as 10kDa dextrans.

      (5) The authors approach their system with the goal of generalizing the cell membrane (the cumulative effect of all cell membrane molecules on viral entry), but what about the inverse? How does the nature of the molecule seeking entry affect the interface? For example, a lipid nanoparticle vs a virus with a short virus-cell distance vs a virus with a large virus-cell distance?

      SPECIFIC QUESTIONS:

      (1) The proposed mechanism indicates that glycosylation status does not produce an effect in the "trapping" of virus, but in later stages of the formation of the virus/membrane interface due to the high energetic costs of displacing highly glycosylated molecules at the vicinity of the virus/membrane interface. It is suggested to present a correlation between the levels of glycans in the Calu-3 cell monolayers and the number of viral particles bound to cell surface at different pulse times. Results may be quantified following the same method as shown in Figure 2 for the correlation between glycosylation levels and viral infection (in this case the resulting output could be number of viral particles bound as a function of glycan content).

      (2) The use of the purified glycosylated and non-glycosylated ectodomains of MUC1 and CD-43 to establish a relationship between glycosylation and protein density into lipid bilayers on silica beads is an elegant approach. An assessment of the impact of glycosylation in the structural conformation of both proteins, for instance determining the Flory radius of the glycosylated and non-glycosylated ectodomains by the FRET-FLIM approach used in Figure 4 would serve to further support the hypothesis of the article.

      (3) The MUC1 glycoprotein is reported to have a dramatic effect in reducing viral infection shown in Fig 1F. On the contrary, in a different experiment shown in Fig2D and Fig2H MUC1 has almost no effect in reducing viral infection. It is not clear how these two findings can be compatible.

      (4) Why is there a shift in the use of the glycan marker? How does this affect the conclusions? For the infection correlation relating protein expression with glycan content the PNA-lectin was used together with flow cytometry. For imaging the infection and correlating with glycan content the SSA-lectin is used.

      (5) The authors in several instances comment on the relevance and importance of the total glycan content. Nevertheless, these conclusions are often drawn when using only one glycan-binding lectin. In fact, the anti-correlation with viral infection is distinct for the various lectins (Fig 2D and Fig 2H). Would it make more sense to use a combination of lectins to get a full glycan spectrum?

      (6) Fig 3A shows virus binding to HEK cells upon MUC1 expression. Please provide the surface expression of the MUC1 so that the data can be compared to Fig 1F. Nevertheless, it is not clear why the authors used MUC expression as a parameter to assess virus binding. Alternatively, more conclusive data supporting the hypothesis would be the absence of a correlation between total glycan content and virus binding capacity.

      (7) While the use of the Flory model could provide a simplification for a (disordered) flexible structure such as MUC1, where the number of amino acids equals N in the Flory model, this generalisation will not hold for all the proteins. Because folding will dramatically change the effective polypeptide chain-length and reduce available positioning of the amino acids, something the authors clearly measured (Fig 4G), this generalisation is not correct. In fact, the generalisation does not seem to be required because the authors provide an estimation for the effective Flory radius using their FRET approach

    1. eLife Assessment

      This manuscript reports valuable findings on the role of the Srs2 protein in turning off the DNA damage signaling response initiated by Mec1 (human ATR) kinase. The data provide solid evidence that Srs2 interaction with PCNA and ensuing SUMO modification is required for checkpoint downregulation. However, while the model that Srs2 acts at gaps after camptothecin-induced DNA damage is reasonable, direct experimental evidence for this is currently lacking. The work will be of interest to cell biologists studying genome integrity.

    2. Reviewer #1 (Public review):

      Overall, the data presented in this manuscript is of good quality. Understanding how cells control RPA loading on ssDNA is crucial to understanding DNA damage responses and genome maintenance mechanisms. The authors used genetic approaches to show that disrupting PCNA binding and SUMOylation of Srs2 can rescue the CPT sensitivity of rfa1 mutants with reduced affinity for ssDNA. In addition, the authors find that SUMOylation of Srs2 depends on binding to PCNA and the presence of Mec1.

      Comments on revisions:

      I am satisfied with the revisions made by the authors, which helped clarify some points that were confusing in the initial submission.

    3. Reviewer #2 (Public review):

      This revised manuscript mostly addresses previous concerns by doubling down on the model without providing additional direct evidence of interactions between Srs2 and PCNA, and that "precise sites of Srs2 actions in the genome remain to be determined." One additional Srs2 allele has been examined, showing some effect in combination with rfa1-zm2.

      Many of the conclusions are based on reasonable assumptions about the consequences of various mutations, but direct evidence of changes in Srs2 association with PNCA or other interactors is still missing. There is an assumption that a deletion of a Rad51-interacting domain or a PCNA-interacting domain have no pleiotropic effects, which may not be the case. How SLX4 might interact with Srs2 is unclear to me, again assuming that the SLX4 defect is "surgical" - removing only one of its many interactions.

      One point of concern is the use of t-tests without some sort of correction for multiple comparisons - in several figures. I'm quite sceptical about some of the p < 0.05 calls surviving a Bonferroni correction. Also in 4B, which comparison is **? Also, admittedly by eye, the changes in "active" Rad53 seem much greater than 5x. (also in Fig. 3, normalizing to a non-WT sample seems odd).

      What is the WT doubling time for this strain? From the FACS it seems as if in 2 h the cells have completed more than 1 complete cell cycle. Also in 5D. Seems fast...

      I have one over-arching confusion. Srs2 was shown initially to remove Rad51 from ssDNA and the suppression of some of srs2's defects by deleting rad51 made a nice, compact story, though exactly how srs2's "suppression of rad6" fit in isn't so clear (since Rad6 ties into Rad18 and into PCNA ubiquitylation and into PCNA SUMOylation). Now Srs2 is invoked to remove RPA. It seems to me that any model needs to explain how Srs2 can be doing both. I assume that if RPA and Rad51 are both removed from the same ssDNA, the ssDNA will be "trashed" as suggested by Symington's RPA depletion experiments. So building a model that accounts for selective Srs2 action at only some ssDNA regions might be enhanced by also explaining how Rad51 fits into this scheme.

      As a previous reviewer has pointed out, CPT creates multiple forms of damage. Foiani showed that 4NQO would activate the Mec1/Rad53 checkpoint in G1- arrested cells, presumably because there would be single-strand gaps but no DSBs. Whether this would be a way to look specifically at one type of damage is worth considering; but UV might be a simpler way to look.

      As also noted, the effects on the checkpoint and on viability are quite modest. Because it isn't clear (at least to me) why rfa1 mutants are so sensitive to CPT, it's hard for me to understand how srs2-zm2 has a modest suppressive effect: is it by changing the checkpoint response or facilitating repair or both? Or how srs2-3KR or srs2-dPIM differ from Rfa1-zm2 in this respect. The authors seem to lump all these small suppressions under the rubric of "proper levels of RPA-ssDNA" but there are no assays that directly get at this. This is the biggest limitation.

      Srs2 has also been implicated as a helicase in dissolving "toxic joint molecules" (Elango et al. 2017). Whether this activity is changed by any of the mutants (or by mutations in Rfa1) is unclear. In their paper, Elango writes: "Rare survivors in the absence of Srs2 rely on structure-specific endonucleases, Mus81 and Yen1, that resolve toxic joint-molecules" Given the involvement of SLX4, perhaps the authors should examine the roles of structure-specific nucleases in CPT survival?

      Experiments that might clarify some of these ambiguities are proposed to be done in the future. For now, we have a number of very interesting interactions that may be understood in terms of a model that supposes discriminating among gaps and ssDNA extensions by the presence of PCNA, perhaps modified by SUMO. As noted above, it would be useful to think about the relation to Rad6.

    4. Reviewer #3 (Public review):

      The superfamily I 3'-5' DNA helicase Srs2 is well known for its role as an anti-recombinase, stripping Rad51 from ssDNA, as well as an anti-crossover factor, dissociating extended D-loops and favoring non-crossover outcome during recombination. In addition, Srs2 plays a key role in in ribonucleotide excision repair. Besides DNA repair defects, srs2 mutants also show a reduced recovery after DNA damage that is related to its role in downregulating the DNA damage signaling or checkpoint response. Recent work from the Zhao laboratory (PMID: 33602817) identified a role of Srs2 in downregulating the DNA damage signaling response by removing RPA from ssDNA. This manuscript reports further mechanistic insights into the signaling downregulation function of Srs2.

      Using the genetic interaction with mutations in RPA1, mainly rfa1-zm2, the authors test a panel of mutations in Srs2 that affect CDK sites (srs2-7AV), potential Mec1 sites (srs2-2SA), known sumoylation sites (srs2-3KR), Rad51 binding (delta 875-902), PCNA interaction (delta 1159-1163), and SUMO interaction (srs2-SIMmut). All mutants were generated by genomic replacement and the expression level of the mutant proteins was found to be unchanged. This alleviates some concern about the use of deletion mutants compared to point mutations. Double mutant analysis identified that PCNA interaction and SUMO sites were required for the Srs2 checkpoint dampening function, at least in the context of the rfa1-zm2 mutant. There was no effect of this mutants in a RFA1 wild type background. This latter result is likely explained by the activity of the parallel pathway of checkpoint dampening mediated by Slx4, and genetic data with an Slx4 point mutation affecting Rtt107 interaction and checkpoint downregulation support this notion. Further analysis of Srs2 sumoylation showed that Srs2 sumoylation depended on PCNA interaction, suggesting sequential events of Srs2 recruitment by PCNA and subsequent sumoylation. Kinetic analysis showed that sumoylation peaks after maximal Mec1 induction by DNA damage (using the Top1 poison camptothecin (CPT)) and depended on Mec1. This data are consistent with a model that Mec1 hyperactivation is ultimately leading to signaling downregulation by Srs2 through Srs2 sumoylation. Mec1-S1964 phosphorylation, a marker for Mec1 hyperactivation and a site found to be needed for checkpoint downregulation after DSB induction, did not appear to be involved in checkpoint downregulation after CPT damage. The data are in support of the model that Mec1 hyperactivation when targeted to RPA-covered ssDNA by its Ddc2 (human ATRIP) targeting factor, favors Srs2 sumoylation after Srs2 recruitment to PCNA to disrupt the RPA-Ddc2-Mec1 signaling complex. Presumably, this allows gap filling and disappearance of long-lived ssDNA as the initiator of checkpoint signaling, although the study does not extend to this step.

      Strengths<br /> (1) The manuscript focuses on the novel function of Srs2 to downregulate the DNA damage signaling response and provide new mechanistic insights.<br /> (2) The conclusions that PCNA interaction and ensuing Srs2-sumoylation are involved in checkpoint downregulation are well supported by the data.

      Weaknesses<br /> (1) Additional mutants of interest could have been tested, such as the recently reported Pin mutant, srs2-Y775A (PMID: 38065943), and the Rad51 interaction point mutant, srs2-F891A (PMID: 31142613).<br /> (2) The use of deletion mutants for PCNA and RAD51 interaction is inferior to using specific point mutants, as done for the SUMO interaction and the sites for post-translational modifications.<br /> (3) Figure 4D and Figure 5A report data with standard deviations, which is unusual for n=2. Maybe the individual data points could be plotted with a color for each independent experiment to allow the reader to evaluate the reproducibility of the results.

      Comments on revisions:

      In this revision, the authors adequately addressed my concerns. The only issue I see remaining is the site of Srs2 action. The authors argue in favor of gaps and against R-loops and ssDNA resulting from excessive supercoiling. The authors do not discuss ssDNA resulting from processing of one-sided DSBs, which are expected to result from replication run-off after CPT damage but are not expected to provide the 3'-junction for preferred PCNA loading. Can the authors exclude PCNA at the 5'-junction at a resected DSB?

    5. Author response:

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

      eLife Assessment

      This manuscript reports valuable findings on the role of the Srs2 protein in turning off the DNA damage signaling response initiated by Mec1 (human ATR) kinase. The data provide solid evidence that Srs2 interaction with PCNA and ensuing SUMO modification is required for checkpoint downregulation. However, experimental evidence with regard to the model that Srs2 acts at gaps after camptothecin-induced DNA damage is currently lacking. The work will be of interest to cell biologists studying genome integrity but would be strengthened by considering the possible role of Rad51 and its removal. 

      We thank editors and reviewers for their constructive comments and address their main criticisms below. 

      (1)  Srs2 action sites. Our data provide support to the model that Srs2 removal of RPA is favored at ssDNA regions with proximal PCNA, but not at ssDNA regions lacking proximal PCNA. A prominent example of the former type of ssDNA regions is an ssDNA gap with a 3’ DNA end permissive for PCNA loading. Examples of the latter type of ssDNA sites include those within R-loops and negatively supercoiled regions, both lacking 3’ DNA end required for PCNA loading. The former type of ssDNA regions can recruit other DNA damage checkpoint proteins, such as 9-1-1, which requires a 5’ DNA end for loading; thus, these ssDNA regions are ideal for Srs2’s action in checkpoint dampening. In contrast, ssDNA within supercoiled and Rloop regions, both of which can be induced by CPT treatment (Pommier et al, 2022), lacks the DNA ends required for checkpoint activation. RPA loaded at these sites plays important roles, such as recruiting Rloop removal factors (Feng and Manley, 2021; Li et al, 2024; Nguyen et al, 2017), and they are not ideal sites for Srs2’s checkpoint dampening functions. Based on the above rationale and our data, we suggest that Srs2 removal of RPA is favored only at a subset of ssDNA regions prone to checkpoint activation and can be avoided at other ssDNA regions where RPA mainly helps DNA protection and repair. We have modified the text and model drawing to better articulate the implications of our work, that is, Srs2 can distinguish between two types of ssDNA regions by using PCNA proximity as a guide for RPA removal_._ We noted that the precise sites of Srs2 actions in the genome remain to be determined. 

      (2)  Rad51 in the Srs2-RPA antagonism. In our previous report (Dhingra et al, 2021), we provided several lines of evidence to support the conclusion that Rad51 is not relevant to the Srs2-RPA antagonism, despite it being the best-studied protein that is regulated by Srs2. For example, while rad51∆ rescues the hyperrecombination phenotype of srs2∆ cells as shown by others, we found that rad51∆ did not affect the hypercheckpoint phenotype of srs2∆. In contrast, rfa1-zm1/zm2 have the opposite effects. The differential effects of rad51∆ and rfa1-zm1/zm2 were also seen for the srs2-ATPase dead allele (srs2-K41A). For example, rfa1-zm2 rescued the hyper-checkpoint defect and the CPT sensitivity of srs2-K41A, while rad51∆ had neither effect. These and other data described by Dhingra et al (2021) suggest that Srs2’s effects on checkpoint vs. recombination can be separated and that Rad51 removal by Srs2 is distinct from the Srs2RPA antagonism in checkpoint regulation. Given the functional separation summarized above, in our current work investigating which Srs2 features affect the Srs2-RPA antagonism, we did not focus on the role of Rad51. However, we did examine all known features of Srs2, including its Rad51 binding domain. Consistent with our conclusion summarized above, deleting the Rad51 binding domain in Srs2 (srs2∆Rad51BD) has no effect on rfa1-zm2 phenotype in CPT (Figure 2D). This data provides yet another evidence that Srs2 regulation of Rad51 is separable from the Srs2-RPA antagonism. Our work provides a foundation for future examination of how Srs2 regulates RPA and Rad51 in different manners and if there is a crosstalk between them in specific contexts. We have added this point to the revised text.

      Public Reviews: 

      Reviewer #1.

      Overall, the data presented in this manuscript is of good quality. Understanding how cells control RPA loading on ssDNA is crucial to understanding DNA damage responses and genome maintenance mechanisms. The authors used genetic approaches to show that disrupting PCNA binding and SUMOylation of Srs2 can rescue the CPT sensitivity of rfa1 mutants with reduced affinity for ssDNA. In addition, the authors find that SUMOylation of Srs2 depends on binding to PCNA and the presence of Mec1. Noted weaknesses include the lack of evidence supporting that Srs2 binding to PCNA and its SUMOylation occur at ssDNA gaps, as proposed by the authors. Also, the mutants of Srs2 with impaired binding to PCNA or impaired SUMOylation showed no clear defects in checkpoint dampening, and in some contexts, even resulted in decreased Rad53 activation. Therefore, key parts of the paper would benefit from further experimentation and/or clarification. 

      We thank the reviewer for the positive comments, and we address her/his remark regarding ssDNA gaps below. In addition, we provide evidence that redundant pathways can mask checkpoint dampening phenotype of the srs2-∆PIM and -3KR alleles.

      Major Comments 

      (1) The central model proposed by the authors relies on the loading of PCNA at the 3' junction of an ssDNA gap, which then mediates Srs2 recruitment and RPA removal. While several aspects of the model are consistent with the data, the evidence that it is occurring at ssDNA gaps is not strong. The experiments mainly used CPT, which generates mostly DSBs. The few experiments using MMS, which mostly generates ssDNA gaps, show that Srs2 mutants lead to weaker rescue in this context (Figure S1). How do the authors explain this discrepancy? In the context of DSBs, are the authors proposing that Srs2 is engaging at later steps of HRdriven DSB repair where PCNA gets loaded to promote fill-in synthesis? If so, is RPA removal at that step important for checkpoint dampening? These issues need to be addressed and the final model adjusted. 

      Our data provide supports to the model that Srs2 removal of RPA is favored at ssDNA regions with proximal PCNA, but not at ssDNA regions lacking proximal PCNA (Figure 7). A prominent example of the former type is ssDNA gap with 3’ DNA end permissive for PCNA loading. Examples of the latter type of ssDNA sites are present within R-loops and negatively supercoiled regions, and these ssDNA sites lack 3’ DNA ends required for PCNA loading. In principle, the former can recruit other DNA damage checkpoint proteins, such as 9-1-1, which requires 5’ DNA end for loading, thus it is ideal for Srs2’s action in checkpoint dampening. In contrast, ssDNA within supercoiled and R-loop regions, which can be induced by CPT treatment (Pommier et al., 2022), lacks DNA ends required for checkpoint activation. RPA loaded at these sites plays important roles such as recruiting R-loop removal factors (Feng and Manley, 2021; Li et al., 2024; Nguyen et al., 2017), and these are not ideal sites for Srs2 removal of RPA to achieve checkpoint dampening. Our work suggests that Srs2 removal of RPA is favored only at a subset of ssDNA regions prone to checkpoint activation and can be avoided at other ssDNA regions where RPA mainly helps DNA protection and repair. We have modified the text and the model to clarify our conclusions and emphasized that Srs2 can distinguish between two types of ssDNA regions using PCNA proximity as a guide for RPA removal. 

      We note that in addition to DSBs, CPT also induces both types of ssDNA mentioned above. For example, CPT can lead to ssDNA gap formation upon excision repair or DNA-protein crosslink repair of trapped Top1 (Sun et al, 2020). The resultant ssDNA regions contain 3’ DNA end for PCNA loading, thus favoring Srs2 removal of RPA. CPT treatment also depletes the functional pool of Top1, thus causing topological stress and increased levels of DNA supercoiling and R-loops (Petermann et al, 2022; Pommier et al., 2022). As mentioned above, R-loops and supercoiled regions do not favor Srs2 removal of RPA due to a lack of PCNA loading. We have now adjusted the text to clarify that CPT can lead to the generation of two types of ssDNA regions as stated above. We have also adjusted the model drawing to indicate that while ssDNA gaps can be logical Srs2 action sites, other types of ssDNA regions with proximal PCNA (e.g., resected ssDNA tails) could also be targeted by Srs2. Our work paves the way to determine the precise ssDNA regions for Srs2’s action. 

      Multiple possibilities should be considered in explaining the less potent suppression of rfa1 mutants by srs2 alleles in MMS compared to CPT conditions. For example, MMS and CPT affect checkpoints differently. While CPT only activates the DNA damage checkpoint, MMS additionally induces DNA replication checkpoint (Menin et al, 2018; Redon et al, 2003; Tercero et al, 2003). It is possible that the Srs2-RPA antagonism is more relevant to the DNA damage checkpoint compared with the DNA replication checkpoint. Further investigation of this possibility among other scenarios will shed light on differential suppression seen here. We have included this discussion in the revised text.

      (2) The data in Figure 3 showing that Srs2 mutants reduce Rad53 activation in the rfa1-zm2 mutant are confusing, especially given the claim of an anti-checkpoint function for Srs2 (in which case Srs2 mutants should result in increased Rad53 activation). The authors propose that Rad53 is hyperactivated in rfa1-zm2 mutant because of compromised ssDNA protection and consequential DNA lesions, however, the effects sharply contrast with the central model. Are the authors proposing that in the rfa1-zm2 mutant, the compromised protection of ssDNA supersedes the checkpoint-dampening effect?  Perhaps a schematic should be included in Figure 3 to depict these complexities and help the reader. The schematic could also include the compensatory dampening mechanisms like Slx4 (on that note, why not move Figure S2 to a main figure?... and even expand experiments to better characterize the compensatory mechanisms, which seem important to help understand the lack of checkpoint dampening effect in the Srs2 mutants) 

      Partially defective alleles often do not manifest null phenotype. In this case, while srs2∆ increases Rad53 activation (Dhingra et al., 2021), srs2-∆PIM and -3KR did not (Figure 3A-3B). However, srs2-∆PIM did increase Rad53 activation when combined with another checkpoint dampening mutant slx4<sup>RIM</sup> (now Figure 4B-4C). This result suggests that defects of partially defective srs2 alleles can be masked by Slx4. Further, srs2-∆PIM and 3KR rescued rfa1-zm2’s checkpoint abnormality (now Figure 3B-3C), suggesting that Srs2 binding to PCNA and its sumoylation contribute to the Srs2-RPA antagonism in the DNA damage checkpoint response.

      Partially defective alleles that impair specific features of a protein without producing null phenotype have been used widely to reveal biological mechanisms. For example, a partially defective allele of the checkpoint protein Rad9 perturbing binding to gamma-H2A (rad9-K1088M) does not cause DNA damage sensitivity on its own, due to the compensation from other checkpoint factors (Hammet et al, 2007). However_, rad9-K1088M_ rescues the DNA damage sensitivity and persistent G2/M checkpoint of slx4 mutants, providing strong evidence for the notion that Slx4 dampens checkpoint via regulating Rad9 (Ohouo et al, 2013).

      We have now indicated that our model highlights the checkpoint recovery process and does not depict another consequence of the Srs2-RPA antagonism, that is, rfa1 DNA binding mutants can lead to increased levels of DNA lesions and consequently stronger checkpoint activation, which are rescued by lessening Srs2’s ability to strip RPA from DNA (Dhingra et al., 2021). We have stated these points more clearly in the text and added a schematic (Figure 3A) to outline the genetic relationship and interpretations. We also moved Figure S2 to the main figures (Figure 4), as suggested by the reviewer. Better characterizing the compensatory mechanisms among the multiple checkpoint dampening pathways requires substantial amounts of work that will be pursued in the future.

      (3) The authors should demarcate the region used for quantifying the G1 population in Figure 3B and explain the following discrepancy: By inspection of the cell cycle graph, all mutants have lower G1 peak height compared to WT (CPT 2h). However, in the quantification bar graph at the bottom, ΔPIM has higher G1 population than the WT. 

      We now describe how the G1 region of the FACS histogram was selected to derive the percentage of G1 cells in Figure 3B (now Figure 3C). Briefly, the G1 region from the “G1 sample” was used to demarcate the G1 region of the “CPT 2h” sample. We noticed that a mutant panel was mistakenly put in the place of wild-type, and this error is now corrected. The conclusion remains that srs2-∆PIM and srs2-3KR improved rfa1-zm2 cells’ ability to exit G2/M, while they themselves do not show difference from the wild-type control for the percentage of G1 cells after 2hr CPT treatment. We have added statistics in Figure 3C that support this conclusion.

      Reviewer #2:

      This is an interesting paper that delves into the post-translational modifications of the yeast Srs2 helicase and proteins with which it interacts in coping with DNA damage. The authors use mutants in some interaction domains with RPA and Srs2 to argue for a model in which there is a balance between RPA binding to ssDNA and Srs2's removal of RPA. The idea that a checkpoint is being regulated is based on observing Rad53 and Rad9 phosphorylation (so there are the attributes of a checkpoint), but evidence of cell cycle arrest is lacking. The only apparent delay in the cell cycle is the re-entry into the second S phase (but it could be an exit from G2/M); but in any case, the wild-type cells enter the next cell cycle most rapidly. No direct measurement of RPA residence is presented. 

      We thank the reviewer for the helpful comments. Previous studies have shown that CPT does not induce the DNA replication checkpoint, and thus does not slow down or arrest S phase progression; however, CPT does induce the DNA damage checkpoint, which causes a delay (not arrest) in G2/M phase and re-entering into the second G1 (Menin et al., 2018; Redon et al., 2003). Our result is consistent with these findings, showing that CPT induces G2/M delay but not arrest. We have now made this point clearer in the text.

      We have previously reported chromatin-bound RPA levels in rfa1-zm2, srs2, and their double mutants, as well as in vitro ssDNA binding by wild-type and mutant RPA complexes (Dhingra et al., 2021). These data showed that Srs2 loss or its ATPase dead mutant led to 4-6-fold increase of RPA levels on chromatin, which was rescued by rfa1-zm2 (Dhingra et al., 2021). On its own, rfa1-zm2 did not cause defective chromatin association, despite modestly reducing ssDNA binding in vitro (Dhingra et al., 2021). This discrepancy could be due to a lack of sensitivity of the chromatin fractionation assay in revealing moderate changes of RPA residence on DNA in vivo. Our functional assays (Figure 2-3) were more effective in identifying the Srs2 features pertaining to RPA regulation. 

      Strengths:

      Data concern viability assays in the presence of camptothecin and in the post-translational modifications of Srs2 and other proteins.  

      Weaknesses:

      There are a couple of overriding questions about the results, which appear technically excellent. Clearly, there is an Srs2-dependent repair process here, in the presence of camptothecin, but is it a consequence of replication fork stalling or chromosome breakage? Is repair Rad51-dependent, and if so, is Srs2 displacing RPA or removing Rad51 or both? If RPA is removed quickly what takes its place, and will the removal of RPA result in lower DDC1-MEC1 signaling? 

      Srs2 can affect both the checkpoint response and DNA repair processes in CPT conditions. However, rfa1zm2 mainly affects the former role of Srs2; this allows us to gain a deeper understanding of this role, which is critical for cell survival in CPT (Dhingra et al., 2021). Building on this understanding, our current study identified two Srs2 features that could afford spatial and temporal regulation of RPA removal from DNA, providing a rationale for how cells can properly utilize an activity that can be beneficial yet also dangerous if it were to lack regulation. Study of Srs2-mediated DNA repair in CPT conditions, either in Rad51-dependent or -independent manner, to deal with replication fork stalling or DNA breaks will require studies in the future.

      Moreover, it is worth noting that in single-strand annealing, which is ostensibly Rad51 independent, a defect in completing repair and assuring viability is Srs2-dependent, but this defect is suppressed by deleting Rad51. Does deleting Rad51 have an effect here? 

      We have previously shown that rad51∆ did not rescue the hyper-checkpoint phenotype of srs2∆ cells in CPT conditions, while rfa1-zm1 and -zm2 did (Dhingra et al., 2021). This differential effect was also seen for the srs2 ATPase-dead allele (Dhingra et al., 2021). These and other data described by Dhingra et al (2021) suggest that Srs2’s effects on checkpoint vs. recombination are separable at least in CPT condition, and that the Srs2-RPA antagonism in checkpoint regulation is not affected by Rad51 removal (unlike in SSA).

      Neither this paper nor the preceding one makes clear what really is the consequence of having a weakerbinding Rfa1 mutant. Is DSB repair altered? Neither CPT nor MMS are necessarily good substitutes for some true DSB assay. 

      We have previously showed that rfa1-zm1/zm2 did not affect the frequencies of rDNA recombination, gene conversation, or direct repeat repair (Dhingra et al., 2021). Further, rfa1-zm1/zm2 did not suppress the hyperrecombination phenotype of srs2∆, while rad51∆ did (Dhingra et al., 2021). In a DSB system, wherein the DNA repeats flanking the break were placed 30 kb away from each other, srs2∆ led to hyper-checkpoint and lethality, both of which were rescued by rfa1-zm mutants (Dhingra et al., 2021). In this assay, rfa1-zm1/zm2 did not show sensitivity, suggesting largely proficient DNA repair. Collectively, these data suggest that moderately weakening DNA binding of Rfa1 does not lead to detectable effect on the recombinational repair examined thus far, rather it affects Srs2-mediated checkpoint downregulation. In-depth studies of rfa1-zm mutations in the context of various DSB repair steps will be interesting to pursue in the future.

      With camptothecin, in the absence of site-specific damage, it is difficult to test these questions directly. (Perhaps there is a way to assess the total amount of RPA bound, but ongoing replication may obscure such a measurement). It should be possible to assess how CPT treatment in various genetic backgrounds affects the duration of Mec1/Rad53-dependent checkpoint arrest, but more than a FACS profile would be required. 

      Quantitative measurement of RPA residence time on DNA in cellular context and the duration of the

      Mec1/Rad53-mediated cell cycle delay/arrest will be informative but requires further technology development. Our current work provides a foundation for such quantitative assessment.

      It is also notable that MMS treatment does not seem to yield similar results (Fig. S1). 

      Figure S1 showed that srs2-∆PIM and srs2-3KR had weaker suppression of rfa1-zm2 growth on MMS plates than on CPT plates. Multiple possibilities should be considered in explaining the less potent suppression of rfa1 mutants by srs2 in MMS compared with CPT conditions. For example, MMS and CPT affect checkpoints differently. While CPT only activates the DNA damage checkpoint, MMS additionally induces DNA replication checkpoint (Menin et al., 2018; Redon et al., 2003; Tercero et al., 2003). It is therefore possible that the Srs2RPA antagonism is more relevant for the DNA damage checkpoint control compared with the DNA replication checkpoint. Further investigation of this possibility will shed light on differential suppression seen here. We have included this discussion in the revised text.

      Reviewer #3:

      The superfamily I 3'-5' DNA helicase Srs2 is well known for its role as an anti-recombinase, stripping Rad51 from ssDNA, as well as an anti-crossover factor, dissociating extended D-loops and favoring non-crossover outcome during recombination. In addition, Srs2 plays a key role in ribonucleotide excision repair. Besides DNA repair defects, srs2 mutants also show a reduced recovery after DNA damage that is related to its role in downregulating the DNA damage signaling or checkpoint response. Recent work from the Zhao laboratory (PMID: 33602817) identified a role of Srs2 in downregulating the DNA damage signaling response by removing RPA from ssDNA. This manuscript reports further mechanistic insights into the signaling downregulation function of Srs2. 

      Using the genetic interaction with mutations in RPA1, mainly rfa1-zm2, the authors test a panel of mutations in Srs2 that affect CDK sites (srs2-7AV), potential Mec1 sites (srs2-2SA), known sumoylation sites (srs2-3KR), Rad51 binding (delta 875-902), PCNA interaction (delta 1159-1163), and SUMO interaction (srs2SIMmut). All mutants were generated by genomic replacement and the expression level of the mutant proteins was found to be unchanged. This alleviates some concern about the use of deletion mutants compared to point mutations. The double mutant analysis identified that PCNA interaction and SUMO sites were required for the Srs2 checkpoint dampening function, at least in the context of the rfa1-zm2 mutant. There was no effect of these mutants in a RFA1 wild-type background. This latter result is likely explained by the activity of the parallel pathway of checkpoint dampening mediated by Slx4, and genetic data with an Slx4 point mutation affecting Rtt107 interaction and checkpoint downregulation support this notion. Further analysis of Srs2 sumoylation showed that Srs2 sumoylation depended on PCNA interaction, suggesting sequential events of Srs2 recruitment by PCNA and subsequent sumoylation. Kinetic analysis showed that sumoylation peaks after maximal Mec1 induction by DNA damage (using the Top1 poison camptothecin (CPT)) and depended on Mec1. These data are consistent with a model that Mec1 hyperactivation is ultimately leading to signaling downregulation by Srs2 through Srs2 sumoylation. Mec1-S1964 phosphorylation, a marker for Mec1 hyperactivation and a site found to be needed for checkpoint downregulation after DSB induction did not appear to be involved in checkpoint downregulation after CPT damage. The data are in support of the model that Mec1 hyperactivation when targeted to RPA-covered ssDNA by its Ddc2 (human ATRIP) targeting factor, favors Srs2 sumoylation after Srs2 recruitment to PCNA to disrupt the RPA-Ddc2-Mec1 signaling complex. Presumably, this allows gap filling and disappearance of long-lived ssDNA as the initiator of checkpoint signaling, although the study does not extend to this step. 

      Strengths 

      (1) The manuscript focuses on the novel function of Srs2 to downregulate the DNA damage signaling response and provide new mechanistic insights. 

      (2) The conclusions that PCNA interaction and ensuing Srs2-sumoylation are involved in checkpoint downregulation are well supported by the data. 

      We thank the reviewer for carefully reading our work and for his/her positive comments. 

      Weaknesses 

      (1) Additional mutants of interest could have been tested, such as the recently reported Pin mutant, srs2Y775A (PMID: 38065943), and the Rad51 interaction point mutant, srs2-F891A (PMID: 31142613). 

      Residue Y775 of Srs2 was shown to serve as a separation pin in unwinding D-loops and dsDNA with 3’ overhang in vitro; however, srs2-Y775A lacks cellular phenotype in assays for gene conversion, crossover, and genetic interactions. As such, the biological role of this residue has not been clear. In addressing reviewer’s comment, we obtained srs2-Y775A, and the control strains as described in the recent publication (Meir et al, 2023). While srs2-Y775A on its own did not affect CPT sensitivity, it improved rfa1-zm_2 mutant growth on media containing CPT. This result suggests that Y775 can influence RPA regulation during in checkpoint dampening. Given that truncated Srs2 (∆Cter 276 a.a.) containing Y775A showed normal RPA stripping activity _in vitro, it is possible that cellular assay using rfa1-zm2 is more sensitive for revealing defect of this activity or full-length protein is required for manifest Y775A effect. Future experiments distinguishing these possibilities can provide more clarity. Nevertheless, our result reveals the first phenotype of Srs2 separation pin mutant. We have added this new result (Figure S4) and our interpretation.

      We have already included data showing that a srs2 mutant lacking the Rad51 binding domain (srs2∆Rad51BD, ∆875-902) did not affect rfa1-zm2 growth in CPT nor caused defects in CPT on its own (Figure 2D). This data suggest that Rad51 binding is not relevant to the Srs2-RPA antagonism in CPT, a conclusion fully supported by data in our previous study (Dhingra et al., 2021). Collectively, these findings do not provide a strong rationale to test a point mutation within the Rad51BD region. 

      (2) The use of deletion mutants for PCNA and RAD51 interaction is inferior to using specific point mutants, as done for the SUMO interaction and the sites for post-translational modifications. 

      We generally agree with this view. However, it is less of a concern in the context of the Rad51 binding site mutant (srs2-∆Rad51BD) since it behaved as the wild-type allele in our assays. The srs2-∆PIM mutant (lacking 4 amino acids) has been examined for PCNA binding in vitro and in vivo (Kolesar et al, 2016; Kolesar et al, 2012); to our knowledge no detectable defect was reported. Thus, we believe that this allele is suitable for testing whether Srs2’s ability to bind PCNA is relevant to RPA regulation.

      (3) Figure 4D and Figure 5A report data with standard deviations, which is unusual for n=2. Maybe the individual data points could be plotted with a color for each independent experiment to allow the reader to evaluate the reproducibility of the results. 

      We have included individual data points as suggested and corrected figure legend to indicate that three independent biological samples per genotype were examined in both panels.

      References:

      Dhingra N, Kuppa S, Wei L, Pokhrel N, Baburyan S, Meng X, Antony E, Zhao X (2021) The Srs2 helicase dampens DNA damage checkpoint by recycling RPA from chromatin. Proc Natl Acad Sci U S A 118: e2020185118.

      Feng S, Manley JL (2021) Replication Protein A associates with nucleolar R loops and regulates rRNA transcription and nucleolar morphology. Genes Dev 35: 1579-1594.

      Fiorani S, Mimun G, Caleca L, Piccini D, Pellicioli A (2008) Characterization of the activation domain of the Rad53 checkpoint kinase. Cell Cycle 7: 493-499.

      Hammet A, Magill C, Heierhorst J, Jackson SP (2007) Rad9 BRCT domain interaction with phosphorylated H2AX regulates the G1 checkpoint in budding yeast. EMBO Rep 8: 851-857.

      Kolesar P, Altmannova V, Silva S, Lisby M, Krejci L (2016) Pro-recombination role of Srs2 protein requires SUMO (Small Ubiquitin-like Modifier) but is independent of PCNA (Proliferating Cell Nuclear Antigen) interaction. J Biol Chem 291: 7594-7607.

      Kolesar P, Sarangi P, Altmannova V, Zhao X, Krejci L (2012) Dual roles of the SUMO-interacting motif in the regulation of Srs2 sumoylation. Nucleic Acids Res 40: 7831-7843.

      Li Y, Liu C, Jia X, Bi L, Ren Z, Zhao Y, Zhang X, Guo L, Bao Y, Liu C et al (2024) RPA transforms RNase H1 to a bidirectional exoribonuclease for processive RNA-DNA hybrid cleavage. Nat Commun 15: 7464.

      Meir A, Raina VB, Rivera CE, Marie L, Symington LS, Greene EC (2023) The separation pin distinguishes the pro- and anti-recombinogenic functions of Saccharomyces cerevisiae Srs2. Nat Commun 14: 8144.

      Memisoglu G, Lanz MC, Eapen VV, Jordan JM, Lee K, Smolka MB, Haber JE (2019) Mec1(ATR) autophosphorylation and Ddc2(ATRIP) phosphorylation regulates dna damage checkpoint signaling. Cell Rep 28: 1090-1102 e1093.

      Menin L, Ursich S, Trovesi C, Zellweger R, Lopes M, Longhese MP, Clerici M (2018) Tel1/ATM prevents degradation of replication forks that reverse after Topoisomerase poisoning. EMBO Rep 19: e45535.

      Nguyen HD, Yadav T, Giri S, Saez B, Graubert TA, Zou L (2017) Functions of Replication Protein A as a sensor of R loops and a regulator of RNaseH1. Mol Cell 65: 832-847 e834.

      Ohouo PY, Bastos de Oliveira FM, Liu Y, Ma CJ, Smolka MB (2013) DNA-repair scaffolds dampen checkpoint signalling by counteracting the adaptor Rad9. Nature 493: 120-124.

      Papouli E, Chen S, Davies AA, Huttner D, Krejci L, Sung P, Ulrich HD (2005) Crosstalk between SUMO and ubiquitin on PCNA is mediated by recruitment of the helicase Srs2p. Mol Cell 19: 123-133.

      Petermann E, Lan L, Zou L (2022) Sources, resolution and physiological relevance of R-loops and RNA-DNA hybrids. Nat Rev Mol Cell Biol 23: 521-540.

      Pommier Y, Nussenzweig A, Takeda S, Austin C (2022) Human topoisomerases and their roles in genome stability and organization. Nat Rev Mol Cell Biol 23: 407-427.

      Redon C, Pilch DR, Rogakou EP, Orr AH, Lowndes NF, Bonner WM (2003) Yeast histone 2A serine 129 is essential for the efficient repair of checkpoint-blind DNA damage. EMBO Rep 4: 678-684.

      Sun Y, Saha S, Wang W, Saha LK, Huang SN, Pommier Y (2020) Excision repair of topoisomerase DNAprotein crosslinks (TOP-DPC). DNA Repair (Amst) 89: 102837.

      Tercero JA, Longhese MP, Diffley JFX (2003) A central role for DNA replication forks in checkpoint activation and response. Mol Cell 11: 1323-1336.

      Reviewer #1 (Recommendations For The Authors): 

      (1) "the srs2-ΔPIM (Δ1159-1163 amino acids)". "11" should not be italic.

      Corrected.

      (2) "the srs2-SIMmut (1170 IIVID 1173 to 1170 AAAAD 1173)". "1173" should be 1174.

      Corrected.

      (3) Can Slx4-RIM mutant rescue rfa1-zm2 CPT sensitivity?  

      We found that unlike srs2∆, slx4∆ failed to rescue rfa1-zm2 CPT sensitivity (picture on the right). On the other hand, slx4∆ counteracts Rad9-dependent Rad53 activation as shown by Ohouo et al (2013). 

      Author response image 1.

      (4) One genotype (rfa1-zm2 srs2-3KR) is missing in Figure 5B.

      Corrected.

      (5) In Fig. S2C, FACS plots do not match the bar graph (see major concern 3). 

      Corrected and is described in more detail in Major Concern #3.

      Reviewer #2 (Recommendations For The Authors): 

      Figure 1. The colors in A are not well-conserved in B.

      Colors for srs2-7AV and -2SA in panel B are now matched with those in panel A.

      Figure 2. Is srs2-SIMmut the same as srs2-sim? 

      This mutant allele is now referred to as srs2-SIM<sup>mut</sup> throughout the text and figures.

      The suppression of rfa1-zm2 and (less strongly) rfa-t33 by the Srs2 mutants is interesting. Based on previous data, the suppression is apparently mutual, though it isn't shown here, unless we misunderstand. 

      We have previously shown that rfa1-zm2 and srs2∆ showed mutual suppression (Dhingra et al 2021 PNAS) and have included an example in Figure S1A. Unlike srs2∆, srs2-∆PIM and -3KR showed little damage sensitivity and DDC defects, likely due to the compensation by the Slx4-mediated checkpoint dampening (detailed in the Public Review section). Suppression is not applicable toward mutants lacking a phenotype, though the mutants could confer suppression when there is a functional relationship with another mutant, as we see here toward rfa1-zm2.

      Is Srs2 interaction with PCNA dependent on its ubiquitylation or SUMO? Does PCNA mutant K164R mimic this mutation? (this may well be known; our ignorance). 

      It was known that Srs2 can bind unmodified PCNA, though SUMO enhances this interaction; however, a very small percentage of PCNA is sumoylated in cells and PCNA sumoylation affects both Srs2-dependent and independent processes (e.g., (Papouli et al, 2005). As such, the genetic interaction of K164R with rfa1-zm2 can be difficult to interpret.

      Why srs2-7AV or srs2-sim make rfa1-zm2 even more sensitive is also not obvious. The authors take refuge in the statement that Srs2 "has multiple roles in cellular survival of genotoxic stress" but don't attempt to be more precise. 

      Our understanding of srs2-7AV and -sim is limited; thus, more specific speculation cannot be made at this time.

      Figure 3. It is striking (Figure 3A) that all the cells have reached G2 an hour after releasing from alpha-factor arrest, even though presumably CPT treatment must impair replication. It is even more striking that there is apparently no G2/M arrest in the presumably damaged cells as the WT (Figure 3B) has the most rapid progression through the cell cycle. How does this compare with cells in the absence of CPT? The idea that CPT is triggering Rad53-mediated response is hard to understand if there is in fact no delay in the cell cycle. Instead, the several mutants appear to delay re-entry into S... Or maybe it is actually an exit from G2/M? 

      This phenomenon needs a better explanation. 

      CPT does not induce the DNA replication checkpoint nor S phase delay, explaining apparent G2 content by the one hour time point; however, CPT does induce the DNA damage checkpoint, and a delay (not arrest) in G2/M (Menin et al., 2018; Redon et al., 2003; Tercero et al., 2003). We confirmed these findings. In our hand, wildtype G1 cells released into the cell cycle in the absence of CPT complete the first cell cycle within 80 minutes, such that most cells are in the second G1 phase by 90 min. In contrast, when wild-type cells were treated with CPT, G2/M exit was only partial at 120min (e.g., Figure 3B). These features differentiate CPT treatment from MMS treatment, which induces both types of checkpoints and lengthening the time that cells reach G2. We have highlighted this unique feature of CPT in checkpoint induction.

      What is "active Rad53"? If the authors mean they are using a phospho-specific Ab versus Rad53, they should explain this. It's impossible to know if total Rad53 is altered from Figure 3A. A blot with an antibody that detects both phosphorylated and nonphosphorylated Rad53 would help. 

      The F9 antibody used here detects phosphorylated Rad53 forms induced by Mec1 activation and does not detect unphosphorylated Rad53 (Fiorani et al, 2008). We changed “active Rad53” to “phosphorylated Rad53”. We used Pgk1 as a loading control to ensure equal loading, which help to quantify the relative amount of “active Rad53” in cells. This method has been used widely in the field.

      Also is there a doublet of Rad53 in the right two lanes and in WT? Rad53 often shows more than one slowmigrating species, so this isn't necessarily a surprise. Were both forms used in quantitation? 

      Both forms are used for quantification. 

      Figure 4A. Is there a di-SUMO form above the band marked Srs2-Su? Is this known? Is it counted? 

      Mono-sumoylated form of Srs2 is the most abundant form of sumoylated Srs2, though we detected a sumoylated Srs2 band that can represent its di-sumo form. We did quantify both forms in the plot.

      B. The dip at 1.5 h in Rad9-P is curious. It would be useful to know what % of Rad9 is phosphorylated in a repair-defective (rad52?) background with CPT treatment. And would such rad52 cells show a long arrest? 

      This dip is reproducible and may reflect that a population of cells escape G2/M delay at this timepoint.  

      Figure 5. It seems clear that the autophosphorylation site of Mec1, which was implicated in turning off a longdelayed G2/M arrest has no effect here, but presumably, a kinase-dead Mec1 (or deletion) does? The idea that a checkpoint is being regulated seems to come more from an assumption than from any direct data; as noted above, the only apparent delay in the cell cycle is the re-entry into S. There clearly is Rad53 and Rad9 phosphorylation so there are the attributes of a checkpoint.  If PI3KK phosphorylation is important, can this be accomplished by Tel1 as well as Mec1? 

      A mec1 helicase dead or null would not activate the checkpoint at the first place, therefore will not be useful to address whether Mec1 autophosphorylation is implicated in turning off checkpoint. A recent study from the Haber lab provided evidence that Mec1 autophosphorylation at S1964 helps to turn off the checkpoint in a DSB situation (Memisoglu et al, 2019). The role of Tel1 in checkpoint dampening will be interesting to examine in the future.  

      Figure 6. Two Rfa1 phospho-sites don't appear to be important, but do the known multiple phosphorylations of Rfa2 play a role?  

      Figure 6D examined three Rfa2 phosphorylation sites and found no genetic interaction with srs2∆.   

      Summary:  There are a lot of interesting data here, but they don't strongly support the author's model in the absence of a more direct way to monitor RPA binding and removal. This could be done using some sitespecific damage, but hard to do with CPT or MMS (which themselves don't appear to have the same effect).  The abstract suggests Srs2 is "temporally and spatially regulated to both allow timely checkpoint termination and to prevent superfluous RPA removal." But where is the checkpoint termination if there's no evident checkpoint? And "superfluous" is probably not the right word (= unnecessary); probably the authors intend "excessive"? As noted above, it also isn't clear if the displacement is of RPA or of Rad51, which normally replaces RPA and which is well-known to be itself displaced by Srs2. Again, if CPT is causing enough damage to kill orders of magnitudes of cells (are the plate and liquid concentrations comparable, we suddenly wonder) then why isn't there some stronger evidence for a cell cycle response to the DDC? 

      As described in the Public Review section, we have previously shown that a lack of Srs2-mediated checkpoint downregulation leads to a 4-6 fold increase of RPA on chromatin, which was rescued by rfa1-zm2 (Dhingra et al., 2021). On its own, rfa1-zm2 did not cause defective chromatin association in our assays, despite modestly reducing ssDNA binding in vitro (Dhingra et al., 2021). This discrepancy could be due to a lack of sensitivity of chromatin fractionation assay in revealing moderate changes of RPA residence on DNA. Considering this, we decided to employ functional assays (Figure 2-3) that are more effective in identifying the specific Srs2 features pertaining to RPA regulation. 

      We respectfully disagree with the reviewer’s point that there is “no evident checkpoint” in CPT.  Previous studies have shown that CPT induces the DNA damage checkpoint as evidenced by Mec1 activation and phosphorylation of Rad53 and Rad9, and delaying exit from G2/M (Dhingra et al., 2021; Menin et al., 2018; Redon et al., 2003). Our data are fully consistent with these reports. It is important to note that DNA damage checkpoint can manifest at a range of strengths depending on the genotoxic conditions and treatment, but the fundamental principles are the same. For example, we found that the Srs2-RPA antagonism not only affects the checkpoint downregulation in CPT, but also does so in MMS treatment and in a DSB system. We focused on CPT condition in this work, since CPT only induces the DNA damage checkpoint but not DNA replication checkpoint while MMS induces both. Further investigating the Srs2-RPA antagonism in a DSB system can be interesting to pursue in the future.  

      We believe that “superfluous removal” is appropriately used when discussing RPA regulation at genomic sites wherein it supports ssDNA protection and DNA repair, rather than DDC. Examples of these sites include R-loops and negatively supercoiled regions. These sites lack 3’ and 5’ DNA ends at the ss-dsDNA junctions for loading PCNA and the 9-1-1 checkpoint factors, and thus are not designated for checkpoint regulation.

      We addressed the reviewer’s point regarding Rad51 in the Public Review section. We disagree with reviewer’s view that “Rad51 normally replaces RPA”. RPA is involved in many more processes than Rad51 wherein it is not replaced by Rad51.  

      Regarding toxicity of CPT, our view is that it stems from a combination of checkpoint regulation and other processes that also involve the Srs2-RPA antagonism. While this work focused on the checkpoint aspect of this antagonism, future studies will be conducted to address the latter.

      One reference is entered as Lee Zhou and Stephen J. Elledge as opposed to "Zhou and Elledge."

      Corrected.  

      Reviewer #3 (Recommendations For The Authors): 

      (1) It would be nice to see the additional point mutants (srs2-Y775A, srs2-F891A) be tested, as they showed little to no phenotypes in the previously reported analyses, which did not specifically test the function surveyed here. 

      This point is addressed in the Public Reviews section.

      (2) Maybe the caveat of using deletion versus point mutations could be discussed. 

      This point is addressed in the Public Reviews section.

      (3) Please plot individual data points of the two independent experiments in Figures 4D and 5A so that the reader can evaluate reproducibility. N=2 does not really allow deriving SD.

      This point is addressed in the Public Reviews section and three individual data points are now included in both panels.

      (4) It will help the reader to have the exact strains used in each experiment listed in each figure legend.  Minor point.

      The strain table is now updated to address this point.

      (5) Page 7 middle paragraph: The reference to Figure 4A in line 11 should probably be Figure S3A. 

      Corrected.

    1. eLife Assessment

      This study provides useful in vitro evidence to support a mechanism whereby dyslipidemia could accelerate renal functional decline through the activation of the AT1R/LOX1 complex by oxLDL and AngII. As such, it improves the knowledge regarding the complex interplay between dyslipidemia and renal disease and provides a solid basis for the discovery of novel therapeutic strategies for patients with lipid disorders. The methods, data, and analyses partly support the presented findings, although the observed variability and need for further in vivo validation require additional research in this key area.

    2. Reviewer #1 (Public review):

      Summary:

      In the present study, Dr. Ihara demonstrated a key role of oxLDL in enhancing Ang II-induced Gq signaling by promoting the AT1/LOX1 receptor complex formation.

      Strengths:

      This study is very exciting and the work is also very detailed, especially regarding the mechanism of LOX1-AT1 receptor interaction and its impact on oxidative stress, fibrosis and inflammation.

      Weaknesses:

      The direct evidence for the interaction between AT1 and LOX1 receptors in cell membrane localization is relatively weak.

    3. Reviewer #2 (Public review):

      While the findings might be valid, there is enough uncertainty that these results should not be considered anything other than preliminary, warranting a more thorough and rigorous investigation.

      Comments on revisions:

      As the author mentioned that due to the receptor internalisation of AT1 and/or LOX1 induced by AngII or Ox-LDL makes it difficult to detect receptor interaction at the membrane by Co-IP. If so, the GPCR internalisation related pathway should be activated, such as GRKs, arrestin2 could be activated and enhanced during this process, whether they could further provide the evidence for these changes in different groups by Western blot or IF images.

      If the authors don't know why the results across experiments can vary so greatly nor control them, how do we know that their interpretation of the very modest intra-experimental variability they observe is correct? They explain away the difference in biosensor activity response to the likely respective insertion sites that were used. While this can be true, and even might be true, it is important to note that the publication they cite shows that the sensors in the third loop and the C-terminus respond very similarly. In fact, the authors concluded: "Our results also suggest that positioning conformational biosensors into ICL3 and the C-tail effectively reports canonical G protein-mediated signaling downstream of the AT1R." Moreover, it is unclear why the less sensitive biosensor (as least as measured by degree of DBRET) is the one that appears to show enhancement. I suppose one could argue that the activity is maximal using the C-tail and one must use a less responsive reporter to detect the effect, but this is a rationalization for an unexplained result rather than a validated mechanistic explanation. If the other results were more compelling, perhaps this would be less of an issue. Finally, they did not explain why a control, non-specific antibody wasn't used for the studies presented in panel 2d. This would have been an easy study to have done in the interim. It also would have been important to test the effect of the LOX1-ab on the effects of AngII treatment alone.

      In their response to the gene expression studies, the authors attribute the lack of a robust response for some genes to the low dose of oxLDL that was used but give no justification for their choice for this low dose. More importantly, they present the data for a number of hand-picked genes rather than a global assessment of response. Their justification---cost constraints---isn't sufficient to justify this incomplete analysis. Their selective rt-PCR results are a pilot study.

      There is no direct evidence in this study that shows that "partial" EMT is occurring in vivo. The rt-PCR studies presented in Fig 8 are not sufficient. Even if one accepts their incomplete analysis of transcriptomic studies using RT-PCR rather than a complete transcriptomic assessment, the study was done on bulk RNA from the entire kidney. The source material includes all cell types, not just epithelial cells, so there is no way to be sure that EMT is occurring. As noted elsewhere, they found no histologic evidence for injury and had no immunostaining results demonstrating "partial EMT" of damaged renal epithelial cells.

      All of the evidence described is indirect, and the responses, while plausible, are generally excuses for lack of truly unequivocally positive results. The authors acknowledge the potential confounders of lower BP response in the Lox1-KO, unexpected weight loss in response to high fat diet, the lack of meaningful histologic evidence of injury, and they also acknowledge the absence of increased Gq signaling in the kidney, which is central to their model, but defend the entire model based on some minor changes in urinary 8-OHdG and albumin levels and a curated set of transcriptional changes. Their data could support their model---loss of Lox1 seems to reduce the levels somewhat, but the data are preliminary.

      There remain serious reservations about the immunostaining results, with explanations and new data not reassuring. The authors report that they are unable to co-stain for Lox1 and AT1R because both were generated in rabbit, but this reviewer didn't ask for co-staining of the two markers. Rather, it was co-staining showing that Lox1 and ATR1 in fact stain in a specific manner to the same nephron segments. The authors have added a supplementary figure showing co-staining for LOX1/AT1R with megalin, a marker for proximal tubules. However, several aspects of this are problematic:

      i. The pattern in the new Supp Fig 10 does not look like that in Fig 9. In the latter, staining is virtually everywhere, all nephron segments, and predominantly basolateral. In Supp Fig 10, they note that the pattern is primarily in the microvilli of the proximal tubule, where megalin is present. The new studies also seem to be a bit more specific, ie there are some tubules that appear to not stain with the markers.

      ii. It is difficult to be certain that the megalin staining isn't simply "bleed-through" of the signal from the other antibody. The paper doesn't describe the secondary antibody used for megalin to be sure that the emission spectra completely non-overlapping and it isn't clear that the microscope that was used offers necessary precision.

      iii. Their explanation for the pattern of AT1R staining is unconvincing. AT1R immunolocalization is known to be challenging, prompting Schrankl et al to do a definitive study using RNAscope to localize its expression in mice, rats and humans (Am J Physiol Renal Physiol 320: F644-F653, 2021). It argues against the pattern seen in Figure 9 (diffuse tubular expression), though it does suggest it is present in proximal tubules in mice. But perhaps more problematic for their model is that AT1R is not expressed in human tubules (or at least the RNA is undetectable).

      Why isn't there more colocalization apparent for the AT1R and LOX1 if they form a co-receptor complex? They say that the complexes may be very dynamic, yet their movie in Suppl Fig 1 does not really support that. Not only are there few overlapping puncta in the static image, there is very little change over the duration of the movie. We don't see complexes form and then disappear and we see few new complexes form.

      The explanation for why the number of replicates is variable is not reassuring. The authors note that it was because of the higher variability of the results, necessitating a higher "N" to achieve significance, but this has the appearance of P-chasing.

    4. Author response:

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

      Reviewer #1 (Public Review):

      Summary:

      This study demonstrates a key role of oxLDL in enhancing Ang II-induced Gq signaling by promoting the AT1/LOX1 receptor complex formation. Importantly, Gq-mediated calcium influx was only observed in LOX1 and AT1 both expressing cells, and AT1-LOX1 interaction aggravated renal damage and dysfunction under the condition of a high-fat diet with Ang II infusion, so this study indicated a new therapeutic potential of AT1-LOX1 receptor complex in CKD patients with dyslipidemia and hypertension.

      Strengths:

      This study is very exciting and the work is also very detailed, especially regarding the mechanism of LOX1-AT1 receptor interaction and its impact on oxidative stress, fibrosis, and inflammation.

      Weaknesses:

      The direct evidence for the interaction between AT1 and LOX1 receptors in cell membrane localization is relatively weak. Here I raise some questions that may further improve the study.

      Major points:

      (1) The authors hypothesized that in the interaction of AT1/LOX1 receptor complex in response to ox-LDL and AngII, there should be strong evidence of fluorescence detection of colocalization for these two membrane receptors, both in vivo and in vitro. Although the video evidence for AT1 internalization upon complex activation is shown in Figure S1, the more important evidence should be membrane interaction and enhanced signal of intracellular calcium influx.

      Thank you for your valuable feedback. We agree that demonstrating the colocalization and interaction of AT1 and LOX-1 receptors at the membrane is critical to supporting our hypothesis.

      In response, we have previously provided visual evidence of membrane co-localization of the AT1/LOX-1 receptor complex using an in situ PLA assay with anti-FLAG and antiV5 antibodies in CHO cells expressing FLAG-tagged AT1 and V5-tagged LOX-1 (Yamamoto et al., FASEB J 2015). This was further supported by immunoprecipitation of membrane proteins in CHO cells co-expressing LOX-1 and AT1, which confirmed the presence of the receptor complex. In the current study, we offer additional evidence of enhanced intracellular calcium influx following simultaneous stimulation with oxLDL and Ang II, confirming the functional activation of the AT1/LOX-1 receptor complex (Fig. 1g-j and Fig. 3e-h). Together, these findings provide substantial support for the colocalization of AT1 and LOX-1 and their influence on downstream signaling in our in vitro experiments.

      However, we acknowledge the limitation of direct evidence for membrane co-localization of LOX-1 and AT1 in vivo. This constraint is attributed to the fact that both available anti-AT1 and anti-LOX-1 antibodies are derived from rabbits, making coimmunofluorescence or PLA challenging in our study. To address this, we employed coimmunofluorescent staining with megalin, a well-established marker for proximal renal tubules, as shown in Fig. S10. We found that both AT1 and LOX-1 co-localized with megalin, particularly at the brush borders, indicating their presence in the same renal compartments relevant to AT1/LOX-1 signaling.

      We have revised the manuscript to highlight the functional evidence from calcium influx assays, supported by prior PLA results, demonstrating the interaction between LOX-1 and AT1. Additionally, we included a figure showing the co-localization of AT1 and LOX-1 with megalin in proximal renal tubules to reinforce these findings. Lastly, we have emphasized in the discussion the limitation regarding the lack of direct in vivo evidence for membrane co-localization of LOX-1 and AT1.

      (2) Co-IP experiment should be provided to prove the AT1/LOX1 receptor interaction in response to ox-LDL and AngII in AT1 and LOX1 both expressing cells but not in AT1 only expressing cells.

      We thank the reviewer for the insightful suggestion to validate the AT1/LOX1 receptor interaction under various stimulation conditions. In our previous study (Yamamoto et al., FASEB J 2015), we demonstrated the interaction between AT1 and LOX1 receptors through Co-IP and in situ PLA assays in cells overexpressing both receptors, without stimulation. These experiments provided solid evidence of the receptor interaction under static conditions at the cell membrane.

      However, as noted in the previous work, we did not perform Co-IP experiments under AngII or oxLDL stimulation. The primary reason for this is that both AngII and oxLDL trigger internalization of the AT1 and/or LOX1 receptors, which may complicate the detection of receptor interaction at the membrane via Co-IP. This is supported by our realtime imaging, which showed a reduction in AT1 and/or LOX1 puncta following stimulation, indicating internalization of the receptors (Fig. 2a).

      While we acknowledge the reviewer’s interest in investigating the interaction under AngII stimulation, we believe that the current data—especially from the PLA and Co-IP assays under static conditions—strongly support the interaction of AT1 and LOX1 receptors at the membrane.

      (3) The authors mentioned that the Gq signaling-mediated calcium influx may change gene expression and cellular characteristics, including EMT and cell proliferation. They also provided evidence that oxidative stress, fibrosis, and inflammation were all enhanced after activating both receptors and inhibiting Gq was effective in reversing these changes. However, single stimulation with ox-LDL or AngII also has strong effects on ROS production, inflammation, and cell EMT, which has been extensively proved by previous studies. So, how to distinguish the biased effect of LOX1 or AT1r alone or the enhanced effect of receptor conformational changes mediated by their receptor interaction? Is there any better evidence to elucidate this point?

      Thank you for raising this important point regarding the distinction between the individual effects of LOX-1 or AT1R activation and the enhanced effects mediated by their interaction. In our study, the concentration of oxLDL used (2–10 μg/ml) was significantly lower than concentrations typically employed in other studies (which often exceed 20 μg/ml). As a result, oxLDL alone produced minimal effects, aside from a reduction in cell proliferation observed in the BrdU assay. This suggests that oxLDL, at the concentrations used in our experiments, does not elicit a strong cellular response on its own.

      The key to distinguishing the effect of the LOX-1/AT1 interaction lies in the amplification of Gq signaling, a pathway specifically activated by AngII. The distinction between the individual effects of LOX-1 or AT1R and the enhanced effects due to their interaction is centered on the increased activation of Gq signaling. In our experiments, co-treatment with oxLDL and AngII led to a significant increase in IP1 levels and calcium influx— both critical indicators of Gq signaling activation. While AngII alone also raised IP1 levels, the combined treatment with oxLDL further amplified the Gq signaling response, as reflected in the enhanced calcium influx. Importantly, oxLDL alone did not alter IP1 levels, even at high concentrations (100 μg/ml) (Takahashi et al., iScience 2021).

      This enhancement of Gq signaling provides strong evidence of the synergistic interaction between LOX-1 and AT1, which surpasses the individual effects of either receptor alone. The LOX-1/AT1 interaction is thus crucial for the observed amplification of AngIIspecific signaling pathways. The combination of increased IP1 levels and calcium influx serves as compelling evidence of this interaction, clearly differentiating the effects of individual receptor activation from the enhanced response driven by receptor conformational changes and interaction.

      Thank you again for your insightful comment, which has helped us to better articulate the significance of receptor interaction in this study.

      (4) How does the interaction between AT1 and LOX1 affect the RAS system and blood pressure? What about the serum levels of rennin, angiotensin, and aldosterone in ND-fed or HFD-fed mice?

      Thank you for your insightful question regarding the effects of AT1 and LOX-1 interaction on the renin-angiotensin system (RAS) and blood pressure, as well as the plasma levels of renin, angiotensin, and aldosterone in normal diet (ND)-fed and high-fat diet (HFD)-fed mice.

      OxLDL binds to LOX-1, amplifying AT1 receptor activation and Gq signaling, which enhances the effects of Ang II. This interaction between AT1 and LOX-1 can lead to increased vasoconstriction, oxidative stress, and inflammation, which contribute to elevated blood pressure. This pathway may play a crucial role in modulating the RAS, particularly under conditions of elevated oxLDL, such as those induced by a HFD. Regarding the components of the RAS, we focused on plasma aldosterone levels, as this is a direct consequence of Ang II signaling. As shown in Fig. S7, when mice were treated with a pressor dose of Ang II infusion and subjected to a HFD to elevate oxLDL levels, we did not observe a significant increase in plasma aldosterone levels (102.8 ± 11.6pg/mL vs. 141.8 ± 15.0 pg/mL, P = 0.081).

      In terms of blood pressure, Fig. 7b shows that no significant changes were observed under these treatment conditions, despite the AT1/LOX-1 interaction. These findings suggest that while oxLDL, via the AT1/LOX-1 interaction, can enhance Ang II signaling, its effect on blood pressure was not apparent in our study. This may be due to several factors, including heterogeneous cellular responses to the combined treatment across different cell types, as shown by the lack of reaction in vascular endothelial cells, vascular smooth muscle cells, and macrophages (Fig. S2). This may also be attributed to the high concentration of angiotensin II used in this study, which could have saturated aldosterone production under our experimental conditions. We have revised the manuscript to reflect these points. 

      Thank you again for your thoughtful comment, which has allowed us to expand and refine the discussion on this important aspect of our study.

      Reviewer #2 (Public Review):

      (1)  Individuals with chronic kidney disease often have dyslipidemia, with the latter both a risk factor for atherosclerotic heart disease and a contributor to progressive kidney disease. Prior studies suggest that oxidized LDL (oxLDL) may cause renal injury through the activation of the LOX1 receptor. The authors had previously reported that LOX1 and AT1 interact to form a complex at the cell surface. In this study, the authors hypothesize that oxLDL, in the setting of angiotensin II, is responsible for driving renal injury by inducing a more pronounced conformational change of the AT1 receptor which results in enhanced Gq signaling.

      They go about testing the hypothesis in a set of three studies. In the first set, they engineered CHO cell lines to express AT1R alone, LOX1 in combination with AT1R, or LOX1 with an inactive form of AT1R and indirectly evaluated Gq activity using IP1 and calcium activity as read-outs. They assessed activity after treatment with AngII, oxLDL, or both in combination and found that treatment with both agents resulted in the greatest level of activity, which could be effectively blocked by a Gq inhibitor but not a Gi inhibitor nor a downstream Rho kinase inhibitor targeting G12/13 signaling. These results support their hypothesis, though variability in the level of activation was dramatically inconsistent from experiment to experiment, differing by as much as 20-fold. In contrast, within the experiment, differences between the AngII and AngII/oxLDL treatments, while nominally significant and consistent with their hypothesis, generally were only 10-20%. Another example of unexplained variability can be found in Figures 1g-1j. AngII, at a concentration of 10-12, has no effect on calcium flux in one set of studies (Figure 1g, h) yet has induced calcium activity to a level as great as AngII + oxLDL in another (Figure 1i). The inconsistency of results lessens confidence in the significance of these findings. In other studies with the LOX1-CHO line, they tested for conformational change by transducing AT1 biosensors previously shown to respond to AngII and found that one of them in fact showed enhanced BRET in the setting of oxLDL and AngII compared to AngII alone, which was blocked by an antibody to AT1R. The result is supportive of their conclusions. Limiting enthusiasm for these results is the fact that there isn't a good explanation as to why only 1 sensor showed a difference, and the study should have included a non-specific antibody to control for non-specific effects.

      We sincerely appreciate the reviewer’s thorough and insightful feedback, especially regarding the variability observed in our experimental results. As the reviewer pointed out, the differences in activation levels between the calcium influx assay and the IP1 assay, particularly between AngII and AngII/oxLDL co-treatment, were indeed significant. These differences can be attributed to the inherent sensitivity of these assays, which are used to indirectly evaluate Gq activity. Despite the variability, we believe that the reliability of our results is supported by the consistent directional trends across both assays, which align with our hypothesis.

      Regarding the inconsistencies in intracellular calcium dynamics observed in Fig. 1i, we have performed additional analysis of calcium kinetics during ligand stimulation, similar to the analysis in Fig. 1g. As shown in Author response image 1, the background signal in the experiment related to Fig. 1i was relatively higher than in Fig. 1g and 1h. This elevated background, which may have been influenced by variations between cells and experimental days, resulted in a higher percent change from baseline in samples treated with AngII alone. However, the combined effect of AngII with oxLDL was still apparent. This clarification further supports the consistency of our findings.

      Author response image 1.

      In reference to the BRET sensor experiments, we acknowledge the reviewer’s concern regarding the variability in sensor responses. As outlined in Devost et al. (J Biol Chem. 2017), the sensitivity of AT1 intramolecular FlAsH-BRET biosensors in detecting conformational changes induced by AngII is highly dependent on the insertion site of the FlAsH sequence. In our experiments, co-treatment with oxLDL and AngII enhanced AT1 conformational changes, but this effect was only detectable with the CHO-LOX-1-AT1-3p3 sensor (with FlAsH inserted in the third intracellular loop), and not with the CHO-LOX-1-AT1-C-tail P1 sensor (with FlAsH inserted at the C-terminal tail). This differential sensitivity likely explains why only one sensor showed a significant response, highlighting the critical role of FlAsH insertion site selection in these assays. We hope these clarifications address the reviewer’s concerns and improve confidence in the significance of our findings.

      (2) The authors then repeated similar studies using publicly available rat kidney epithelial and fibroblast cell lines that have an endogenous expression of AT1R and LOX1. In these studies, oxLDL in combination with AngiI also enhanced Gq signaling, while knocking down either AT1R or LOX1, and treatment with inhibitors of Gq and AT1R blocked the effects. Like the prior set of studies, however, the effects are very modest and there was significant inter-experimental variability, reducing confidence in the significance of the findings. The authors then tested for evidence that the enhanced Gq signaling could result in renal injury by comparing qPCR results for target genes. While the results show some changes, their significance is difficult to assess. A more global assessment of gene expression patterns would have been more appropriate. In parallel with the transcriptional studies, they tested for evidence of epithelial-mesenchymal transition (EMT) using a single protein marker (alpha-smooth muscle actin) and found that its expression increased significantly in cells treated with oxLDL and AngII, which was blocked by inhibition of Gq inhibition and AT1R. While the data are sound, their significance is also unclear since EMT is a highly controversial cell culture phenomenon. Compelling in vivo studies have shown that most if not all fibroblasts in the kidney are derived from interstitial cells and not a product of EMT. In the last set of studies using these cell lines, the authors examined the effects of AngII and oxLDL on cell proliferation as assayed using BrdU. These results are puzzling---while the two agents together enhanced proliferation which was effectively blocked by an inhibitor to either AT1R or Gq, silencing of LOX1 had no effect.

      Thank you for your thorough review and comments. We acknowledge your concerns regarding the modest effects observed and the variability in experimental outcomes. We would like to address your points systematically.

      (1) Gq signaling and experimental variability:

      Regarding the question of Gq signaling in Fig. 3, as previously mentioned, the observed differences in the IP1 assay are likely due to the sensitivity of the assay and the technical issues associated with detecting calcium influx and IP1 levels. While the overall differences between treatments may appear modest, the most critical comparison— between AngII alone and AngII combined with oxLDL—consistently showed significant differences, which aligns with the calcium influx results shown in Fig. 1. Notably, we found that the EC50 for IP1 production decreased by 80% in response to co-treatment with oxLDL and AngII, compared to AngII treatment alone. These findings demonstrate the robustness of Gq signaling enhancement with co-treatment, even if the absolute differences in the IP1 assay appear small.

      (2) Gene expression in Fig. 4:

      Regarding the gene expression analysis in Fig. 4, we used relatively low concentrations of oxLDL (5 μg/ml) compared to the higher concentrations typically employed in other studies (mostly exceeding 20 μg/ml). This may explain the lack of robust responses in some conditions. However, in combination with AngII, the co-treatment significantly upregulated several genes, particularly pro-inflammatory markers such as IL-6, TNFα, IL1β, and MCP-1 in NRK49F cells. These results suggest that the co-treatment induces a complex response, potentially activating multiple downstream signaling pathways beyond just Gq signaling, which may obscure more straightforward effects.

      While we agree that a more global assessment of gene expression would provide further insights, due to cost constraints, we focused on key representative genes that are highly relevant to inflammation and fibrosis in this study.

      (3) EMT in renal fibrosis:

      We appreciate the reviewer’s insightful comments regarding the role of EMT in renal fibrosis. Regarding full EMT, in which epithelial cells completely transition into mesenchymal cells, previous studies using the unilateral ureteral obstruction (UUO) model suggest that full EMT may not play a significant role (J Clin Invest. 2011 Feb;121(2):468-74). The role of full EMT remains controversial in the context of renal fibrosis, with most kidney fibroblasts thought to originate from interstitial cells rather than through full EMT.

      Recent studies, however, suggest that partial epithelial-mesenchymal transition (pEMT) could be involved in CKD, especially in association with inflammation, oxidative stress, and elevated TGF-β levels—conditions also present in our model involving Ang II infusion combined with an HFD. pEMT refers to a state in which epithelial cells acquire mesenchymal traits, such as increased α-SMA expression and secretion of pro-fibrotic cytokines, while remaining attached to the basement membrane without fully transitioning into fibroblasts (Front Physiol. 2020 Sep 15;11:569322). This phenomenon has been observed in kidney fibrosis models, including UUO, which shares inflammatory and oxidative stress conditions with our Ang II and HFD treatment model. The observed increase in α-SMA in our model may thus indicate a pEMT-like state, indirectly contributing to fibrosis through the secretion of growth factors and cytokines.

      We are mindful of the importance of not overstating EMT's role. Accordingly, we interpret increased α-SMA expression as a potential marker of the pEMT process rather than definitive evidence of its presence or direct role in fibroblast formation. Furthermore, we acknowledge limitations in providing direct in vivo evidence for pEMT and recognize that further mechanistic studies are needed to elucidate its specific role in renal fibrosis, despite inherent challenges.

      In response to the reviewer’s concern, we have revised the manuscript to clarify that our data support the possibility of pEMT contributing to fibrosis in this model, without overstating its impact. We also acknowledge the challenges in translating in vitro pEMT findings to in vivo models, where detecting the subtle effects of pEMT is inherently challenging.

      (4) BrdU assay and fibroblast proliferation (Fig. 6b):

      In Fig. 6b, the BrdU assay shows that fibroblast proliferation was significantly enhanced by the co-treatment with AngII and oxLDL, and this effect was abolished by LOX-1 knockdown, similar to the results observed with AT1 knockdown. These findings strongly suggest a combinatorial effect of AT1/LOX-1 interaction in promoting fibroblast proliferation, supporting the idea that the co-treatment operates through a coordinated mechanism involving both receptors. Notably, LOX-1 silencing did not affect the proliferation induced by AngII alone, as this response is independent of LOX-1.

      We will incorporate these points into the Discussion section of the manuscript, specifically regarding the differences in sensitivity between the Ca influx and IP1 assays, as well as the emerging role of partial EMT in renal fibrosis. This will provide a clearer context for the interpretation of our findings and further strengthen the discussion on the significance of these phenomena.

      Thank you again for your valuable feedback, which has helped us improve the clarity and depth of our manuscript.

      (3) The final set of studies looked to test the hypothesis in mice by treating WT and Lox1KO mice with different doses of AngII and either a normal or high-fat diet (to induce oxLDL formation). The authors found that the combination of high dose AngII and a highfat diet (HFD) increased markers of renal injury (urinary 8-ohdg and urine albumin) in normal mice compared to mice treated with just AngII or HFD alone, which was blunted in Lox1-KO mice). These results are consistent with their hypothesis. However, there are other aspects of these studies that are either inconsistent or complicating factors that limit the strength of the conclusions. For example, Lox1- KO had no effect on renal injury marker expression in mice treated with low-dose AngII and HFD. It also should be noted that Lox1-KO mice had a lower BP response to AngII, which could have reduced renal injury independent of any effects mediated by the AT1R/LOX1 interaction. Another confounding factor was the significant effect the HFD diet had on body weight. While the groups did not differ based on AngII treatment status, the HFD consistently was associated with lower total body weight, which is unexplained. Next, the authors sought to find more direct evidence of renal injury using qPCR of candidate genes and renal histology. The transcriptional results are difficult to interpret; moreover, there were no significant histologic differences between groups. They conclude the study by showing the pattern of expression of LOX1 and AT1R in the kidney by immunofluorescence and conclude that the proteins overlap in renal tubules and are absent from the glomerulus. Unfortunately, they did not co-stain with any other markers to identify the specific cell types. However, these results are inconsistent with other studies that show AT1R is highly expressed in mesangial cells, renal interstitial cells, near the vascular pole, JG cells, and proximal tubules but generally absent from most other renal tubule segments.

      Thank you for your valuable comments and for raising these important points. We appreciate the opportunity to clarify several aspects of our study and address the limitations and inconsistencies you have pointed out.

      (1) Renal injury markers (urinary albumin and 8-OHdG) and the effect of LOX-1 loss of- function:

      Our results showed that the combination of high-dose AngII and HFD led to a significant increase in renal injury markers, such as urinary albumin and 8-OHdG, in WT mice. In LOX-1 KO mice, this increase was significantly blunted, supporting a protective role of LOX-1 loss-of-function. However, as you noted, at low-dose AngII, there was no significant difference in urinary 8-OHdG between ND-fed and HFD-fed mice. Despite this, we observed a significant increase in urinary albumin in HFD-fed WT mice compared to ND-fed mice under low-dose AngII, and this difference was abolished in LOX-1 KO mice. Moreover, gene expression analysis showed that oxidative stress markers such as p67phox and p91phox (Fig. 8b), as well as p40phox, p47phox (Fig. S8), and inflammatory markers like IL1β (Fig. 8b), were significantly elevated in HFD-fed WT mice even with low-dose AngII, while these increases were absent in LOX-1 KO mice. These results suggest that the LOX-1/AT1 interaction contributes to renal injury under both low- and high-dose AngII conditions.

      We acknowledge that the treatment duration may have influenced our results, as urine and renal tissue samples were only examined at a single time point (1.5 months after treatment initiation). The impact of AT1/LOX-1 interaction may evolve over time, and different treatment durations might yield varying outcomes. This is a limitation of our study, which we have addressed in the revised manuscript.

      (2) Blood pressure and its effect on renal injury:

      As shown in Fig. 7b and Fig S6f, LOX-1 KO mice exhibited a lower blood pressure response to high-dose AngII compared to WT mice, which could indeed have contributed to the reduced renal injury in the LOX-1 KO group, independent of the AT1/LOX-1 interaction. However, it is important to note that the differences in renal injury markers between AngII alone and AngII + HFD were largely abolished in LOX-1 KO mice, suggesting the in vivo relevance of the LOX-1/AT1 interaction observed in vitro. Additionally, as shown in Fig. 7d (urinary albumin), Fig. 8b (p67phox, p91phox), and Fig. S8b (p40phox, p47phox), even under subpressor doses of AngII, where no significant blood pressure differences were observed, HFD-fed WT mice exhibited exacerbated renal injury compared to ND-fed mice. These effects were ameliorated in LOX-1 KO mice, indicating that the protective effects in LOX-1 KO mice are at least partly independent of blood pressure changes and that the AT1/LOX-1 interaction plays a significant role in modulating renal injury under co-treatment with AngII and HFD.

      (3) HFD and body weight changes:

      We agree with your observation regarding the effect of HFD on body weight, which was consistently lower in HFD-fed groups, despite no differences in AngII treatment status. This is an atypical presentation compared to previous studies mostly showing increased body weight by feeding of HFD. The HFD used in this study was intended to elevate oxLDL levels, as previously reported (Atherosclerosis 200:303–309 (2008)). As shown in Fig. S6d and S6e, this can be attributed to reduced food intake in HFD-fed mice. Although modest, this weight reduction may influence renal function. This point is added in the limitation.

      (4) Histological findings and qPCR results:

      As discussed in the manuscript, despite significant changes in urinary markers and gene expression, we did not observe histological evidence of fibrosis or mesangial expansion, even under co-treatment with AngII and HFD. This may be due to the relatively short treatment period of 4 weeks, and a longer duration might be necessary to detect such changes. Additionally, we acknowledge that we did not detect increased Gq signaling in kidney tissue, which is another limitation of the study. Nevertheless, the gene expression data on oxidative stress, fibrosis, inflammation, and renal injury markers (e.g., p67phox, IL1β) are consistent with our hypothesis that the AT1/LOX-1 interaction exacerbates renal injury under AngII and HFD conditions.

      (5) Immunostaining for AT1 and LOX-1:

      Due to the use of rabbit-derived antibodies for both AT1 and LOX-1, it was technically not feasible to perform co-immunostaining for both receptors simultaneously. Instead, we performed co-immunofluorescent staining using megalin, a well-established marker of proximal renal tubules, to help localize these receptors. As shown in Fig. S10, both AT1 and LOX-1 were co-localized with megalin, particularly at the brush borders of proximal tubules. This pattern suggests the presence of these receptors in renal compartments relevant to AT1/LOX-1 signaling. While we did not perform additional co-staining with other markers to identify specific cell types, the strong localization with megalin provides robust evidence of their expression in proximal renal tubules, which is consistent with the literature on AT1R in this nephron segment. We acknowledge that previous studies have identified AT1R expression in mesangial cells, renal interstitial cells, the vascular pole, juxtaglomerular (JG) cells, and proximal tubules. In our immunofluorescence experiments, we did not detect significant AT1R expression in the glomerulus or mesangium. This finding aligns with other reports showing strong expression of AT1R in proximal tubules (Am J Physiol Renal Physiol. 2021 Apr 1;320(4)), although it does not exclude the possibility of AT1 expression in other compartments, given the sensitivity limitations of the immunofluorescence. Our focus on proximal tubules allowed us to observe clear AT1/LOX-1 co-localization in this region, particularly in the context of oxLDL and AngII signaling. Given that the AT1/LOX-1 interaction is crucial in kidney disease pathogenesis, this co-localization in proximal tubules highlights a key site of action for these receptors in the renal system.

      In summary, while our study focused on the co-localization of AT1 and LOX-1 in proximal tubules, we agree that further exploration of AT1R expression in other renal cell types would provide a more comprehensive understanding of its role across different kidney compartments. We have addressed this in the revised discussion.

      Reviewer #1 (Recommendations For The Authors):

      Minor points:

      (1) In this study, AT1/LOX1 receptor complex was mainly observed in some renal cells, how about other types of cells that also highly express LOX1 and AT1r? Such as cardiomyocytes? Vascular endothelial cells?

      Thank you for your insightful comment. In our study, we demonstrated that enhanced Gq signaling through co-treatment with AngII and oxLDL was not observed in other cell types, including vascular endothelial cells, smooth muscle cells, and macrophages, as indicated by the lack of an IP1 increase in response to the co-treatment (Fig. S2). The factors contributing to this heterogeneous response remain unclear, and further investigation is needed to explore this observation more thoroughly.

      (2) Has the author detected such an effect on the AT2 receptor?

      We greatly appreciate the reviewer’s insightful inquiry regarding the potential interaction between the AT2 receptor and LOX-1. In our previous work (Yamamoto et al., FASEB J 2015), we conducted an immunoprecipitation (IP) assay to investigate the interaction between LOX-1 and AT2 on cell membranes. The results of this assay demonstrated that, unlike AT1, LOX-1 exhibits minimal binding to the AT2 receptor under the experimental conditions tested. Specifically, our IP studies showed that while LOX-1 readily coimmunoprecipitated with AT1, indicating a strong interaction, this was not the case with AT2, where the binding was negligible. These findings suggest that the interaction between LOX-1 and AT1 is receptor-specific and that LOX-1 does not significantly associate with AT2 to influence signaling pathways.

      (3) Which kind of ARBs are more effective for the inhibition of this AT1/LOX1 receptor conformational change?

      Thank you for your insightful question regarding the effectiveness of ARBs in inhibiting the AT1/LOX-1 receptor conformational change. Based on our current understanding, any ARB should similarly block the downstream signaling resulting from the interaction between AT1 and LOX-1. This is because all ARBs function by inhibiting the binding of Ang II to AT1, thereby preventing receptor activation and the conformational changes that facilitate its interaction with LOX-1. Additionally, our previous study (FASEB J. 2015) demonstrated that even in the absence of Ang II, the activation of AT1 via the binding of oxLDL to LOX-1 was similarly blocked by ARBs, including olmesartan, telmisartan, valsartan, and losartan.

      When oxLDL and Ang II are co-treated, the Gq signaling pathway is significantly amplified due to the interaction between LOX-1 and AT1. In this setting, all ARBs act by competitively inhibiting Ang II binding to AT1, effectively reducing Gq signaling. 

      However, a subtle but important difference arises when considering the inverse agonist activity of certain ARBs. Olmesartan, telmisartan, and valsartan are thought to act not only as competitive inhibitors of Ang II but also as inverse agonists, meaning they reduce the baseline activity of the AT1 receptor by preventing the conformational changes in the absence of Ang II. This inverse agonist property is particularly relevant in pathological conditions where AT1 receptor activation can occur independently of Ang II binding, such as in the presence of oxLDL. In these cases, ARBs with inverse agonist activity may offer an additional therapeutic advantage by reducing receptor activation beyond what is achieved by simple antagonism.

      Thus, while the general efficacy of ARBs in blocking the AT1/LOX-1 interaction could be under similar conditions of oxLDL and Ang II co-treatment, ARBs with inverse agonist properties may provide additional benefit by further reducing AT1 activity. 

      We have revised the manuscript to clarify these points and to highlight the role of inverse agonist activity in ARB efficacy under these conditions.

      Thank you again for your valuable comment, which has allowed us to refine our discussion on the relative efficacy of ARBs in inhibiting AT1/LOX-1 receptor interaction.

      Reviewer #2 (Recommendations For The Authors):

      My comments were pretty thorough in the public review. The only other comments I would add are the following:

      (1) Why are there so few overlapping LOX1 and ATR puncta in Supplementary Figure 1 if the receptors co-localize? The figure would suggest a very small proportion of the receptors actually are co-localized.

      Thank you for your insightful comment regarding the apparent scarcity of overlapping LOX-1 and AT1R puncta in Fig. S1. We agree that at first glance, the low number of colocalized puncta may raise questions about the extent of interaction between these receptors. However, based on our previous findings reported in FASEB J 2015, we believe this phenomenon can be explained by the dynamic nature of the LOX-1 and AT1 interaction.

      As we reported in FASEB J 2015, the interaction between LOX-1 and AT1 is sensitive to buffer conditions. Specifically, in non-reducing conditions, LOX-1 and AT1 form complexes, whereas in reducing buffer, this interaction is not observed. This suggests that the interaction between these receptors is not stabilized by strong covalent (disulfide) bonds but is instead transient, likely involving non-covalent interactions. Thus, LOX-1 and AT1 may form and dissociate repeatedly, contributing to a dynamic receptor complex rather than a permanent colocalization. This transient interaction could explain the relatively low number of overlapping puncta observed at a given time point in the liveimaging analysis.

      Moreover, as you pointed out, it is likely that only a small fraction of LOX-1 and AT1 are physically co-localized at any one moment. However, when these receptors do interact, co-treatment with oxLDL and Ang II has been shown to significantly enhance Gq signaling. This suggests that the functional consequence of the LOX-1/AT1 interaction, particularly in response to stimuli such as oxLDL and Ang II, is more critical than the frequency of receptor colocalization at any one time.

      We have revised the manuscript to include this explanation and to clarify the dynamic nature of the LOX-1/AT1 interaction. This revision also highlights the importance of considering not just the number of colocalized receptors but also the functional outcomes of their interaction, such as enhanced Gq signaling in response to co-treatment.

      Thank you again for your careful observation, which has allowed us to better communicate the complexity of the receptor dynamics in our study.

      (2) Tubulin is misspelled in Figure 5 ("tublin").

      Thank you for pointing out the typographical error in Fig. 5. We have corrected the spelling of "tubulin" in the revised figure. We appreciate your attention to detail, and we apologize for the oversight.

      (3) Why does the number of replicates differ for some experimental sets (i.e. Figure 1h vs other panels in Figure 1, Figure 2d vs other panels in Figure 2, Figure 7: Lox-1KO treated with High dose AngII and HFD? There aren't obvious reasons why the number of replicates should differ so much within a set of studies.

      We are grateful to the reviewer for highlighting the discrepancies in the number of replicates across different figures in our manuscript. We would like to provide detailed explanations for each case.

      (1) Fig. 1h vs Other Panels in Fig. 1:

      The calcium influx assay (Fig. 1h) required a higher number of replicates due to the inherent biological variability associated with calcium signaling. To achieve statistical significance and account for variability in these measurements, we conducted additional replicates. Other panels, such as those measuring IP1 accumulation (Fig. 1a–f), displayed more consistent and reproducible results, allowing us to use fewer replicates while still maintaining statistical power.

      (2) Fig. 2d vs Fig. 2b and 2c: 

      The difference in the number of replicates between Fig. 2d (N=8) and Fig. 2b and 2c (N=4) is due to the distinct nature of the measurements and the variability expected in each assay. In Fig. 2d, which measures the effects of a LOX-1 neutralizing antibody on BRET, additional replicates were needed to ensure the robustness of the statistical analysis due to the greater complexity and sensitivity of the assay. The inclusion of an antibody treatment introduces more variability, necessitating a higher number of replicates (N=8) to confidently assess the effects of the neutralizing antibody. In contrast, Fig. 2b and 2c involved BRET measurements of AT1 conformational changes without antibody intervention. These assays are more reproducible and have less experimental variability, allowing for a smaller sample size (N=4) while still achieving reliable and statistically significant results. The differences in sample size across these panels were carefully considered to ensure appropriate statistical power for each specific experimental condition.

      (3) Fig. 7: LOX-1 KO Mice Treated with High-dose AngII vs Saline:

      We acknowledge the reviewer’s concern regarding the higher number of LOX-1 KO mice treated with high-dose Ang II compared to the saline group. The number of saline-treated mice was indeed sufficient for reliable statistical analysis. However, the decision to increase the number of mice in the high-dose Ang II group was driven by the anticipated higher variability in the physiological responses under these conditions, such as blood pressure and renal injury. To ensure that we captured the full spectrum of responses and to maintain robust statistical power in the high-dose group, we opted to include more mice in this cohort. 

      We hope this response provides clarity on the rationale behind the varying number of replicates across different experiments. We have rigorously applied appropriate statistical methods to account for these differences, ensuring that the conclusions drawn are robust and scientifically sound. We appreciate the reviewer’s understanding of the experimental constraints and variations that can arise in complex studies such as these.

    1. eLife assessment

      This fundamental work describes for the first time the combined gene expression and chromatin structure at the genome level in isolated chondrocytes and classical (cranial) and non-classical (notochordal) osteoblasts. In a compelling analysis of RNA-Seq and ATAC data, the authors characterize the two osteoblast populations relative to their associated chondrocyte cells and further proceed with a convincing analysis of the crucial entpd5a gene regulatory elements by investigating their respective transcriptional activity and specificity in developing zebrafish.

    2. Reviewer #1 (Public Review):

      Summary:

      This work uses transgenic reporter lines to isolate entpd5a+ cells representing classical osteoblasts in the head and non-classical (osterix-) notochordal sheath cells. The authors also include entpd5a- cells, col2a1a+ cells to represent the closely associated cartilage cells. In a combination of ATAC and RNA-Seq analysis, the genome-wide transcriptomic and chromatin status of each cell population is characterized, validating their methodology and providing fundamental insights into the nature of each cell type, especially the less well-studied notochordal sheath cells. Using these data, the authors then turn to a thorough and convincing analysis of the regulatory regions that control the expression of the entpd5a gene in each cell population. Determination of transcriptional activities in developing zebrafish, again combined with ATAC data and expression data of putative regulators, results in a compelling and detailed picture of the regulatory mechanisms governing the expression of this crucial gene.

      Strengths:

      The major strength of this paper is the clever combination of RNA-Seq and ATAC analysis, further combined with functional transcriptional analysis of the regulatory elements of one crucial gene. This results in a very compelling story.

      Weaknesses:

      No major weaknesses were identified, except for all the follow-up experiments that one can think of, but that would be outside of the scope of this paper.

    3. Reviewer #2 (Public Review):

      Summary:

      Complementary to mammalian models, zebrafish has emerged as a powerful system to study vertebrate development and to serve as a go-to model for many human disorders. All vertebrates share the ancestral capacity to form a skeleton. Teleost fish models have been a key model to understand the foundations of skeletal development and plasticity, pairing with more classical work in amniotes such as the chicken and mouse. However, the genetic foundation of the diversity of skeletal programs in teleosts has been hampered by mapping similarities from amniotes back and not objectively establishing more ancestral states. This is most obvious in systematic, objective analysis of transcriptional regulation and tissue specification in differentiated skeletal tissues. Thus, the molecular events regulating bone-producing cells in teleosts have remained largely elusive. In this study, Petratou et al. leverage spatial experimental delineation of specific skeletal tissues -- that they term 'classical' vs 'non-classical' osteoblasts -- with associated cartilage of the endo/peri-chondrial skeleton and inter-segmental regions of the forming spine during development of the zebrafish, to delineate molecular specification of these cells by current chromatin and transcriptome analysis. The authors further show functional evidence of the utility of these datasets to identify functional enhancer regions delineating entp5 expression in 'classical' or 'non-classical' osteoblast populations. By integration with paired RNA-seq, they delineate broad patterns of transcriptional regulation of these populations as well as specific details of regional regulation via predictive binding sites within ATACseq profiles. Overall the paper was very well written and provides an essential contribution to the field that will provide a foundation to promote modeling of skeletal development and disease in an evolutionary and developmentally informed manner.

      Strengths:

      Taken together, this study provides a comprehensive resource of ATAC-seq and RNA-seq data that will be very useful for a wide variety of researchers studying skeletal development and bone pathologies. The authors show specificity in the different skeletal lineages and show the utility of the broad datasets for defining regulatory control of gene regulation in these different lineages, providing a foundation for hypothesis testing of not only agents of skeletal change in evolution but also function of genes and variations of unknown significance as it pertains to disease modeling in zebrafish. The paper is excellently written, integrating a complex history and experimental analysis into a useful and coherent whole. The terminology of 'classical' and 'non-classical' will be useful for the community in discussing the biology of skeletal lineages and their regulation.

      Weaknesses:

      Two items arose that were not critical weaknesses but areas for extending the description of methods and integration into the existing data on the role of non-classical osteoblasts and establishment/canalization of this lineage of skeletal cells.

      (1) In reading the text it was unclear how specific the authors' experimental dissection of the head/trunk was in isolating different entp5a osteoblast populations. Obviously, this was successful given the specificity in DEG of results, however, analysis of contaminating cells/lineages in each population would be useful - e.g. using specific marker genes to assess. The text uses terms such as 'specific to' and 'enriched in' without seemingly grounded meaning of the accuracy of these comments. Is it really specific - e.g. not seen in one or other dataset - or is there some experimental variation in this?

      (2) Further, it would be valuable to discuss NSC-specific genes such as calymmin (Peskin 2020) which has species and lineage-specific regulation of non-classical osteoblasts likely being a key mechanistic node for ratcheting centra-specific patterning of the spine in teleost fishes. What are dynamics observed in this gene in datasets between the different populations, especially when compared with paralogues - are there obvious cis-regulatory changes that correlate with the co-option of this gene in the early regulation of non-classical osteoblasts? The addition of this analysis/discussion would anchor discussions of the differential between different osteoblasts lineages in the paper.

    4. Reviewer #3 (Public Review):

      Summary:

      This study characterizes classical and nonclassical osteoblasts as both types were analyzed independently (integrated ATAC-seq and RNAseq). It was found that gene expression in classical and nonclassical osteoblasts is not regulated in the same way. In classical osteoblasts, Dlx family factors seem to play an important role, while Hox family factors are involved in the regulation of spinal ossification by nonclassical osteoblasts. In the second part of the study, the authors focus on the promoter structure of entpd5a. Through the identification of enhancers, they reveal complex modes of regulation of the gene. The authors suggest candidate transcription factors that likely act on the identified enhancer elements. All the results taken together provide comprehensive new insights into the process of bone development, and point to spatio-temporally regulated promoter/enhancer interactions taking place at the entpd5a locus.

      Strengths:

      The authors have succeeded in justifying a sound and consistent buildup of their experiments, and meaningfully integrating the results into the design of each of their follow-up experiments. The data are solid, insightfully presented, and the conclusion valid. This makes this manuscript of great value and interest to those studying (fundamental) skeletal biology.

      Weaknesses:

      The study is solidly constructed, the manuscript is clearly written and the discussion is meaningful - I see no real weaknesses.

    1. eLife Assessment

      Graca et al. reports a fundamental missing link in the ethanol metabolism of mycobacteria and illuminates the role of a flavoprotein dehydrogenase that acts as an electron shuttle between an uncommon redox cofactor and the electron transport chain. Overall, the data presented are compelling, supported by a range of well designed and meticulous experiments. The findings will be of broad interest to researchers investigating bacterial metabolism.

    2. Reviewer #1 (Public review):

      Using genetically engineered Mycolicibacterium smegmatis strains, the authors tried to decipher the role of the last gene in the mycofactocin operon, mftG. They found that MftG was essential for growth in the presence of ethanol as the sole carbon source, but not for the metabolism of ethanol, evidenced by the equal production of acetaldehyde in the mutant and wild type strains when grown with ethanol (Fig 3). The phenotypic characterization of ΔmftG cells revealed a growth-arrest phenotype in ethanol, reminiscent of starvation conditions (Fig 4). Investigation of cofactor metabolism revealed that MftG was not required to maintain redox balance via NADH/NAD+, but was important for energy production (ATP) in ethanol. Since mycobacteria cannot grow via substrate-level phosphorylation alone, this pointed to a role of MftG in respiration during ethanol metabolism. The accumulation of reduced mycofactocin points to impaired cofactor cycling in the absence of MftG, which would impact the availability of reducing equivalents to feed into the electron transport chain for respiration (Fig 5). This was confirmed when looking at oxygen consumption in membrane preparations from the mutant and wild type strains with reduced mycofactocin electron donors (Fig 7). The transcriptional analysis supported the starvation phenotype, as well as perturbations in energy metabolism.

      The link between mycofactocin oxidation and respiration is shown by whole-cell and membrane respiration measurements. I look forward to seeing what the electron acceptor/s are for MftG. Overall, the data and conclusions support the role of MftG in ethanol metabolism as a mycofactocin redox enzyme.

    3. Reviewer #3 (Public review):

      Summary:

      The work by Graca et al. describes a GMC flavoprotein dehydrogenase (MftG) in the ethanol metabolism of mycobacteria and provides evidence that it shuttles electrons from the mycofactocin redox cofactor to the electron transport chain.

      Strengths:

      Overall, this study is compelling, exceptionally well-designed and thoroughly conducted. An impressively diverse set of different experimental approaches is combined to pin down the role of this enzyme and scrutinize the effects of its presence or absence in mycobacteria cells growing on ethanol and other substrates. Other strengths of this work are the clear writing style and stellar data presentation in the figures, which makes it easy also for non-experts to follow the logic of the paper. Overall, this work therefore closes an important gap in our understanding of ethanol oxidation in mycobacteria, with possible implications for the future treatment of bacterial infections.

      Weaknesses:

      I see no major weaknesses in this work, which in my opinion leaves no doubt about the role of MftG.

    4. Reviewer #4 (Public review):

      Summary:

      The manuscript by Graça et al. explores the role of MftG in the ethanol metabolism of mycobacteria. The authors hypothesise that MftG functions as a mycofactocin dehydrogenase, regenerating mycofactocin by shuttling electrons to the respiratory chain of mycobacteria. Although the study primarily uses M. smegmatis as a model microorganism, the findings have more general implications for understanding mycobacterial metabolism. Identifying the specific partner to which MftG transfers its electrons within the respiratory chain of mycobacteria would be an important next step, as pointed out by the authors.

      Strengths

      The authors have used a wide range of tools to support their hypothesis, including co-occurrence analyses, gene knockout and complementation experiments, as well as biochemical assays and transcriptomics studies.<br /> An interesting observation that the mftG deletion mutant grown on ethanol as the sole carbon source exhibited a growth defect resembling a starvation phenotype.<br /> MftG was shown to catalyse the electron transfer from mycofactocinol to components of the respiratory chain, highlighting the flexibility and complexity of mycobacterial redox metabolism.

      The authors have taken on the majority of recommendations by the reviewers and made changes in the manuscript accordingly. I don't have any further suggestions.

    5. Author response:

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

      Public Reviews:

      Reviewer #1 (Public review):

      Using a knock-out mutant strain, the authors tried to decipher the role of the last gene in the mycofactocin operon, mftG. They found that MftG was essential for growth in the presence of ethanol as the sole carbon source, but not for the metabolism of ethanol, evidenced by the equal production of acetaldehyde in the mutant and wild type strains when grown with ethanol (Fig 3). The phenotypic characterization of ΔmftG cells revealed a growth-arrest phenotype in ethanol, reminiscent of starvation conditions (Fig 4). Investigation of cofactor metabolism revealed that MftG was not required to maintain redox balance via NADH/NAD+, but was important for energy production (ATP) in ethanol. Since mycobacteria cannot grow via substrate-level phosphorylation alone, this pointed to a role of MftG in respiration during ethanol metabolism. The accumulation of reduced mycofactocin points to impaired cofactor cycling in the absence of MftG, which would impact the availability of reducing equivalents to feed into the electron transport chain for respiration (Fig 5). This was confirmed when looking at oxygen consumption in membrane preparations from the mutant and would type strains with reduced mycofactocin electron donors (Fig 7). The transcriptional analysis supported the starvation phenotype, as well as perturbations in energy metabolism, and may be beneficial if described prior to respiratory activity data.

      The data and conclusions support the role of MftG in ethanol metabolism.

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

      Reviewer #3 (Public review):

      Summary:

      The work by Graca et al. describes a GMC flavoprotein dehydrogenase (MftG) in the ethanol metabolism of mycobacteria and provides evidence that it shuttles electrons from the mycofactocin redox cofactor to the electron transport chain.

      Strengths:

      Overall, this study is compelling, exceptionally well designed and thoroughly conducted. An impressively diverse set of different experimental approaches is combined to pin down the role of this enzyme and scrutinize the effects of its presence or absence in mycobacteria cells growing on ethanol and other substrates. Other strengths of this work are the clear writing style and stellar data presentation in the figures, which makes it easy also for non-experts to follow the logic of the paper. Overall, this work therefore closes an important gap in our understanding of ethanol oxidation in mycobacteria, with possible implications for the future treatment of bacterial infections.

      Weaknesses:

      I see no major weaknesses of this work, which in my opinion leaves no doubt about the role of MftG.

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

      Reviewer #4 (Public review):

      Summary:

      The manuscript by Graça et al. explores the role of MftG in the ethanol metabolism of mycobacteria. The authors hypothesise that MftG functions as a mycofactocin dehydrogenase, regenerating mycofactocin by shuttling electrons to the respiratory chain of mycobacteria. Although the study primarily uses M. smegmatis as a model microorganism, the findings have more general implications for understanding mycobacterial metabolism. Identifying the specific partner to which MftG transfers its electrons within the respiratory chain of mycobacteria would be an important next step, as pointed out by the authors.

      Strengths:

      The authors have used a wide range of tools to support their hypothesis, including co-occurrence analyses, gene knockout and complementation experiments, as well as biochemical assays and transcriptomics studies.

      An interesting observation that the mftG deletion mutant grown on ethanol as the sole carbon source exhibited a growth defect resembling a starvation phenotype.

      MftG was shown to catalyse the electron transfer from mycofactocinol to components of the respiratory chain, highlighting the flexibility and complexity of mycobacterial redox metabolism.

      Weaknesses:

      Could the authors elaborate more on the differences between the WT strains in Fig. 3C and 3E? in Fig. 3C, the ethanol concentration for the WT strain is similar to that of WT-mftG and ∆mftG-mftG, whereas the acetate concentration in thw WT strain differs significantly from the other two strains. How this observation relates to ethanol oxidation, as indicated on page 12.

      This is a good question, and we agree with the reviewer that the sum of processes leading to the experimental observations shown in Figure 3 are not completely understood. For instance, when looking at ethanol concentrations, evaporation is a dominating effect and the situation is furthermore confounded by the fact that the rate of ethanol evaporation appears to be inversely correlated to the optical density of the samples (see Figure 3E and compare media control as well as the samples of DmftG and DmftG at OD<sub>600</sub> = 1). Additionally, the growth rate and thus the OD<sub>600</sub> of all strains monitored are different at each time point, thus further complicating the analysis. This is why we assume that the rate of ethanol oxidation is mirrored more clearly by acetate formation, at least in the early phase before 48 h (Figure 3E),i.e., before acetate consumption becomes dominant in DmftG-mftG and WT-mftG. Here, we see that the rate of acetate formation is zero for media controls, low for DmftG, but high for WT as well as DmftG-mftG and WT-mftG. The latter two strains also showed an earlier starting point of growth as well as acetate formation and the following phase of acetate depletion.

      All of these observations are in line with our general statement, i.d., “Parallel to the accelerated and enhanced growth described above (Figure 3A), the overexpression strains displayed higher rates of ethanol consumption as well as an earlier onset of acetate overflow metabolism and acetate consumption (Figure 3D).” We are still convinced that this summary describes the findings well and avoids unnecessary speculation.

      The authors conclude from their functional assays that MftG catalyses single-turnover reactions, likely using FAD present in the active site as an electron acceptor. While this is plausible, the current experimental set up doesn't fully support this conclusions, and the language around this claim should be softened.

      This is a fair point. We revised our claim accordingly. In particular, we changed:

      Page 28: we added “possibly”

      Page 28 we changed “single-turnover reactions” to “reactions reminiscent of a single-turnover process”.

      The authors suggest in the manuscript that the quinone pool (page 24) may act as the electron acceptor from mycofactocinol, but later in the discussion section (page 30) they propose cytochromes as the potential recipients. If the authors consider both possibilities valid, I suggest discussing both options in the manuscript.

      This is true. However, no change to the manuscript is necessary, since both options were discussed on page 30.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The authors addressing some of the original recommendations is appreciated e.g. title change. Other recommendations that were not adequately addressed would mostly improve the clarity and help comprehension for the reader, but they are at the author's discretion.

      Reviewer #3 (Recommendations for the authors):

      Abstract: "Here, we show that MftG enzymes strictly require mft biosynthetic genes and are found in 75% of organisms harboring these genes". I read this sentence several times and I am still somewhat confused and not sure what exactly is meant here. I suggest to rephrase, e.g., to "Here, we show that in 75% of all organisms that harbour the mft biosynthetic genes, MftG enzymes are also encoded and functionally associated with these genes" (if that was meant; also the abbreviation mft should be introduced in the abstract or otherwise the full name be used).

      We thank the reviewer for the good hint. We changed the sentence to “Here, we show that MftG enzymes are almost exclusively found in genomes containing mycofactocin biosynthetic genes and are present in 75% of organisms harboring these genes”.

      p.3, 2nd paragraph: "Although the role of MFT in alcohol metabolism is well established, further biological roles of mycofactocin appear to exist." Mycofactocin is once written as MFN and once in full length, which is slightly confusing. Consider rephrasing, e.g., to "...further biological roles of this cofactor appear to exist".

      Thank you, we adopted the suggested change.

      Fig. 1: Consider adding MftG in brackets after "mycofactocin dehydrogenase" in panel B.

      Good suggestion. We added (MftG) to the figure.

      Fig. 3: Legend should be corrected. The color of the signs should be teal diamond for "M. smegmatis double presence of the mftG gene" and orange upward facing triangle for "Medium with 10 g L-1 of ethanol without bacterial inoculation". Aside from the coloration, the order should ideally also be identical to the one shown in the upper right part.

      Thank you for the valuable hint! We corrected the legend and unified the legends in the figure caption and figure.

      p.20 : It is not exactly clear to me why "semipurified cell-free extracts from M. smegmatis ∆mftG-mftGHis6 " were used here rather than the purified enzyme. Was the purification by HisTrap columns not feasible or was the protein unstable when fully purified? In any case, it would help the reader to quickly state the reason in this section.

      Indeed, the problem with M. smegmatis as an expression host was a combination of low protein yield and poor binding to Ni-NTA columns. In E. coli, poor expression, low solubility or poor binding was the issue. Unfortunately, the usage of other affinity tags resulted in either poor expression or inactive protein. We have shortly mentioned the major issues on page 21 and prefer not to focus on failed attempts too much.

      p. 21: "We, therefore, concluded that MftG can indeed interact with mycofactocins as electron donors but might require complex electron acceptors, for instance, proteins present in the respiratory chain." I agree. For the future it might be worthwhile to determine the redox potential of MftG, which could provide hints on the natural electron acceptor.

      Thank you for the suggestion. We will consider this question in our future work.

      p. 23: "In M. smegmatis, cyanide is a known inhibitor of the cytochrome bc/aa3 but not of cytochrome bd (34), therefore, the decrease of oxygen consumption when MFTs were added to the membrane fractions in combination with KCN (Figure 7), revealed that MFT-induced oxygen consumption is indeed linked to mycobacterial respiration." It might be a good idea to quickly recapitulate the functions of these cytochromes here. Also, I think it should read "bc1aa3" (also correct in legend of Fig. 8 that says "bcc-aa3").

      Thank you for the good observation. We changed all instances to the correct designation (bc1-aa3).

      Reviewer #4 (Recommendations for the authors):

      Abstract: revise the wording "MftG enzymes strictly require mft biosynthetic genes". It should be either mftG gene with the mft biosynthetic genes or MftG enzyme with the Mft biosynthetic proteins. I also suggest replacing "require" with a more appropriate term.

      This was taken care of. See above.

      Page 3, end of the first paragraph; does the alcohol dehydrogenase refer to Mno/Mdo?

      Partially, yes, but also to other alcohol dehydrogenases.

      Page 4, radical SAM; define upon first use

      Good, point, we changed “radical SAM” to radical S-adenosyl methionine (rSAM)

      Page 6; Rossman fold refers to the fold and not only the FAD binding pocket.

      Good point. We deleted “(Rossman fold)”

      Page 11; not exactly sure what this means "the growth curve of the complemented strain, which could be dysregulated in mftG expression"

      By “dysregulated” expression, we mean that the expression of mftG could be higher or lower than in the WT and could follow different regulatory signals than in the wild type. Since this phenomenon is not well understood, we would like to avoid speculative discussions.

      Page 11; Figures 2E and 2C should be 3E and 3C. Likewise on page 12 Figure 2D.

      Thank you very much for the valuable hint. We corrected the figure numbers as suggested.

      Page 12; the last Figure 3D in the page should be 3E?

      Yes, good catch, we corrected the Figure number.

      Page 17, KO; define upon first use.

      Good suggestion, we changed both instances of “KO” to “knockout”

      Page 24; revise: "for instance. For example"

      We deleted “for instance”.

      Page 26; change 6.506 to 6,506

      Corrected.

      Page 23; "In M. smegmatis, cyanide is a known inhibitor ..." is too long and not easy to understand/follow.

      Good suggestion. We simplified the sentence to “Therefore, the decrease of oxygen consumption in the presence of KCN (Figure 7) revealed…”

      Page 29; "single-turnover reactions could be observed". There are no experiments to support this statement, except the results shown in Figure 7F. I suggest softening the language, as it has been done on page 21. To claim single-turnover, a proper kinetic analysis would be necessary, which is not included in the current manuscript.

      This is true and has been taken care of. See above.

      Figure 1; Indicate mycofactocin dehydrogenase as MftG

      Done.

      Figure 5A; what is the significance of comparing ∆mftG glucose with WT ethanol?

      We agree, that, although the difference of the two columns is significant, this does not have any relevant meaning. Therefore, we removed the bracket with p-value in Panel A.

      Make HdB-Tyl/HdB-tyloxapol usage consistent throughout the document. Likewise, re the usage of mycobacteria/Mycobacteria/Mycobacteria

      Thank you for the valuable hint, we unified the usage throughout the document

    1. eLife Assessment

      In this valuable study, Roiuk et al employed a combination of ribosome profiling and reporter assays to provide convincing evidence that eIF2A is not involved in translational regulation in cultured human cells. In conjunction with several recent publications (spanning yeast to mammalian systems), these findings disaffirm the previously proposed role of eIF2A in directing protein synthesis, including its implication in translational reprogramming under stress. Whilst clearly delinating something eIF2A does not do, identifying cellular role(s) for eIF2A could further strengthen this article.

    2. Reviewer #1 (Public review):

      Summary:

      Beyond what is stated in the title of this paper, not much needs to be summarized. eIF2A in HeLa cells promotes translation initiation of neither the main ORFs nor short uORFs under any of the conditions tested.

      Strengths:

      Very comprehensive, in fact, given the huge amount of purely negative data, an admirably comprehensive and well-executed analysis of the factor of interest.

      Weaknesses:

      The study is limited to the HeLa cell line, focusing primarily on KO of eIF2A and neglecting the opposite scenario, higher eIF2A expression which could potentially result in an increase in non-canonical initiation events.

    3. Reviewer #2 (Public review):

      Summary

      Roiuk et al describe a work in which they have investigated the role of eIF2A in translation initiation in mammals without much success. Thus, the manuscript focuses on negative results. Further, the results, while original, are generally not novel, but confirmatory, since related claims have been made before independently in different systems with Haikwad et al study recently published in eLife being the most relevant.

      Despite this, we find this work highly important. This is because of a massive wealth of unreliable information and speculations regarding eIF2A role in translation arising from series of artifacts that began at the moment of eIF2A discovery. This, in combination with its misfortunate naming (eIF2A is often mixed up with alpha subunit of eIF2, eIF2S1) has generated a widespread confusion among researchers who are not experts in eukaryotic translation initiation. Given this, it is not only justifiable but critical to make independent efforts to clear up this confusion and I very much appreciate the authors' efforts in this regard.

      Strengths

      The experimental investigation described in this manuscript is thorough, appropriate and convincing.

      Weaknesses

      However, we are not entirely satisfied with the presentation of this work which we think should be improved.

    4. Reviewer #3 (Public review):

      Summary:

      This is a valuable study providing solid evidence that the putative non-canonical initiation factor eIF2A has little or no role in the translation of any expressed mRNAs in cultured human (primarily HeLa) cells. Previous studies have implicated eIF2A in GTP-independent recruitment of initiator tRNA to the small (40S) ribosomal subunit, a function analogous to canonical initiation factor eIF2, and in supporting initiation on mRNAs that do not require scanning to select the AUG codon or that contain near-cognate start codons, especially upstream ORFs with non-AUG start codons, and may use the cognate elongator tRNA for initiation. Moreover, the detected functions for eIF2A were limited to, or enhanced by, stress conditions where canonical eIF2 is phosphorylated and inactivated, suggesting that eIF2A provides a back-up function for eIF2 in such stress conditions. CRISPR gene editing was used to construct two different knock-out cell lines that were compared to the parental cell line in a large battery of assays for bulk or gene-specific translation in both unstressed conditions and when cells were treated with inhibitors that induce eIF2 phosphorylation. None of these assays identified any effects of eIF2A KO on translation in unstressed or stressed cells, indicating little or no role for eIF2A as a back-up to eIF2 and in translation initiation at near-cognate start codons, in these cultured cells.

      The study is very thorough and generally well executed, examining bulk translation by puromycin labeling and polysome analysis and translational efficiencies of all expressed mRNAs by ribosome profiling, with extensive utilization of reporters equipped with the 5'UTRs of many different native transcripts to follow up on the limited number of genes whose transcripts showed significant differences in translational efficiencies (TEs) in the profiling experiments. They also looked for differences in translation of uORFs in the profiling data and examined reporters of uORF-containing mRNAs known to be translationally regulated by their uORFs in response to stress, going so far as to monitor peptide production from a uORF itself. The high precision and reproducibility of the replicate measurements instil strong confidence that the myriad of negative results they obtained reflects the lack of eIF2A function in these cells rather than data that would be too noisy to detect small effects on the eIF2A mutations. They also tested and found no evidence for a recent claim that eIF2A localizes to the cytoplasm in stress and exerts a global inhibition of translation. Given the numerous papers that have been published reporting functions of eIF2A in specific and general translational control, this study is important in providing abundant, high-quality data to the contrary, at least in these cultured cells.

      Strengths:

      The paper employed two CRISPR knock-out cell lines and subjected them to a combination of high-quality ribosome profiling experiments, interrogating both main coding sequences and uORFs throughout the translatome, which was complemented by extensive reporter analysis, and cell imaging in cells both unstressed and subjected to conditions of eIF2 phosphorylation, all in an effort to test previous conclusions about eIF2A functioning as an alternative to eIF2.

      Weaknesses:

      There is some question about whether their induction of eIF2 phosphorylation using tunicamycin was extensive enough to state forcefully that eIF2A has little or no role in the translatome when eIF2 function is strongly impaired. Also, similar conclusions regarding the minimal role of eIF2A were reached previously for a different human cell line from a study that also enlisted ribosome profiling under conditions of extensive eIF2 phosphorylation; although that study lacked the extensive use of reporters to confirm or refute the identification by ribosome profiling of a small group of mRNAs regulated by eIF2A during stress.

    5. Author response:

      Reviewer #1:

      Summary:

      Beyond what is stated in the title of this paper, not much needs to be summarized. eIF2A in HeLa cells promotes translation initiation of neither the main ORFs nor short uORFs under any of the conditions tested.

      Strengths:

      Very comprehensive, in fact, given the huge amount of purely negative data, an admirably comprehensive and well-executed analysis of the factor of interest.

      Weaknesses:

      The study is limited to the HeLa cell line, focusing primarily on KO of eIF2A and neglecting the opposite scenario, higher eIF2A expression which could potentially result in an increase in non-canonical initiation events.

      We thank the reviewer for the positive evaluation. As suggested by the reviewer in the detailed recommendations, we will clarify in the title, abstract and text that our conclusions are limited to HeLa cells. Furthermore, as suggested we will test the effect of eIF2A overexpression on the luciferase reporter constructs, and will upload a revised manuscript.

      Reviewer #2:

      Summary

      Roiuk et al describe a work in which they have investigated the role of eIF2A in translation initiation in mammals without much success. Thus, the manuscript focuses on negative results. Further, the results, while original, are generally not novel, but confirmatory, since related claims have been made before independently in different systems with Haikwad et al study recently published in eLife being the most relevant.

      Despite this, we find this work highly important. This is because of a massive wealth of unreliable information and speculations regarding eIF2A role in translation arising from series of artifacts that began at the moment of eIF2A discovery. This, in combination with its misfortunate naming (eIF2A is often mixed up with alpha subunit of eIF2, eIF2S1) has generated a widespread confusion among researchers who are not experts in eukaryotic translation initiation. Given this, it is not only justifiable but critical to make independent efforts to clear up this confusion and I very much appreciate the authors' efforts in this regard.

      Strengths

      The experimental investigation described in this manuscript is thorough, appropriate and convincing.

      Weaknesses

      However, we are not entirely satisfied with the presentation of this work which we think should be improved.

      We thank the reviewer for the positive evaluation. We will revise the manuscript according to the reviewer's suggestions made in the detailed recommendations.

      Reviewer #3:

      Summary:

      This is a valuable study providing solid evidence that the putative non-canonical initiation factor eIF2A has little or no role in the translation of any expressed mRNAs in cultured human (primarily HeLa) cells. Previous studies have implicated eIF2A in GTP-independent recruitment of initiator tRNA to the small (40S) ribosomal subunit, a function analogous to canonical initiation factor eIF2, and in supporting initiation on mRNAs that do not require scanning to select the AUG codon or that contain near-cognate start codons, especially upstream ORFs with non-AUG start codons, and may use the cognate elongator tRNA for initiation. Moreover, the detected functions for eIF2A were limited to, or enhanced by, stress conditions where canonical eIF2 is phosphorylated and inactivated, suggesting that eIF2A provides a back-up function for eIF2 in such stress conditions. CRISPR gene editing was used to construct two different knock-out cell lines that were compared to the parental cell line in a large battery of assays for bulk or gene-specific translation in both unstressed conditions and when cells were treated with inhibitors that induce eIF2 phosphorylation. None of these assays identified any effects of eIF2A KO on translation in unstressed or stressed cells, indicating little or no role for eIF2A as a back-up to eIF2 and in translation initiation at near-cognate start codons, in these cultured cells.

      The study is very thorough and generally well executed, examining bulk translation by puromycin labeling and polysome analysis and translational efficiencies of all expressed mRNAs by ribosome profiling, with extensive utilization of reporters equipped with the 5'UTRs of many different native transcripts to follow up on the limited number of genes whose transcripts showed significant differences in translational efficiencies (TEs) in the profiling experiments. They also looked for differences in translation of uORFs in the profiling data and examined reporters of uORF-containing mRNAs known to be translationally regulated by their uORFs in response to stress, going so far as to monitor peptide production from a uORF itself. The high precision and reproducibility of the replicate measurements instil strong confidence that the myriad of negative results they obtained reflects the lack of eIF2A function in these cells rather than data that would be too noisy to detect small effects on the eIF2A mutations. They also tested and found no evidence for a recent claim that eIF2A localizes to the cytoplasm in stress and exerts a global inhibition of translation. Given the numerous papers that have been published reporting functions of eIF2A in specific and general translational control, this study is important in providing abundant, high-quality data to the contrary, at least in these cultured cells.

      Strengths:

      The paper employed two CRISPR knock-out cell lines and subjected them to a combination of high-quality ribosome profiling experiments, interrogating both main coding sequences and uORFs throughout the translatome, which was complemented by extensive reporter analysis, and cell imaging in cells both unstressed and subjected to conditions of eIF2 phosphorylation, all in an effort to test previous conclusions about eIF2A functioning as an alternative to eIF2.

      Weaknesses:

      There is some question about whether their induction of eIF2 phosphorylation using tunicamycin was extensive enough to state forcefully that eIF2A has little or no role in the translatome when eIF2 function is strongly impaired. Also, similar conclusions regarding the minimal role of eIF2A were reached previously for a different human cell line from a study that also enlisted ribosome profiling under conditions of extensive eIF2 phosphorylation; although that study lacked the extensive use of reporters to confirm or refute the identification by ribosome profiling of a small group of mRNAs regulated by eIF2A during stress.

      We thank the reviewer for the positive evaluation. We will revise the manuscript according to the recommendations made in the detailed recommendations. Regarding the two points mentioned here:

      (1) the reason eIF2alpha phosphorylation does not increase appreciably is because unfortunately the antibody is very poor. The fact that the Integrated Stress Response (ISR) is induced by our treatment can be seen, for instance, by the fact that ATF4 protein levels increase strongly (in the very same samples where eIF2alpha phosphorylation does not increase much, in Suppl. Fig. 5E). We will strengthen the conclusion that the ISR is indeed activated with additional experiments/data as suggested by the reviewer.

      (2) We agree that our results are in line with results from the previous study mentioned by the reviewer, so we will revise the manuscript to mention this other study more extensively in the discussion.

    1. eLife Assessment

      Notch1 is expressed uniformly throughout the mouse endocardium during the initial stages of heart valve formation, yet it remains unclear how Notch signaling is activated specifically in the AVC region to induce valve formation. To answer this question, the authors used a combination of in vivo and ex vivo experiments in mice to demonstrate ligand-independent activation of Notch1 by circulation induced-mechanical stress and provide evidence for stimulation of a novel mechanotransduction pathway involving post-translational modification of mTORC2 and Protein Kinase C (PKC) upstream of Notch1. These findings represent an important advance in our understanding of valve formation and the conclusions are supported by convincing data.

    2. Reviewer #2 (Public review):

      Summary:

      In mice, Notch1 is expressed uniformly throughout the endocardium during the initial stages of heart valve formation. How, then, is Notch activated specifically in the valve forming regions? To answer this question, the authors use a combination of in vivo and ex vivo experiments to demonstrate the critical role of hemodynamic forces on Notch1 activation and provide strong evidence for a novel mechanotransduction pathway involving PKC and mTORC2.

      Strengths:

      (1) Novel insights into the role of PKC and mTOR were obtained using a combination of mutant studies and pharmacological studies.<br /> (2) Novel insights on the role of mechanical forces on caveolin-1 localisation.<br /> (3) Mechanical forces were manipulated using the class III antiarrhythmic drug dofetilide, which transiently blocks heartbeat. Care was taken to minimise the confounding effects of hypoxia.

      Weaknesses:

      The authors suggest that shear stress activates the mTORC2-PKC-Notch signalling pathway by altering the membrane lipid microstructure. Although this is a fascinating hypothesis, more evidence will be needed to prove this. In particular, it is not clear how the general addition of cholesterol in dofetilide-treated hearts would result in a rescue of regionalized membrane distribution within the AVC and in high-shear stress areas.

    3. Reviewer #3 (Public review):

      Summary:

      The overall goal of this manuscript is to understand how Notch signaling is activated in specific regions of the endocardium, including the OFT and AVC, that undergo EMT to form the endocardial cushions. Using dofetilide to transiently block circulation in E9.5 mice, the authors show that Notch receptor cleavage still occurs in the valve-forming regions due to mechanical sheer stress as Notch ligand expression and oxygen levels are unaffected. The authors go on to show that changes in lipid membrane structure activate mTOR signaling, which causes phosphorylation of PKC and Notch receptor cleavage. The data are largely convincing and support their hypothesis. The conclusions are also novel and significantly add to the field of endocardial cushion biology.

      The strengths of the manuscript include the dual pharmacological and genetic approaches to block blood flow in the mouse, the inclusion of many controls including those for hypoxia, the quality of the imaging, and the clarity of the text. In the revision, the authors put forth a good faith effort to address experimentally or textually the concerns of the reviewers. Most weaknesses that were identified in the first submission were addressed and the main claims are convincing. In general, the authors achieved their aims and the results support their conclusions.

    4. Author response:

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

      Public Review:

      The overall goal of this manuscript is to understand how Notch signaling is activated in specific regions of the endocardium, including the OFT and AVC, that undergo EMT to form the endocardial cushions. Using dofetilide to transiently block circulation in E9.5 mice, the authors show that Notch receptor cleavage still occurs in the valve-forming regions due to mechanical sheer stress as Notch ligand expression and oxygen levels are unaffected. The authors go on to show that changes in lipid membrane structure activate mTOR signaling, which causes phosphorylation of PKC and Notch receptor cleavage.

      The strengths of the manuscript include the dual pharmacological and genetic approaches to block blood flow in the mouse, the inclusion of many controls including those for hypoxia, the quality of the imaging, and the clarity of the text. However, several weaknesses were noted surrounding the main claims where the supporting data are incomplete.

      PKC - Notch1 activation:

      (1) Does deletion of Prkce and Prkch affect blood flow, and if so, might that be suppressing Notch1 activation indirectly?

      To address this concern, we performed echocardiography of Prkce<sup>+/-</sup>;Prkch<sup>+/-</sup>, Prkce<sup>-/-</sup>;Prkch<sup>+/-</sup>, and Prkce<sup>+/-</sup>;Prkch<sup>-/-</sup> mouse hearts (Figure 3-supplement figure 2D), showing no significant effect in heartbeat and blood flow. (Line 308)

      (2) It would be helpful to visualize the expression of prkce and prkch by in situ hybridization in E9.5 embryos.

      We now added immunofluorescence staining results for both PKCE and PKCH as shown in Figure 3-supplement figure 2B. In E9.5 embryonic heart, PKCH is mainly expressed in the endocardium overlying AV canal and the base of trabeculae, overlapping with the expression pattern of NICD and pPKC<sup>Ser660</sup>. PKCE is expressed in both endocardium and myocardium. In the endocardium, PKCE is mainly expressed in the endocardium overlying AV canal (Line312-314)

      (2) PMA experiments: Line 223-224: A major concern is related to the conclusion that "blood flow activates Notch in the cushion endocardium via the mTORC2-PKC signaling pathway". To make that claim, the authors show that a pharmacological activation with a potent PKC activator, PMA, rescues NICD levels in the AVC in dofetilide-treated embryos. This claim would also need proof that a lack of blood flow alters the activity of mTORC2 to phosphorylate the targets of PKC phosphorylation. Also, this observation does not explain the link between PKC activity and Notch activation.

      Both AKT Ser473 and PKC Ser660 are well characterized phosphorylation sites regulated by mTORC2 (Baffi TR et. al, mTORC2 controls the activity of PKC and Akt by phosphorylating a conserved TOR interaction motif. Sci Signal. 2021;14.). pAKT<sup>Ser473</sup> is widely used as an indicator of mTORC2 activity. Therefore, the reduced staining intensity of pAKT<sup>Ser473</sup> and pPKC<sup>Ser660</sup> observed in the dofetilide treated embryos should reflect the reduced activity of their common upstream activator mTORC2. This information is provided in Line 317-321.

      As PMA is a well-characterized specific activator of PKC, we believe the rescue of NICD by PMA could explain the link between PKC activity and Notch activation.

      (3) In addition, the authors hypothesise that shear stress lies upstream of PKC and Notch activation, and that because shear stress is highest at the valve-forming regions, PKC and Notch activity is localised to the valve-forming regions. Since PMA treatment affects the entire endocardium which expresses Notch1, NICD should be seen in areas outside of the AVC in the PMA+dofetilide condition. Please clarify.

      As shown in Figure 3C and Figure 3-supplement figure 2B, pPKC, PKCH and PKCE expression are all confined in the AVC region. This explains PMA activates NICD specifically in the valve-forming region. This information is added in Line 312-314.

      Lipid Membrane:

      (1) It is not clear how the authors think that the addition of cholesterol changes the lipid membrane structure or alters Cav-1 distribution. Can this be addressed? Does adding cholesterol make the membrane more stiff? Does increased stiffness result from higher shear stress?

      We do not know how exactly addition of cholesterol alters membrane structure and influence mTORC2-PKC-Notch signaling. As cholesterol is an important component of lipid raft and caveolae, it is possible that enrichment of cholesterol might alter the membrane structure to make the lipid raft structure less dependent on sheer stress. This hypothesis need to be tested in further in vitro studies. This information is added to Line 433-436.

      (2) The loss of blood flow apparently affects Cav1 membrane localization and causes a redistribution from the luminal compartment to lateral cell adhesion sites. Cholesterol treatment of dofetilide-treated hearts (lacking blood flow) rescued Cav1 localization to luminal membrane microdomains and rescued NICD expression. It remains unclear how the general addition of cholesterol would result in a rescue of regionalized membrane distribution within the AVC and in high-shear stress areas.

      We do not know the exact mechanism. As replied in the previous question, future cell-based work is needed to address these important questions. (Line 433-436)

      (3) The authors do not show the entire heart in that rescue treatment condition (cholesterol in dofetilide-treated hearts). Also, there is no quantification of that rescue in Figure 4B. Currently, only overview images of the heart are shown but high-resolution images on a subcellular scale (such as electron microscopy) are needed to resolve and show membrane microdomains of caveolae with Cav1 distribution. This is important because Cav-1could have functions independent of caveolae.

      In Figure 4C, most panels display the large part of the heart including AVC, atrium and ventricle. The images in the third column appear to be more restricted to AVC. We have now replaced these images to reveal AVC and part of the atrium and ventricle. 

      The quantification has also been provided in Figure 4C. We also added a new panel of scanning EM of AVC endocardium, showing numerous membrane invaginations on the luminal surface of the endocardial cells. The size of the invaginations ranges from 50 to 100 nm, consistent with the reported size of caveolae. Dofetilide significantly reduced the number of membrane invaginations, which recovered after restore of blood flow at 5 hours post dofetilide treatment. The reduction of membrane invaginations could also be rescued by ex vivo cholesterol treatment. This information is added to Line 342-349.

      Figure Legends, missing data, and clarity:

      (1) The number of embryos used in each experiment is not clear in the text or figure legends. In general, figure legends are incomplete (for instance in Figure 1).

      Thanks for reminding. we have now added numbers of embryos in the figure legends.

      (2) Line 204: The authors refer to unpublished endocardial RNAseq data from E9.5 embryos. These data must be provided with this manuscript if it is referred to in any way in the text.

      The RNAseq data of PKC isoforms is now provided in Figure3-Figure supplement 2A, Line 301-302.

      (3) Figure 1 shows Dll4 transcript levels, which do not necessarily correlate with protein levels. It would be important to show quantifications of these patterns as Notch/Dll4 levels are cycling and may vary with time and between different hearts.

      The Dll4 immuno-staining in Figure 1B,C is indeed Dll4 protein, not transcript. The quantification is added in Figure 1—Figure supplement 1C. Line 215.

      (4) Line 212-214: The authors describe cardiac cushion defects due to the loss of blood flow and refer to some quantifications that are not completely shown in Figure 3. For instance, quantifications for cushion cellularity and cardiac defects at three hours (after the start of treatment?) are missing.

      The formation of the defects is a developmental process and time dependent. To address this concern, we quantified the cushion cellularity at 5 hours post dofetilide treatment and showed that cell density significantly decreased in the dofetilide treated embryos, albeit less pronounced than the difference at E10.5. (Line 256-257)

      (5) Related to Figure 5. The work would be strengthened by quantification of the effects of dofetilide and verapamil on heartbeat at the doses applied. Is the verapamil dosage used here similar to the dose used in the clinic?

      We are grateful to this suggestion. The effect of dofetilide on heartbeat has already been shown in Figure 2A. We have now additionally measured the heartbeat rate of verapamil treated embryos, and provided the results in Figure 5E. For verapamil injection in mice, a single i.p. dose of 15 mg/kg was used, which is equivalent to 53 mg/m<sup>2</sup> body surface. Verapamil is used in the clinic at dosage ranging from 200 to 480 mg/day, equivalent to 3.33 - 8 mg/kg or 117 - 282 mg/m<sup>2</sup> body surface. Therefore, the dosage used in the mouse is not excessively high compared to the clinic uses. (Line 361-365) 

      Overstated Claims:

      (1) The authors claim that the lipid microstructure/mTORC2/PKC/Notch pathway is responsive to shear stress, rather than other mechanical forces or myocardial function. Their conclusions seem to be extrapolated from various in vitro studies using non-endocardial cells. To solidify this claim, the authors would need additional biomechanical data, which could be obtained via theoretical modelling or using mouse heart valve explants. This issue could also be addressed by the authors simply softening their conclusions.

      We aggrege with the reviewer’s comment. We have now revised the statement as “Our data support a model that membrane lipid microdomain acts as a shear stress sensor and transduces the mechanical cue to activate intracellular mTORC2-PKC-Notch signaling pathway in the developing endocardium. (line 416-418) It is noteworthy that the methodology used to alter blood flow in this study inevitably affects myocardial contraction. Additional work to uncouple sheer stress with other changes of mechanical properties of the myocardium with the aid of theoretical modelling or using mouse heart valve explants is needed to fully characterize the effect of sheer stress on mouse endocardial development.” (Line 436-440)

      (2) Line 263-264: In the discussion, the authors conclude that "Strong fluid shear stress in the AVC and OFT promotes the formation of caveolae on the luminal surface of the endocardial cells, which enhances PKCε phosphorylation by mTORC2." This link was shown rather indirectly, rather than by direct evidence, and therefore the conclusion should be softened. For example, the authors could state that their data are consistent with this model.

      We have revised the statement as “Strong fluid shear stress in the AVC and OFT enhances PKC phosphorylation by mTORC2 possibly by maintaining a particular membrane microstructure.” (Line 372-374)

      (3) In the Discussion, it says: "Mammalian embryonic endocardium undergoes extensive EMT to form valve primordia while zebrafish valves are primarily the product of endocardial infolding (Duchemin et al., 2019)." In the paper cited, Duchemin and colleagues described the formation of the zebrafish outflow tract valve. The zebrafish atrioventricular valve primordia is formed via partial EMT through Dll-Notch signaling (Paolini et al. Cell Reports 2021) and the collective cell migration of endocardial cells into the cardiac jelly. Then, a small subset of cells that have migrated into the cardiac jelly give rise to the valve interstitial cells, while the remainder undergo mesenchymal-to-endothelial transition and become endothelial cells that line the sinus of the atrioventricular valve (Chow et al., doi: 10.1371/journal.pbio.3001505). The authors should modify this part of the Discussion and cite the relevant zebrafish literature.

      Thanks for valuable comments. We have now revised the statement as “Mammalian embryonic endocardium undergoes extensive EMT to form valve primordia while zebrafish atrioventricular valve primordia is formed via partial EMT and the collective cell migration of endocardial cells into the cardiac jelly followed by tissue sheet delamination.” with relevant references added. (Line 411-414)

      Recommendations to the Authors:

      (1) One issue that the authors could address is the organization of figures. There are several cases where positive data that are central to the conclusions are placed in the supplement and should be moved to the main figures. Places where this occurred are listed below:

      - The Tie2 conditional deletion of Dll4 showing retention of NICD in the OFT and AVC regions is highly supportive of the model. The authors should consider moving these data to main Figure 1.

      Thanks for the suggestion. We have reorganized the figure as requested.

      - The ligand expression data in Figure 2- Supplement Figure 1 A is VERY important to the conclusions drawn from the dofetilide treatment. The authors should move these data to main Figure 2.

      The ligand expression data in Figure 2- Supplement Figure 1A are now moved to Figure 2B.

      - In Figure 3A - the area in the field of view should be stated in the Figure (is it the AVC?) Figure 3 - Supplement 1 proximal OFT data should be moved to main Figure 3 as it is central to the conclusions. Negative DA data can be left in the supplement. Again, for Figure 3 - Supplement 1 Stauroporine treatment data should be moved to the main figure as it is positive data that are central to the conclusions.

      Thanks for the suggestion. We have reorganized the figure as requested.

      (2) Antibody used for Twist1 detection is not listed in the resource table.

      Twist1 is purchased from abcam, the detailed information is now available in the resource table.

      (3) Missing arrowhead in Figure 4A, last row.

      Sorry for the negligence. Arrowhead is now added.

      (4) Line 286. "OFT" pasted on the word "endothelium".

      “OFT” is now removed.

      (5) Related to Figure 2C. The fast response of NICD to flow cessation was used as an argument to support post-translational modification. It is not clear why Sox9 and Twist1 expression also responds so quickly.

      Sox9 and Twist1 expression does seem to respond very quickly. Whether there exists additional regulatory pathways such as Wnt, Vegf signaling that also respond to sheer stress needs to be investigated in the future.

      (6) Line 200: The sentence should end with a period.

      Sorry for the oversight. It is now corrected.

      (7) Lines 34 to 35: the authors phrase that Notch is "allowed" to be specifically activated in the AVC and outflow tract by shear stress.

      We have rephrased the statement with “enabling Notch to be specifically activated in AVC and OFT by regional increased shear stress.” Line 27

      (8) Lines 96-100: At the end of the introduction, the text is copied from the abstract. New text should be written or summarized in a different way.

      The last sentence of introduction is now changed to “The results uncovered a new mechanism whereby mechanical force serves as a primary cue for endocardial patterning in mammalian embryonic heart.” (Line 93-95)

      (9) Line 125: The term "agreed with the Dll4 transcript.."should be replaced with a better term like "overlapped" or "was identical with".

      The word “agreed” is now “overlapped”. (Line 219)

      (10) Line 291: "Thus, through these sophisticated mechanisms, the developing mouse hearts may achieve three purposes:"- The English should be adjusted here since it sounds like hearts are aiming to achieve a purpose, which is unlikely what was meant by the authors.

      This sentence is rephrased to “Thus, in the developing mouse hearts: (1) VEGF signaling is reduced to permit endocardial EMT; (2) Dll4 expression is reduced to prevent widespread endocardial Notch activation and make endocardium sensitive to flow; (3) a proper cushion size and shape is maintained by limiting the flanking endocardium to undergo EMT despite physically close to the field of BMP2 derived from of AVC myocardium (Figure 6).” (Line 402-406)

    1. eLife Assessment

      This manuscript reports useful data suggesting a critical role of two cyclin-dependent kinases, CDK8 and CDK19, in spermatogenesis. However, the data supporting the conclusion remains incomplete. This work may be of interest to reproductive biologists and physicians working on male fertility.

    2. Reviewer #1 (Public review):

      Summary:

      In this paper, Bruter and colleagues report effects of inducible deletion of the genes encoding the two paralogous kinases of the Mediator complex in adult mice. The physiological roles of these two kinases, CDK8 and CDK19, are currently rather poorly understood; although conserved in all eukaryotes, and among the most highly conserved kinases in vertebrates, individual knockouts of genes encoding CDK8 homologues in different species have revealed generally rather mild and specific effects, in contrast to Mediator itself. Here, the authors provide evidence that neither CDK8 nor CDK19 are required for adult homeostasis but they are functionally redundant for maintenance of reproductive tissue morphology and fertility in males.

      Strengths:

      The morphological data on the atrophy of the male reproductive system and the arrest of spermatocyte meiosis are solid and are reinforced by single cell transcriptomics data, which is a challenging technique to implement in vivo. The main findings are important and will be of interest to scientists in the fields of transcription and developmental biology.

      Weaknesses:

      There are several major weaknesses.

      The first is that data on general health of mice with single and double knockouts is not shown, nor are there any data on effects in any other tissues. This gives the impression that the only phenotype is in the male reproductive system, which would be misleading if there were phenotypes in other tissues that are not reported. Furthermore, given that the new data show differing expression of CDK8 and CDK19 between cell types in the testis, data for the genitourinary system in single knockouts are very sparse; data are described for fertility in figure 1E, ploidy and cell number in figure 3B and C, plasma testosterone and luteinizing hormone levels in figure 6C and 6D and morphology of testis and prostate tissue for single Cdk8 knockout in supplementary figure 1C (although in this case the images do not appear very comparable between control and CDK8 KO, thus perhaps wider fields should be shown), but, for example, there is no analysis of different meiotic stages or of gene expression in single knockouts. This might have provided insight into the sterility of induced CDK8 knockout.

      The second major weakness is that the correlation between double knockout and reduced expression of genes involved in steroid hormone biosynthesis is portrayed as a likely causal mechanism for the phenotypes observed. While this is a possibility, there are no experiments performed to provide evidence that this is the case. Furthermore, there is no evidence shown that CDK8 and/or CDK19 are directly responsible for transcription of the genes concerned.

      Finally, the authors propose that the phenotypes are independent of the kinase activity of CDK8 or CDK19 because treatment of mice for a month with an inhibitor does not recapitulate the effects of the knockout, and nor does expression of two steroidogenic genes change in cultured Leydig cells upon treatment with an inhibitor. However, there are no controls for effective target inhibition shown.

      Comments on revisions:

      This manuscript is in some ways improved - mainly by toning down the conclusions - but a few major weaknesses have not been addressed. I do not agree that it is not justified to perform experiments to investigate the sterility of single CDK8 knockout mice since this could be important and given that the new data show that while there is some overlap in expression of the two prologues, there are also significant differences in the testis. At the least, it would have been interesting and easy to do to show the expression of CDK8 and CDK19 in the single cell transcriptomics, since this might help to identify the different populations.

      The only definitive way of concluding a kinase-independent phenotype is to rescue with a kinase dead mutant. While I agree that the inhibitors have been well validated, since they did not have any effects, it is hard to be sure that they actually reached their targets in the tissue concerned. This could have been done by cell thermal shift assay. In the absence of any data on this, the conclusion of a kinase-independent effect is weak.

      Figure 2 legend includes (G) between (B) and (C), and appears to, in fact, refer to Fig 1E, for which the legend is missing the description.

      Finally, Figure S1C appears wrong. Goblet cells are not in the crypt but on the villi (so the graph axis label is wrong), and there are normally between 5 and 15 per villus, so the iDKO figure is normal, but there are a surprisingly high number of goblet cells in the controls. And normally there are 10-15 Paneth cells/crypt, so it looks like these have been underestimated everywhere. I wonder how the counting was done - if it is from images such as those shown here then I am not surprised as the quality is insufficient for quantification. How many crypts and villi were counted? Given the difficulty in counting and the variability per crypt/villus, with quantitative differences like this it is important to do quantifications blind. I personally wouldn't conclude anything from this data and I would recommend to either improve it or not include it. If these data are shown, then data showing efficient double knockout in this tissue should also accompany it, by IF, Western or PCR. Otherwise, given a potentially strong phenotype, repopulation of the intestine by unrecombined crypts might have occurred - this is quite common (see Ganuza et al, EMBO J. 2012).

    3. Reviewer #2 (Public review):

      Summary:

      The authors tried to test the hypothesis that Cdk8 and Cdk19 stabilize the cytoplasmic CcNC protein, the partner protein of Mediator complex including CDK8/19 and Mediator protein via a kinase-independent function by generating induced double knockout of Cdk8/19. However the evidence presented suffer from a lack of focus and rigor and does not support their claims.

      Strengths:

      This is the first comprehensive report on the effect of a double knockout of CDK8 and CDK19 in mice on male fertility, hormones and single cell testicular cellular expression. The inducible knockout mice led to male sterility with severe spermatogenic defects, and the authors attempted to use this animal model to test the kinase-independent function of CDK8/19, previously reported for human. Single cell RNA-seq of knockout testis presented a high resolution of molecular defects of all the major cell types in the testes of the inducible double knockout mice. The authors also have several interesting findings such as reentry into cell cycles by Sertoli cells, loss of Testosterone in induced dko that could be investigated further.

      Weaknesses:

      The claim of reproductive defects in the induced double knockout of CDK8/19 resulted from the loss of CCNC via a kinase-independent mechanism is interesting but was not supported by the data presented. While the construction and analysis of the systemic induced knockout model of Cdk8 in Cdk19KO mice is not trivial, the analysis and data is weakened by systemic effect of Cdk8 loss, making it difficult to separate the systemic effect from the local testis effect.

      The analysis of male sterile phenotype is also inadequate with poor image quality, especially testis HE sections. Male reproductive tract picture is also small and difficult to evaluate. The mice crossing scheme is unusual as you have three mice to cross to produce genotypes, while we could understand that it is possible to produce pups of desired genotypes with different mating schemes, such vague crossing scheme is not desirable and of poor genetics practice. Also using TAM treated wild type as control is ok, but a better control will be TAM treated ERT2-cre; CDK8f/f or TAM treated ERT2 Cre CDK19/19 KO, so as to minimize the impact from well-recognized effect of TAM.

      While the authors proposed that the inducible loss of CDK8 in the CDK19 knockout background is responsible for spermatogenic defects, it was not clear in which cells CDK8/19 genes are interested and which cell types might have a major role in spermatogenesis. The authors also put forward the evidence that reduction/loss of Testosterone might be the main cause of spermatogenic defects, which is consistent with the expression change in genes involved in steroigenesis pathway in Leydig cells of inducible double knockout. But it is not clear how the loss of Testosterone contributed to the loss of CcnC protein.

      The authors should clarify or present the data on where CDK8 and CDK19 as well as CcnC are expressed so as to help the readers to understand which tissues that both CDK might be functioning and cause the loss of CcnC. It should be easier to test the hypothesis of CDK8/19 stabilize CcnC protein using double knock out primary cells, instead of the whole testis.

      Since CDK8KO and CDK19KO both have significantly reduced fertility in comparison with wildtype, it might be important to measure the sperm quantity and motility among CDK8 KO, CDK19KO and induced DKO to evaluate spermatogenesis based on their sperm production.

      Some data for the inducible knockout efficiency of Cdk8 were presented in Supplemental figure 1, but there is no legend for the supplemental figures, it was not clear which band represented deletion band, which tissues were examined? Tail or testis? It seems that two months after the injection of Tam, all the Cdk8 were completely deleted, indicating extremely efficient deletion of Tam induction by two-month post administration. Were the complete deletion of Cdk8 happening even earlier ? an examination of timepoints of induced loss would be useful and instructional as to when is the best time to examine phenotypes.

      The authors found that Sertoli cells re-entered cell cycle in the inducible double knockout but stop short of careful characterization other than increased expression of cell cycle genes.

      Overall this work suffered from a lack of focus and rigor in the analysis and lack of sufficient evidence to support their main conclusions.

      Comments on revisions:

      This reviewer appreciated the authors' effort in improving the quality of this manuscript during their revision. While some concerns remain, the revision is a much improved work and the authors addressed most of my major concerns.<br /> Figure 2E CDK8 and CDK19 immunofluorescent staining images seem to show CDK8 and CDK19 location are completely distinct and in different cells, the authors need to elaborate on this results and discuss what such a distinct location means in line of their double knockout data.

      Minor comments:

      Supplemental figure 1(C) legend typo : (C) Periodic acid-Schiff stained sections of ilea of tamoxifen treated R26/Cre/ERI2 and DKO mice.

      While the effort to identify and generate new antibodies is appreciated, the specificity of the antibodies used should be examined and presented if available.

    4. Author response:

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

      The mice crossing scheme is unusual as you have three mice to cross to produce genotypes, while we could understand that it is possible to produce pups of desired genotypes with different mating schemes, such a vague crossing scheme is not desirable and of poor genetics practice.

      We thank the reviewer for this suggestion. Indeed, our scheme is not a representation of the actual breeding scheme but just a brief explanation of lineages used for the acquisition of the triple transgenic mice. We will include the full crossing scheme into the revision.

      We added to the text the explanation that all used genotypes were maintained as homozygotes and put a full breeding scheme in the supplementary figure S1A

      It is worth mentioning that single knockouts seem to show a corresponding upregulation of the level of the paralogue kinase, indicating that any lack of phenotypes might be due to feedback compensation, which would be an interesting finding if confirmed; this has not been mentioned.

      We thank the reviewer for raising an important point about the paralog upregulation. Indeed, our data on primary cells (supplementary 1B) suggests the upregulation of CDK19 in CDK8KO and vice versa. We will point this out in discussion. We plan to examine the data for the testis as soon as more tissues are available.

      We addressed this question by performing additional western blot (added to the paper fig. 2D) and found no paralogue upregulation in testes. To do that we also manufactured novel rabbit anti-mouse CDK19 antibodies described in Materials and Methods.

      The authors should clarify or present the data on where CDK8 and CDK19  as well as CcnC are expressed so as to help the readers understand which tissues both CDK might be functioning in and cause the loss of CcnC.

      Due to a limited sensitivity of single cell sequencing (only ~5,000 transcripts are sequenced from total of average 500,000 transcripts per cell, so the low expressed transcripts are not sequenced in all cells) it is challenging to firmly establish CDK8/19 positive and -negative tissues from single cell data because both transcripts are minor. This image will be included in the next version.

      In this version we have added staining by CDK8 and CDK19 antibodies on paraffin sections, showing expression in variety of cells. Additionally, we have analyzed Cdk8/CcnC presence in different testicular cell types by flow cytometry. Both methods show that not only spermatogonial stem cells express Cdk8 as was shown in McCleland et al. 2005, but also some 1n cells, 4n cells and a significant part of cKit<sup>- </sup>2n cells. We added a corresponding paragraph and figures (2E-K) to the paper. We consider this a more definitive answer to the question than RNA data.

      Furthermore, data for the genitourinary system in single knockouts are very sparse; data are described for fertility in Figure 1H, ploidy, and cell number in Figures 2B and C, plasma testosterone and luteinizing hormone levels in Figures 5C and 5D, and morphology of testis and prostate tissue for single Cdk8 knockout in Supplementary Figure 1C (although in this case the images do not appear very comparable between control and CDK8 KO, thus perhaps wider fields should be shown), but, for example, there is no analysis of different meiotic stages or of gene expression in single knockouts. It is worth mentioning that single knockouts seem to show a corresponding upregulation of the level of the paralogue kinase, indicating that any lack of phenotypes might be due to feedback compensation, which would be an interesting finding if confirmed; this has not been mentioned.

      We agree that a description of the single KO could be beneficial, but we expect no big differences with the WT or Cre-Ert. We found neither histological differences nor changes in cell counts or ratios of cell types. Our ethical committee also has concerns about sacrificing mice without major phenotypic changes, without a well formulated hypothesis about the observed effects. We plan to add histological pictures to the next version of the article.

      We have updated histological figures with new figures for iDKO and Cre+Tam mice with additional fields of view and better quality staining (2A-B).

      The second major weakness is that the correlation between double knockout and reduced expression of genes involved in steroid hormone biosynthesis is portrayed as a causal mechanism for the phenotypes observed. While this is a possibility, there are no experiments performed to provide evidence that this is the case. Furthermore, there is no evidence showing that CDK8 and/or CDK19 are directly responsible for the transcription of the genes concerned.

      We agree with the reviewer that the effects on CDK8/CDK19/CCNC could lead to the observed transcriptional changes in multiple indirect steps. There are, however, major technical challenges in examining the binding of transcription factors in the tissue, especially in Leydig cells which are a relatively minor population.  We will clarify it in the revision and strengthen this point in the discussion.

      We have added corresponding explanation in the Discussion: “We hypothesize that all these changes are caused by disruption of testosterone synthesis in Leydig cells, although, at this point, we cannot definitively prove that the affected genes are regulated by CDK8/19 directly.”

      The claim of reproductive defects in the induced double knockout of CDK8/19 resulted from the loss of CCNC via a kinase-independent mechanism is interesting but was not supported by the data presented. While the construction and analysis of the systemic induced knockout model of Cdk8 in Cdk19KO mice is not trivial, the analysis and data are weakened by the systemic effect of Cdk8 loss, making it difficult to separate the systemic effect from the local testis effect.

      We agree with the reviewer that the effects on the testes could be due to the systemic loss of CDK8 rather than specifically in the testis, and we will clarify it in the revision. We will also clarify that although our results are suggestive that the effects of CDK8/19 knockout are kinase-independent, and that the loss of Cyclin C is a likely explanation for the kinase independence, but we do not claim that it is *the* mechanism.

      In this version we added several caveats indicating that the proposed mechanism is likely, but not the only one possible.

      Also using TAM-treated wild type as control is ok, but a better control will be TAM-treated ERT2-cre; CDK8f/f or TAM-treated ERT2 Cre CDK19/19 KO, so as to minimize the impact from the well-recognized effect of TAM.  

      We used TAM-treated ERT2-cre for most of the experiments, and did not observe any major histological or physiological differences with the WT+TAM. We will make sure to present them in the revision.

      The authors found that Sertoli cells re-entered the cell cycle in the inducible double knockout but stopped short of careful characterization other than increased expression of cell cycle genes.

      Unfortunately, we were not able to perform satisfactory Ki67 staining to address this point.

      Dko should be appropriately named iDKO (induced dKO). We will make the corresponding change.

      We performed necropsy ? not the right wording here.

      Colchicine-like apoptotic bodies ? what does this mean? Not clear.

      We made appropriate changes - all DKO were renamed iDKO, necropsy changed to autopsy and cells designated as “apoptotic”.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Given the proprietary claims of the authors ("We have for the first time generated mice with the systemic inducible Cdk8 knockout on the background of Cdk19 constitutive knockout"), it does not appear acceptable and indeed might be misleading, to not describe the overall phenotypes of the mice. Are mice normal size/weight? Does an autopsy reveal anything other than atrophied genital tissue in males? Do the authors find a phenotype in the intestinal epithelium, as previously reported? (N.B. this could potentially clarify a discrepancy in the literature since the loss of the secretory lineages in double knockouts reported by the Firestein lab was not reproduced by intestinal organoid double knockout in the paper by the Fisher lab).

      We have removed the statement “for the first time”, although to the best of our knowledge this is the fact. We did not attempt to describe all the phenotypic effects of the Cdk8/19 knockout in this paper, since some of the phenotypic observations related to mouse weight and behavior varied between different laboratories involved and require additional analysis. The effect on the urogenital system was by far the most striking histological feature observed and it was carefully addressed in this paper. Other findings require additional experiments and are out of the scope of this paper and we plan to focus on them later. As per suggestion of the reviewer we performed histological analysis of DKO intestines and found the same decrease in the Paneth and goblet cells numbers as described by Dannappel et al. We added corresponding figures (Supplemental fig. 1C) to the paper.

      If the authors wish to reinforce their claims about causality of steroidogenic gene expression and phenotype, they could try rescuing the phenotype by treating mice with testosterone.

      As stated in Discussion, we hypothesized that injection of testosterone would not rescue the phenotype, as the androgen receptor signaling is also affected. However we would like to perform such an experiment, but we were not able to procure testosterone pellets at this time.

      If they wish to claim a direct effect of CDK8/19 on the expression of steroidogenic genes, they could also assess CDK8/19 binding to promoters of the genes analysed by ChIP.

      There are big technical challenges in examining the binding of transcription factors in the primary tissue, especially in Leydig cells, a minor population, so we cannot perform such an experiment.

      In order to conclude that their CDK8/19 inhibitor treatment worked, they could show target engagement by cell thermal shift assay, loss of CDK8/19 kinase-dependent gene expression, or loss of CDK8/19 substrate phosphorylation (eg interferon-induced STAT1 S727 phosphorylation) under the conditions used. Alternatively, they could show rescue with a kinase-dead allele.

      As noted in public comments - we thank the reviewer for raising this concern. The target selectivity and target engagement by the inhibitors used in this study (Senexin B and SNX631-6) have been described in other models and published. CDK8/19 engagement and target selectivity of Senexin B, used in our vitro studies, have been extensively characterized in cell-based assays (Chen et al., Cells 2019, 8(11), 1413; Zhang et al., J Med Chem. 2022 Feb 24;65(4):3420-3433.) Similar characterization has been published for SNX631-6 and its equipotent analog SNX631, which showed drastic antitumor activity when  used in vivo at the same dosing regimen as in this paper (Li et al., J Clin Invest. 2024;134(10):e176709). The comparison of the pharmacokinetics data obtained in the present study and in vitro activity of SNX631-6 in a cell-based assay suggests that the tissue concentrations of this drug should have provided substantial inhibition of Cdk8/19. Unfortunately, there are no known phosphorylation substrates specific for Cdk8/19 that can be used as pharmacodynamic markers. The widely used STAT1 phosphorylation at S727 is exerted not only by CDK8/19 but also by other kinases and shows variable response to CDK8/19 inhibition (Chen et al., Cells 2019, 8(11), 1413). In the revised MS, we have added a Western blot with pSTAT1 S727 staining of WT, 8KO, 19KO and iDKO testes. Cdk8/19 knockout did not decrease and apparently even increased the level of pSTAT1 S727, which demonstrates that this marker of CDK8/19 activity it is not suitable for our tissue type. While the evidence that Cdk8/19 kinase inhibition in the testes after in vivo drug treatment does not match the phenotype of iDKO is admittedly indirect, the same result has been obtained in the cell culture studies with Sertoli cells, where the inhibitor concentration (1 µM Senexin B) was much higher than needed for the maximal Cdk8/19 inhibition.

      Finally, I did not find any legends to supplementary figures anywhere.

      We apologize for not including legends for supplementary figures, and will correct that in the next version of the manuscript.

      Additionally, we addressed the question about the sufficiency of the lipid supply for steroidogenesis in testes. There was a possibility that steroidogenesis is impossible due to the lack of cholesterol input, but OilRed staining revealed that the situation is the opposite: lipid content in iDKO testes is significantly higher than in WT testes. We added corresponding text to the article and the supplementary Fig. S6.

    1. eLife Assessment

      Data presented in this useful report suggest a potentially new model for chemotaxis regulation in the gram-negative bacterium P. putida. Data supporting interactions between CheA and the copper-binding protein CsoR, reveal potential mechanisms for coordinating chemotaxis and copper resistance. There was, however, concern about the large number of CheA interactors identified in the initial screen and it was felt that the study was incomplete without a substantial number of additional experiments to test the model and bolster the authors' conclusions.

    2. Reviewer #2 (Public review):

      Summary:

      This manuscript focuses on the apparent involvement of a proposed copper-responsive regulator in the chemotactic response of Pseudomonas putida to Cu(II), a chemorepellent. Broadly, this area is of interest because it could provide insight into how soil microbes mitigate metal stress. Additionally, copper has some historical agricultural use as an antimicrobial, thus can accumulate in soil. The manuscript bases its conclusions on an in vitro screen to identify interacting partners of CheA, an essential kinase in the P. putida chemotaxis-signaling pathway. Much of the subsequent analysis focuses on a regulator of the CsoR/RcnR family (PP_2969).

      Weaknesses:

      The data presented in this work does not support the model (Figure 8). In particular, PP_2969 is linked to Ni/Co resistance not Cu resistance. Further, it is not clear how the putative new interactions with CheA would be integrated into diverse responses to various chemoattract/repellents. These two comments are justified below.

      PP_2969

      • The authors present a sequence alignment (Figure S5) that is the sole based for their initial assignment of this ORF as a CsoR protein. There is conservation of the primary coordinating ligands (highlighted with asterisks) known to be involved in Cu(I) binding to CsoR (ref 31). There are some key differences, though, in residues immediately adjacent to the conserved Cys (the preceding Ala, which is Tyr in the other sequences). The effect of these change may be significant in a physiological context.

      • The gene immediately downstream of PP_2969 is homologous to E. coli RcnA, a demonstrated Ni/Co efflux protein, suggesting that P2969 may be Ni or Co responsive. Indeed PP_2970 has previously been reported as Ni/Co responsive (J. Bact 2009 doi:10.1128/JB.00465-09). The host cytosol plays a critical role in determining metal-response, in addition to the protein, which can explain the divergence from the metal response expected from the alignment.

      • The previous JBact study also explains the lack of an effect (Figure 5b) of deleting PP_2969 on copper-efflux gene expression (copA-I, copA-II, and copB-II) as these are regulated by CueR not PP_2969 consistent with the previous report. Deletion of CsoR/RcnR family regulator will result in constitutive expression of the relevant efflux/detoxification gene, at a level generally equivalent to the de-repression observed in the presence of the signal.

      • Further, CsoR proteins are Cu(I) responsive so measuring Cu(II) binding affinity is not physiologically relevant (Figures 5a and S5b). The affinities of demonstrated CsoR proteins are 10-18 M and these values are determined by competition assay. The MTS assay and resulting affinities are not physiologically relevant.

      • The DNA-binding assays are carried out at protein concentrations well above physiological ranges (Figs 5c and d, and S5c, d). The weak binding will in part result from using DNA-sequences upstream of the copA genes and not from from PP_2970.

      CheA interactions

      There is no consideration given to the likely physiological relevance of the new interacting partners for CheA.

      • How much CheA is present in the cell (copies) and how many copies of other proteins are present? How would specific responses involving individual interacting partners be possible with such a heterogenous pool of putative CheA-complexes in a cell. For PP_2969, the affinity reported (Figure 5A) may lay at the upper end of the CsoR concentration range (for example, CueR in Salmonella is present at ~40 nM).

      • The two-hybrid system experiment uses a long growth time (60 h) before analysis. Even low LacZ activity levels will generate a blue colour, depending upon growth medium (see doi: 10.1016/0076-6879(91)04011-c). It is also not clear how Miller units can be accurately or precisely determined from a solid plate assay (the reference cited describes a protocol for liquid culture).

      Comments on revised version:

      The authors have replied in detail to the various comments about the original manuscripts. However, the responses are generally lengthy rationalisations of the original interpretation of the data and do not fundamentally address critical concerns raised about the physiological relevance of the results. The response appears to rest on the assumption that the numerous interacting partners obtained from the initial screen are all true positives and that all subsequent experimental results are interpreted to justify that assumption. In the case of CsoR, the experimental results and interpretation are inconsistent with previously published studies of the metal and DNA-binding properties of CsoR proteins. The following points reiterate comments from the previous review, in the hopes that the authors will, at the very least, consider the likelihood that the "CsoR" protein they have identified is in fact responsive to a different metal. Further, that the authors consider multiple possible interpretations of the data, particularly those that are inconsistent with the model/hypothesis (and take this into account in their experimental design.

      • (Figure 4) Almost all purified proteins will bind Cu(II) most tightly in vitro, followed by Zn(II) and Ni(II). This behaviour is a consequence of the Irving-Williams affinity series (doi.org/10.1038/162746a0 and doi.org/10.1039/JR9530003192, especially Figure 4) and is not considered an indicator of physiological metal preference. Biomolecules will exhibit the same behaviour as small organic ligands towards first row transition ions because of the flexibility of their structures. Thus, the results obtained are unsurprising and, because of the method used, have no physiological relevance.

      • The authors cite other in vivo work as evidence for varied metal-response by regulator proteins. However, experiments in these citations are of limited relevance because some focus on other structural classes of metalloregulator proteins (so not relevant here) while others focus on changes in metal accumulation by overexpression of the regulator protein, with no examination of the metal-specificity of the efflux protein (the key determinant of the physiological response of the regulator protein - why turn on expression of an efflux protein that can't pump out a particular metal? Finally, adding equivalent concentrations of metals to growing cells is not a good comparison as metals are toxic at different concentrations. The regulators will only have evolved to be just good enough, not perfect, with respect to selectivity. Laboratory experimental conditions often explore non-physiological conditions.

      • It is also important to re-emphasise the authors' own statements on lines 90-93 that P. putida has a CueR protein. This is consistent with the phylogenetic distribution of CueR proteins in gram-negative bacteria. The CsoR proteins, in contrast, are found only in gram-positive bacteria. This inconsistency is ignored by the authors.

      • The implications of the Irving-Williams series on metal-specific responses of bacterial metalloregulator proteins are described in the following references: 10.1016/j.cbpa.2021.102095, 10.1074/jbc.R114.588145, and 10.1038/s41589-018-0211-4). The last reference of this set provides an experimental basis for why metalloregulator affinities for Cu (and Zn and Ni) are so tight (and why the values obtained in Figure 4 in this manuscript are not relevant).

      • Similarly, the previous experimental studies of CsoR proteins not cited by the authors (10.1021/ja908372b 10.1021/bi900115w) provide rigourous experimental approaches for measuring metal and DNA-binding affinities and further highlight the weakness of the experimental design in this manuscript.

      • The DNA-binding assays are not physiologically relevant because they do not use DNA from the operator regulated by the candidate protein (why this was not explored in the revision is difficult to understand). The mobility shift observed at these high protein concentrations will result from non-specific binding. It is unsurprising that Cu(II) has an effect on DNA binding as it is added at such high concentrations relative to both protein and DNA so as to compete for DNA-binding with the protein (which binds weakly because there is no specific recognition site). The 10:1 ratio of Cu:CsoR is 10-times higher than needed as this class of proteins will show decreases in DNA-affinity in the presence of the correct metal at 1:1 stoichiometry. As indicated above, the authors need to consider alternative interpretations for their results rather than try to rationalise the results to fit the model.

      The points raised above readily address the authors' own comments in the response as to their surprise at some of the results and their inconsistency with the model.

      Even if the authors were to identify the correct metal to which the protein responds, there are still fundamental issues with experimental design and interpretation that would need to be addressed to indicate any link between the protein and chemotaxis.

    3. Author response:

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

      Reviewer #1 (Public Review):

      This report contains two parts. In the first part, several experiments were carried out to show that CsoR binds to CheA, inhibits CheA phosphorylation, and impairs P. putida chemotaxis. The second part provides some evidence that CsoR is a copper-binding protein, binds to CheA in a copper-dependent manner, and regulates P. putida response to copper, a chemorepellent. Based on these results, a working model is proposed to describe how CsoR coordinates chemotaxis and resistance to copper in P. putida. While the second part of the study is relatively solid, there are some major concerns about the first part.

      Critiques:

      (1) The rigor from prior research is not clear. In addition to talking about other bacterial chemotaxis, the Introduction should briefly summarize previous work on P. putida chemotaxis and copper resistance.

      We summarized previous results on P. putida copper resistance and added those results to the introduction section of the revised manuscript. As for chemotaxis, most studies in P. putida focused on the sensing/responding of the bacteria to different chemical compounds and the methyl-accepting chemotaxis proteins (MCPs) involved in the sensing, which is not relevant to the main content of this study. The component of the chemotaxis system in P. putida is similar to that in E. coli, and the signaling mechanism is presumably similar.

      (2) The rationale for identifying those CheA-binding proteins is vague. CheA has been extensively studied and its functional domains (P1 to P5) have been well characterized. Compared to its counterparts from other bacteria, does P. putida CheA contain a unique motif or domain? Does CsoR bind to other bacterial CheAs or only to P. putida CheA?

      The original purpose of the pull-down assay was to detect the interaction between CheA and c-di-GMP metabolizing enzymes, which was another project. However, we ignored that most c-di-GMP metabolizing enzymes were membrane proteins, and we made a mistake by using whole-cell lysate in the pull-down experiment. Thus, we failed to identify c-di-GMP metabolizing enzymes in “target” proteins of the pull-down assay. However, we found several novel “target” proteins in the pull-down assay. We wondered about the function of these proteins and the physiological roles of the interaction between CheA and these proteins, which was the primary purpose of this study. Although the function of CheA has been well characterized, most previous results focused on the role of CheA in chemotaxis, and its role in other bacterial processes was poorly studied. To extend our knowledge about CheA, we analyzed the results of the pull-down assay and decided to test the interaction between CheA and identified proteins, as well as the physiological roles of the interaction.

      BLAST results showed that the CheA of P. putida shared 41.12% sequence similarity with the CheA of E. coli, and the CheA of P. putida had a similar domain pattern to those CheAs from other bacteria. To test whether  CsoR<sub>P. putida</sub> interacted with CheA from other bacteria, we performed a BTH assay to investigate the interaction between  CsoR<sub>P. putida</sub> and eight CheAs, including CheA from E. coli, CheA from A. caldus, CheA from B. diazoefficiens, CheA from B. subtilis, CheA from L. monocytogenes, CheA from P. fluorescens, CheA from P. syringae, and CheA from P. stutzeri. As shown in the following Fig. 1,  CsoR<sub>P. putida</sub> could interact with CheA from A. caldus, B. subtilis, L. monocytogenes, P. fluorescens, P. syringae, and P. stutzeri. Besides, among these strains, cheA and csoR coexist in A. caldus, B. diazoefficiens, B. subtilis, L. monocytogenes, P. fluorescens, P. syringae, and P. stutzeri. We previously tested the interaction of the two proteins from these bacterial species. The results showed that the CheA-CsoR interaction existed between proteins from A. caldus, B. subtilis, P. syringae, and P. stutzeri (Fig. 7 in the manuscript). However, CheA and CsoR from B. diazoefficiens, L. monocytogenes, and P. fluorescens showed no apparent interaction (Fig. 7 in the manuscript). These results suggested that unique amino acid sequences in the two proteins might be required to achieve interaction.

      (3) Line 133-136, "Collectively, using pull-down, BTH, and BiFC assays, we identified 16 new CheA-interacting proteins in P. putida." It is surprising that so many proteins were identified but none of them were chemotaxis proteins, in particular those known to interact with CheA, such as CheW, CheY and CheZ, which raises a concern about the specificity of these methods. BTH and BiFC often give false-positive results and thus should be substantiated by other approaches such as co-IP, surface plasmon resonance (SPR), or isothermal titration calorimetry (ITC) along with mutagenesis studies.

      The response regulator CheY and the phosphatase CheZ (two proteins known to be associated with CheA) were identified in the pull-down assay (Table S1), and the two proteins showed high Log<sub>2</sub>(fold change) values, indicating that they were obtained in the pull-down assay with high amount in the experimental group and low amount in the control group. Our study aimed to identify new CheA-interacting proteins; thus, the two proteins (CheY and CheZ) were not included in subsequent investigations. The CheA-interacting proteins were initially obtained through an in vitro assay (pull-down), followed by an in vivo assay (BTH and BiFC) to test the interaction further. Only proteins that showed positive results in all three assays were considered trustworthy CheA-interacting proteins and kept for further study.

      (4) Line 147-149, "Fig. 2a, five strains (WT+pcsoR, WT+pispG, WT+pnfuA, WT+pphaD, and WT+pPP_1644) displayed smaller colony than the control strain (WT+pVec), indicating a weaker chemotaxis ability in these five strains." If copper is a chemorepellent, these strains should swim away from high concentrations of copper; thus, the sizes of colonies couldn't be used to measure this response. In the cited reference (reference 29), bacterial response to phenol was measured using a response index (RI).

      Except for CsoR, the rest of the CheA-interacting proteins had no direct connection with copper and were involved in different processes (Table S1). A reasonable speculation is that these proteins involved in different processes can integrate signals from specific processes into chemotaxis by regulating CheA autophosphorylation, leading to better regulation of chemotaxis according to intracellular physiological state. We used semisolid nutrient agar plates to test and compare bacterial chemotaxis ability. In a fixed attractant/repellent gradient, chemokine, such as copper, can lead to two subpopulations traveling at different speeds, with the slower one being held back by the chemokinetic drift. In the case of semisolid plate migration, bacteria with chemotaxis ability formed large colonies by generating their gradient by consuming nutrients/producing toxic metabolic waste and following attractant/repellent gradients leading outward from the colony origin (Cremer et al., 2019. Nature 575:658–663). The observation of successive sharp circular bands (rings) progressing outward from the inoculation point was taken to confirm the chemotaxis genotype, and mutants without chemotaxis spread out uniformly and formed a small colony (Wolfe and Berg, PNAS. 1989, 86:6973-6977). In our experiment, we were unsure about the signals/chemokines of each target protein, so we could not design a fixed attractant/repellent gradient. Besides, all target proteins interacted with CheA, which is a crucial factor in chemotaxis, and we assume that these proteins would affect chemotaxis under overexpression conditions. Thus, we used semisolid nutrient plates to test and compare bacterial chemotaxis ability.

      (5) Figures 2 and 3 show both CsoR and PhaD bind to CheA and inhibit CheA autophosphorylation. Do these two proteins share any sequence or structural similarity? Does PhaD also bind to copper? Otherwise, it is difficult to understand these results.

      Thanks a lot. This is an enlightening comment. CsoR is a protein with a size of 10.8 kDa, and PhaD is 23.1 kDa. Because of the difference in size, we took it for granted that the two proteins were not similar. We recently compared their sequence on NCBI BLAST. Although both CsoR and PhaD are transcriptional regulators and interact with CheA, they have no significant sequence similarity. In terms of protein structure, we predicted their structures using AlphaFold. The results showed that CsoR consisted of three α-helixes and PhaD consisted of nine α-helixes (new Fig. S5a and S5b in the manuscript). We further compared their structure using Pymol but found no significant similarity between the two proteins (new Fig. S5c in the manuscript).

      PhaD is a TetR family transcriptional regulator located adjacent to the genes involved in PHA biosynthesis, and it behaves as a carbon source-dependent activator of the pha cluster related to polyhydroxyalkanoates (PHAs) biosynthesis (de Eugenio et al., Environ Microbiol. 2010, 12:1591-1603; Tarazona et al., Environ Microbiol. 2020, 22:3922-3936). Bacterial PHAs are isotactic polymers synthesized under unfavorable growth conditions in the presence of excess carbon sources. PHAs are critical in central metabolism, acting as dynamic carbon reservoirs and reducing equivalents (Gregory et al., Trends Mol Med. 2022, 28:331-342). The interaction between PhaD and CheA leads us to speculate that there might be some connection between PHA synthesis and bacterial chemotaxis. For example, chemotaxis helps bacteria move towards specific carbon sources that favor PHA synthesis, and the interaction between PhaD and CheA weakens chemotaxis, causing bacteria to linger in areas rich in these carbon sources. This is an interesting hypothesis worth testing in the future.

      (6) Line 195-196, "CsoR/PhaD had no apparent influence on the phosphate transfer between CheA and CheY". CheA controls bacterial chemotaxis through CheY phosphorylation. If this is true, how do CsoR and PhaD affect chemotaxis?

      During the autophosphorylation assay, CheA was mixed with CsoR/PhaD and incubated for about 10 min before adding [<sup>32</sup>P]ATP[γP]. Thus, the effect of CsoR/PhaD on CheA autophosphorylation happened through the assay, and a significant inhibition effect was observed in the final result. Regarding transphosphorylation, CheA was mixed with ATP and incubated for about 30 min, at which time the autophosphorylation of CheA happened. Then, CsoR/PhaD and CheY were added to the phosphorylated CheA to investigate transphosphorylation. CsoR and PhaD affected chemotaxis via inhibiting CheA autophosphorylation, which was a crucial step in chemotaxis signaling, and the decrease in CheA autophosphorylation caused decreased chemotaxis.

      (7) Figure 3 shows that CsoR/PhaD bind to CheA through P1, P3, and P4. This result is intriguing. All CheA proteins contain these three domains. If this is true, CsoR/PhaD should bind to other bacterial CheAs too. That said, this experiment is premature and needs to be confirmed by other approaches.

      As replied to comment (2) above, we performed a BTH assay to investigate whether  CsoR<sub>P. putida</sub> interacts with CheA from other bacterial species. The results revealed that  CsoR<sub>P. putida</sub> interacted with CheA from A. caldus, B. subtilis, L. monocytogenes, P. fluorescens, P. syringae, and P. stutzeri, but not with CheA from E. coli and B. diazoefficiens. This result suggested that CheA-CsoR interaction required specific/unique amino acid sequence patterns in the two proteins, and similar domain composition alone was insufficient.

      (8) Figure 5, does PhaD contain these three residues (C40, H65, and C69)? If not, how does PhaD inhibit CheA autophosphorylation and chemotactic response to copper?

      No, there is no significant sequence similarity between PhaD and CsoR, and PhaD contains none of the three residues of CsoR (C40, H65, and C69). The size of the two proteins is also quite different (CsoR 10.8 kDa, PhaD 23.1 kDa). The structure alignment also revealed no apparent similarity between the predicted structures of PhaD and CsoR (new Fig. S5c in the manuscript). Nevertheless, CsoR and PhaD interacted with CheA through its P1, P3, and P4 domains. It is interesting how the two proteins interacted with CheA, but we currently have no answer.

      (9) Does deletion of cosR or cheA have any impact on P. putida resistance to high concentrations of copper?

      No, deletion of cosR/cheA had no noticeable impact on P. putida's resistance to high concentrations of copper. We performed a growth assay to test the effect of CsoR and CheA on copper resistance under both liquid and solid medium conditions. The copper concentration was set at 0, 200, 500, 1000 μM. With the increase of copper concentration, the growth of bacteria was gradually inhibited, but the growth trends of csoR mutant, cheA mutant, and complementary strains were similar to that of the wild-type strain (new Fig. S6b and S6c in the manuscript). We speculated that this might be attributed to CsoR being a repressor and inhibiting gene expression in the absence of copper. When copper existed, the inhibitory effect of CsoR was relieved, which is the same as that in the csoR mutant. Besides, although deletion of cosR led to a slight increase (about 1.3-fold) in the expression of copper resistance genes (Fig. 4b in the manuscript), its effect on gene expression was much weaker than its homologous protein in other bacterial species. In M. tuberculosis, B. subtilis, C. glutamicum, L. monocytogenes, and S. aureus, deletion of csoR resulted in an about 10-fold increase in the expression of target genes in the absence of copper. This difference might be attributed to several vital regulators that activated the expression of copper-resistance genes in response to copper in P. putida, such as CueR and CopR (Adaikkalam and Swarup, Microbiology. 2002, 148:2857-2867; Hofmann et al., Int J Mol Sci, 2021, 22:2050; Quintana et al., J Biol Chem, 2017, 292:15691-15704). CueR positively regulated the expression of cueA, encoding a copper-transporting P1-type ATPase that played a crucial role in copper resistance. CopR was essential for expressing several genes implicated in cytoplasmic copper homeostasis, such as copA-II, copB-II, and cusA. The existence of these positive regulators makes the function of CosR a secondary or even dispensable insurance in the expression of copper-resistance genes. Consistent with this, there is no CosR homolog in P. aeruginosa, and copper homeostasis is mainly controlled by CueR and CopR.

      Reviewer #2 (Public Review):

      This manuscript focuses on the apparent involvement of a proposed copper-responsive regulator in the chemotactic response of Pseudomonas putida to Cu(II), a chemorepellent. Broadly, this area is of interest because it could provide insight into how soil microbes mitigate metal stress. Additionally, copper has some historical agricultural use as an antimicrobial, thus can accumulate in soil. The manuscript bases its conclusions on an in vitro screen to identify interacting partners of CheA, an essential kinase in the P. putida chemotaxis-signaling pathway. Much of the subsequent analysis focuses on a regulator of the CsoR/RcnR family (PP_2969).

      Weaknesses:

      The data presented in this work does not support the model (Figure 8). In particular, PP_2969 is linked to Ni/Co resistance, not Cu resistance. Further, it is not clear how the putative new interactions with CheA would be integrated into diverse responses to various chemoattract/repellents. These two comments are justified below.

      Thanks a lot for all these comments. Before designing experiments to explore the function of PP_2969, we found three clues: (i) its sequence showed 38% similarity to the copper-responsive regulator CsoR of M. tuberculosis, and the three conserved amino acids essential for copper-binding were conserved in PP_2969; (ii) it located next to a Ni<sup>2+</sup>/Co<sup>2+</sup> transporter (PP_2968) on the genome; (iii) a previous report revealed that PP_2969 (also named MreA) expression increased during metal stress, and overexpression of PP_2969 in P. putida and E. coli led to metal accumulation (Zn, Cd, and Cr) (Lunavat et al., Curr Microbiol. 2022, 79:142). These clues indicate that the function of PP_2969 is related to metal-binding, but it remains to be explored which metal(s) PP_2969 binds to. Thus, we played MST assay to test the interaction between PP_2969 and metals, including copper (Cu<sup>2+</sup>), zinc (Zn<sup>2+</sup>), nickel (Ni<sup>2+</sup>), cobalt (Co<sup>2+</sup>), cadmium (Cd<sup>2+</sup>), and magnesium (Mg<sup>2+</sup>). The result showed that PP_2969 was bound to three metal ions (Cu<sup>2+</sup>, Zn<sup>2+</sup>, Ni<sup>2+</sup>), and the binding to Cu<sup>2+</sup> was the strongest. Besides, the EMSA assay revealed that Cu<sup>2+</sup>/Ni<sup>2+</sup>/Zn<sup>2+</sup> inhibited the interaction between PP_2969 and promoter DNA, and Cu<sup>2+</sup> showed the most substantial inhibitory effect at the same concentration. These results suggested that PP_2969 was mainly bound to Cu<sup>2+</sup>, followed by Zn<sup>2+</sup> and Ni<sup>2+</sup>. To further test whether PP_2969 functioned as a metal-responsive repressor and which metal resistance was related to its target gene, we constructed a PP_2969 deletion mutant and complementary strain and performed a qPCR assay to compare the expression of metal resistance-related genes. 14 metal-resistant-related genes were chosen as targets. The results showed that PP_2969 deletion led to a weak but significant increase (about 1.3-fold) in expression of 10 genes, including three copper-resistance genes (copA-I, copA-II, and copB-II), one nickel-resistance gene (nikB), two cadmium-resistance genes (cadA-I and cadA-III), one cobalt-resistance gene (cbtA), and three multiple metal-resistance genes (czcC-I, czcB-II, and PP_0026) (Fig. 4b, Fig. S5a in the manuscript). Meanwhile, complementation with a multicopy plasmid containing the PP_2969 gene decreased the gene expression in Δ_PP_2969_. Although PP_2969 regulated the expression of multiple metal resistance genes, it showed the most robust binding to Cu<sup>2+</sup>. Thus, we considered its primary function as a Cu<sup>2+</sup>-responsive regulator.

      As for the second comment, “How would the putative new interactions with CheA be integrated into diverse responses to various chemoattract/repellents?”, We have some speculations based on our results and previous reports. For example, PP_2969 interacted with CheA and decreased its autophosphorylation activity, and copper inhibited the interaction between CheA and PP_2969. In the absence of copper, PP_2969 binds to promoters to inhibit the expression of copper resistance genes, and it also binds to CheA to inhibit its autophosphorylation, resulting in lower chemotaxis. When the bacteria move to an area of high copper concentration, PP_2969 binds to copper and falls off the DNA promoter, leading to higher expression of copper resistance genes. Meanwhile, copper-binding of PP_2969 decreases its interaction with CheA, increasing CheA autophosphorylation promoting chemotaxis, and bacteria swim away from the high copper concentration. Another attractive target protein is PhaD, a TetR family transcriptional regulator located adjacent to the genes involved in PHA biosynthesis, and it behaves as a carbon source-dependent activator of the pha cluster related to polyhydroxyalkanoates (PHAs) biosynthesis (de Eugenio et al., Environ Microbiol. 2010, 12:1591-1603; Tarazona et al., Environ Microbiol. 2020, 22:3922-3936). Bacterial PHAs are isotactic polymers synthesized under unfavorable growth conditions in the presence of excess carbon sources. PHAs are critical in central metabolism, acting as dynamic carbon reservoirs and reducing equivalents (Gregory et al., Trends Mol Med. 2022, 28:331-342). The interaction between PhaD and CheA leads us to speculate that there might be some connection between PHA synthesis and bacterial chemotaxis. For example, chemotaxis helps bacteria move towards particular carbon sources that favor PHA synthesis; the regulator PhaD activates the genes related to PHA synthesis. Meanwhile, the interaction between PhaD and CheA weakens chemotaxis, causing bacteria to linger in areas rich in these carbon sources. Collectively, we speculate that by interacting with CheA and modulating its autophosphorylation, target proteins such as CsoR/PhaD integrate signals from their original process pathway into chemotaxis signaling.

      PP_2969

      (1) The authors present a sequence alignment (Figure S5) that is the sole basis for their initial assignment of this ORF as a CsoR protein. There is a conservation of the primary coordinating ligands (highlighted with asterisks) known to be involved in Cu(I) binding to CsoR (ref 31). There are some key differences, though, in residues immediately adjacent to the conserved Cys (the preceding Ala, which is Tyr in the other sequences). The effect of these changes may be significant in a physiological context.

      We constructed a point mutation in PP_2969 by replacing the Ala residue before the conserved Cys with a Tyr (CsoR<sub>A39Y</sub>) and then analyzed the effect of this mutation on CsoR. As shown in Author response image 1a, CsoR<sub>A39Y</sub> showed similar promoter-binding ability as the wild-type CsoR and the presence of Cu<sup>2+</sup> abolished the interaction between CsoR<sub>A39Y</sub> and DNA, suggesting that the A39 residue in PP_2969 was not essential for the DNA-binding and Cu<sup>2+</sup>-binding abilities. Besides, CsoR<sub>A39Y</sub> interacted with CheA as the wild-type CsoR did (Author response image 1b), indicating that the Ala39 residue was not required to interact with CheA.

      The CsoR from B. subtilis has a Tyr before the conserved Cys, which is the same as other sequences, and the BTH result showed that interaction existed between CsoR and CheA from B. subtilis (Fig. 7 in the manuscript).

      Author response image 1.

      The effect of CsoR point mutation (CsoR<sub>A39Y</sub>) on the DNA-binding and Cu<sup>2+</sup>-binding abilities of CsoR. (a) Analysis for interactions between CsoR/CsoR<sub>A39Y</sub> and copA-I promoter DNA using EMSA. The concentrations of CsoR/CsoR<sub>A39Y</sub> and Cu<sup>2+</sup> added in each lane are shown above the gel. Free DNA and protein-DNA complexes are indicated. (b) The interaction between CsoR/CsoR<sub>A39Y</sub> and CheA was tested by BTH. Blue indicates protein-protein interaction in the colony after 60 h of incubation, while white indicates no protein-protein interaction. CK+ represents positive control, and CK- represents negative control.

      (2) The gene immediately downstream of PP_2969 is homologous to E. coli RcnA, a demonstrated Ni/Co efflux protein, suggesting that P2969 may be Ni or Co responsive. Indeed PP_2970 has previously been reported as Ni/Co responsive (J. Bact 2009 doi:10.1128/JB.00465-09). The host cytosol plays a critical role in determining metal response, in addition to the protein, which can explain the divergence from the metal response expected from the alignment.

      Correction: The gene immediately upstream (not downstream) of PP_2969 (the ID is PP_2968, not PP_2970) is homologous to E. coli RcnA, a demonstrated Ni/Co efflux protein. The previous JBact study (J. Bact 2009 doi:10.1128/JB.00465-09) named PP_2968 as MrdH, and mrdH disruption led to sensitivity to cadmium, zinc, nickel, and cobalt, but not copper. Their results also revealed that MrdH was a broad-spectrum metal efflux transporter with a substrate range including Cd<sup>2+</sup>, Zn<sup>2+</sup>, and Ni<sup>2+</sup>. However, the role of MrdH in Cu<sup>2+</sup> efflux was not tested. Commonly, metal efflux transporter has a broad substrate spectrum, allowing transporters to influence bacterial resistance to a variety of metals (Munkelt et al., J Bacteriol. 2004, 186:8036-8043; Grass et al., J Bacteriol. 2005, 187:1604-1611; Nies et al., J Ind Microbiol. 1995, 14:186-199; Kelley et al., Metallomics. 2021, 13:mfaa002). Our results showed that PP_2969 bound to Cu<sup>2+</sup>, Zn<sup>2+</sup>, and Ni<sup>2+</sup> under our experimental conditions, and CsoR regulated the expression of genes related to Cu<sup>2+</sup>, Zn<sup>2+</sup>, and Ni<sup>2+</sup> resistance, indicating that CsoR was involved in resistance to these metals. But the binding of CsoR to Cu<sup>2+</sup> was the strongest, and Cu<sup>2+</sup> showed the most substantial inhibitory effect on CsoR-DNA interaction. Thus, we considered its primary function as a Cu<sup>2+</sup>-responsive regulator.

      (3) The previous JBact study also explains the lack of an effect (Figure 5b) of deleting PP_2969 on copper-efflux gene expression (copA-I, copA-II, and copB-II) as these are regulated by CueR not PP_2969 consistent with the previous report. Deletion of CsoR/RcnR family regulator will result in constitutive expression of the relevant efflux/detoxification gene, at a level generally equivalent to the de-repression observed in the presence of the signal.

      We performed qPCR to test the effect of PP_2969 on gene expression, and we chose 14 target genes, including copper-resistance genes, nickel-resistance genes, zinc-resistance genes, cadmium-resistance genes, and cobalt-resistance genes. The results showed that PP_2969 deletion led to a weak but significant increase (about 1.3-fold) in the expression of 10 genes (Fig. 4b, new Fig. S5a in the manuscript), and complementation with a multicopy plasmid containing PP_2969 gene decreased the gene expression in Δ_PP_2969_. We were confused about these results. Why was the effect of PP_2969 on gene expression so weak? Did we pick the wrong target genes? In other bacteria, deletion of csoR led to an about ten-fold increase in gene expression, generally equivalent to the de-repression observed in the presence of metal. Thus, to further identify target genes, we performed RNA-seq to compare the gene expression in WT and Δ_PP_2969_ without copper. The result surprised us because no gene expression levels changed more than two-fold (data not shown). This result might be attributed to several vital regulators that activated the expression of metal-resistance genes in response to metal in P. putida, such as CueR and CopR (Adaikkalam and Swarup, Microbiology. 2002, 148:2857-2867; Hofmann et al., Int J Mol Sci, 2021, 22:2050; Quintana et al., J Biol Chem, 2017, 292:15691-15704). CueR positively regulated the expression of cueA, encoding a copper-transporting P1-type ATPase that played a crucial role in copper resistance. CopR was essential for expressing several genes implicated in cytoplasmic copper homeostasis, such as copA-II, copB-II, and cusA. The existence of these positive regulators might make the function of CosR a secondary or even dispensable insurance in the expression of copper-resistance genes. Consistent with this, there is no CosR homolog in P. aeruginosa, and copper homeostasis is mainly controlled by CueR and CopR.

      (4) Further, CsoR proteins are Cu(I) responsive so measuring Cu(II) binding affinity is not physiologically relevant (Figures 5a and S5b). The affinities of demonstrated CsoR proteins are 10-18 M and these values are determined by competition assay. The MTS assay and resulting affinities are not physiologically relevant.

      Thank you for this enlightening comment. This question also confused us during our experiment. The first study on CsoR from Mycobacterium tuberculosis showed that CsoR bound a single-monomer mole equivalent of Cu(I) to form a trigonally coordinated complex, and that was a convincing result from protein structure analysis (Liu et al., Nat Chem Biol. 2007, 3:60-68). They further revealed that the presence of Cu(I) in the EMSA assay abolished the DNA-binding ability of CsoR, but the impact of Cu(II) was not tested. Besides, their results also showed that adding CuCl<sub>2</sub> in the medium induced the expression of the cso operon involved in copper resistance. Perhaps Cu(II) converted to Cu(I) and then bound to CsoR in bacterial cells. Later studies in diverse bacterial species (including Listeria monocytogenes, Corynebacterium glutamicum, Deinococcus radiodurans, and Thermus thermophilus) showed that in vitro assays with Cu(II) abolished the DNA-binding ability of CsoR, indicating that CsoR bound to both Cu (I) and Cu(II) (Corbett et al., Mol Microbiol. 2011, 81:457-472; Teramoto et al., Biosci Biotechnol Biochem. 2012, 76:1952-1958; Zhao et al., Mol Biosyst. 2014, 10:2607-2616; Sakamoto et al., Microbiology. 2010, 156:1993-2005). Here, our results from in vitro assays (MST and EMSA) showed that CsoR bound to Cu(II) and Cu(II) affected the interaction between CsoR and promoter DNA. Compounds containing Cu(I) are poorly soluble in water and easily oxidized by Cu(II). DTT can reduce Cu(II) to Cu(I) (Krzel et al., J Inorg Biochem. 2001, 84:77-88). To test whether Cu(I) bound to CsoR and affected its DNA-binding ability, we recently performed an EMSA assay with the addition of CuCl<sub>2</sub>/DTT/CuCl<sub>2</sub>+DTT. As shown in Fig. 4d, the addition of DTT (0.1 and 1 mM) decreased CsoR-DNA interaction in the presence of 0.2 mM CuCl<sub>2</sub>, while the addition of DTT alone had no apparent influence on CsoR-DNA interaction, indicating that DTT enhanced the inhibition of CuCl<sub>2</sub> on CsoR-DNA interaction, and the Cu(I) converted from Cu(II) by DTT had stronger inhibitory effect than Cu(II) on CsoR-DNA interaction. Together, these results suggested that CsoR bound to Cu(I) more strongly than it bound to Cu(II). We have added these results to the new version of manuscript.

      (5) The DNA-binding assays are carried out at protein concentrations well above physiological ranges (Figures 5c and d, and S5c, d). The weak binding will in part result from using DNA sequences upstream of the copA genes and not from PP_2970.

      We performed the vitro DNA-binding assay several times, and the lowest CsoR concentration used to obtain a shifted band was about 3 μM, and a higher concentration (15 μM) caused total DNA binding. Thus, we used the concentration of 15 and 20 μM to test the effect of metal on protein-DNA interaction in the assay. We also realized that these concentrations were above physiological ranges. We considered that the in vitro DNA-binding assay was only a mimic of the in vivo process, and the extracellular physiological conditions in EMSA might restrict the activity of CsoR. Besides, we recently performed EMSA to investigate the interaction between CsoR and its own promoter (csoRpro). As shown in Author response image 2, CsoR bound to csoRpro with a similar intensity to that it bound to copA-Ipro. Thus, the weak binding was not caused by the promoter used in the assay. 

      Author response image 2.

      The binding of CsoR to its own promoter (csoRpro) and copA-I promoter (copA-1pro) in EMSA. The concentrations of CsoR added in each lane are shown above the gel. Free DNA and CsoR-DNA complex are indicated.

      CheA interactions

      (1) There is no consideration given to the likely physiological relevance of the new interacting partners for CheA.

      Thank you for this comment. The initial purpose of this research was to identify new CheA-interacting proteins to broaden our knowledge of CheA and bacterial chemotaxis. Thus, we are currently focusing on the effect of the interaction on CheA and chemotaxis and trying to find the link between different processes and bacterial chemotaxis. We infer that the interaction between these new interacting partners and CheA can integrate signals from different pathways into the chemotaxis signaling pathway so that bacteria can better sense and adapt to different environments. Besides, the other role of the interaction, which is the influence of CheA on these new interacting partners, is also an exciting question that remains to be answered. Among the 16 new CheA-interacting proteins, five showed significant influence on chemotaxis, and the remaining 11 proteins had no obvious impact on chemotaxis (Fig. 2a in the manuscript). CsoR and PhaD inhibited CheA autophosphorylation, and here we focused on the effect of CsoR on chemotaxis. We also investigated the impact of CheA on CsoR, such as gene regulation and copper resistance. However, the results showed that CheA had no obvious influence on these functions of CsoR. The interactions between CheA and these proteins may be physiologically biased, with some interactions affecting the function of CheA and others mainly affecting the function of partners. Future studies on the function of these new CheA-interacting proteins and the role of CheA in regulating their functions would further expand our knowledge of CheA.

      (2) How much CheA is present in the cell (copies) and how many copies of other proteins are present? How would specific responses involving individual interacting partners be possible with such a heterogenous pool of putative CheA-complexes in a cell? For PP_2969, the affinity reported (Figure 5A) may lay at the upper end of the CsoR concentration range (for example, CueR in Salmonella is present at ~40 nM).

      Thank you for this insightful comment. We don’t know the copy number of CheA and other proteins in the cell. We were also initially surprised and felt skeptical about the reliability of CheA interaction with so many proteins. CheA interacts with CheY, CheW, and CheB in the classical chemotaxis pathway. This study found 16 new CheA-interacting proteins using pull-down assay and subsequent analysis. Moreover, in another unpublished result, we found that CheA interacted with eight c-di-GMP-metabolizing proteins, and CheA transferred the phosphate group to one of them. Together, it seemed that CheA could interact with at least 27 proteins. With such a heterogeneous pool of CheA-complexes, performing a specific response seemed difficult. However, several previous studies have reported the example of one protein interacting with dozens of proteins. For example, the c-di-GMP effector LapD in Pseudomonas fluorescens and Pseudomonas putida can interact with a dozen different c-di-GMP-metabolizing proteins (Giacalone et al., mBio. 2018, 9:e01254-18; Nie et al., Mol Microbiol. 2024, 121:1-17.) In Escherichia coli, a subset of DGCs and PDEs operated as central interaction hubs in a larger “supermodule” by interacting with dozens of proteins (Sarenko et al., mBio. 2017, 8:e01639-17). We infer that the expression of different CheA-interacting proteins might happen at different growth stages or under different conditions, and their interaction with CheA under that stage/condition changed bacterial chemotaxis or the process in which the target protein was involved.

      (3) The two-hybrid system experiment uses a long growth time (60 h) before analysis. Even low LacZ activity levels will generate a blue color, depending upon growth medium (see doi: 10.1016/0076-6879(91)04011-c). It is also not clear how Miller units can be accurately or precisely determined from a solid plate assay (the reference cited describes a protocol for liquid culture).

      We didn’t observe a blue color on the colony after 60 h growth on a plate under our experimental conditions. The BTH experiment was described as follows: After transforming the two plasmids into E. coli BTH101 cells, the plates containing transformants were placed at 28° for 48 h, at which time the colonies of the transformants were big enough to be picked up and incubated in a liquid medium for 24 h at 28°. Then, 5 μL of the culture was spotted onto an LB agar plate supplemented with antibiotics, X-gal, and IPTG and incubated for 60 h at 28° before taking the photos. After the photos were taken, the bacteria on the plate were scraped off and resuspended with buffer, and then the LacZ activity of the bacteria was tested. According to our experience, culture at 28°(lower than 30°) is a critical condition, and we have not observed false positives in BTH assays under this condition.

      Reviewer #1 (Recommendations For The Authors):

      In addition to genetic and biochemical approaches, structural studies should be conducted to elucidate the molecular interaction between CheA and CsoR with/without copper.

      It would be more logical to first establish the role of CsoR in copper regulation and chemotaxis (the second part of this report) and then investigate its underpinning mechanism (the first part).

      Thank you for these recommendations. Structural analysis can reveal more details about the molecular mechanism of CheA-CsoR interaction, but we currently don’t have sufficient experimental conditions for such structural analysis.

      As for the presentation logic of the results, we wrote the manuscript following the sequence of experiments. Firstly, screening of CheA interacting proteins (pull-down assay) was conducted, and then the influence of interacting proteins on the chemotaxis of strains and CheA autophosphorylation activity was detected. Based on these results, we obtained two proteins, CsoR and PhaD, and decided to go deeper into the function of CsoR and its effect on chemotaxis. We considered that this writing logic reflected our research design better and could also lay a foundation for future exploration of the functions of other interacting proteins and the physiological significance of interactions.

      Reviewer #2 (Recommendations For The Authors):

      A huge amount of effort has gone into this work.

      It would be good to see at least one of the newly identified interactions turn out to be physiologically relevant.

      The experimental tools appear to be available to do this, but it is critical to consider how these tools can lead to attempts to prove rather than test and possibly refute a model or hypothesis. In particular, please consider some of the comments about the physiological relevance of affinities when generating models.

      Thank you for these recommendations. Our study aimed to screen new interacting proteins of CheA and explore how new interacting proteins affect CheA activity and bacterial chemotaxis, thereby broadening our understanding of chemotaxis. However, the impact of each protein-protein interaction has two sides: the influence of A to B and B to A. During experimental design, we focused more on the influence of identified interacting proteins on CheA function and chemotaxis but paid less attention to the function of interacting proteins and the influence of the interaction on their function. Moreover, our study found that the influence of protein-protein interaction was biased. In the interaction between CsoR and CheA, CsoR mainly affected the function of CheA and then affected the chemotaxis, while CheA had no significant effect on the function of CsoR. This might be attributed to the weak effect of CsoR in regulating metal resistance in P. putida, and we speculated that this interaction was more about favoring the sensing and avoiding metal stress. In addition, we planned to explore the interaction between CheA and another interacting protein (PhaD) in the future, reveal the effect of the interaction on PhaD function (regulation of PHAS synthesis in bacteria), and explore the effect of the interaction on CheA function and chemotaxis, to find out whether the association existed between PHAS anabolism and bacterial chemotaxis. Besides, for those proteins that did not have significant effects on CheA autophosphorylation and bacterial chemotaxis, we speculated that CheA might affect their function/activity through interactions, which meant that the physiological effects of the interaction mainly reflected through the interacting protein rather than CheA. These are speculations that need to be tested by experiments.

    1. eLife Assessment

      This important study identifies a new class of small molecules that activate the integrated stress response (ISR) via the kinase HRI. Convincing evidence, including the image analysis pipeline, indicates that two of these compounds promote mitochondrial elongation and protect against mitochondrial fragmentation caused by chemical stress conditions or by genetic alterations. These findings open an avenue for new strategies for mitochondrial dysfunction targeting linked to ISR alterations.

    2. Reviewer #1 (Public review):

      Summary:

      This manuscript (Baron, Oviedo et al., 2024) builds on a previous study from the Wiseman lab (Perea, Baron et al., 2023) and describes the identification of novel nucleoside mimetics that activate the HRI branch of the ISR and drive mitochondrial elongation. The authors develop an image processing and analysis pipeline to quantify the effects of these compounds on mitochondrial networks and show that these HRI activators mitigate ionomycin driven mitochondrial fragmentation. They then show that these compounds rescue mitochondrial morphology defects in patient-derived MFN2 mutant cell lines.

      Strengths:

      The identification of new ISR modulators opens new avenues for biological discovery surrounding the interplay between mitochondrial form/function and the ISR, a topic that is of broad interest. Conceptually, this work suggests that such compounds might represent new potential therapeutics for certain mitochondrial disorders. Additionally, the development of a quantitative image analysis pipeline is valuable and has the potential to extract subtle effects of various treatments on mitochondrial morphology.

      Weaknesses:

      While the ISR modulators described here correct the morphology of mitochondria in MFN2.D414V mutant cells, the impact of these compounds on the function of mitochondria in the mutant cells remains unaddressed. Sharma et al., 2022 provide data for a deficit in mitochondrial OCR in MFN2.D414V cells which, if rescued by these compounds, would strengthen the argument that pharmacological ISR kinase activation is a strategy for targeting the functional consequences of the dysregulation of mitochondrial form.

    3. Reviewer #2 (Public review):

      Summary.

      Mitochondrial dysfunction is associated with a wide spectrum of genetic and age-related diseases. Healthy mitochondria form a dynamic reticular network and constantly fuse, divide, and move. In contrast, dysfunctional mitochondria have altered dynamic properties resulting in fragmentation of the network and more static mitochondria. It has recently been reported that different types of mitochondrial stress or dysfunction activate kinases that control the integrated stress response, including HRI, PERK and GCN2. Kinase activity results in decreased global translation and increased transcription of stress response genes via ATF4, including genes that encode mitochondrial protein chaperones and proteases (HSP70 and LON). In addition, the ISR kinases regulate other mitochondrial functions including mitochondrial morphology, phospholipid composition, inner membrane organization, and respiratory chain activity. Increased mitochondrial connectivity may be a protective mechanism that could be initiated by pharmacological activation of ISR kinases, as was recently demonstrated for GCN2.

      A small molecule screening platform was used to identify nucleoside mimetic compounds that activate HRI. These compounds promote mitochondrial elongation and protect against acute mitochondrial fragmentation induced by a calcium ionophore. Mitochondrial connectivity is also increased in patient cells with a dominant mutation in MFN2 by treatment with the compounds.

      Strengths:

      (1) The screen leverages a well-characterized reporter of the ISR: translation of ATF4-FLuc is activated in response to ER stress or mitochondrial stress. Nucleoside mimetic compounds were screened for activation of the reporter, which resulted in the identification of nine hits. The two most efficacious in dose response tests were chosen for further analysis (0357 and 3610). The authors clearly state that the compounds have low potency. These compounds were specific to the ISR and did not activate the unfolded protein response or the heat shock response. Kinases activated in the ISR were systematically depleted by CRISPRi revealing that the compounds activate HRI.<br /> (2) The status of the mitochondrial network was assessed with an Imaris analysis pipeline and attributes such as length, sphericity, and ellipsoid principal axis length were quantified. The characteristics of the mitochondrial network in cells treated with the compounds were consistent with increased connectivity. Rigorous controls were included. These changes were attenuated with pharmacological inhibition of the ISR.<br /> (3) Treatment of cells with the calcium ionophore results in rapid mitochondrial fragmentation. This was diminished by pre-treatment with 0357 or 3610 and control treatment with thapsigargin and halofuginone.<br /> (4) Pathogenic mutations in MFN2 result in the neurodegenerative disease Charcot-Marie-Tooth Syndrome Type 2A (CMT2A). Patient cells that express Mfn2-D414V possess fragmented mitochondrial networks and treatment with 0357 or 3610 increased mitochondrial connectivity in these cells.

      Weaknesses:

      The weakness is the limited analysis of cellular changes following treatment with the compounds.<br /> (1) Unclear how 0357 or 3610 alter other aspects of cellular physiology. While this would be satisfying to know, it may be that the authors determined that broad, unbiased experiments such as RNAseq or proteomic analysis are not justified due to the limited translational potential of these specific compounds.<br /> (2) There are many changes in Mfn2-D414V patient cells including reduced respiratory capacity, reduced mtDNA copy number, and fewer mitochondrial-ER contact sites. These experiments are relatively narrow in scope and quantifying more than mitochondrial structure would reveal if the compounds improve mitochondrial function, as is predicted by their model.

      Comments on revisions:

      Many reviewer concerns have been addressed or will be addressed in forthcoming manuscripts.

    4. Reviewer #3 (Public review):

      Summary:

      Mitochondrial injury activates eiF2α kinases-PERK, GCN2, HRI and PKR-which collectively regulate the Integrated Stress Response (ISR) to preserve mitochondrial function and integrity. Previous work has demonstrated that stress-induced and pharmacologic stress-independent ISR activation promotes adaptive mitochondrial elongation via the PERK and GCN2 kinases, respectively. Here, the authors demonstrate that pharmacologic ISR inducers of HRI and GCN2 enhance mitochondrial elongation and suppress mitochondrial fragmentation in two disease models, illustrating the therapeutic potential of pharmacologic ISR activators. Specifically, the authors first used an innovative ISR translational reporter to screen for nucleoside mimetic compounds that induce ISR signaling, and identified two compounds, 0357 and 3610, that preferentially activate HRI. Using a mitochondrial-targeted GFP MEF cell line, the authors next determined that these compounds (as well as the GCN2 activator, halofuginone) enhance mitochondrial elongation in an ISR-dependent manner. Moreover, pretreatment of MEFs with these ISR kinase activators suppressed pathological mitochondrial fragmentation caused by a calcium ionophore. Finally, pharmacologic HRI and GCN2 activation was found to preserve mitochondrial morphology in human fibroblasts expressing a pathologic variant in MFN2, a defect that leads to mitochondrial fragmentation and is a cause of Charcot Marie Tooth Type 2A disease.

      Strengths:

      This well-written manuscript has several notable strengths, including the demonstration of the potential therapeutic benefit of ISR modulation. New chemical entities with which to further interrogate this stress response pathway are also reported. In addition, the authors used an elegant screen to isolate compounds that selectively activate the ISR and identify which of the four kinases was responsible for activation. Special attention was also paid to a thorough evaluation of the effect of their compounds on other stress response pathways (i.e. the UPR, and heat and oxidative stress responses), thereby minimizing the potential for off-target effects. The implementation of automated image analysis rather than manual scoring to quantify mitochondrial elongation is not only practical but also adds to the scientific rigor, as does the complementary use of both the calcium ionophore and MFN2 models to enhance confidence and the broad therapeutic potential for pharmacology ISR manipulation.

      Weaknesses:

      The only minor concerns are with regard to effects on cell health and the timing of pharmacological administration.

      Comments on revisions:

      In this revised manuscript the authors demonstrate that pharmacological activation of the eiF2α kinases, HRI and GCN2, induce adaptive mitochondrial elongation and suppress mitochondrial fragmentation in two disease models, illustrating the translational potential of pharmacological ISR modulation.

      In revising their manuscript the authors adequately addressed the concerns. In response to comments about the potential toxicity of their compounds, 0357 and 3610, the authors furnish evidence that neither compound significantly reduced viability of HEK293 cells (Figure S1G). Understandably, the authors focused the present work on the acute effects of their compounds. Several other attributes are noteworthy: First, that injury attributable to chronic ISR activation in cell culture may ultimately be circumvented by altering the in vivo pharmacodynamic and pharmacodynamic properties of the compounds, thereby preserving the translation potential for these (and related) compounds. Second, the authors also reasonably explain that the rapidity of ionomycin-induced injury, necessitating that the inducers are administered prior to treatment. Their assessment of the effects of the compounds on mitochondrial fragmentation in MFN2 mutant fibroblasts-in combination with the preserved viability of HEK293 cells-is sufficient to demonstrate the practical pharmacological potential for these (or related) agents.

    5. Author response:

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

      Public Reviews:

      Reviewer #1 (Public review):

      Summary: 

      This manuscript (Baron, Oviedo et al., 2024) builds on a previous study from the Wiseman lab (Perea, Baron et al., 2023) and describes the identification of novel nucleoside mimetics that activate the HRI branch of the ISR and drive mitochondrial elongation. The authors develop an image processing and analysis pipeline to quantify the effects of these compounds on mitochondrial networks and show that these HRI activators mitigate ionomycin-driven mitochondrial fragmentation. They then show that these compounds rescue mitochondrial morphology defects in patient-derived MFN2 mutant cell lines. 

      Strengths: 

      The identification of new ISR modulators opens new avenues for biological discovery surrounding the interplay between mitochondrial form/function and the ISR, a topic that is of broad interest. It also reinforces the possibility that such compounds might represent new potential therapeutics for certain mitochondrial disorders. The development of a quantitative image analysis pipeline is valuable and has the potential to extract the subtle effects of various treatments on mitochondrial morphology. 

      We thank the reviewer for the positive feedback on our manuscript. We address all of the reviewer’s valuable concerns in the revised submission, as highlighted below. 

      Weaknesses: 

      I have three main concerns.

      First, support for the selectivity of compounds 0357 and 3610 acting downstream of HRI comes from using knockdown ISR kinase cell lines and measuring the fluorescence of ATF4-mApple (Figure 1G and 1H). However, the selectivity of these compounds acting through HRI is not shown for mitochondrial morphology. Is mitochondrial elongation blocked in HRI knockdown cells treated with the compounds? While the ISRIB treatment does block mitochondrial elongation, ISRIB acts downstream of all ISR kinases and doesn't necessarily define selectivity for the HRI branch of the ISR. Additionally, are the effects of these compounds on ATF4 production and mitochondrial elongation blocked in a non-phosphorylatable eIF2alpha mutant? 

      We thank the reviewer for highlighting this point. As indicated by the reviewer, we show that compounddependent increases in mitochondrial elongation are blocked by co-treatment with ISRIB, indicating that this effect can be attributed to ISR activation. We prefer the use of this highly selective pharmacologic approach to block ISR activation, as opposed to the MEF<sup>A/A</sup> cells, as the use of pharmacologic approaches provide more temporal control over ISR inhibition and can prevent the type of chronic disruption to mitochondria associated with these types of genetic perturbations. However, the reviewer is correct that ISRIB blocks downstream of all ISR kinases, meaning that we cannot explicitly demonstrate that 0357 and 3610 induce mitochondrial elongation downstream of HRI-dependent ISR activation using this tool. Thus, to address this point, we have clarified the discussion of these results to make it clear that our results show that our compounds induce mitochondrial elongation downstream of the ISR, omitting the direct implications of HRI in this phenotype. 

      This point of selectivity/specificity of the compounds gets at a semantic stumbling block I encountered in the text where it was often stated "stress-independent activation" of ISR kinases. Nucleoside mimetics are likely a very biologically active class of molecules and are likely driving some level of cell stress independent of a classical ISR, UPR, heat-shock response, or oxidative stress response. 

      A major challenge in defining stress-independent activation of stress-responsive signaling pathways is the fact that the activation of these pathways is often used as a primary marker of cellular stress. While this can be overcome by transcriptome-wide profiling (e.g., RNAseq), the reviewer is correct that our focused profiling of select stress-responsive signaling pathways is insufficient to claim the stress-independent activation of the ISR by our prioritized compounds. To address this, we removed this terminology from the revised submission.  

      Second, it is difficult for me to interpret the data for the quantification of mitochondrial morphology. In the legend for Figure 2, it is stated that "The number of individual measurements for each condition are shown above." Are the individual measurements the number of total cells quantified? If not, how many total cells were analyzed? If the individual measurements are distinct mitochondrial structures that could be quantified why are the n's for each parameter (bounding box, ellipsoid principal axis, and sphericity) so different? Does this mean that for some mitochondria certain parameters were not included in the analysis? For me, it seems more intuitive that each mitochondrial unit should have all three parameters associated with it, but if this isn't the case it needs to be more carefully described why. 

      The number of individual measurements refers to the number of 3D segmentations generated using the “surfaces’ module in Imaris. As the reviewer noted, we expect each surface segmentation to represent a single “mitochondrial unit.” We have now further clarified this in the figure legend. 

      Regarding differences in sample size for each group, we used an outlier test (i.e., ROUT outlier test in PRISM 10) to remove apparent outliers in our data. Often, these outliers result from errors in the automatic quantification that inaccurately merge two mitochondria into one large segmentation. This explains the discrepancy in the number of measurements made for each experimental group. We have made this point more clear in the Materials and Methods section of the revised manuscript.  

      Third, the impact of these compounds on the physiological function of mitochondria in the MFN2.D414V mutants needs to be measured. Sharma et al., 2021 showed a clear deficit in mitochondrial OCR in MFN2.D414V cells which, if rescued by these compounds, would strengthen the argument that pharmacological ISR kinase activation is a strategy for targeting the functional consequences of the dysregulation of mitochondrial form.

      In this manuscript, we demonstrate that pharmacologic activation of the ISR using 0357 and 3610 rescue mitochondrial morphology in patient fibroblasts expressing the disease-associated MFN2<sup>D414V</sup> mutant. The reviewer is correct that there are other mitochondrial phenotypes linked to the expression of this mutant. We are currently pursuing this question with more potent ISR activating compounds developed in our laboratory identified using the HTS screening platform described in this manuscript. However, this work, which builds on the studies described herein, uses other ISR activating compounds, which we feel would be best described in subsequent manuscripts that can fully define the activity of these new compounds.  

      Reviewer #2 (Public review): 

      Summary. 

      Mitochondrial dysfunction is associated with a wide spectrum of genetic and age-related diseases. Healthy mitochondria form a dynamic reticular network and constantly fuse, divide, and move. In contrast, dysfunctional mitochondria have altered dynamic properties resulting in fragmentation of the network and more static mitochondria. It has recently been reported that different types of mitochondrial stress or dysfunction activate kinases that control the integrated stress response, including HRI, PERK, and GCN2. Kinase activity results in decreased global translation and increased transcription of stress response genes via ATF4, including genes that encode mitochondrial protein chaperones and proteases (HSP70 and LON). In addition, the ISR kinases regulate other mitochondrial functions including mitochondrial morphology, phospholipid composition, inner membrane organization, and respiratory chain activity. Increased mitochondrial connectivity may be a protective mechanism that could be initiated by pharmacological activation of ISR kinases, as was recently demonstrated for GCN2. 

      A small molecule screening platform was used to identify nucleoside mimetic compounds that activate HRI. These compounds promote mitochondrial elongation and protect against acute mitochondrial fragmentation induced by a calcium ionophore. Mitochondrial connectivity is also increased in patient cells with a dominant mutation in MFN2 by treatment with the compounds.

      Strengths: 

      (1) The screen leverages a well-characterized reporter of the ISR: translation of ATF4-FLuc is activated in response to ER stress or mitochondrial stress. Nucleoside mimetic compounds were screened for activation of the reporter, which resulted in the identification of nine hits. The two most efficacious dose-response tests were chosen for further analysis (0357 and 3610). The authors clearly state that the compounds have low potency. These compounds were specific to the ISR and did not activate the unfolded protein response or the heat shock response. Kinases activated in the ISR were systematically depleted by CRISPRi revealing that the compounds activate HRI.

      (2) The status of the mitochondrial network was assessed with an Imaris analysis pipeline and attributes such as length, sphericity, and ellipsoid principal axis length were quantified. The characteristics of the mitochondrial network in cells treated with the compounds were consistent with increased connectivity. Rigorous controls were included. These changes were attenuated with pharmacological inhibition of the ISR. 

      (3) Treatment of cells with the calcium ionophore results in rapid mitochondrial fragmentation. This was diminished by pre-treatment with 0357 or 3610 and control treatment with thapsigargin and halofuginone 

      (4) Pathogenic mutations in MFN2 result in the neurodegenerative disease Charcot-Marie-Tooth Syndrome Type 2A (CMT2A). Patient cells that express Mfn2-D414V possess fragmented mitochondrial networks and treatment with 0357 or 3610 increased mitochondrial connectivity in these cells.

      We appreciate the reviewer’s positive response to these aspects of our manuscript. We address the reviewer’s valuable comments in the revised submission as highlighted below. 

      Weaknesses: 

      The weakness is the limited analysis of cellular changes following treatment with the compounds. 

      (1) Unclear how 0357 or 3610 alter other aspects of cellular physiology. While this would be satisfying to know, it may be that the authors determined that broad, unbiased experiments such as RNAseq or proteomic analysis are not justified due to the limited translational potential of these specific compounds.

      The reviewer is correct. The low potency of 0357 and 3610 limit the translational potential for these compounds. However, building on the work described herein, we recently identified more potent HRI activating compounds with higher translational potential. Using RNAseq profiling, we found that these compounds show transcriptomewide selectivity for the ISR and can promote adaptive remodeling of mitochondrial morphology and function in cellular models of multiple other diseases. These compounds will be further described in subsequent studies that expand on the efforts outlined here demonstrating the potential for pharmacologic HRI activators to promote adaptive mitochondrial remodeling.   

      (2) There are many changes in Mfn2-D414V patient cells including reduced respiratory capacity, reduced mtDNA copy number, and fewer mitochondrial-ER contact sites. These experiments are relatively narrow in scope and quantifying more than mitochondrial structure would reveal if the compounds improve mitochondrial function, as is predicted by their model.

      In this manuscript, we demonstrate that pharmacologic activation of the ISR using 0357 and 3610 rescue mitochondrial morphology in patient fibroblasts expressing the disease-associated MFN2<sup>D414V</sup> mutant. The reviewer is correct that there are other mitochondrial phenotypes linked to the expression of this mutant. We are currently pursuing this question with more potent ISR activating compounds developed in our laboratory using the HTS screening platform described in this manuscript. However, this work, which builds on the studies described herein, uses other ISR activating compounds, which we feel would be best described in subsequent manuscripts that can fully define the activity of these new compounds.  

      Reviewer #3 (Public review):

      Summary: 

      Mitochondrial injury activates eiF2α kinases - PERK, GCN2, HRI, and PKR - which collectively regulate the Integrated Stress Response (ISR) to preserve mitochondrial function and integrity. Previous work has demonstrated that stress-induced and pharmacologic stress-independent ISR activation promotes adaptive mitochondrial elongation via the PERK and GCN2 kinases, respectively. Here, the authors demonstrate that pharmacologic ISR inducers of HRI and GCN2 enhance mitochondrial elongation and suppress mitochondrial fragmentation in two disease models, illustrating the therapeutic potential of pharmacologic ISR activators. Specifically, the authors first used an innovative ISR translational reporter to screen for nucleoside mimetic compounds that induce ISR signaling and identified two compounds, 0357 and 3610, that preferentially activate HRI. Using a mitochondrial-targeted GFP MEF cell line, the authors next determined that these compounds (as well as the GCN2 activator, halofuginone) enhance mitochondrial elongation in an ISR-dependent manner. Moreover, pretreatment of MEFs with these ISR kinase activators suppressed pathological mitochondrial fragmentation caused by a calcium ionophore. Finally, pharmacologic HRI and GCN2 activation were found to preserve mitochondrial morphology in human fibroblasts expressing a pathologic variant in MFN2, a defect that leads to mitochondrial fragmentation and is a cause of Charcot Marie Tooth Type 2A disease. 

      Strengths: 

      This well-written manuscript has several notable strengths, including the demonstration of the potential therapeutic benefit of ISR modulation. New chemical entities with which to further interrogate this stress response pathway are also reported. In addition, the authors used an elegant screen to isolate compounds that selectively activate the ISR and identify which of the four kinases was responsible for activation. Special attention was also paid to a thorough evaluation of the effect of their compounds on other stress response pathways (i.e. the UPR, and heat and oxidative stress responses), thereby minimizing the potential for off-target effects. The implementation of automated image analysis rather than manual scoring to quantify mitochondrial elongation is not only practical but also adds to the scientific rigor, as does the complementary use of both the calcium ionophore and MFN2 models to enhance confidence and the broad therapeutic potential for pharmacology ISR manipulation. 

      We thank the reviewer for their positive response to our manuscript. We address the reviewer’s remaining concerns as outlined below. 

      Weaknesses: 

      The only minor concerns are with regard to effects on cell health and the timing of pharmacological administration. 

      The two compounds described in this manuscript were found to not induce any overt toxicity over a 24 h period in cell culture models. In the revised manuscript, we show data showing that treatment with increasing doses of either 0357 or 3610 do not significantly reduce cellular viability in HEK293 cells (Fig. S1G). 

      With regards to treatments, we include all of the relevant information for the timing and dosage of compound treatment in the revised manuscript. 

      Recommendations for the authors:

      Reviewer #1 (Recommendations for Authors)

      (1) Figure S1 "B. ATF4-Gluc activity" -> Fluc, The number of replicates is not consistently stated for each experiment. p-values are not given for D and F. 

      We have changed the legend for Fig. S1B to ATF4-FLuc. We show individual replicates for all experiments for all panels described in this figure, except panels C and G, in the revised Figure S1. We explicitly state the number of replicates in panel C and G in the accompanying figure legend. We have repeated the qPCR described in panels C,F and statistics are included in the revised manuscript.

      (2) Figure 2 - no p-values for BtdCPU.

      Yes. We found that BtdCPU-dependent increases in mitochondrial fragmentation (described in Fig. 2A-D) were not significant when analyzing all the data included in these figures by Brown-Forsythe and Welch ANOVA test. However, the DMSO and BtdCPU conditions were significantly different when directly compared using a Welch’s t-test (p<0.005). Since the statistics in this manuscript are being analyzed by ANOVA, we decided not to include a significance marker for BtdCPU, as it was not observed in this more stringent test and is not the main focus of this manuscript.  

      (3) Figure S4 (Supplement to Figure 5) -> Supplement to Figure 4. 

      We have corrected this error in the revised manuscript. 

      (4) Error in references - duplicated 24 and 46, duplicated 10 and 11.

      This is now corrected in the revised submission.

      Reviewer #2 (Recommendations for the authors): 

      I would love to see an assessment of mitochondrial function and mtDNA in the D414 cells following treatment. 

      As indicated above, we are continuing to probe the impact of more potent HRI activating compounds in patientderived cell models expressing disease-relevant MFN2 mutants. Initial experiments suggest that this approach can mitigate additional pathologies beyond deficient elongation in these cells, although we are continuing to pursue these results with our improved HRI activating compounds. We are excited by these results, but feel that they are best suited for a follow-up manuscript describing these new HRI activators.   

      Reviewer #3 (Recommendations for the authors):

      The only suggestion to broaden the manuscript's impact might be to perform a basic assessment of the impact of pharmaceutical ISR activation on cell viability. Though mitochondrial elongation is often considered a surrogate for mitochondrial health, whether mitochondrial elongation improves cell viability (or not) would be informative. Similarly, the authors did not address the time-dependent effects of the ISR modulators, choosing to focus on the acute rather more chronic outcomes. Finally, does simultaneous (rather than pre-) treatment with an activator and the ionomycin produce similar effects on mitochondrial morphology, especially since therapeutics are typically administered post-injury?

      We now include cell viability experiments showing that the two HRI activators discussed in this manuscript, 0357 and 3610, do not significantly reduce viability in HEK293 cells. This work is included in the revised manuscript (see Fig. S1G). 

      With respect to acute vs chronic outcomes of ISR activation. As highlighted by the reviewer, we primarily focus this work on defining the impact of acute ISR treatment on mitochondrial morphology. As discussed above, we now show that our prioritized ISR activating compounds 0357 and 3610 do not significantly impact cellular viability over a 24 h timecourse. However, as suggested by the reviewer, additional studies on the potential implications of chronic pharmacologic ISR activation on mitochondrial biology remains to be further explored.

      We are continuing to address this in subsequent studies using more potent ISR kinase activating compounds established in our lab. However, we would like to highlight that detrimental phenotypes linked to chronic ISR kinase activation in cell culture does not preclude the translational potential for this approach, as in vivo PK/PD of these compounds can be controlled to prevent complications arising from chronic pathway activity. We previously demonstrated the potential for controlling compound activity through its PK/PD in our establishment of highly selective activators of other stress-responsive signaling pathways such as the IRE1/XBP1s arm of the UPR (e.g., Madhavan et al (2022) Nat Comm).   

      We appreciate the reviewer’s comments regarding the timing of compound treatment in them ionomycin experiment. Ionomycin works extremely quick to induce fragmentation (minutes), which would be prior to activation of the ISR induced by these compounds (hours). Thus, co-treatment would lead to fragmentation. It is an interesting question to ask if co-treatment with ISR activators could rescue this fragmentation as the pathway is activated, but we did not explicitly address this question in this manuscript. However, we do show that pharmacologic GCN2 or HRI activators can rescue mitochondrial morphology in patient fibroblasts expressing a MFN2 mutant, where mitochondria are fragmented, indicating that our approach can restore mitochondrial morphology in this context. We feel that these results, in combination with others described in our manuscript, demonstrate the potential for this approach to mitigate pathologic mitochondrial fragmentation associated with different conditions.

    1. eLife Assessment

      This fundamental work describes for the first time the combined gene expression and chromatin structure at the genome level in isolated chondrocytes and classical (cranial) and non-classical (notochordal) osteoblasts. In a compelling analysis of RNA-Seq and ATAC data, the authors characterize the two osteoblast populations relative to their associated chondrocyte cells and further proceed with a convincing analysis of the crucial entpd5a gene regulatory elements by investigating their respective transcriptional activity and specificity in developing zebrafish.

    2. Reviewer #1 (Public review):

      Summary:

      This work uses transgenic reporter lines to isolate entpd5a+ cells representing classical osteoblasts in the head and non-classical (osterix-) notochordal sheath cells. The authors also include entpd5a- cells, col2a1a+ cells to represent the closely associated cartilage cells. In a combination of ATAC and RNA-Seq analysis, the genome-wide transcriptomic and chromatin status of each cell population is characterized, validating their methodology and providing fundamental insights into the nature of each cell type, especially the less well-studied notochordal sheath cells. Using these data, the authors then turn to a thorough, and convincing analysis of the regulatory regions that control the expression of the entpd5a gene in each cell population. Determination of transcriptional activities in developing zebrafish, again combined with ATAC data and expression data of putative regulators results in a compelling, and detailed picture of the regulatory mechanisms governing expression of this crucial gene.

      Strengths:

      The major strength of this paper is the clever combination of RNA-Seq and ATAC analysis, further combined with functional transcriptional analysis of the regulatory elements of one crucial gene. This results in a very compelling story.

      Weaknesses:

      No major weakness, except for all the follow-up experiments that one can think of, but that would be outside of the scope of this paper.

      Comments on revisions:

      The description of Supplementary Figure 1 is still confusing: in the results section, it says "We photo converted and directly imaged entpd5a:Kaede positive embryos starting from the 15 somite- stage (s), when we could first detect the fluorophore along the newly-formed notochord progenitor cells (Suppl. Fig. 1E). We repeated photoconversion and imaging at 18, 21 and 24s (Suppl. Fig. 1F-H). ...(Suppl. Fig 1E)"<br /> In the response, the authors say "we could see new Kaede expression under the control of the entpd5a promoter region within 1.5 hours of photoconversion, as shown in Suppl. Figure 1E-H."<br /> In the legend to Suppl. Fig. 1, it says "Using the entpd5a:Kaede photoconversion line we first detect entpd5a expression at the 15 somite-stage (E). Following the same embryo, active expression of the gene continues until prior to 24 hpf (F-H)."<br /> So my questions are: -was there a delay between photoconversion and imaging - was the same delay used for all pictures - was there indeed additional photoconversion for Fig.1 F-H before imaging?<br /> This could be stated in Materials and Methods, and maybe in the legend to Suppl. Fig. 1

      All other issues have been addressed.

    3. Reviewer #2 (Public review):

      Summary:

      Complementary to mammalian models, zebrafish has emerged as a powerful system to study vertebrate development and serve as a go-to model for many human disorders. All vertebrates share the ancestral capacity to form a skeleton. Teleost fish models have been a key model to understand the foundations of skeletal development and plasticity, pairing with more classical work in amniotes such as the chicken and mouse. However, the genetic foundation of the diversity of skeletal programs in teleosts have been hampered by mapping similarities from amniotes back and not objectively establishing more ancestral states. This is most obvious in systematic, objective analysis of transcriptional regulation and tissue specification in differentiated skeletal tissues. Thus, the molecular events regulating bone-producing cells in teleosts have remained largely elusive. In this study, Petratou et al. leverage spatial experimental delineation of specific skeletal tissues -- that they term 'classical' vs 'non-classical' osteoblasts -- with associated cartilage of the endo/peri-chondrial skeleton and inter-segmental regions of the forming spine during development of the zebrafish, to delineate molecular specification of these cells by current chromatin and transcriptome analysis. The authors further show functional evidence of the utility of these datasets to identify functional enhancer regions delineating entp5 expression delineated in 'classical' or 'non-classical' osteoblast populations. By integration with paired RNA-seq, they delineate broad patterns of transcriptional regulation of these populations as well as specific detail of regional regulation via predictive binding sites within ATACseq profiles. Overall the paper was very well written and provides an essential contribution to the field that will provide a foundation to promote modeling of skeletal development and disease in an evolutionary and developmentally informed manner.

      Strengths:

      Taken together, this study provides a comprehensive resource of ATAC-seq and RNA-seq data that will be very useful for a wide variety of researchers studying skeletal development and bone pathologies. The authors show specificity in the different skeletal lineages and show utility of the broad datasets for defining regulatory control of gene regulation in these different lineages, providing the foundation for hypothesis testing of not only agents of skeletal change in evolution but also function of genes and variations of unknown significance as it pertains to disease modeling in zebrafish. The paper is excellently written, integrating a complex history and experimental analysis into a useful and coherent whole. The terminology of 'classical' and 'non-classical' will be useful for the community in discussing biology of skeletal lineages and their regulation.

      Weaknesses:

      Two items arose that proposed areas for extending the description to integrate the data into the existing data on role of non-classical osteobasts and establishment/canalization of this lineage of skeletal cells.

      (1) It was unclear how specific the authors' experimental dissection of head/trunk was in isolating different entp5a osteoblast populations. Obviously, this was successful given the specificity in DEG of results, however an analysis of contaminating cells/lineages in each population would be useful - e.g. maybe use specific marker genes to assess. The text uses terms such as 'specific to' and 'enriched in' without seemingly grounded meaning of the accuracy of these comments. Is it really specific e.g. not seen in one or other dataset, or is there some experimental variation in this?

      (2) Further, it would be valuable to discuss NSC-specific genes such as calymmin (Peskin 2020) which has species and lineage specific regulation of non-classical osteoblasts likely being a key mechanistic node for ratcheting centra-specific patterning of the spine in teleost fishes. What are dynamics observed in this gene in datasets between the different populations, especially when compared with paralogues - is there obvious cis-regulatory changes that correlate with the co-option of this gene in early regulation of non-classical osteoblasts? The addition of this analysis/discussion would anchor discussions of a differential between different osteoblasts lineages in the paper.

      Comments on revisions: All issues have been addressed.

    4. Reviewer #3 (Public review):

      Summary:

      This study characterizes classical and nonclassical osteoblasts as both types were analyzed independently (integrated ATAC-seq and RNAseq). It was found that gene expression in classical and nonclassical osteoblasts is not regulated in the same way. In classical osteoblasts Dlx family factors seem to play an important role, while Hox family factors are involved in the regulation of spinal ossification by nonclassical osteoblasts. In the second part of the study, the authors focus on the promoter structure of entpd5a. Through the identification of enhancers they reveal complex modes of regulation of the gene. The authors suggest candidate transcription factors that likely act on the identified enhancer elements. All the results taken together provide comprehensive new insights into the process of bone development, and point to spatio-temporally regulated promoter/enhancer interactions taking place at the entpd5a locus.

      Strengths:

      The authors have succeeded in justifying a sound and consistent buildup of their experiments, and meaningfully integrate the results into the design of each of their follow-up experiments. The data are solid, insightfully presented, and the conclusion valid. This makes this manuscript of great value and interest to those studying (fundamental) skeletal biology.

      Weaknesses:

      The study is solidly constructed, the manuscript is clearly written and the discussion is meaningful - I see no real weaknesses.

    5. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This work uses transgenic reporter lines to isolate entpd5a+ cells representing classical osteoblasts in the head and non-classical (osterix-) notochordal sheath cells. The authors also include entpd5a- cells, col2a1a+ cells to represent the closely associated cartilage cells. In a combination of ATAC and RNA-Seq analysis, the genome-wide transcriptomic and chromatin status of each cell population is characterized, validating their methodology and providing fundamental insights into the nature of each cell type, especially the less well-studied notochordal sheath cells. Using these data, the authors then turn to a thorough and convincing analysis of the regulatory regions that control the expression of the entpd5a gene in each cell population. Determination of transcriptional activities in developing zebrafish, again combined with ATAC data and expression data of putative regulators, results in a compelling and detailed picture of the regulatory mechanisms governing the expression of this crucial gene.

      Strengths:

      The major strength of this paper is the clever combination of RNA-Seq and ATAC analysis, further combined with functional transcriptional analysis of the regulatory elements of one crucial gene. This results in a very compelling story.

      Weaknesses:

      No major weaknesses were identified, except for all the follow-up experiments that one can think of, but that would be outside of the scope of this paper.

      Reviewer #2 (Public Review):

      Summary:

      Complementary to mammalian models, zebrafish has emerged as a powerful system to study vertebrate development and to serve as a go-to model for many human disorders. All vertebrates share the ancestral capacity to form a skeleton. Teleost fish models have been a key model to understand the foundations of skeletal development and plasticity, pairing with more classical work in amniotes such as the chicken and mouse. However, the genetic foundation of the diversity of skeletal programs in teleosts has been hampered by mapping similarities from amniotes back and not objectively establishing more ancestral states. This is most obvious in systematic, objective analysis of transcriptional regulation and tissue specification in differentiated skeletal tissues. Thus, the molecular events regulating bone-producing cells in teleosts have remained largely elusive. In this study, Petratou et al. leverage spatial experimental delineation of specific skeletal tissues -- that they term 'classical' vs 'non-classical' osteoblasts -- with associated cartilage of the endo/peri-chondrial skeleton and inter-segmental regions of the forming spine during development of the zebrafish, to delineate molecular specification of these cells by current chromatin and transcriptome analysis. The authors further show functional evidence of the utility of these datasets to identify functional enhancer regions delineating entp5 expression in 'classical' or 'non-classical' osteoblast populations. By integration with paired RNA-seq, they delineate broad patterns of transcriptional regulation of these populations as well as specific details of regional regulation via predictive binding sites within ATACseq profiles. Overall the paper was very well written and provides an essential contribution to the field that will provide a foundation to promote modeling of skeletal development and disease in an evolutionary and developmentally informed manner.

      Strengths:

      Taken together, this study provides a comprehensive resource of ATAC-seq and RNA-seq data that will be very useful for a wide variety of researchers studying skeletal development and bone pathologies. The authors show specificity in the different skeletal lineages and show the utility of the broad datasets for defining regulatory control of gene regulation in these different lineages, providing a foundation for hypothesis testing of not only agents of skeletal change in evolution but also function of genes and variations of unknown significance as it pertains to disease modeling in zebrafish. The paper is excellently written, integrating a complex history and experimental analysis into a useful and coherent whole. The terminology of 'classical' and 'non-classical' will be useful for the community in discussing the biology of skeletal lineages and their regulation.

      Weaknesses:

      Two items arose that were not critical weaknesses but areas for extending the description of methods and integration into the existing data on the role of non-classical osteoblasts and establishment/canalization of this lineage of skeletal cells.

      (1) In reading the text it was unclear how specific the authors' experimental dissection of the head/trunk was in isolating different entp5a osteoblast populations. Obviously, this was successful given the specificity in DEG of results, however, analysis of contaminating cells/lineages in each population would be useful - e.g. using specific marker genes to assess. The text uses terms such as 'specific to' and 'enriched in' without seemingly grounded meaning of the accuracy of these comments. Is it really specific - e.g. not seen in one or other dataset - or is there some experimental variation in this?

      We thank the reviewer for pointing this out. Given that the separation from head and trunk is done manually, there will be some experimental variability. We have used anatomical hallmarks (cleithrum and swim bladder), and therefore would expect the variability to be small. Regarding classical osteoblasts contaminating trunk tissue, head removal was consistently performed using the aforementioned anatomical hallmarks in a manner that ensures that the cleithrum does not remain in the trunk tissue.  In order to alleviate concerns regarding trunk cell populations contaminating cranial populations, and to further clarify our strategy, we add the following statement to the Materials and Methods section: “The procedure does not allow for a complete separation of notochordal non-classical osteoblasts from cranial classical osteoblasts, as the notochord extends into the cranium. However, the amount of sheath cells in that portion of the notochord is negligible, compared both to the number of classical (cranial) osteoblasts in head samples, and to notochord cells isolated in trunk samples.”

      (2) Further, it would be valuable to discuss NSC-specific genes such as calymmin (Peskin 2020) which has species and lineage-specific regulation of non-classical osteoblasts likely being a key mechanistic node for ratcheting centra-specific patterning of the spine in teleost fishes. What are dynamics observed in this gene in datasets between the different populations, especially when compared with paralogues - are there obvious cis-regulatory changes that correlate with the co-option of this gene in the early regulation of non-classical osteoblasts? The addition of this analysis/discussion would anchor discussions of the differential between different osteoblasts lineages in the paper.

      This is an interesting concept and idea, that we will consider in a possible revision or, if requiring substantial additional efforts, in a possible new research line. An excellent starting point for further studies using our datasets.

      Reviewer #3 (Public Review):

      Summary:

      This study characterizes classical and nonclassical osteoblasts as both types were analyzed independently (integrated ATAC-seq and RNAseq). It was found that gene expression in classical and nonclassical osteoblasts is not regulated in the same way. In classical osteoblasts, Dlx family factors seem to play an important role, while Hox family factors are involved in the regulation of spinal ossification by nonclassical osteoblasts. In the second part of the study, the authors focus on the promoter structure of entpd5a. Through the identification of enhancers, they reveal complex modes of regulation of the gene. The authors suggest candidate transcription factors that likely act on the identified enhancer elements. All the results taken together provide comprehensive new insights into the process of bone development, and point to spatio-temporally regulated promoter/enhancer interactions taking place at the entpd5a locus.

      Strengths:

      The authors have succeeded in justifying a sound and consistent buildup of their experiments, and meaningfully integrating the results into the design of each of their follow-up experiments. The data are solid, insightfully presented, and the conclusion valid. This makes this manuscript of great value and interest to those studying (fundamental) skeletal biology.

      Weaknesses:

      The study is solidly constructed, the manuscript is clearly written and the discussion is meaningful - I see no real weaknesses.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Minor issues that may need to be addressed or detailed:

      Supplementary Figures 1I-J, text page 4, line 24: "photoconversion and imaging": this needs some more detailed description: green fluorescent cells should be actively expressing Kaede, but only if there is a delay between photoconversion and imaging. What is the reason that Supplementary Figure 1F shows mainly green fluorescent cells, contrary to 1G-J?

      In our experiments, we could see new Kaede expression under the control of the entpd5a promoter region within 1.5 hours of photoconversion, as shown in Suppl. Figure 1E-H, suggesting that this time window was sufficient for protein generation. The reason for Suppl. Fig 1F showing more green fluorescence we believe relates to the high rate of transcriptional activity at that stage, in the entirety of the notochord progenitor cells. In addition, this is an effect which we attribute to the relatively small number of cells producing red fluorescence at that stage, due to photoconversion of only a few Kaede+ cells at the 15 somites stage (Suppl. Fig. 1E). Therefore, the masking effect of the green fluorescence by the red is not as significant as in G and H, where the red fluorescence resulting from photoconversion right after imaging at 18s and 21s, respectively, significantly overlaps with new green fluorescence. This can be seen in the image as the presence of orange fluorescence in G and H, instead of the clear red shown in E, I and J.

      In addition to this, we would like to point out that in Suppl. Fig. 1I, J the reason that green fluorescence is only detected in the ventral region of the notochord, is because the promoter of entpd5a only remains active in the ventral-most sheath cells at that stage. This is stated in the results section of the main text, first subsection, paragraph 3. The reason for this very interesting, strictly localised expression pattern remains unclear.

      Somewhat intriguing: green fluorescence in Figure 1B, C (osx:GAL4FF) and Supplementary Figure 1C (entpd5a:GAL4FF) in the CNS? Would that be an artefact of the GAL4FF/UAS:GFP system?

      We are confident that the fluorescence pointed out by the reviewer is not an artefact of the GAL4FF/UAS system, for a few reasons. Firstly, osx (Sp7) has been shown to be expressed and to function in the nervous system in mice (Park et al, BBRC, 2011; Elbaz et al, Neuron, 2023). Secondly, not only osx, but also entpd5a can be readily detected in a subset of cranial and spinal neurons in early development using the entpd5a:GAL4FF; UAS:GFP transgenic line (Suppl. Fig 1C). Finally, when establishing transgenic lines with the entpd5a(1.1):GFP construct, expression was almost invariably present in diverse elements of the nervous system, but not in bone (data not shown). This led us to hypothesise that the minimal promoter of entpd5a (and possibly also that of osx) is activated by transcription factors active in the nervous system, and this effect is likely controlled by the surrounding enhancers, but also the genome location. It is unclear at present what the endogenous neural expression of the two genes is like, and we did not further investigate this in this study, as the focus was on the skeleton.

      Figure 2: What exactly is "Corrected Total Cell Fluorescence"? Is it green + red fluorescence?

      We thank the reviewer for pointing out the absence of more information on this. Corrected total cell fluorescence does not correspond to green+ red fluorescence, rather it is calculated as follows for a single channel:

      CTCF = Integrated Density – (Area of selected cell X Mean fluorescence of background readings)

      More details can be found in the following website: https://theolb.readthedocs.io/en/latest/imaging/measuring-cell-fluorescence-using-imagej.html

      We have edited the Materials and Methods section under “Imaging and image analysis” to include the aforementioned information.

      Page 11, line 34: The authors may have missed the recently published "Raman et al., Biomolecules 2024 Vol. 14; doi:10.3390/biom14020139" describing RNA-Seq in 4 dpf osterix+ osteoblasts.

      We thank the reviewer for drawing our attention to the Raman et al publication. The reference has now been added in the manuscript.

      Figure 5A and B: use a higher resolution version to make the numbers and gene names more readable. Figures 5C and 6A could also use a larger font for the text and numbers.

      High resolution files are now included with the revised manuscript, which should significantly help in making figures more easily readable. Although we agree with the reviewer that larger fonts would improve readability, due to the nature of the graphs (very small spaces in some cases, where the numbers would have to fit) this would not be easy to achieve. However, we believe that this issue will be resolved with the availability of higher resolution files. If readability remains a concern, we would be happy to attempt re-organising the graphs to allow for larger fonts.

      Reviewer #2 (Recommendations For The Authors):

      I suggest no further experiments, but do suggest that a few points be clarified.

      In the Discussion, the text "the less evolved osteoblasts of fish and amphibians..." is not accurate. These cells are not less evolved as they represent an independent lineage to tetrapods that have evolved with different stresses for a similar time. However, as teleost fishes and amphibians share characteristics and all share a common ancestor, these signatures represent a putative ancestral state of skeletal differentiation not seen in amniotes, including humans.

      We thank the reviewer for pointing out the unfortunate phrasing. The text has now been modified as follows: “Specifically, the osteoblasts of teleost fish and amphibians, whose characteristics are putatively closer to a more ancestral state of skeletal differentiation compared to amniotes, appear to share gene expression with chondrocytes”.

      The title could potentially be shortened to reach a broader audience by removing the initial clause of 'integration of ATAC and RNA seq' as this is a commonly performed analysis - "Chromatin and transcriptomic signature in classical and non-classical zebrafish osteoblasts indicate mechanisms of ancestral skeletal differentiation" is more descriptive of the findings and not focused on the method.

      We have discussed this internally, but would prefer to retain the current title. The reason is (1) because we would like to see our methodology and datasets be used as platform for further studies, and the current title, in our opinion, facilitates this. In regards to replacing “mechanisms of entpd5a regulation” with “mechanisms of ancestral skeletal differentiation”, we think this does not give an accurate description of our work, which is primarily focused on elucidating entpd5a promoter dynamics.

      All datasets should be made available as soon as possible for use in the field.

      The datasets (raw and processed) are available on the GEO database. The corresponding accession numbers can be found in our data availability statement.

      Minor comments:

      (1) Figure 1A. The labels are missing for grey and light blue structures.

      These structures are together making up the “notochord sheath”, which is comprised of the basal lamina (grey), the medial layer of fibrillar collagen (light blue) and the outer layer of loosely arranged matrix (lighter blue). We modified the figure legend to indicate that the three layers all correspond to the notochord sheath.

      (2) Figure 2A. The constructs in the lower part of the panel are not discussed in the legend and seem out of place in terms of data type and analysis.

      We would argue that indicating which non-coding regions and which ATAC peaks were responsible for driving GFP expression in each construct aids in a better understanding of our results. We thank the reviewer for pointing out the lack of mention of these constructs in the figure legend. This issue has now been resolved.

      (3) Be wary of red/green color combinations, especially in the figures where these are juxtaposed with each other.

      We apologise for the use of red/green colour. Although it is not possible for this manuscript to change the colour patterns, we will make sure to avoid the use of these colours in conjunction in the future.

      (4) The use of fish as a term should be classified as teleost fish, as authors are not addressing non-teleost basal ray-finned fishes or the fact that tetrapods are within bony fishes overall.

      This is well spotted, we have now remedied this by editing the manuscript. Where the term “fish” was used, we now state “teleost fish”.

      (5) Age information is missing in several Figures (e.g. 1D and 2C).

      In some of the figures space constrains did not allow for including the stage on the figure itself. However, we have made sure that in those cases the stage is incorporated in the figure legend.

      (6) The resolution of several Figures (e.g. Figure 5 and Supplementary Figure 3) is low.

      We address this issue by providing high resolution figures with the revised manuscript.

      (7) In the sentence (top page before Discussion) "The same conclusion was reached upon isolation from these three..", it was unclear what 'upon isolation' referred to.

      We agree with the reviewer that this phrasing is unclear. To enhance clarity, the manuscript now reads as follows: “The same conclusion was reached upon isolation of the DEGs highlighted by our RNA-seq results, from the three aforementioned groups of genes associated with ATAC peaks for each cell population.”

    1. eLife Assessment

      In this potentially important study, the authors report results of QM/MM simulations and kinetic measurements for the phosphoryl-transfer step in adenylate kinase. The results point to the mechanistic proposal that the transition state ensemble is broader in the most efficient form of the enzyme (i.e., in the presence of Mg2+ in the active site) and thus a different activation entropy. With a broad set of computations and experimental analyses, the level of evidence is considered solid by some reviewers. On the other hand, there remain limitations in the computational analyses, especially regarding free energy profiles using different methodologies (shape of free energy profiles with DFTB vs. PBE QM/MM, and barriers with steered MD and umbrella sampling) and the activation entropy, leading some reviewers to the evaluation that the level of evidence is incomplete.

    2. Reviewer #1 (Public Review):

      Summary:

      This study investigated the phosphoryl transfer mechanism of the enzyme adenylate kinase, using SCC-DFTB quantum mechanical/molecular mechanical (QM/MM) simulations, along with kinetic studies exploring the temperature and pH dependence of the enzyme's activity, as well as the effects of various active site mutants. Based on a broad free energy landscape near the transition state, the authors proposed the existence of wide transition states (TS), characterized by the transferring phosphoryl group adopting a meta-phosphate-like geometry with asymmetric bond distances to the nucleophilic and leaving oxygens. In support of this finding, kinetic experiments were conducted with Ca2+ ions at different temperatures and pH, which revealed a reduced entropy of activation and unique pH-dependence of the catalyzed reaction.

      Strengths:

      A combined application of simulation and experiments is a strength.

      Weaknesses:

      The conclusion that the enzyme-catalyzed reaction involves a wide transition state is not sufficiently clarified with some concerns about the determined free energy profiles compared to the experimental estimate. (See Recommendations for the authors.)

      Comments on revisions:

      While the authors have made some improvements in clarifying the manuscript, questions still remain about their conclusion regarding the wide-TS, which appears this may be a misinterpretation of the simulation results. Also, they should clearly point out the large discrepancies between DFTB QM/MM and PBE QM/MM results (shape of free energy files) and also between steered MD and umbrella sampling results (barriers). Another question is the large change in activation entropy (between the reaction with and without divalent cations). This difference may be difficult to attribute sorely to the difference in the reaction geometries near TS.

    3. Reviewer #2 (Public Review):

      Summary:

      The authors report results of QM/MM simulations and kinetic measurements for the phosphoryl-transfer step in adenylate kinase. The main assertion of the paper is that a wide transition state ensemble is a key concept in enzyme catalysis as a strategy to circumvent entropic barriers. This assertion is based on observation of a "structurally wide" set of energetically equivalent configurations that lie along the reaction coordinate in QM/MM simulations, together with kinetic measurements that suggest a decrease of the entropy of activation.

      Strengths:

      The study combines theoretical calculations and supporting experiments.

      Weaknesses:

      The current paper hypothesizes a "wide" transition state ensemble as a catalytic strategy and key concept in enzyme catalysis. Overall, it is not clear the degree to which this hypothesis is fully supported by the data. The reasons are as follows:

      (1) Enzyme catalysis reflects a rate enhancement with respect to a baseline reaction in solution. In order to assert that something is part of a catalytic strategy of an enzyme, it would be necessary to demonstrate from simulations that the activation entropy for the baseline reaction is indeed greater and the transition state ensemble less "wide". Alternatively stated, when indicating there is a "wide transition state ensemble" for the enzyme system - one needs to indicate that is with respect to the non-enzymatic reaction. However, these simulations were not performed and the comparisons not demonstrated. The authors state "This chemical step would take about 7000 years without the enzyme" making it impossible to measure; nonetheless, the simulations of the nonenzymatic reaction would be fairly straightforward to perform in order to demonstrate this key concept that is central to the paper. Rather, the authors examine the reaction in the absence of a catalytically important Mg ion.

      (2) The observation of a "wide conformational ensemble" is not a quantitative measure of entropy. In order to make a meaningful computational prediction of the entropic contribution to the activation free energy, one would need to perform free energy simulations over a range of temperatures (for the enzymatic and non-enzymatic systems). Such simulations were not performed, and the entropy of activation was thus not quantified by the computational predictions. The authors instead use a wider TS ensemble as a proxy for larger entropy, and miss an opportunity to compare directly to the experimental measurements.

      Comments on revisions:

      Overall, I do not think the authors have been able to quantitatively support their conclusion, and the qualitative support is somewhat weak. This makes the interpretation of the computational results somewhat speculative. Nonetheless, comparison was made for models with and without divalent ions, and the experimental data is valuable.

    4. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This study investigated the phosphoryl transfer mechanism of the enzyme adenylate kinase, using SCC-DFTB quantum mechanical/molecular mechanical (QM/MM) simulations, along with kinetic studies exploring the temperature and pH dependence of the enzyme's activity, as well as the effects of various active site mutants. Based on a broad free energy landscape near the transition state, the authors proposed the existence of wide transition states (TS), characterized by the transferring phosphoryl group adopting a meta-phosphate-like geometry with asymmetric bond distances to the nucleophilic and leaving oxygens. In support of this finding, kinetic experiments were conducted with Ca2+ ions at different temperatures and pH, which revealed a reduced entropy of activation and unique pH-dependence of the catalyzed reaction.

      Strengths:

      A combined application of simulation and experiments is a strength.

      Weaknesses:

      The conclusion that the enzyme-catalyzed reaction involves a wide transition state is not sufficiently clarified with some concerns about the determined free energy profiles compared to the experimental estimate. (See Recommendations for the authors.)

      Reviewer #2 (Public Review):

      Summary:

      The authors report results of QM/MM simulations and kinetic measurements for the phosphoryl-transfer step in adenylate kinase. The main assertion of the paper is that a wide transition state ensemble is a key concept in enzyme catalysis as a strategy to circumvent entropic barriers. This assertion is based on observation of a "structurally wide" set of energetically equivalent configurations that lie along the reaction coordinate in QM/MM simulations, together with kinetic measurements that suggest a decrease of the entropy of activation.

      Thank you for your feedback. The reviewer’s questions are answered below, hoping to clarify them.

      Strengths:

      The study combines theoretical calculations and supporting experiments.

      Weaknesses:

      The current paper hypothesizes a "wide" transition state ensemble as a catalytic strategy and key concept in enzyme catalysis. Overall, it is not clear the degree to which this hypothesis is fully supported by the data. The reasons are as follows:

      (1) Enzyme catalysis reflects a rate enhancement with respect to a baseline reaction in solution. In order to assert that something is part of a catalytic strategy of an enzyme, it would be necessary to demonstrate from simulations that the activation entropy for the baseline reaction is indeed greater and the transition state ensemble less "wide". Alternatively stated, when indicating there is a "wide transition state ensemble" for the enzyme system - one needs to indicate that is with respect to the non-enzymatic reaction. However, these simulations were not performed and the comparisons not demonstrated. The authors state "This chemical step would take about 7000 years without the enzyme" making it impossible to measure; nonetheless, the simulations of the nonenzymatic reaction would be fairly straight forward to perform in order to demonstrate this key concept that is central to the paper. Rather, the authors examine the reaction in the absence of a catalytically important Mg ion.

      Thank you for your thoughtful feedback. QM/MM calculations for uncatalysed phosphoryl-transfer reactions involving either diphosphates or triphosphates have been well documented in the literature showing narrow and symmetric TSE (Klan et al., JACS 2006, 128 (47) 15310-15323; Cui Wang et al., JPCB 2015, 119(9), 3720-3726). We added these references to the revised manuscript.

      (2) The observation of a "wide conformational ensemble" is not a quantitative measure of entropy. In order to make a meaningful computational prediction of the entropic contribution to the activation free energy, one would need to perform free energy simulations over a range of temperatures (for the enzymatic and non-enzymatic systems). Such simulations were not performed, and the entropy of activation was thus not quantified by the computational predictions. The authors instead use a wider TS ensemble as a proxy for larger entropy, and miss an opportunity to compare directly to the experimental measurements.

      Although we share the reviewers desire to quantify entropies from QM/MM simulations, we agree with discussions in the literature that calculating quantitative entropies from performing QM/MM simulations at different temperatures is not reliable. We therefore felt strongly to stay with a qualitative assessment of entropy differences from our simulations. As the reviewer highlighted, our study combines theoretical calculations and experiments. The entropy of activation is well estimated by the experiments from the experimental accuracy of these temperature-dependent changes in rate constants for the chemical step.  Our computational results agree well with the experimental results and were further validated in 2 rounds of reviews/revisions by additional different free energy calculation methods (MSMD and US), plus committor analysis.

      Reviewer #3 (Public Review):

      Summary:

      By conducting QM/MM free energy simulations, the authors aimed to characterize the mechanism and transition state for the phosphoryl transfer in adenylate kinase. The qualitative reliability of the QM/MM results has been supported by several interesting experimental kinetic studies. However, the interpretation of the QM/MM results is not well supported by the current calculations.

      Thank you for your feedback. We appreciate the recognition of our experimental validation but understand your concern about the interpretation of our QM/MM results. To address this, we answer the specific questions below and added clearer explanations of the computational approach, including its limitations. We also better aligned the QM/MM results with both experimental data and theoretical expectations to strengthen the overall interpretation.

      Strengths:

      The QM/MM free energy simulations have been carefully conducted. The accuracy of the semi-empirical QM/MM results was further supported by DFT/MM calculations, as well as qualitatively by several experimental studies.

      Weaknesses:

      (1) One key issue is the definition of the transition state ensemble. The authors appear to define this by simply considering structures that lie within a given free energy range from the barrier. However, this is not the rigorous definition of transition state ensemble, which should be defined in terms of committor distribution. This is not simply an issue of semantics, since only a rigorous definition allows a fair comparison between different cases - such as the transition state in an enzyme vs in solution, or with and without the metal ion. For a chemical reaction in a complex environment, it is also possible that many other variables (in addition to the breaking and forming P-O bonds) should be considered when one measures the diversity in the conformational ensemble.

      In the revised manuscript, the authors included committor analysis. However, the discussion of the result is very brief. In particular, if we use the common definition of the transition state ensemble (TSE) as those featuring the committor around 0.5, the reaction coordinate of the TSE would span a much narrower range than those listed in Table 1. This point should be carefully addressed.

      The reviewer is right, the TSE is rigorously defined in terms of the committor distribution. We actually calculated the committor distribution for the reaction with and without Mg. We now added the figure showing the committor distribution for both reactions (Figure 3 – supplement 9). We did not include these results before because the committor distribution histogram would need more points to have a more accurate shape, requiring a high computational cost. We followed the reviewer’s suggestion and updated table 1 with the values from the committor distribution analysis.

      (2) While the experimental observation that the activation entropy differs significantly with and without the Ca2+ ion is interesting, it is difficult to connect this result with the "wide" transition state ensemble observed in the QM/MM simulations so far. Even without considering the definition of the transition state ensemble mentioned above, it is unlikely that a broader range of P-O distances would explain the substantial difference in the activation entropy measured in the experiment. Since the difference is sufficiently large, it should be possible to compute the value by repeating the free energy simulations at different temperatures, which would lead to a much more direct evaluation of the QM/MM model/result and the interpretation.

      See our answer above about this point.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major comments:

      One of the remaining issues with this revision is the assertion of the wide transition states in the presence of Mg2+ ion. When discussing the transition state of phosphoryl transfer reactions, it is important to consider their nature as involving both the cleavage and formation of P-O bonds. While these two events can occur in concert with a single transition state, many studies have shown that one event often precedes the other. Sometimes, there is a slight drop in free energy between the two events, forming a transient intermediate. However, due to its very short lifetime, this intermediate state may not be detectable experimentally. Depending on the sequence of events, the transition state or the transient intermediate may exhibit characteristics of a metaphosphate or phosphorane-like species. Based on the DFTB simulation results presented in the paper, it appears that the reaction forms a metaphosphate-like transition state. In the present reaction, since the two oxygen atoms involved in the reaction are very good leaving groups with similar reactivity, it is not surprising to observe the two events near the TS with very similar relative free energies, and therefore, the free energy profile can be very flat near the TS. This is consistent with the statement that "the transferring phosphate can be much closer to the leaving oxygen than the attacking oxygen and vice versa" on page 9. In my opinion, however, this should not be considered as a wide transition state but rather a consequence of the two events occurring very close to each other along the reaction coordinate. This distinction can be considered a semantic issue, and as long as the authors clearly discuss this issue and clarify the meaning of the TS ensemble, the reviewer is okay with that. In its current form, the statement of the wide TS ensemble may lead to a misleading interpretation of the reaction under study.

      An intermediate is clearly defined as a minimum in the free energy landscape. We see no evidence in any of your simulations of a minimum flanked by two transitions states, nor do we see any evidence in our NMR relaxation data or crystal structure ensemble refinement. We report our experimental and computational results, so that the reader can directly interpret the free energy landscapes for this system, avoiding semantics due to language ambiguity.

      Second, based on the kinetic study, the free energy of the catalytic reaction is approximately zero. The authors suggest that at pH near 7, the ADP exists as a roughly

      50-50 mixture between the singly protonated and fully charged states and consequently, the reaction free energies between the two scenarios cancel each other out. However, this argument is not correct. If [ADP(H)]/[ADP] is close to 1, the two reaction free energies, one with +6 kcal/mol and the other with -6 kcal/mol, imply that the protonation of the products (either ATP or AMP) requires ~12 kcal/mol (i.e., 9 pKa unit shift). Given the symmetric nature of the reaction and the similar pKa values between ATP, ADP versus AMP, such a large shift in the pKa of the product state is not expected, and for the calculated results to be accurate, the pKa shifts in the reactant state versus the product state must be opposite, with a total relative shift of 9 pKa units. This is difficult to understand given the nature of the reaction catalyzed by the adenylate kinase enzyme.

      We thank this reviewer for this question, which made us realize that we cannot compare the free energies of our QM/MD simulations with the experimentally determined ADP and ATP/AMP ratios. In the experiment we determine the entire pool of ADP and AMP/ATP bound to the enzyme, but could not distinguish if the protonated and or nonprotonated states are contributing to the measured observed rate constants (Kerns, S. et al.,(2015). In the present study, we now discovered that the nonprotonated forms have a lower activation barrier, but the protonated states also contribute to the overall reaction. Therefore, we removed this paragraph from our discussion.

      Minor comments:

      The difference in the free energy barrier determined by the MSMD and umbrella sampling is not negligible. Considering that umbrella sampling is commonly used in this type of research, the MSMD method appears to overestimate the barrier by more than 3 kcal/mol. Would the TS geometries obtained by umbrella sampling be comparable to those obtained by MSMD?

      This is an excellent suggestion, since the umbrella sampling is the more accurate method. The TSE from both methods are indeed comparable, and we added new figure panels about this results to Fig. 4.

      Figure 5 shows that the enthalpy of activation is similar for reactions with and without Ca2+. Do the authors expect the enthalpy of activation to decrease when Ca2+ is replaced by Mg2+ without a significant change in the entropy of activation? Any justification?

      In (Kerns, S. et al.,(2015) we had experimentally determined the dependence of the observed rate of the P-transfer on the nature of the divalent metal, with Mg2+ being by far superior to the other divalent metals. We proposed that this majorly is an effect on the enthalpy of activation, that other divalent metals provide poor orbital overlap, in agreement with published work on P-transfer reactions that show selectivity for a specific metal.

      Please provide proper citations for SHAKE and WHAM.

      The citations were added.

      Reviewer #2 (Recommendations For The Authors):

      The authors did not really address in the revised manuscript the main points of the previous review, which included examination of non-enzymatic reaction (via simulation, not measurement) and quantification of the connection between the reported wide TS ensemble and the increase in entropy (by additional simulations). The authors should also add reference to the AM1/d-PhoT model of Nam et al. which is now discussed.

      QM/MM calculations for uncatazlysed phosphoryl-transfer reactions involving either diphosphates or triphosphates have been well documented in the literature showing narrow and symmetric TSE (Klahn et al., JACS 2006, 128 (47) 15310-15323; Cui Wang et al., JPCB 2015, 119(9), 3720-3726). We added these references to the revised manuscript.

      The reference to AM1/d-PhoT model was added.

      Reviewer #3 (Recommendations For The Authors):

      In the revised ms, the authors indeed addressed many of the points raised in the previous round of review. In addition to the issue of TSE and committor mentioned above, another point that needs to be carefully explained is the very significant difference between umbrella sampling results and those in Fig. 1C - especially for the case without Mg2+ - the difference of more than 20 kcal/mol is not something that can be ignored at a qualitative level.

      We thank the reviewer for pointing out that the difference in free energy profiles between umbrella sampling (US) and MSMD, especially in the case without Mg<sup>2</sup>+ needs to be addressed.

      We believe that the key reason for this difference lies in the methodological approaches of these techniques.

      Umbrella sampling is an equilibrium enhanced sampling method, that allows for a balanced and thorough exploration of the free energy landscape, the MSMD is a non-equilibrium method and estimation depends of the averaging scheme used and the number of trajectories. In the present work, the free energy was estimated using an exponential average. This averaging scheme has a slow convergence, small variance and may overestimate the free energy barrier, specially if the barrier as seen in the absence of Mg is quite high. This factor could explain the significant difference between umbrella sampling and MSMD combined with Jarzynski’s equality.

      We have added new panels to Fig. 4 to compare the TSE from the more accurate umbrella sampling to the MSMD simulations, buttressing the validity of our original findings. We revised the manuscript discuss the differences between the MSMD and the umbrella sampling free energy profiles.

    1. eLife Assessment

      This valuable work suggests a new physical model of centrosome maturation: a catalytic growth model with a shared enzyme pool. The authors provide compelling evidence to show that the model is able to reproduce various experimental results such as centrosome size scaling with cell size and centrosome growth curves in C. elegans, and that the final centrosome size is more robust to differences in initial centrosome size. While direct experimental support for this theory is currently lacking, the authors propose concrete experiments that could distinguish their shared-enzyme model from previously proposed alternatives.

    2. Reviewer #1 (Public review):

      The work analyzes how centrosomes mature before cell division. A critical aspect is the accumulation of pericentriolar material (PCM) around the centrioles to build competent centrosomes that can organize the mitotic spindle. The present work builds on the idea that the accumulation of PCM is catalyzed either by the centrioles themselves (leading to a constant accumulation rate) or by enzymes activated by the PCM itself (leading to autocatalytic accumulation). These ideas are captured by a previous model derived for PCM accumulation in C. elegans (Zwicker et al, PNAS 2014) and are succinctly summarized by Eq. 1. The main addition of the present work is to allow the activated enzymes to diffuse in the cell, so they can also catalyze the accumulation of PCM in other centrosomes (captured by Eqs. 2-4). The authors show that this helps centrosomes to reach the same size, independent of potential initial mismatches.

      A strength of the paper is the simplicity of the equations, which are reduced to the bare minimum and thus allow a detailed inspection of the physical mechanism, e.g., using linear stability analysis. The possible shortcoming of this approach, namely that all equations assume that the diffusion of molecules is much faster than any of the reactive time scales, is addressed in Appendix 4. The authors show convincingly that their model compensates for initial size differences in centrosomes and leads to more similar final sizes. They carefully discuss parameter values used in their model, and they propose concrete experiments to test the theory. The model could thus stimulate additional experiments and help us understand how cells tightly control their centrosomes, which is crucial for faithful mitosis.

      Comments on revised version:

      The authors addressed my comments satisfactorily.

    3. Reviewer #2 (Public review):

      In this paper, Banerjee & Banerjee argue that a solely autocatalytic assembly model of the centrosome leads to size inequality. The authors instead propose a catalytic growth model with a shared enzyme pool. Using this model, the authors predict that size control is enzyme-mediate and are able to reproduce various experimental results such as centrosome size scaling with cell size and centrosome growth curves in C. elegans.

      The paper contains interesting results and is well-written and easy to follow/understand.

      Comments on revised version:

      The authors made a number of revisions that significantly improved the manuscript, including analyzing the impact of finite diffusion, more thorough stability analysis, and enhanced comparison to experimental results.

    4. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      The work analyzes how centrosomes mature before cell division. A critical aspect is the accumulation of pericentriolar material (PCM) around the centrioles to build competent centrosomes that can organize the mitotic spindle. The present work builds on the idea that the accumulation of PCM is catalyzed either by the centrioles themselves (leading to a constant accumulation rate) or by enzymes activated by the PCM itself (leading to autocatalytic accumulation). These ideas are captured by a previous model derived for PCM accumulation in C. elegans (ref. 8) and are succinctly summarized by Eq. 1. The main addition of the present work is to allow the activated enzymes to diffuse in the cell, so they can also catalyze the accumulation of PCM in other centrosomes (captured by Eqs. 2-4). The authors claim that this helps centrosomes to reach the same size, independent of potential initial mismatches.

      A strength of the paper is the simplicity of the equations, which are reduced to the bare minimum and thus allow a detailed inspection of the physical mechanism. One shortcoming of this approach is that all equations assume that the diffusion of molecules is much faster than any of the reactive time scales, although there is no experimental evidence for this.

      We appreciate the reviewer’s recognition of the strengths of our work. Indeed, the centrosome growth model incorporates multiple timescales corresponding to various reactions, and existing experimental data do not directly provide diffusion constants for the cytosolic proteins. However, we can estimate these diffusion constants using protein mass, based on the Stokes-Einstein relation, and compare the diffusion timescales with the reaction timescales obtained from FRAP analysis. For example, we estimate that the diffusion timescale for centrosomes separated by 5-10 micrometers is much smaller than the reaction timescales deduced from the FRAP experiments. Specifically, for SPD-5, a scaffold protein with a mass of ~150 kDa, the estimated diffusion constant is ~17 µm<sup>2</sup>/s, using the Stokes-Einstein relation and a reference diffusion constant of ~30 µm<sup>2</sup>/s for a 30 kDa GFP protein (reference: Bionumbers book). This results in a diffusion timescale of ~1 second for centrosomes 10 µm apart. In contrast, FRAP recovery timescales for SPD-5 in C. elegans embryos are on the order of several minutes, suggesting that scaffold protein binding reactions are much slower than diffusion. Therefore, a reaction-limited model is appropriate for studying PCM self-assembly during centrosome maturation. We have revised the manuscript to clarify this point and to include a discussion of the diffusion and reaction timescales.

      Spatially extended model with diffusion

      Both the reviewers have pointed out the importance of considering diffusion effects in centrosome size dynamics, and we agree that this is important to explore. We have developed a spatially extended 3D version of the centrosome growth model, incorporating stochastic reactions and diffusion (see Appendix 4). In this model, the system is divided into small reaction volumes (voxels), where reactions depend on local density, and diffusion is modeled as the transport of monomers/building blocks between voxels.

      We find that diffusion can alter the timescales of growth, particularly when the diffusion timescale is comparable to or slower than the reaction timescale, potentially mitigating size inequality by slowing down autocatalysis. However, the main conclusions of the catalytic growth model remain unchanged, showing robust size regulation independent of diffusion constant or centrosome separation (Figure 2—figure supplement 3). Hence, we focused on the effect of subunit diffusion on the autocatalytic growth model. We find that in the presence of diffusion, the size inequality reduces with increasing diffusion timescale, i.e., increasing distance between centrosomes and decreasing diffusion constant (Figure 2—figure supplement 4). However, the lack of robustness in size control in the autocatalyic growth model remains, i.e., the final size difference increases with increasing initial size difference. Notably, in the diffusion-limited regime (very small diffusion or large distances), the growth curve loses its sigmoidal shape, resembling the behavior in the non-autocatalytic limit (Figure 2). These findings are discussed in the revised manuscript.

      Another shortcoming of the paper is that it is not clear what species the authors are investigating and how general the model is. There are huge differences in centrosome maturation and the involved proteins between species. However, this is not mentioned in the abstract or introduction. Moreover, in the main body of the paper, the authors mention C. elegans on pages 2 and 3, but refer to Drosophila on page 4, switching back to C. elegans on page 5, and discuss Drosophila on page 6. This is confusing and looks as if they are cherry-picking elements from various species. The original model in ref. 8 was constructed for C. elegans and it is not clear whether the autocatalytic model is more general than that. In any case, a more thorough discussion of experimental evidence would be helpful.

      We believe one strength of our approach is its applicability across organisms. Our goal in comparing the theoretical model with experimental data from C. elegans and D.

      melanogaster is to demonstrate that the apparent qualitative differences in centrosome growth across species (see e.g., the extent of size scaling discussed in the section “Cytoplasmic pool depletion regulates centrosome size scaling with cell size”) may arise from the same underlying mechanisms in the theoretical model, albeit with different parameter values. We acknowledge differences in regulatory molecules between species, but the core pathways remain conserved see e.g. Raff, Trends in Cell Biology 2019, section: “Molecular Components of the Mitotic Centrosome Scaffold Appear to Have Been Conserved in Evolution from Worms to Humans”. In the revised manuscript, we have expanded the introduction to clarify this point and explain how our theory applies across species. We have also provided a clearer discussion of the experimental systems used throughout the manuscript and the available experimental evidence.

      The authors show convincingly that their model compensates for initial size differences in centrosomes and leads to more similar final sizes. These conclusions rely on numerical simulations, but it is not clear how the parameters listed in Table 1 were chosen and whether they are representative of the real situation. Since all presented models have many parameters, a detailed discussion on how the values were picked is indispensable. Without such a discussion, it is not clear how realistic the drawn conclusions are. Some of this could have been alleviated using a linear stability analysis of the ordinary differential equations from which one could have gotten insight into how the physical parameters affect the tendency to produce equal-sized centrosomes.

      Following the suggestion of the reviewer, we have revised the manuscript to add references and discussions justifying the choice of the parameter values used for the numerical simulations. These references and parameter choices can be found in Table 1 and Table 2, and are also discussed in relevant figure captions and within the manuscript text.

      We thank the reviewer for the excellent suggestion of including linear stability analysis of the ODE models of centrosome growth. We included linear stability analyses of the catalytic and autocatalytic growth models in Appendix 3. Analysis of the catalytic growth model reaffirms the robustness of size equality and the analysis of autocatalytic growth provides an approximate condition of size inequality. We have modified the revised manuscript to discuss these results.

      The authors use the fact that their model stabilizes centrosome size to argue that their model is superior to the previously published one, but I think that this conclusion is not necessarily justified by the presented data. The authors claim that "[...] none of the existing quantitative models can account for robustness in centrosome size equality in the presence of positive feedback." (page 1; similar sentence on page 2). This is not shown convincingly. In fact, ref 8. already addresses this problem (see Fig. 5 in ref. 8) to some extent.

      The linear stability analysis shown in Fig 5 in ref 8 (Zwicker et al, PNAS, 2014) shows that the solutions are stable around the fixed point and it was inferred from this result that Ostwald ripening can be suppressed by the catalytic activity of the centriole, therefore stabilizing the centrosomes (droplets) against coarsening by Ostwald ripening. But, if size discrepancy arises from the growth process (e.g., due to autocatalysis) the timescale of relaxation for such discrepancy is not clear from the above-mentioned result. We show (in figure 2 - figure supplement 3) that for any appreciable amount of positive feedback, the solution moves very slowly around the fixed point (almost like a line attractor) and cannot reach the fixed point in a biologically relevant timescale. Hence the model in ref 8 does not provide a robust mechanism for size control in the presence of autocatalytic growth. We have added this discussion in the Discussion section.

      More importantly, the conclusion seems to largely be based on the analysis shown in Fig. 2A, but the parameters going into this figure are not clear (see the previous paragraph). In particular, the initial size discrepancy of 0.1 µm^3 seems quite large, since it translates to a sphere of a radius of 300 nm. A similarly large initial discrepancy is used on page 3 without any justification. Since the original model itself already showed size stability, a careful quantitative comparison would be necessary.

      We thank the reviewer for the valuable suggestions. The parameters used in Fig. 2A are listed in Table 1 with corresponding references, and we used the parameter values from Zwicker et al. (2014) for rate constants and concentrations.

      The issue of initial size differences between centrosomes is important, but quantitative data on this are not readily available for C. elegans and Drosophila. Centrosomes may differ initially due to disparities in the amount and incorporation rate of PCM between the mother and daughter centrioles. Based on available images and videos (Cabral et al, Dev. Cell, 2019, DOI: https://doi.org/10.1016/j.devcel.2019.06.004), we estimated an initial radius of ~0.5 μm for centrosomes. Accounting for a 5% radius difference would lead to a volume difference of ~0.1 μm<sup>3</sup>, which was used in our analysis (Fig. 2A). These differences likely arise from distinct growth conditions of centrosomes containing different centrioles (older mother and newer daughter).

      More importantly, we emphasize that the initial size difference does not qualitatively alter the results presented in Figure 2. We agree that a quantitative analysis will further clarify our conclusions, and we have revised the manuscript accordingly. For example, Figure 2—figure supplement 3 provides a detailed analysis of how the final centrosome size depends on initial size differences across various parameter values. Additionally, Appendix 3 now includes analytical estimates of the onset of size inequality as a function of these parameters.

      The analysis of the size discrepancy relies on stochastic simulations (e.g., mentioned on pages 2 and 4), but all presented equations are deterministic. It's unclear what assumptions go into these stochastic equations, and how they are analyzed or simulated. Most importantly, the noise strength (presumably linked to the number of components) needs to be mentioned. How is this noise strength determined? What are the arguments for this choice? This is particularly crucial since the authors quote quantitative results (e.g., "a negligible difference in steady-state size (∼ 2% of mean size)" on page 4).

      As described in the Methods, we used the exact Gillespie method (Gillespie, JPC, 1977) to simulate the evolution of the stochastic trajectories of the systems, corresponding to the deterministic growth and reaction kinetics outlined in the manuscript. We've expanded the Methods to include further details on the stochastic simulations and refer to Appendix 1, where we describe the chemical master equations governing autocatalytic growth..

      The noise strength (fluctuations about the mean size of centrosome) does depend on the total monomer concentration (the pool size), and this may affect size inequality. Similar values of the total monomer concentration were used in the catalytic (0.04 uM) and autocatalytic growth (0.33 uM) simulations. These values for the pool size are similar to previous studies (Zwicker et al, PNAS, 2012) and have been optimized to obtain a good fit with experimental growth curves from C. elegans embryo data.

      To present more quantitative results, we have revised our manuscript to add data showing the effect of pool size on centrosome size inequality (Figure 3 - figure supplement 2). We find the size inequality in catalytic growth to increase with decreasing pool size as the origin of this inequality is the stochastic fluctuation in individual centrosome size. The size inequality (ratio of dv/<V>) in the autocatalytic growth does not depend (strongly) on the pool size (dv and <V> both increase similarly with pool size).

      Moreover, the two sets of testable predictions that are offered at the end of the paper are not very illuminative: The first set of predictions, namely that the model would anticipate an "increase in centrosome size with increasing enzyme concentration, the ability to modify the shape of the sigmoidal growth curve, and the manipulation of centrosome size scaling patterns by perturbing growth rate constants or enzyme concentrations.", are so general that they apply to all models describing centrosome growth. Consequently, these observations do not set the shared enzyme pool apart and are thus not useful to discriminate between models. The second part of the first set of predictions about shifting "size scaling" is potentially more interesting, although I could not discern whether "size scaling" referred to scaling with cell size, total amount of material, or enzymatic activity at the centrioles. The second prediction is potentially also interesting and could be checked directly by analyzing published data of the original model (see Fig. 5 of ref. 8). It is unclear to me why the authors did not attempt this.

      In response to the reviewers' valuable feedback, we have revised the manuscript to include results on potential methods for distinguishing catalytic growth from autocatalytic growth. Since the growth dynamics of a single centrosome do not significantly differ between these two models, it is necessary to experimentally examine the growth dynamics of a centrosome pair under various initial size perturbations. In Figure 3-figure supplement 2, we present theoretical predictions for both catalytic and autocatalytic growth models, illustrating the correlation between initial and final sizes after maturation. The figure demonstrates that the initial size difference and final size difference should be correlated only in the autocatalytic growth and the relative size inequality decreases with increasing subunit pool size in catalytic growth while remains almost unchanged in autocatalytic growth. These predictions can be experimentally examined by inducing varying centrosome sizes at the early stage of maturation for different expression levels of the scaffold former proteins.

      A second experimentally testable feature of the catalytic growth model involves sharing of the enzyme between both centrosomes. This could be tested through immunofluorescent staining of the kinase or by constructing a FRET reporter for PLK1 activity, where it can be studied if the active form of the PLK1 is found in the cytoplasm around the centrosomes indicating a shared pool of active enzyme. Additionally, photoactivated localization microscopy could be employed, where fluorescently tagged enzyme can be selectively photoactivated in one centrosome and intensity can be measured at the other centrosome to find the extent of enzyme sharing between the centrosomes.

      We also discuss shifts in centrosome size scaling behavior with cell size by varying parameters of the catalytic growth model (Fig 4). While quantitative analysis of size scaling in Drosophila is currently unavailable, such an investigation could enable us to distinguish catalytic growth mode with other models. We have included this point in the Discussion section.

      “The second prediction is potentially also interesting …” We assume the reviewer is referencing the scenario in Zwicker et al. (ref 8), where differences in centriole activity lead to unequal centrosome sizes. The data in that study represent a case of centrosome growth with variable centriole activity, resulting in size differences in both autocatalytic and catalytic growth models. This differs from our proposed experiment, where we induce unequal centrosome sizes without modifying centriole activity. We have now revised the text to clarify this distinction.

      Taken together, I think the shared enzyme pool is an interesting idea, but the experimental evidence for it is currently lacking. Moreover, the model seems to make little testable predictions that differ from previous models.

      We appreciate the reviewer’s interest in the core idea of our work. As mentioned earlier, we have improved the clarity in model predictions in the revised discussion section. Unfortunately, the lack of publicly available experimental data limits our ability to provide more direct experimental evidence. However, we are hopeful that our theoretical model will inspire future experiments to test these model predictions.

      Reviewer #2 (Public Review):

      Summary:

      In this paper, Banerjee & Banerjee argue that a solely autocatalytic assembly model of the centrosome leads to size inequality. The authors instead propose a catalytic growth model with a shared enzyme pool. Using this model, the authors predict that size control is enzyme-mediate and are able to reproduce various experimental results such as centrosome size scaling with cell size and centrosome growth curves in C. elegans.

      The paper contains interesting results and is well-written and easy to follow/understand.

      We are delighted that the reviewer finds our work interesting, and we appreciate the thoughtful suggestions provided. In response, we have revised the text and figures to incorporate these recommendations. Below, we address each of the reviewer’s comments point by point:

      Suggestions:

      ● In the Introduction, when the authors mention that their "theory is based on recent experiments uncovering the interactions of the molecular components of centrosome assembly" it would be useful to mention what particular interactions these are.

      As the reviewer suggested, we have modified the introduction section to add the experimental observations upon which we build our model.

      ● In the Results and Discussion sections, the authors note various similarities and differences between what is known regarding centrosome formation in C. elegan and Drosophila. It would have been helpful to already make such distinctions in the Introduction (where some phenomena that may be C. elegans specific are implied to hold centrosomes universally). It would also be helpful to include more comments for the possible implications for other systems in which centrosomes have been studied, such as human, Zebrafish, and Xenopus.

      We thank the reviewer for this suggestion. We have modified the Introduction to motivate the comparative study of centrosome growth in different organisms and draw relevant connections to centrosome growth in other commonly studied organisms like Zebrafish and Xenopus.

      ● For Fig 1.C, the two axes are very close to being the same but are not. It makes the graph a little bit more difficult to interpret than if they were actually the same or distinctly different. It would be more useful to have them on the same scale and just have a legend.

      We have modified the Figure 1C in the revised manuscript. The plot now shows the growth of a single and a pair of centrosomes both on the same y-axis scale.

      ● The authors refer to Equation 1 as resulting from an "active liquid-liquid phase separation", but it is unclear what that means in this context because the rheology of the centrosome does not appear to be relevant.

      We used the term “active liquid-liquid phase separation” simply to refer to a previous model proposed by Zwicker et al (PNAS, 2014) where the underlying process of growth results from liquid-liquid phase separation. We agree with the reviewer that the rheological property of the centrosome is not very relevant in our discussions and we have thus removed the sentence from the revised manuscript to avoid any confusion.

      ● The authors reject the non-cooperative limit of Eq 1 because, even though it leads to size control, it does not give sigmoidal dynamics (Figure 2B). While I appreciate that this is just meant to be illustrative, I still find it to be a weak argument because I would guess a number of different minor tweaks to the model might keep size control while inducing sigmoidal dynamics, such as size-dependent addition of loss rates (which could be due to reactions happen on the surface of the centrosome instead of in its bulk, for example). Is my intuition incorrect? Is there an alternative reason to reject such possible modifications?

      The reviewer raises an interesting point here. However, we disagree with the idea that minor adjustments to the model can produce sigmoidal growth curves while still maintaining size control. In the absence of an external, time-dependent increase in building block concentration (which would lead to an increasing growth rate), achieving sigmoidal growth requires a positive feedback mechanism in the growth rate. This positive feedback alone could introduce size inequality unless shared equally between the centrosomes, as it is in our model of catalytic growth in a shared enzyme pool. The proposed modification involving size-dependent addition or loss rates due to surface assembly/disassembly may result in unequal sizes precisely because of this positive feedback. A similar example is provided in Appendix 1, where assembly and disassembly across the pericentriolic material volume lead to sigmoidal growth but also generate significant size inequality and lack of robustness in size control.

      ● While the inset of Figure 3D is visually convincing, it would be good to include a statistical test for completeness.

      Following the reviewer’s suggestion, we present a statistical analysis in Figure 3 - Figure supplement 2 in the modified manuscript to enhance clarity. We show that the size difference values are uncorrelated (Pearson’s correlation coefficient ~ 0) with the initial size difference indicating the robustness of the size regulation mechanism.

      ● The authors note that the pulse in active enzyme in their model is reminiscent of the Polo kinase pulse observed in Drosophila. Can the authors use these published experimental results to more tightly constrain what parameter regime in their model would be relevant for Drosophila? Can the authors make predictions of how this pulse might vary in other systems such as C. elegans?

      Thank you for the insightful suggestion regarding the use of pulse dynamics in experiments to better constrain the model’s parameter regime. In our revised manuscript, we attempted this analysis; however, the data from Wong et al. (EMBO 2022) for Drosophila are presented as normalized intensity in arbitrary units, rather than as quantitative measures of centrosome size or Polo enzyme concentration. This lack of quantitative data limits our ability to benchmark the model beyond capturing qualitative trends. We thus believe that quantitative measurements of centrosome size and enzyme concentration are necessary to achieve a tighter alignment between model predictions and biological data.

      We discuss the enzyme dynamics in C. elegans in the revised manuscript. We find the enzyme dynamics corresponding to the fitted growth curves of C. elegans centrosomes are distinctly different from the ones observed in Drosophila. Instead of the pulse-like feature, we find a step-like increase in (cytosolic) active enzyme concentration.

      ● The authors mention that the shared enzyme pool is likely not diffusion-limited in C. elegans embryos, but this might change in larger embryos such as Drosophila or Xenopus. It would be interesting for the authors to include a more in-depth discussion of when diffusion will or will not matter, and what the consequence of being in a diffusion-limit regime might be.

      Both the reviewers have pointed out the importance of considering diffusion effects in centrosome size dynamics, and we agree that this is important to explore. We have developed a spatially extended 3D version of the centrosome growth model, incorporating stochastic reactions and diffusion (see Appendix 4). In this model, the system is divided into small reaction volumes (voxels), where reactions depend on local density, and diffusion is modeled as the transport of monomers/building blocks between voxels.

      We find that diffusion can alter the timescales of growth, particularly when the diffusion timescale is comparable to or slower than the reaction timescale, potentially mitigating size inequality by slowing down autocatalysis. However, the main conclusions of the catalytic growth model remain unchanged, showing robust size regulation independent of diffusion constant or centrosome separation (Figure 2—figure supplement 3). Hence, we focused on the effect of subunit diffusion on the autocatalytic growth model. We find that in the presence of diffusion, the size inequality reduces with increasing diffusion timescale, i.e., increasing distance between centrosomes and decreasing diffusion constant (Figure 2—figure supplement 4). However, the lack of robustness in size control in the autocatalyic growth model remains, i.e., the final size difference increases with increasing initial size difference. Notably, in the diffusion-limited regime (very small diffusion or large distances), the growth curve loses its sigmoidal shape, resembling the behavior in the non-autocatalytic limit (Figure 2). These findings are discussed in the revised manuscript.

      ● The authors state "Firstly, our model posits the sharing of the enzyme between both centrosomes. This hypothesis can potentially be experimentally tested through immunofluorescent staining of the kinase or by constructing FRET reporter of PLK1 activity." I don't understand how such experiments would be helpful for determining if enzymes are shared between the two centrosomes. It would be helpful for the authors to elaborate.

      Our results indicate the necessity of the centrosome-activated enzyme to be shared for the robust regulation of centrosome size equality. If a FRET reporter of the active form of the enzyme (e.g., PLK1) can be constructed then the localization of the active form of the enzyme may be determined in the cytosol. We propose this based on reports of studying PLK activities in subcellular compartments using FRET as described in Allen & Zhang, BBRC (2006). Such experiments will be a direct proof of the shared enzyme pool. Following the reviewer’s suggestion, we have modified the description of the FRET based possible experimental test for the shared enzyme pool hypothesis in the revised manuscript.

      Additionally, we have added another possible experimental test based on photoactivated localization microscopy (PALM), where tagged enzyme can be selectively photoactivated in one centrosome and intensity measured at the other centrosome to indicate whether the enzyme is shared between the centrosomes.

      Recommendations for the authors:

      The manuscript needs to clarify better what species the model describes, how alternative models were rejected, and how the parameters were chosen.

      In the revised manuscript, we have connect the chemical species in our model to those documented in organisms like Drosophila and C. elegans. This connection is detailed in the main text under the Catalytic Growth Model section and summarized in Table 2. We discuss alternative models and our reasons for excluding them in the first results section on autocatalytic growth, with additional details provided in Appendix 1 and the accompanying supplementary figures. The selection of model parameters is addressed in the main text and methods, with references listed in Table 1. We believe that these revisions, along with our point-by-point responses to reviewer comments, comprehensively address all reviewer concerns.

      Reviewer #1 (Recommendations For The Authors):

      I think the style and structure of the paper could be improved on at least two accounts:

      (1) What's the role of the last section ("Multi-component centrosome model reveals the utility of shared catalysis on centrosome size control.")? It seems to simply add another component, keeping the essential structure of the model untouched. Not surprisingly, the qualitative features of the model are preserved and quantitative features are not discussed anyway.

      This model provides a more realistic description of centrosome growth by incorporating the dynamics of the two primary scaffold-forming subunits and their interactions with an enzyme. It is based on the observation that the major interaction pathways among centrosome components are conserved across many organisms (see Raff, Trends in Cell Biology, 2019 and Table 2), typically involving two scaffold-forming proteins and one enzyme that mediates positive feedback between them. These pathways may involve homologous proteins in different species.

      This model allows us to validate the experimentally observed spatial spread of the two subunits, Cnn and Spd-2, in Drosophila. Additionally, we used it to investigate the impact of relaxing the assumption of a shared enzyme pool on size control. Although similar insights could be obtained using a single-component model, the two-component model offers a more biologically relevant framework. We have highlighted these points in the revised manuscript to ensure clarity.

      (2 ) The very long discussion section is not very helpful. First, it mostly reiterates points already made in the main text. Second, it makes arguments for the choice of modeling (top left column of page 8), which probably should have been made when introducing the model. Third, it introduces new results (lower left column of page 8), which should probably be moved to the main text. Fourth, the interpretation of the model in light of the known biochemistry is useful and should probably be expanded although I think it would be crucial to keep information from different organisms clearly separate (this last point actually holds for the entire manuscript).

      We thank the reviewer for the feedback. We have modified the discussion section to focus more on the interpretation of the results, model predictions and future outlook with possible experiments to validate crucial aspects of the model. We have moved most of the justifications to the main text model description.

      Here are a few additional minor points:

      * page 1: Typo "for for" → "for"

      * Page 8: Typo "to to" → "to"

      We thank the reviewer for the useful recommendations. We have corrected all the typos in the revised manuscript.

      * Why can diffusion be neglected in Eq. 1? This is discussed only very vaguely in the main text (on page 3). Strangely, there is some discussion of this crucial initial step in the discussion section, although the diffusion time of PLK1 is compared to the centrosome growth time there and not the more relevant enzyme-mediate conversion rate or enzyme deactivation rate.

      We now discuss the justification of neglecting diffusion while motivating the model. We have added a more detailed discussion in the Methods section. We estimate the timescale of diffusion for the scaffold formers and the enzyme and compare them with the turnover timescales of the respective proteins Spd-2, Cnn and Polo. We find the proteins to diffuse fast compared to their FRAP recovery timescales indicating reaction timescales to be slower than the timescales of diffusion. Nevertheless, following the reviewer’s suggestion, we have also investigated the effect of diffusion on the growth process in Appendix 4.

      * Page 3: The comparison k_0^+ ≫ k_1^+ is meaningless without specifying the number of subunits n. I even doubt that this condition is the correct one since even if k_0^+ is two orders of magnitude larger than k_1^+, the autocatalytic term can dominate if there are many subunits.

      We thank the reviewer for the insightful comment on the comparison between the growth rates k^+_0 and k^+_1. Indeed, the pool size matters and we have now included a linear stability analysis of the autocatalytic growth equations in Appendix 3 to estimate the condition for size inequality. We have commented on these new findings in the revised manuscript.

      * The Eqs. 2-4 are difficult to follow in my mind. For instance, it is not clear why the variables N_av and N_av^E are introduced when they evidently are equivalent to S_1 and E. It would also help to explicitly mention that V_c is the cell volume. Moreover, do these equations contain any centriolar activity? If so, I could not understand what term mediates this. If not, it might be good to mention this explicitly.

      Following the reviewer’s suggestion, we have modified the equations 2-4 and added the definition of V_c to enhance clarity in the revised manuscript. The centriole activity is given by k^+ in the catalytic model. We now explicitly mention it.

      * Page 4: The observed peak of active enzyme (Fig 3C) is compared to experimental observation of a PLK1 peak at centrosomes in Drosophila (ref. 28). However, if I understand correctly, the peak in the model refers to active enzyme in the entire cell (and the point of the model is that this enzymatic pool is shared everywhere), whereas the experimental measurement quantified the amount of PLK1 at the centrosome (and not the activity of the enzyme). How are the quantity in the model related to the experimental measurements?

      The reviewer is correct in pointing out the difference between the quantities calculated from our model and those measured in the experiment by Wong et al. We have clarified this point in the revised manuscript. We hypothesize that if, in future experiments, the active (phosphorylated) polo can be observed by using a possible FRET reporter of activity then the cytosolic pulse can be observed too. We discuss this point in the revised manuscript.

      * Page 6: The asymmetry due to differences in centriolar activity is apparently been done for both models (Eq. 1 and Eqs. 2-4), referring to a parameter k_0^+ in both cases. How does this parameter enter in the latter model? More generally, I don't really understand the difference in the two rows in Fig. 5 - is the top row referring to growth driven by centriolar activity while the lower row refers to pure autocatalytic growth? If so, what about the hybrid model where both mechanisms enter? This is particularly relevant, since ref. 8 claims that such a hybrid model explains growth curves of asymmetric centrosomes quantitatively. Along these lines, the analysis of asymmetric growth is quite vague and at most qualitative. Can the models also explain differential growth quantitatively?

      We believe the reviewer’s comment on centrosome size asymmetry may stem from a lack of clarity in our initial explanation. In this section, as shown in Figure 5, we compare the full autocatalytic model (where both k_0^+ and k_1^+ are non-zero) with the catalytic model. The confusion might have arisen due to an unclear definition of centriolar activity in the catalytic growth model, which we have clarified in the revised manuscript. Specifically, we use k+ in the catalytic model and k0+ in the autocatalytic model as indicators of centriolar activity.

      Our findings quantitatively demonstrate that variations in centriole activity can robustly drive size asymmetry in catalytic growth, independent of initial size differences. However, in autocatalytic growth, increased initial size differences make the system more vulnerable to a loss of regulation, as positive feedback can amplify these differences, ultimately influencing the final size asymmetry. Our results do not contradict Zwicker et al. (ref 8); rather, they complement it. We show that size asymmetry in autocatalytic growth is governed by both centriole activity and positive feedback, highlighting that centriole activity alone cannot robustly regulate centrosome size asymmetry within this framework.

      * The code for performing the simulations does not seem to be available

      We have now made the main codes available in a GitHub repository. Link: https://github.com/BanerjeeLab/Centrosome_growth_model

    1. eLife Assessment

      This manuscript describes the identification and characterization of 12 specific phosphomimetic mutations in the recombinant full-length human tau protein that trigger tau to form fibrils. This fundamental study will allow in vitro mechanistic investigations. The presented evidence is solid but a higher purity of these fibril types might be required for future studies. This manuscript will be of interest to all scientists in the amyloid formation field.

    2. Reviewer #1 (Public review):

      Summary and Strengths:

      The very well-written manuscript by Lövestam et al. from the Scheres/Goedert groups entitled "Twelve phosphomimetic mutations induce the assembly of recombinant full-length human tau into paired helical filaments" demonstrates the in vitro production of the so-called paired helical filament Alzheimer's disease (AD) polymorph fold of tau amyloids through the introduction of 12 point mutations that attempt to mimic the disease-associated hyper-phosphorylation of tau. The presented work is very important because it enables disease-related scientific work, including seeded amyloid replication in cells, to be performed in vitro using recombinant-expressed tau protein.

      Weaknesses:

      The following points are asked to be addressed by the authors:

      (i) In the discussion it would be helpful to note the findings that in AD the chemical structure tau (including phosphorylation) is what defines the polymorph fold and not the buffer/cellular environment. It would be further interesting to discuss these findings in respect to the relationship between disease and structure. The presented findings suggest that due to a cellular/organismal alteration, such as aging or Abeta aggregation, tau is specifically hyper-phosphorylated which then leads to its aggregation into the paired helical filaments that are associated with AD.

      (ii) The conditions used for each assembly reaction are a bit hard to keep track of and somewhat ambiguous. In order to help the reader, I would suggest making a table to show conditions used for each type of assembly (including the diameter / throw of the orbital shaker) and the results (structural/biological) of those conditions. For example, presumably the authors did not have ThT in the samples used for cryo-EM but the methods section does not specify this. Also, the presence of trace NaCl is proposed as a possible cause for the CTE fold to appear in the 0N4R sample (page 4) but no explanation of why this particular sample would have more NaCl than the others. Furthermore, it appears that NaCl was actually used in the seeded assembly reactions that produced the PHF and not the CTE fold. This would seem to indicate the CTE structure of 0N4R-PAD12 is not actually induced by NaCl (like it was for tau297-391). In order for the reader to better understand the reproducibility of the polymorphs, it would be helpful to indicate in how many different conditions and how many replicates with new protein preparations each polymorph was observed (could be included in the same table)

      (iii) It is not clear how the authors calculate the percentage of each filament type. In Figure 1 it is stated "discarded solved particles (coloured) and discarded filaments in grey" which leaves the reviewer wondering what a "discarded solved particle" is and which filaments were discarded. From the main text one guesses that the latter is probably false positives from automated picking but if so, these should not be referred to as filaments. Also, are the percentages calculated for filaments or segments? In any case, it would be more helpful in such are report to know the best estimate of the ratio of identified filament types without confusing the reader with a measure of the quality of the picking algorithm. Please clarify. Also, a clarification is asked for the significance of the varying degrees of PHF and AD monomer filaments in the various assembly conditions. It could be expected that there is significant variability from sample to sample but it would be interesting to know if there has been any attempt to reproduce the samples to measure this variability. If not, it might be worth mentioning so that the % values are taking with the appropriate sized grain of salt. Finally, the representation of the data in Figure 1 would seem to imply that the 0N3R forms less or no monofilament AD fold because no cross-section is shown for this structure, however it is very similar to (or statistically the same as) the 1:1 mix of 0N3R:0N4R.

      (iv) The interpretation of the NMR data on soluble tau that the mutations on the second site are suppressing in part long range dynamic interaction around the aggregation-initiation site (FIA) is sound. It is in particular interesting to find that the mutations have a similar effect as the truncation at residue 391. An additional experiment using solvent PREs to elaborate on the solvent exposed sequence-resolved electrostatic potential and the intra-molecular long range interactions would likely strengthen the interpretation significantly (Iwahara, for example, Yu et al, in JACS 2024). Figure 6D Figure supplement shows the NMR cross peak intensities between tau 151-391 and PAD12tau151-391. Overall the intensities of the PAD12 tau construct are more intense which could be interpreted with less conformational exchange between long range dynamic interactions. There are however several regions which do not show any intensity anymore when compared with the corresponding wildtype construct such as 259-262, 292-294 which should be discussed/explained.

      (v) Concerning the Cryo-EM data from the different hyper-phosphorylation mimics, it would seem that the authors could at least comment on the proportion of monofilament and paired-filaments even if they could not solve the structures. Nonetheless, based on their previous publications, one would also expect that they could show whether the non-twisted filaments are likely to have the same structure (by comparing the 2D classes to projections of non-twisted models). Also, it is very interesting to note that the twist could be so strongly controlled by the charge distribution on the non-structured regions (and may be also related to the work by Mezzenga on twist rate and buffer conditions). Is the result reported in Figure 2 a one-off case or was it also reproducible?

    3. Reviewer #2 (Public review):

      Summary:

      This manuscript addresses an important impediment in the field of Alzheimer's disease (AD) and tauapathy research by showing that 12 specific phosphomimetic mutations in full-length tau allow the protein to aggregate into fibrils with the AD fold and the fold of chronic traumatic encephalopathy fibrils in vitro. The paper presents comprehensive structural and cell based seeding data indicating the improvement of their approach over previous in vitro attempts on non-full-length tau constructs. The main weaknesses of this work results from the fact that only up to 70% of the tau fibrils form the desired fibril polymorphs. In addition, some of the figures are of low quality and confusing.

      Strengths:

      This study provides significant progress towards a very important and timely topic in the amyloid community, namely the in vitro production of tau fibrils found in patients.

      The 12 specific phosphomimetic mutations presented in this work will have an immediate impact in the field since they can be easily reproduced.

      Multiple high-resolution structures support the success of the phosphomimetic mutation approach.

      Additional data show the seeding efficiency of the resulting fibrils, their reduced tendency to bundle, and their ability to be labeled without affecting core structure or seeding capability.

      Weaknesses:

      Despite the success of making full-length AD tau fibrils, still ~30% of the fibrils are either not PHF, or not accounted for. A small fraction of the fibrils are single filaments and another ~20% are not accounted for. The authors mention that ~20% of these fibrils were not picked by the automated algorithm. However, it would be important to get additional clarity about these fibrils. Therefore, it would improve the impact of the paper if the authors could manually analyze passed-over particles to see if they are compatible with PHF or fall into a different class of fibrils. In addition, it would be helpful if the authors could comment on what can be done/tried to get the PHF yield closer to 90-100%

    4. Author response:

      We thank the reviewers for their constructive comments. While we work on a revision that addresses all points raised, we would already like to point out that both reviewers seem to have misunderstood how we reported the percentages of filament types in our reactions. Because we included all picked images in our calculations (including false positives from the picking, as well as damaged, overlapping or otherwise unsuitable filaments), we may have inadvertently given the impression that these filament preparations are not pure. In fact, the opposite is true: 0N3R PAD12 tau and the mixture of 0N3R:0N4R PAD12 tau assemble into highly pure paired helical filaments with the Alzheimer fold. Discarding images is common practice for high-resolution cryo-EM structure determination. Our reported percentages of discarded images (20-30%) are much lower than in typical cryo-EM studies, which is another reflection of the high quality of these samples. The main impurity lies in smaller fractions (~10%) of single protofilaments with the Alzheimer fold. We will make this clearer in our revised manuscript.

    1. eLife Assessment

      Cardiolipin is known to play an important role in modulating the assembly and function of membrane proteins in bacterial and mitochondrial membranes. In this study, authors convincingly define the molecular determinants of cardiolipin binding on de novo-designed and native membrane proteins combining the coarse-grained molecular dynamics simulation with the state-of-the-art experimental approaches such as native mass spectrometry and cryogenic electron microscopy. The major findings in this study, which are the identification of degenerate cardiolipin binding motifs and their role in membrane protein stability and activity, will provide much needed insight into the still poorly understood nature of protein-cardiolipin interactions.

    2. Reviewer #1 (Public review):

      Summary:

      The study combines predictions from MD simulations with sophisticated experimental approaches including native mass spectrometry (nMS), cryo-EM, and thermal protein stability assays to investigate the molecular determinants of cardiolipin (CDL) binding and binding-induced protein stability/function of an engineered model protein (ROCKET), as well as of the native E. coli intramembrane rhomboid protease, GlpG.

      Strengths:

      State-of-the-art approaches and sharply focused experimental investigation lend credence to the conclusions drawn. Stable CDL binding is accommodated by a largely degenerate protein fold that combines interactions from distant basic residues with greater intercalation of the lipid within the protein structure. Surprisingly, there appears to be no direct correlation between binding affinity/occupancy and protein stability.

      Weaknesses:

      (i) While aromatic residues (in particular Trp) appear to be clearly involved in the CDL interaction, there is no investigation of their roles and contributions relative to the positively charged residues (R and K) investigated here. How do aromatics contribute to CDL binding and protein stability, and are they differential in nature (W vs Y vs F)? (ii) In the case of GlpG, a WR pair (W136-R137) present at the lipid-water on the periplasmic face (adjacent to helices 2/3) may function akin to the W12-R13 of ROCKET in specifically binding CDL. Investigation of this site might prove to be interesting if it indeed does. (iii) Examples of other native proteins that utilize combinatorial aromatic and electrostatic interactions to bind CDL would provide a broader perspective of the general applicability of these findings to the reader (for e.g. the adenine nucleotide translocase (ANT/AAC) of the mitochondria as well as the mechanoenzymatic GTPase Drp1 appear to bind CDL using the common "WRG' motif.)

      Overall, using both model and native protein systems, this study convincingly underscores the molecular and structural requirements for CDL binding and binding-induced membrane protein stability. This work provides much-needed insight into the poorly understood nature of protein-CDL interactions.

    3. Reviewer #2 (Public review):

      Summary:

      The work in this paper discusses the use of CG-MD simulations and nMS to describe cardiolipin binding sites in a synthetically designed that can be extrapolated to a naturally occurring membrane protein. While the authors acknowledge their work illuminates the challenges in engineering lipid binding they are able to describe some features that highlight residues within GlpG that may be involved in lipid regulation of protease activity, although further study of this site is required to confirm it's role in protein activity.

      Comments<br /> Discrepancy between total CDL binding in CG simulations (Fig 1d) and nMS (Fig 2b,c) should be further discussed. Limitations in nMS methodology selecting for tightest bound lipids?<br /> Mutation of helical residues to alanine not only results in loss of lipid binding residues but may also impact overall helix flexibility, is this observed by the authors in CG-MD simulations? Change in helix overall RMSD throughout simulation? The figures shown in Fig.1H show what appear to be quite significant differences in APO protein arrangement between ROCKET and ROCKET AAXWA.<br /> CG-MD force experiments could be corroborated experimentally with magnetic tweezer unfolding assays as has been performed for the unfolding of artificial protein TMHC2. Alternatively this work could benefit to referencing Wang et al 2019 "On the Interpretation of Force-Induced Unfolding Studies of Membrane Proteins Using Fast Simulations" to support MD vs experimental values.<br /> Did the authors investigate if ROCKET or ROCKETAAXWA copurifies with endogenous lipids? Membrane proteins with stabilising CDL often copurify in detergent and can be detected by MS without the addition of CDL to the detergent solution. Differences in retention of endogenous lipid may also indicate differences in stability between the proteins and is worth investigation.<br /> Do the AAXWA and ROCKET have significantly similar intensities from nMS? The AAXWA appears to show slight lower intensities than the ROCKET.<br /> Can the authors extend their comments on why densities are observed only around site 2 in the cryo-em structures when site 1 is the apparent preferential site for ROCKET.<br /> The authors state that nMS is consistent with CDL binding preferentially to Site 1 in ROCKET and preferentially to Site 2 in the ROCKET AAXWA variant, yet it unclear from the text exactly how these experiments demonstrate this.<br /> As carried out for ROCKET AAXWA the total CDL binding to A61P and R66A would add to supporting information of characterisation of lipid stabilising mutations.<br /> Did the authors investigate a double mutation to Site 2 (e.g. R66A + M16A)?<br /> Was the stability of R66A ever compared to the WT or only to AAXWA?<br /> How many CDL sites in the database used are structurally verified?<br /> The work on GlpG could benefit from mutagenesis or discussion of mutagenesis to this site. The Y160F mutation has already been shown to have little impact on stability or activity (Baker and Urban Nat Chem Biol. 2012).

    4. Reviewer #3 (Public review):

      Summary:

      The relationships of proteins and lipids: it's complicated. This paper illustrates how cardiolipins can stabilize membrane protein subunits - and not surprisingly, positively charged residues play an important role here. But more and stronger binding of such structural lipids does not necessarily translate to stabilization of oligomeric states, since many proteins have alternative binding sites for lipids which may be intra- rather than intermolecular. Mutations which abolish primary binding sites can cause redistribution to (weaker) secondary sites which nevertheless stabilize interactions between subunits. This may be at first sight counterintuitive but actually matches expectations from structural data and MD modelling. An analogous cardiolipin binding site between subunits is found in E.coli tetrameric GlpG, with cardiolipin (thermally) stabilizing the protein against aggregation.

      Strengths:

      The use of the artificial scaffold allows testing of hypothesis about the different roles of cardiolipin binding. It reveals effects which are at first sight counterintuitive and are explained by the existence of a weaker, secondary binding site which unlike the primary one allows easy lipid-mediated interaction between two subunits of the protein. Introducing different mutations either changes the balance between primary and secondary binding sites or introduced a kink in a helix - thus affecting subunit interactions which are experimentally verified by native mass spectrometry.

      Weaknesses:

      The artificial scaffold is not necessarily reflecting the conformational dynamics and local flexibility of real, functional membrane proteins. The example of GlpG, while also showing interesting cardiolipin dependency, illustrates the case of a binding site across helices further but does not add much to the main story. It should be evident that structural lipids can be stabilizing in more than one way depending on how they bind, leading to different and possibly opposite functional outcomes.

    5. Author response:

      (1) discuss the non-native properties of ROCKET and compare CDL binding in native proteins

      ROCKET is indeed a non-native protein with exceptional stability, which makes it immune to mutations with subtle effects on structure or dynamics. We would argue that this is an advantage, allowing us to find the features with the most pronounced impact on CDL-mediated stability. The reviewers are right that there certainly are other structural features which impact CDL binding, which cannot be investigated using ROCKET. This is the reason we then apply our findings to GlpG - to translate back to native systems.

      The CDL binding site geometry that we tested experimentally was derived by Corey et al (Sci Adv 2022) from large-scale computational analysis of native protein structures. Our data adds some basic rules for flexibility, which helped us to identify GlpG as a potentially CDL-regulated protein. Following the reviewers’ suggestion, we will screen the dataset from Corey et al. for experimentally confirmed examples of CDL-mediated stabilization and analyze whether they conform to the rules derived from analysis of ROCKET. In this way, we may be able to assess how general our findings are.

      (2) clarify the limitations of combining MS and nMS

      The reviewers correctly point out that there are differences between the MD and MS data: although the binding Site 1 has nearly 100% occupancy in MD, MS shows that ca 50% of the protein is CDL-free and that not all subunits in the tetramer have a CDL bound. Furthermore, MD shows that aromatic residues are important, but this is not tested by MS. Both points relate to the shortcomings of nMS, which requires desolvation, ionization, and detergent stripping to detect protein-lipid complexes. These processes can potentially affect lipid binding, e.g. by leading to loss of lipids that are not tightly bound. As a result, absolute quantitative comparisons between MD and MS are challenging, and contributions from subtle non-electrostatic interactions involving aromatic residues are difficult to detect. For this reason, we use relative changes in lipid interactions between different ROCKET variants to compare MD and MS data. We will discuss these factors in the revision.

      (3) more detailed investigation of the structure-function relationship in GlpG-CDL complexes

      We use the insights from ROCKET to identify a stabilizing CDL site in GlpG and find that CDL binding switches substrate preference from transmembrane to soluble substrates. We do not verify the binding site with mutagenesis in our study, but the MD and MS data are very unambiguous that there is only one site, and its location provides a rationale for how CDL affects substrate binding, which is described in the supplementary data.

      We agree that the regulatory effect of CDL on GlpG activity raises a wide range of interesting questions relating to the mechanism of allosteric inhibition, the evolutionary background, and biological implications of E. coli using changes in membrane CDL content to steer GlpG activity. Work in our labs is on-going to investigate this further, including the mutational analysis suggested by the reviewers, but it moves beyond of the scope of the current study. We will discuss our rationale for the absence of mutagenesis data in the revision.

    1. eLife Assessment

      The bacterial cell wall is crucial to maintain viability. It has previously been suggested that Gram-positive bacteria have a periplasmic region between the cell membrane and peptidoglycan cell wall that this is maintained by the presence of teichoic acids. In this valuable study, Nguyen et al. make clever use of electron microscopy and metabolic labelling to interrogate the role of teichoic acids in supporting the maintenance of the periplasmic region in Streptococcus pneumoniae. The findings are potentially significant but incomplete to fully support the conclusions drawn.

    2. Reviewer #1 (Public review):

      The authors set out to analyse the roles of the teichoic acids of Streptococcus pneumoniae in supporting the maintenance of the periplasmic region. Previous work has proposed the periplasm to be present in Gram positive bacteria and here advanced electron microscopy approach was used. This also showed a likely role for both wall and lipo-teichoic acids in maintaining the periplasm. Next, the authors use a metabolic labelling approach to analyse the teichoic acids. This is a clear strength as this method cannot be used for most other well studied organisms. The labelling was coupled with super-resolution microscopy to be able to map the teichoic acids at the subcellular level and a series of gel separation experiments to unravel the nature of the teichoic acids and the contribution of genes previously proposed to be required for their display. The manuscript could be an important addition to the field but there are a number of technical issues which somewhat undermine the conclusions drawn at the moment. These are shown below and should be addressed. More minor points are covered in the private Recommendations for Authors.

      Weaknesses to be addressed:

      (1) l. 144 Was there really only one sample that gave this resolution? Biological repeats of all experiments are required.

      (2) Fig. 4A. Is the pellet recovered at "low" speeds not just some of the membrane that would sediment at this speed with or without LTA? Can a control be done using an integral membrane protein and Western Blot? Using the tacL mutant would show the behaviour of membranes alone.

      (3) Fig. 4A. Using enzymatic digestion of the cell wall and then sedimentation will allow cell wall associated proteins (and other material) to become bound to the membranes and potentially effect sedimentation properties. This is what is in fact suggested by the authors (l. 1000, Fig. S6). In order to determine if the sedimentation properties observed are due to an artefact of the lysis conditions a physical breakage of the cells, using a French Press, should be carried out and then membranes purified by differential centrifugation. This is a standard, and well-established method (low-speed to remove debris and high-speed to sediment membranes) that has been used for S. pneumoniae over many years but would seem counter to the results in the current manuscript (for instance Hakenbeck, R. and Kohiyama, M. (1982), Purification of Penicillin-Binding Protein 3 from Streptococcus pneumoniae. European Journal of Biochemistry, 127: 231-236).

      (4) l. 303-305. The authors suggest that the observed LTA-like bands disappear in a pulse chase experiment (Fig. 6B). What is the difference between this and Fig. 5B, where the bands do not disappear? Fig. 5C is the WT and was only pulse labelled for 5 min and so would one not expect the LTA-like bands to disappear as in 6B?

      (5) Fig. 6B, l. 243-269 and l. 398-410. If, as stated, most of the LTA-like bands are actually precursor then how can the quantification of LTA stand as stated in the text? The "Titration of Cellular TA" section should be re-evaluated or removed? If you compare Fig. 6C WT extract incubated at RT and 110oC it seems like a large decrease in amount of material at the higher temperature. Thus, the WT has a lot of precursors in the membrane? This needs to be quantified.

      (6) L. 339-351, Fig. 6A. A single lane on a gel is not very convincing as to the role of LytR. Here, and throughout the manuscript, wherever statements concerning levels of material are made, quantification needs to be done over appropriate numbers of repeats and with densitometry data shown in SI.

      (7) 14. l. 385-391. Contrary to the statement in the text, the zwitterionic TA will have associated counterions that result in net neutrality. It will just have both -ve and +ve counterions in equal amounts (dependent on their valency), which doesn't matter if it is doing the job of balancing osmolarity (rather than charge).

    3. Reviewer #2 (Public review):

      The Gram-positive cell wall contains for a large part of TAs, and is essential for most bacteria. However, TA biosynthesis and regulation is highly understudied because of the difficulties in working with these molecules. This study closes some of our important knowledge gaps related to this and provides new and improved methods to study TAs. It also shows an interesting role for TAs in maintaining a 'periplasmic space' in Gram positives. Overall, this is an important piece of work. It would have been more satisfying if the possible causal link between TAs and periplasmic space would have been more deeply investigated with complemented mutants and CEMOVIS. For the moment, there is clearly something happening but it is not clear if this only happens in TA mutants or also in strains with capsules/without capsules and in PG mutants, or in lafB (essential for production of another glycolipid) mutants. Finally, some very strong statements are made suggesting several papers in the literature are incorrect, without actually providing any substantiation/evidence supporting these claims. This work pioneers some new methods that will definitively move the field forward.

    1. eLife Assessment

      This study estimates the fraction of apoptotic motor neurons during the development of the zebrafish spinal cord. The results are useful, but incomplete. Importantly, the data are inadequate to support the title or the conclusions presented in the abstract. A correct title could be: "A surprisingly small percentage of early developing zebrafish motor neurons die through apoptosis in non-limb innervating regions of the spinal cord."

    2. Reviewer #1 (Public review):

      Summary:

      The authors aim at measuring the apoptotic fraction of motorneurons in developing zebrafish spinal cord to assess the extent of neuronal apoptosis during the development of of a vertebrate embryo in an in vivo context

      Strengths:

      The transgenic fish line tg(mnx1:sensor C3) appears to be a good reagent for motorneuron apoptosis studies, while further validation of its motorneuron specificity should be performed

      Weaknesses:

      The results do not support the conclusions. The main "selling point" as summarized in the title is that the apoptotic rate of zebrafish motorneurons during development is strikingly low (~2% ) as compared to the much higher estimate (~50%) by previous studies in other systems. The results used to support the conclusion are that only a small percentage (under 2%) of apoptotic cells were found over a large population at a variety of stages 24-120hpf. This is fundamentally flawed logic, as a short-time window measure of percentage cannot represent the percentage on the long-term. For example, at any year under 1% of human population die, but over 100 years >99% of the starting group will have died. To find the real percentage of motorneurons that died, the motorneurons born at different times must be tracked over long term, or the new motorneuron birth rate must be estimated.

      Similar argument can be applied to the macrophage results.

      The conclusion regarding timing of axon and cell body caspase activation and apoptosis timing also has clear issues. The ~minutes measurement are too long as compared to the transport/diffusion timescale between the cell body and the axon, caspase activity could have been activated in the cell body and either caspase or the cleaved sensor move to the axon in several seconds. The authors' results are not high frequency enough to resolve these dynamics

      Many statements suggest oversight of literature, for example, in abstract "however, there is still no real-time observation showing this dying process in live animals.".

      Many statements should use more scholarly terms and descriptions from the spinal cord or motorneuron, neuromuscular development fields, such as line 87 "their axons converged into one bundle to extend into individual somite, which serves as a functional unit for the development and contraction of muscle cells"

      The transgenic line is perhaps the most meaningful contribution to the field as the work stands. However, mnx1 promoter is well known for its non-specific activation - while the images do suggest the authors' line is good, motorneuron markers should be used to validate the line. This is especially important for assessing this population later as mnx1 may be turned off in mature neurons. The author's response regarding mnx1 specificity does not mitigate the original concern.

      Overall, this work does not substantiate its biological conclusions and therefore do not advance the field. The transgenic line has the potential for addressing the questions raised but requires different sets of experiments. The line and the data as reported are useful on their own by providing a short-term rate of apoptosis of the motorneuron population.

    3. Reviewer #2 (Public review):

      Summary:

      Jia and colleagues developed a fluorescence resonance energy transfer (FRET)-based biosensor to study programmed cell death in the zebrafish spinal cord. They applied this tool to study death of zebrafish spinal motor neurons.

      Strengths:

      Their analysis shows that the tool is a useful biosensor of motor neuron apoptosis in living zebrafish and can reveal which part of the neuron undergoes caspase activation first, achieving two of their aims.

      Weaknesses:

      The third aim, to provide novel insights into the spatiotemporal properties and occurrence rates of motor neuron death requires additional context and investigation, especially to understand the significance of the differences they report between zebrafish motor neuron programmed cell death and what has been previously described in chicks and rodents. For example, mnx1 expresses not only in motor neurons, but also in interneurons. However, the way the authors counted living and dead cells does not take this into consideration, potentially underestimating the percentage of motor neurons that died. Previous studies of chicks and rodents showed widespread differences in the timing of motor neuron programmed cell death and the number of cells that died depending on the spinal cord region examined. The authors have not described which spinal cord segments they examined or whether they examined motor neurons in limb-bearing segments which have been best studied in other species. Previous literature investigated the death of an identified zebrafish motor neuron and provided experimental evidence that it is independent of limitations in muscle innervation area, suggesting it is not coupled to muscle-derived neurotrophic factors. Thus, the authors need to acknowledge that even previous to their study, there was literature suggesting that programmed cell death of at least one motor neuron in zebrafish does not easily fit into the "neurotrophic hypothesis" as it is generally formulated. Finally, the authors need to be mindful that showing that something does not happen in an observational study cannot reveal the capabilities of the cells involved without an experimental test.

    4. Author response:

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

      Reviewer 1:

      We thank the reviewer for the time and effort in providing very useful comments and suggestions for our manuscript.

      (1) The results do not support the conclusions. The main "selling point" as summarized in the title is that the apoptotic rate of zebrafish motorneurons during development is strikingly low (~2% ) as compared to the much higher estimate (~50%) by previous studies in other systems. The results used to support the conclusion are that only a small percentage (under 2%) of apoptotic cells were found over a large population at a variety of stages 24-120hpf. This is fundamentally flawed logic, as a short-time window measure of percentage cannot represent the percentage in the long term. For example, at any year under 1% of the human population dies, but over 100 years >99% of the starting group will have died. To find the real percentage of motorneurons that died, the motorneurons born at different times must be tracked over the long term or the new motorneuron birth rate must be estimated. A similar argument can be applied to the macrophage results. Here the authors probably want to discuss well-established mechanisms of apoptotic neuron clearance such as by glia and microglia cells.

      We chose the time window of 24-120 hpf based on the following two reasons: 1) Previous studies showed that although the time windows of motor neuron death vary in chick (E5-E10), mouse (E11.5-E15.5), rat (E15-E18), and human (11-25 weeks of gestation), the common feature of these time windows is that they are all the developmental periods when motor neurons contact with muscle cells. The contact between zebrafish motor neurons and muscle cells occurs before 72 hpf, which is included in our observation time window of 24-120 hpf. 2) Zebrafish complete hatching during 48-72 hpf, and most organs form before 72 hpf. More importantly, zebrafish start swimming around 72 hpf, indicating that motor neurons are fully functional at 72 hpf. Thus, we are confident that this 24-120 hpf time window covers the time window during which motor neurons undergo programmed cell death during zebrafish early development. We have added this information to the revised manuscript.

      We frequently used “early development” in this manuscript to describe our observation. However, we missed “early” in our title. We therefore have added this ket word of “early” in the title in the revised manuscript.

      Previous studies in zebrafish have shown that the production of spinal cord motor neurons largely ceases before 48 hpf, and then the motor neurons remain largely constant until adulthood (doi: 10.1016/j.celrep.2015.09.050; 10.1016/j.devcel.2013.04.012; 10.1007/BF00304606; 10.3389/fcell.2021.640414). Our observation time window covers the major motor neuron production process. Therefore, we believe that neurogenesis will not affect our findings and conclusions.

      We discussed the engulfment of dead motor neurons by other types of cells in the discussion section.

      (2) The transgenic line is perhaps the most meaningful contribution to the field as the work stands. However, the mnx1 promoter is well known for its non-specific activation - while the images suggest the authors' line is good, motor neuron markers should be used to validate the line. This is especially important for assessing this population later as mnx1 may be turned off in mature neurons.

      The mnx1 promoter has been widely used to label motor neurons in transgenic zebrafish. Previous studies have shown that most of the cells labeled in the mnx1 transgenic zebrafish are motor neurons. In this study, we observed that the neuronal cells in our sensor zebrafish formed green cell bodies inside of the spinal cord and extended to the muscle region, which is an important morphological feature of the motor neurons.

      Reviewer 2:

      We thank the reviewer for the time and effort in making very useful comments and suggestions for our manuscript.

      The FRET-based programmed cell death biosensor described in this manuscript could be very useful. However, the authors have not considered what is already known about the development and programmed cell death of zebrafish spinal motor neurons, and potential differences between motor neuron populations innervating different types of muscles in different vertebrate models. Without this context, the application of their new biosensor tool does not provide new insights into zebrafish motor neuron programmed cell death. In addition, the authors have not carried out controls to show the efficacy and specificity of their morpholinos. Nor have they described how they counted dying motor neurons, or why they chose the specific developmental time points they addressed. These issues are addressed more specifically below.

      (1) Lines 12-13: Previous studies in zebrafish showed death of identified spinal motor neurons.

      Line 103: In Figure 2A the cell body in the middle is that of identified motor neuron VaP. VaP death has previously been described in several publications. The cell body on the right of the same panel appears to belong to an interneuron whose axon can be seen extending off to the left in one of the rostrocaudal axon bundles that traverse the spinal cord. Higher-resolution imaging would clarify this.

      Lines 163-164: Is this the absolute number of motor neurons that died? How were the counts done? Were all the motor neurons in every segment counted? There are approximately 30 identifiable VaP motor neurons in each embryo and they have previously been reported to die between 24-36 hpf. So this analysis is likely capturing those cells.

      Our study examined the overall motor neuron apoptosis rather than a specific type of motor neuron death, so we did not emphasize the death of VaP motor neurons. We agree that the dead motor neurons observed in our manuscript contain VaP motor neurons. However, there were also other types of dead motor neurons observed in our study. The reasons are as follows: 1) VaP primary motor neurons die before 36 hpf, but our study found motor neuron cells died after 36 hpf and even at 84 hpf (revised Figure 4A). 2) The position of the VaP motor neuron is together with that of the CaP motor neuron, that is, at the caudal region of the motor neuron cluster. Although it’s rare, we did observe the death of motor neurons in the rostral region of the motor neuron cluster (revised Figure 2C). 3) There is only one or zero VaP motor neuron in each motor neuron cluster. Although our data showed that usually one motor neuron died in each motor neuron cluster, we did observe that sometimes more than one motor neuron died in the motor neuron cluster (revised Figure 2C). We included this information in the revised discussion.

      (2) Lines 82-83: It is published that mnx1 is expressed in at least one type of spinal interneuron derived from the same embryonic domain as motor neurons.

      The mnx1 promoter has been widely used to label motor neurons in transgenic zebrafish. Previous studies have shown that most of the cells labeled in the mnx1 transgenic zebrafish are motor neurons. In this study, we observed that the neuronal cells in our sensor zebrafish formed green cell bodies inside of the spinal cord and extended to the muscle region, which is an important morphological feature of the motor neurons.

      Furthermore, a few of those green cell bodies turned into blue apoptotic bodies inside the spinal cord and changed to blue axons in the muscle regions at the same time, which strongly suggests that those apoptotic neurons are not interneurons. Although the mnx1 promoter might have labeled some interneurons, this will not affect our major finding that only a small portion of motor neurons died during zebrafish early development.

      (3) Lines 161-162: Although this may be the major time window of neurogenesis, there are many more motor neurons in adults than in larvae. Neither of these references describes the increase in motor neuron numbers over this particular time span, so the rationale for this choice is unclear.

      Lines 168-171: It is known that later developing motor neurons are still being generated in the spinal cord at this time, suggesting that if there is a period of programmed cell death similar to that described in chick and mouse, it would likely occur later. In addition, most of the chick and mouse studies were performed on limb-innervating motor neurons, rather than the body wall muscle-innervating motor neurons examined here.

      Lines 237-238: Especially since new motor neurons are still being generated at this time.

      Previous studies have shown that the production of spinal cord motor neurons largely ceases before 48 hpf in zebrafish, and then the motor neurons remain largely constant until the adulthood (doi: 10.1016/j.celrep.2015.09.050; 10.1016/j.devcel.2013.04.012; 10.1007/BF00304606; 10.3389/fcell.2021.640414). Our observation time window covers the major motor neuron production process. Therefore, we believe that neurogenesis will not affect our data and conclusions.

      The death of motor neurons in limb-innervating motor neurons has been extensively studied in chicks and rodents, as it is easy to undergo operations such as amputation. However, previous studies have shown this dramatic motor neuron death does not only occur in limb-innervating motor neurons but also occurs in other spinal cord motor neurons (doi: 10.1006/dbio.1999.9413). In our manuscript, we studied the naturally occurring motor neuron death in the whole spinal cord during the early stage of zebrafish development.

      (4) Lines 184-187: Previous publications showed that death of VaP is independent of limitations in muscle innervation area, suggesting it is not coupled to muscle-derived neurotrophic factors.

      Lines 328-334: There have been many publications describing appropriate morpholino controls. The authors need to describe their controls and show that they know that the genes they were targeting were downregulated.

      For the morpholinos, we did not confirm the downregulation of the target genes. These morpholino-related data are a minor part of our manuscript and shall not affect our major findings. We have removed the neurotrophic factors and morpholino-related data in the revised manuscript.

    1. eLife Assessment

      This study presents a valuable finding on the role of secretory leukocyte protease inhibitors (SLPI) in developing Lyme disease in mice infected with Borrelia burgdorferi. The evidence supporting the claims of the authors is solid, although there are a few concerns that need to be addressed, including patient sample sizes, and the potential contribution of the greater bacterial burden to the enhanced inflammation. This paper would be of interest to scientists in the infectious inflammatory disease field.

    2. Reviewer #1 (Public review):

      Summary:

      This study demonstrates the significant role of secretory leukocyte protease inhibitor (SLPI) in regulating B. burgdorferi-induced periarticular inflammation in mice. They found that SLPI-deficient mice showed significantly higher B. burgdorferi infection burden in ankle joints compared to wild-type controls. This increased infection was accompanied by infiltration of neutrophils and macrophages in periarticular tissues, suggesting SLPI's role in immune regulation. The authors strengthened their findings by demonstrating a direct interaction between SLPI and B. burgdorferi through BASEHIT library screening and FACS analysis. Further investigation of SLPI as a target could lead to valuable clinical applications.

      The conclusions of this paper are mostly well supported by data, but two aspects need attention:

      (1) Cytokine Analysis:<br /> The serum cytokine/chemokine profile analysis appears without TNF-alpha data. Given TNF-alpha's established role in inflammatory responses, comparing its levels between wild-type and infected B. burgdorferi conditions would provide valuable insight into the inflammatory mechanism.<br /> (2) Sample Size Concerns:<br /> While the authors note limitations in obtaining Lyme disease patient samples, the control group is notably smaller than the patient group. This imbalance should either be addressed by including additional healthy controls or explicitly justified in the methodology section.

    3. Reviewer #2 (Public review):

      Summary:

      This manuscript by Yu and coworkers investigates the potential role of Secretory leukocyte protease inhibitor (SLPI) in Lyme arthritis. They show that, after needle inoculation of the Lyme disease (LD) agent, B. burgdorferi, compared to wild type mice, a SLPI-deficient mouse suffers elevated bacterial burden, joint swelling and inflammation, pro-inflammatory cytokines in the joint, and levels of serum neutrophil elastase (NE). They suggest that SLPI levels of Lyme disease patients are diminished relative to healthy controls. Finally, they find that SLPI may interact directly the B. burgdorferi.

      Strengths:

      Many of these observations are interesting and the use of SLPI-deficient mice is useful (and has not previously been done).

      Weaknesses:

      (a) The known role of SLPI in dampening inflammation and inflammatory damage by inhibition of NE makes the enhanced inflammation in the joint of B. burgdorferi-infected mice a predicted result; (b) The potential contribution of the greater bacterial burden to the enhanced inflammation is not addressed; (c) The relationship of SLPI binding by B. burgdorferi to the enhanced disease of SLPI-deficient mice is not clear; and (d) Several methodological aspects of the study are unclear.

    4. Reviewer #3 (Public review):

      Summary:

      The authors investigated the role of secretory leukocyte protease inhibitors (SLPI) in developing Lyme disease in mice infected with Borrelia burgdorferi. Using a combination of histological, gene expression, and flow cytometry analyses, they demonstrated significantly higher bacterial burden and elevated neutrophil and macrophage infiltration in SLPI-deficient mouse ankle joints. Furthermore, they also showed direct interaction of SLPI with B. burgdorferi, which likely depletes the local environment of SLPI and causes excessive protease activity. These results overall suggest ankle tissue inflammation in B. burgdorferi-infected mice is driven by unchecked protease activity.

      Strengths:

      Utilizing a comprehensive suite of techniques, this is the first study showing the importance of anti-protease-protease balance in the development of periarticular joint inflammation in Lyme disease.

      Weaknesses:

      Due to the limited sample availability, the authors investigated the serum level of SLPI in both in Lyme arthritis patients and patients with earlier disease manifestations.

    5. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study demonstrates the significant role of secretory leukocyte protease inhibitor (SLPI) in regulating B. burgdorferi-induced periarticular inflammation in mice. They found that SLPI-deficient mice showed significantly higher B. burgdorferi infection burden in ankle joints compared to wild-type controls. This increased infection was accompanied by infiltration of neutrophils and macrophages in periarticular tissues, suggesting SLPI's role in immune regulation. The authors strengthened their findings by demonstrating a direct interaction between SLPI and B. burgdorferi through BASEHIT library screening and FACS analysis. Further investigation of SLPI as a target could lead to valuable clinical applications.

      The conclusions of this paper are mostly well supported by data, but two aspects need attention:

      (1) Cytokine Analysis:

      The serum cytokine/chemokine profile analysis appears without TNF-alpha data. Given TNF-alpha's established role in inflammatory responses, comparing its levels between wild-type and infected B. burgdorferi conditions would provide valuable insight into the inflammatory mechanism.

      (2) Sample Size Concerns:

      While the authors note limitations in obtaining Lyme disease patient samples, the control group is notably smaller than the patient group. This imbalance should either be addressed by including additional healthy controls or explicitly justified in the methodology section.

      We thank the reviewer for the careful review and positive comments.

      (1) We did look into the level of TNF-alpha in both WT and SLPI-/- mice with and without B. burgdorferi infection. At serum level, using ELISA, we did not observe any significant difference between all four groups. At gene expression level, using RT-qPCR on the tibiotarsal tissue, we also did not observe any significant differences. Our RT-qPCR result is consistent with the previous microarray study using the whole murine joint tissue (DOI: 10.4049/jimmunol.177.11.7930). The microarray study did not show significant changes in TNF-alpha level in C57BL/6 mice following B. burgdorferi infection. The above data suggest that TNF-alpha does not involve in SLPI-regulated immune responses in the murine tibiotarsal tissue following B. burgdorferi infection. A brief discussion will be added, and the above data will be provided as a supplemental figure in the revised manuscript.

      (2) We agree with the reviewer that the control group is smaller than the patient group. Among the archived samples that are available, the number of adult healthy controls are limited. It has been shown that the serum level of SLPI in healthy volunteers is in average about 40 ng/ml  (DOI: 10.3389/fimmu.2019.00664 and 10.1097/00003246-200005000-00003). The median level in the healthy control in our data was 38.92 ng/ml, which is comparable to the previous results. A brief discussion will be added in the revised manuscript.

      Reviewer #2 (Public review):

      Summary:

      This manuscript by Yu and coworkers investigates the potential role of Secretory leukocyte protease inhibitor (SLPI) in Lyme arthritis. They show that, after needle inoculation of the Lyme disease (LD) agent, B. burgdorferi, compared to wild type mice, a SLPI-deficient mouse suffers elevated bacterial burden, joint swelling and inflammation, pro-inflammatory cytokines in the joint, and levels of serum neutrophil elastase (NE). They suggest that SLPI levels of Lyme disease patients are diminished relative to healthy controls. Finally, they find that SLPI may interact directly the B. burgdorferi.

      Strengths:

      Many of these observations are interesting and the use of SLPI-deficient mice is useful (and has not previously been done).

      We appreciate the reviewer’s careful reading and positive comments.

      Weaknesses:

      (a) The known role of SLPI in dampening inflammation and inflammatory damage by inhibition of NE makes the enhanced inflammation in the joint of B. burgdorferi-infected mice a predicted result;

      We agree that the observation of the elevated NE level and the enhanced inflammation is theoretically likely. Indeed, that was the hypothesis that we explored, and often what is theoretically possible does not turn out to occur. In addition, despite the known contribution of neutrophils to the severity of murine Lyme arthritis, the importance of the neutrophil serine proteases and anti-protease has not been specifically studied, and neutrophils secrete many factors. Therefore, our data fill an important gap in the knowledge of murine Lyme arthritis development – and set the stage for the further exploration of this hypothesis in the genesis of human Lyme arthritis.

      (b) The potential contribution of the greater bacterial burden to the enhanced inflammation is not addressed;

      We agree with the reviewer’s viewpoint that the increased infection burden in the tibiotarsal tissue of the infected SLPI-/- mice could contribute to the enhanced inflammation. A brief discussion of this possibility will be added to the revised manuscript.

      (c) The relationship of SLPI binding by B. burgdorferi to the enhanced disease of SLPI-deficient mice is not clear; and

      We agree with the reviewer that we have not shown the importance of the SLPI-B. burgdorferi binding in the development of periarticular inflammation. It is an ongoing project in our lab to identify the SLPI binding partner in B. burgdorferi. Our hypothesis is that SLPI could bind and inhibit an unknown B. burgdorferi virulence factor that contributes to murine Lyme arthritis. We will include the above discussion in the revised manuscript.

      (d) Several methodological aspects of the study are unclear.

      We appreciate the critique and will modify the method session in greater detail in the revised manuscript.

      Reviewer #3 (Public review):

      Summary:

      The authors investigated the role of secretory leukocyte protease inhibitors (SLPI) in developing Lyme disease in mice infected with Borrelia burgdorferi. Using a combination of histological, gene expression, and flow cytometry analyses, they demonstrated significantly higher bacterial burden and elevated neutrophil and macrophage infiltration in SLPI-deficient mouse ankle joints. Furthermore, they also showed direct interaction of SLPI with B. burgdorferi, which likely depletes the local environment of SLPI and causes excessive protease activity. These results overall suggest ankle tissue inflammation in B. burgdorferi-infected mice is driven by unchecked protease activity.

      Strengths:

      Utilizing a comprehensive suite of techniques, this is the first study showing the importance of anti-protease-protease balance in the development of periarticular joint inflammation in Lyme disease.

      We greatly appreciate the reviewer’s careful reading and positive comments.

      Weaknesses:

      Due to the limited sample availability, the authors investigated the serum level of SLPI in both in Lyme arthritis patients and patients with earlier disease manifestations.

      We agree with the reviewer that it would be ideal to have more samples from Lyme arthritis patients. However, among the available archived samples, samples from Lyme arthritis patients are limited. For the samples from patients with single EM, the symptom persisted into 3-4 month after diagnosis, the same timeframe when arthritis is developed. We will add the above discussion in the revised manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) In Figure 2, for histological scoring, do they have similar n numbers?

      In panel B, 20 infected WT mice and 19 infected SLPI-/- mice were examined. In panel D, 13 infected WT and SLPI-/- mice were examined. Without infection, WT and SLPI-/- mice do not develop spontaneous arthritis. Due to the slow breeding of the SLPI-/- mice, a small number of uninfected control animals were used.

      (2) In Figure 3, for macrophage population analysis, maybe consider implementing Ly6G-negative gating strategy to prevent neutrophil contamination in macrophage population?

      We appreciate reviewer’s suggestion. We will analyze the data using the Ly6G-negative gating strategy and provide the result in a supplemental figure. We will compare the results using the two gating strategies in the revised manuscript.

      Reviewer #2 (Recommendations for the authors):

      (1) The investigators should address the possibility that much of the enhanced inflammatory features of infected SLPI-deficient mice are simply due to the higher bacterial load in the joint.

      We agree with the reviewer’s viewpoint that the increased infection burden in the tibiotarsal tissue of the infected SLPI-/- mice could contribute to the enhanced inflammation. A brief discussion of this possibility will be added to the revised manuscript.

      (2) Fig. 1. (A) There is no statistically significant difference in the bacterial load in the heart or skin, in contrast to the tibiotarsal joint. It would be of interest to know whether other tissues that are routinely sampled to assess the bacterial load, such as injection site, knee, and bladder, also harbored increased bacterial load in SLPI-deficient mice. (B) Heart and joint burden were measured at "21-28" days. The two time points should be analyzed separately rather than pooled.

      (A) We appreciate the reviewer’s suggestion. We agree that looking into the infection load in other tissues is helpful. However, studies into murine Lyme arthritis have been predominantly focused on tibiotarsal tissue, which displays the most consistent and prominent swelling that’s easy to observe and measure. Thus, we focused on the tibiotarsal joint in our study. (B) We collected the heart and joint tissue approximately 3-week post infection within a 3-day window based on the feasibility and logistics of the laboratory. Using “21-28 d”, we meant to describe between 21-24 days post infection. We apologize for the mislabeling and will correct it in the revised manuscript, stating approximately 3 weeks in the results, and defining approximately 3-weeks as between 21-24 days in the methods.

      (3) Fig. 2. (A) The same ambiguity as to the days post-infection as cited above in Point 2B exists in this figure. (B) Panel B: Caliper measurements to assess joint swelling should be utilized rather than visual scoring. (In addition, the legend should make clear that the black circles represent mock-infected mice.)

      (A) The histology scoring, and histopathology examination were performed at the same time as heart and joint tissue collection, approximately 3 weeks post infection within a 3-day window based on the feasibility and logistics of the laboratory. We apologize for the mislabeling and will correct it in the revised manuscript.  (B) We appreciate the reviewer’s suggestion. However, our extensive experience is that caliper measurement can alter the assessment of swelling by placing pressure on the joints and did not produce consistent results. Double blinded scoring was thus performed. Histopathology examination was performed by an independent pathologist and confirmed the histology score and provided additional measurements.

      (4) Fig. 3. (A) See Point 2B. (B) For Panels C-E, uninfected controls are lacking.

      We apologize for this omission. Uninfected controls will be provided in the revised manuscript.

      (5) Fig. 4. Fig. 4. Some LD subjects were sampled multiple times (5 samples from 3 subjects with Lyme arthritis; 13 samples from 4 subjects with EM), and samples from same individuals apparently are treated as biological replicates in the statistical analysis. In contrast, the 5 healthy controls were each sampled only once.

      We agree with the reviewer that the control group is smaller than the patient group. Among the archived samples that are available, the number of adult healthy controls are limited, and sampled once. We used these samples to establish the baseline level of SLPI in the serum. It has been shown that the serum level of SLPI in healthy volunteers is in average about 40 ng/ml  (DOI: 10.3389/fimmu.2019.00664 and 10.1097/00003246-200005000-00003). The median level in the healthy control in our data was 38.92 ng/ml, which is comparable to the previous results. A brief discussion will be added in the revised manuscript.

      (6) Fig. 5. (A) Panel A: does binding occur when intact bacteria are used? (B) Panels B, C: Were bacteria probed with PI to indicate binding likely to occur to surface? How many biological replicates were performed for each panel? Is "antibody control" a no SLPI control? What is the blue line?

      Actively growing B. burgdorferi were collected and used for binding assays. We do not permeabilize the bacteria for flow cytometry. Thus, all the binding detected occurs to the bacterial surface. Three biological replicates were performed for each panel. The antibody control is no SLPI control. For panel D, the bacteria were stained with Hoechst, which shows the morphology of bacteria. We apologize for the missing information. A complete and detailed description of Figure 5 will be provided in the revised manuscript. 

      (7) Sup Fig. 1. (A) Panel A: Was this experiment performed multiple times? I.e., how many biological replicates? (B) Panel B: Strain should be specified.

      The binding assay to B. burgdorferi B31A was performed two times. In panel B, B. burgdorferi B31A3 was used. We apologize for the missing information. A complete and detailed description will be provided in the revised manuscript. 

      (8) Fig. S2. It is not clear that the condition (20% serum) has any bactericidal activity, so the potential protective activity of SLPI cannot be determined. (Typical serum killing assays in the absence of specific antibody utilized 40% serum.)

      In Fig. S2, panel B, the first two bars (without SLPI, with 20% WT anti serum) showed around 40% viability. It indicates that the 20% WT anti serum has bactericidal activity. Serum was collected from B. burgdorferi-infected WT mice at 21 dpi, which should contain polyclonal antibody against B. burgdorferi.

      Reviewer #3 (Recommendations for the authors):

      It was a pleasure to review! I congratulate the authors on this elegant study. I think the manuscript is very well-written and clearly conveys the research outcomes. I only have minor suggestions to improve the readability of the text.

      We greatly appreciate the reviewer’s recognition of our work.

      Line 92: Please briefly summarize the key results of the study at the end of the introduction section.

      We appreciate the reviewer’s suggestion. A brief summary will be added in the revised manuscript.

      Line 108: Why is the inflammation significantly occurred only in ankle joints of SLPI-I mice? Could you please provide a brief explanation?

      The inflammation may also happen in other joints the B. burgdorferi infected SLPI-/- mice, which has not been studied. The study into murine Lyme arthritis has been predominantly done in the tibiotarsal tissue, which displays the most prominent swelling that’s easy to observe and measure. Thus, we focused on the tibiotarsal joint in our study.

      Line 136: Please also include the gene names in Figure 3.

      We apologize for the omission. Gene names will be included in the revised manuscript.

      Line 181: Please briefly introduce BASEHIT. Why did you use this tool? What are the benefits?

      We appreciate the reviewer’s suggestion. We will provide more background information on BASEHIT in the revised manuscript.

    1. eLife Assessment

      This valuable study investigates how the proteins of the Cdv division system in Metallosphaera sedula archaea sequentially interact with curved membranes in vitro, extending our understanding of this reduced ESCRT-like machinery. While the data support key aspects of protein recruitment and membrane remodeling, missing controls and statistical analysis information, unaddressed discrepancies, and limitations in recapitulating native geometry leave the data incomplete to fully support the proposed conclusions. The work will be of interest to evolutionary and synthetic biologists as membrane biophysicists but would benefit from additional experiments and a more cautious interpretation of results.

    2. Reviewer #1 (Public review):

      Summary:

      The authors aimed to elucidate the recruitment order and assembly of the Cdv proteins during Sulfolobus acidocaldarius archaeal cell division using a bottom-up reconstitution approach. They employed liposome-binding assays, EM, and fluorescence microscopy with in vitro reconstitution in dumbbell-shaped liposomes to explore how CdvA, CdvB, and the homologues of ESCRT-III proteins (CdvB, CdvB1, and CdvB2) interact to form membrane remodeling complexes.<br /> The study sought to reconstitute the Cdv machinery by first analyzing their assembly as two sub-complexes: CdvA:CdvB and CdvB1:CdvB2ΔC. The authors report that CdvA binds lipid membranes only in the presence of CdvB and localizes preferentially to membrane necks. Similarly, the findings on CdvB1:CdvB2ΔC indicate that truncation of CdvB2 facilitates filament formation and enhances curvature sensitivity in interaction with CdvB1. Finally, while the authors reconstitute a quaternary CdvA:CdvB:CdvB1:CdvB2 complex and demonstrate its enrichment at membrane necks, the mechanistic details of how these complexes drive membrane remodeling by subcomplexes removal by the proteasome and/or CdvC remain speculative.<br /> Although the work highlights intriguing similarities with eukaryotic ESCRT-III systems and explores unique archaeal adaptations, the conclusions drawn would benefit from stronger experimental validation and a more comprehensive mechanistic framework.

      Strengths:

      The study of machinery assembly and its involvement in membrane remodeling, particularly using bottom-up reconstituted in vitro systems, presents significant challenges. This is particularly true for systems like the ESCRT-III complex, which localizes uniquely at the lumen of membrane necks prior to scission. The use of dumbbell-shaped liposomes in this study provides a promising experimental model to investigate ESCRT-III and ESCRT-III-like protein activity at membrane necks.<br /> The authors present intriguing evidence regarding the sequential recruitment of ESCRT-III proteins in crenarchaea-a close relative of eukaryotes. This finding suggests that the hierarchical recruitment characteristic of eukaryotic systems may predate eukaryogenesis, which is a significant and exciting contribution. However, the broader implications of these findings for membrane remodeling mechanisms remain speculative, and the study would benefit from stronger experimental validation and expanded contextualization within the field.

      Weaknesses:

      This manuscript presents several methodological inconsistencies and lacks key controls to validate its claims. Additionally, there is insufficient information about the number of experimental repetitions, statistical analyses, and a broader discussion of the major findings in the context of open questions in the field.

    3. Reviewer #2 (Public review):

      Summary:

      The Crenarchaeal Cdv division system represents a reduced form of the universal and ubiquitous ESCRT membrane reverse-topology scission machinery, and therefore a prime candidate for synthetic and reconstitution studies. The work here represents a solid extension of previous work in the field, clarifying the order of recruitment of Cdv proteins to curved membranes.

      Strengths:

      The use of a recently developed approach to produce dumbbell-shaped liposomes (De Franceschi et al. 2022), which allowed the authors to assess recruitment of various Cdv assemblies to curved membranes or membrane necks; reconstitution of a quaternary Cdv complex at a membrane neck.

      Weaknesses:

      The manuscript is a bit light on quantitative detail, across the various figures, and several key controls are missing (CdvA, B alone to better interpret the co-polymerisation phenotypes and establish the true order of recruitment, for example) - addressing this would make the paper much stronger. The authors could also include in the discussion a short paragraph on implications for our understanding of ESCRT function in other contexts and/or in archaeal evolution, as well as a brief exploration of the possible reasons for the discrepancy between the foci observed in their liposome assays and the large rings observed in cells - to better serve the interests of a broad audience.

    4. Reviewer #3 (Public review):

      Summary:

      In this report, De Franceschi et al. purify components of the Cdv machinery in archaeon M. sedula and probe their interactions with membrane and with one-another in vitro using two main assays - liposome flotation and fluorescent imaging of encapsulated proteins. This has the potential to add to the field by showing how the order of protein recruitment seen in cells is related to the differential capacity of individual proteins to bind membranes when alone or when combined.

      Strengths:

      Using the floatation assay, they demonstrate that CdvA and CdvB bind liposomes when combined. While CdvB1 also binds liposomes under these conditions, in the floatation assay, CdvB2 lacking its C-terminus is not efficiently recruited to membranes unless CdvAB or CdvB1 are present. The authors then employ a clever liposome assay that generates chained spherical liposomes connected by thin membrane necks, which allows them to accurately control the buffer composition inside and outside of the liposome. With this, they show that all four proteins accumulate in necks of dumbbell-shaped liposomes that mimic the shape of constricting necks in cell division. Taken altogether, these data lead them to propose that Cdv proteins are sequentially recruited to the membrane as has also been suggested by in vivo studies of ESCRT-III dependent cell division in crenarchaea.

      Weaknesses:

      These experiments provide a good starting point for the in vitro study the interaction of Cdv system components with the membrane and their consecutive recruitment. However, several experimental controls are missing that complicate their ability to draw strong conclusions. Moreover, some results are inconsistent across the two main assays which make the findings difficult to interpret.

      (1) Missing controls.

      Various protein mixtures are assessed for their membrane-binding properties in different ways. However, it is difficult to interpret the effect of any specific protein combination, when the same experiment is not presented in a way that includes separate tests for all individual components. In this sense, the paper lacks important controls.

      For example, Fig 1C is missing the CdvB-only control. The authors remark that CdvB did not polymerise (data not shown) but do not comment on whether it binds membrane in their assays. In the introduction, Samson et al., 2011 is cited as a reference to show that CdvB does not bind membrane. However, here the authors are working with protein from a different organism in a different buffer, using a different membrane composition and a different assay. Given that so many variables are changing, it would be good to present how M. sedula CdvB behaves under these conditions.

      Similarly, there is no data showing how CdvB alone or CdvA alone behave in the dumbbell liposome assay. Without these controls, it's impossible to say whether CdvA recruits CdvB or the other way around.

      The manuscript would be much stronger if such data could be added.

      (2) Some of the discrepancies in the data generated using different assays are not discussed.

      The authors show that CdvB2∆C binds membrane and localizes to membrane necks in the dumbbell liposome assay, but no membrane binding is detected in the flotation assay. The discrepancy between these results further highlights the need for CdvB-only and CdvA-only controls.

      (3) Validation of the liposome assay.

      The experimental setup to create dumbbell-shaped liposomes seems great and is a clever novel approach pioneered by the team. Not only can the authors manipulate liposome shape, they also state that this allows them to accurately control the species present on the inside and outside of the liposome. Interpreting the results of the liposome assay, however, depends on the geometry being correct. To make this clearer, it would seem important to include controls to prove that all the protein imaged at membrane necks lie on the inside of liposomes. In the images in SFig3 there appears to be protein outside of the liposome. It would also be helpful to present data to show test whether the necks are open, as suggested in the paper, by using FRAP or some other related technique.

      (4) Quantification of results from the liposome assay.

      The paper would be strengthened by the inclusion of more quantitative data relating to the liposome assay. Firstly, only a single field of view is shown for each condition. Because of this, the reader cannot know whether this is a representative image, or an outlier? Can the authors do some quantification of the data to demonstrate this? The line scan profiles in the supplemental figures would be an example of this, but again in these Figures only a single image is analyzed.

      We would recommend that the authors present quantitative data to show the extent of co-localization at the necks in each case. They also need a metric to report instances in which protein is not seen at the neck, e.g. CdvB2 but not CdvB1 in Fig2I, which rules out a simple curvature preference for CdvB2 as stated in line 182.

      Secondly, the authors state that they see CdvB2∆C recruited to the membrane by CdvB1 (lines 184-187, Fig 2I). However, this simple conclusion is not borne out in the data. Inspecting the CdvB2∆C panels of Fig 2I, Fig3C, and Fig3D, CdvB2∆C signal can be seen at positions which don't colocalize with other proteins. The authors also observe CdvB2∆C localizing to membrane necks by itself (Fig 2E). Therefore, while CdvB1 and CdvB2∆C colocalize in the flotation assay, there is no strong evidence for CdvB2∆C recruitment by CdvB1 in dumbbells. This is further underscored by the observation that in the presented data, all Cdv proteins always appear to localize at dumbbell necks, irrespective of what other components are present inside the liposome. Although one nice control is presented (ZipA), this suggests that more work is required to be sure that the proteins are behaving properly in this assay. For example, if membrane binding surfaces of Cdv proteins are mutated, does this lead to the accumulation of proteins in the bulk of the liposome as expected?

      (5) Rings.

      The authors should comment on why they never observe large Cdv rings in their experiments. In crenarchaeal cell division, CdvA and CdvB have been observed to form large rings in the middle of the 1 micron cell, before constriction. Only in the later stages of division are the ESCRTs localized to the constricting neck, at a time when CdvA is no longer present in the ring. Therefore, if the in vitro assay used by the authors really recapitulated the biology, one would expect to see large CdvAB rings in Figs 1EF. This is ignored in the model. In the proposed model of ring assembly (line 252), CdvAB ring formation is mentioned, but authors do not discuss the fact that they do not observe CdvAB rings - only foci at membrane necks. The discussion section would benefit from the authors commenting on this.

      (6) Stoichiometry

      It is not clear why 100% of the visible CdvA and 100% of the the visible CdvB are shifted to the lipid fraction in 1C. Perhaps this is a matter of quantification. Can the authors comment on the stoichiometry here?

      (7) Significance of quantification of MBP-tagged filaments.

      Authors use tagging and removal of MBP as a convenient, controllable system to trigger polymerisation of various Cdv proteins. However, it is unclear what is the value and significance of reporting the width and length of the short linear filaments that are formed by the MBP-tagged proteins. Presumably they are artefactual assemblies generated by the presence of the tag? Similar Figure 2C doesn't seem a useful addition to the paper.

    5. Author response:

      We thank the three Reviewers for the extensive evaluation of our work, which was largely positive and constructive. Prompted by their reviews and the many suggestions, we plan to do additional control experiments to add further data in a revised manuscript in order to improve the statistics and quantitation. Furthermore, we plan to expand the discussion. We agree that a more comprehensive mechanistic framework would be welcome but note that the system is a complex multicomponent system which is challenging. We plan to expand the work in future follow-up research.

    1. eLife Assessment

      This important study reveals a role for IκBα in the regulation of embryonic stem cell pluripotency. The solid data in mouse embryonic stem cells include separation of function mutations in IκBα to dissect its non-canonical role as a chromatin regulator and its canonical function as NF-κB inhibitor. The conclusions could be strengthened by including better markers of differentiation status and additional controls or orthogonal approaches.

    2. Reviewer #1 (Public review):

      Summary:

      This study probes the role of the NF-κB inhibitor IκBa in the regulation of pluripotency in mouse embyronic stem cells (mESCs). It follows from previous work that identified a chromatin-specific role for IκBa in the regulation of tissue stem cell differentiation. The work presented here shows that a fraction of IκBa specifically associates with chromatin in pluripotent stem cells. Using three Nfkbia-knockout lines, the authors show that IκBa ablation impairs the exit from pluripotency, with embryonic bodies (an in vitro model of mESC multi-lineage differentiation) still expressing high levels of pluripotency markers after sustained exposure to differentiation signals. The maintenance of aberrant pluripotency gene expression under differentiation conditions is accompanied by pluripotency-associated epigenetic profiles of DNA methylation and histone marks. Using elegant separation of function mutants identified in a separate study, the authors generate versions of IκBa that are either impaired in histone/chromatin binding or NF-κB binding. They show that the provision of the WT IκBa, or the NF-κB-binding mutant can rescue the changes in gene expression driven by loss of IκBa, but the chromatin-binding mutant can not. Thus the study identifies a chromatin-specific, NF-κB-independent role of IκBa as a regulator of exit from pluripotency.

      Strengths:

      The strengths of the manuscript lie in: (a) the use of several orthogonal assays to support the conclusions on the effects of exit from pluripotency; (b) the use of three independent clonal Nfkbia-KO mESC lines (lacking IκBa), which increase confidence in the conclusions; and (c) the use of separation of function mutants to determine the relative contributions of the chromatin-associated and NF-κB-associated IκBa, which would otherwise be very difficult to unpick.

      Weaknesses:

      In this reviewer's view, the term "differentiation" is used inappropriately in this manuscript. The data showing aberrant expression of pluripotency markers during embryoid body formation are supported by several lines of evidence and are convincing. However, the authors call the phenotype of Nfkbia-KO cells a "differentiation impairment" while the data on differentiation markers are not shown (beyond the fact that H3K4me1, marking poised enhancers, is reduced in genes underlying GO processes associated with differentiation and organ development). Data on differentiation marker expression from the transcriptomic and embryoid body immunofluorescent experiments, for example, should be at hand without the need to conduct many more experiments and would help to support the conclusions of the study or make them more specific. The lack of probing the differentiation versus pluripotency genes may be a missed opportunity in gaining in-depth understanding of the phenotype associated with loss of the chromatin-associated function of IκBa.

    3. Reviewer #2 (Public review):

      Summary:

      This manuscript investigates the role of IκBα in regulating mouse embryonic stem cell (ESC) pluripotency and differentiation. The authors demonstrate that IκBα knockout impairs the exit from the naïve pluripotent state during embryoid body differentiation. Through mechanistic studies using various mutants, they show that IκBα regulates ESC differentiation through chromatin-related functions, independent of the canonical NF-κB pathway.

      Strengths:

      The authors nicely investigate the role of IκBα in pluripotency exit, using embryoid body formation and complementing the phenotypic analysis with a number of genome-wide approaches, including transcriptomic, histone marks deposition, and DNA methylation analyses. Moreover, they generate a first-of-its-kind mutant set that allows them to uncouple IκBα's function in chromatin regulation versus its NF-κB-related functions. This work contributes to our understanding of cellular plasticity and development, potentially interesting a broad audience including developmental biologists, chromatin biology researchers, and cell signaling experts.

      Weaknesses:<br /> - The study's main limitation is the lack of crucial controls using bona fide naïve cells across key experiments, including DNA methylation analysis, gene expression profiling in embryoid bodies, and histone mark deposition. This omission makes it difficult to evaluate whether the observed changes in IκBα-KO cells truly reflect naïve pluripotency characteristics.<br /> - Several conclusions in the manuscript require a more measured interpretation. The authors should revise their statements regarding the strength of the pluripotency exit block, the extent of hypomethylation, and the global nature of chromatin changes.<br /> - From a methodological perspective, the manuscript would benefit from additional orthogonal approaches to strengthen the knockout findings, which may be influenced by clonal expansion of ES cells.

      Overall, this study makes an important contribution to the field. However, the concerns raised regarding controls, data interpretation, and methodology should be addressed to strengthen the manuscript and support the authors' conclusions.

    4. Author response:

      eLife Assessment

      This important study reveals a role for IκBα in the regulation of embryonic stem cell pluripotency. The solid data in mouse embryonic stem cells include separation of function mutations in IκBα to dissect its non-canonical role as a chromatin regulator and its canonical function as NF-κB inhibitor. The conclusions could be strengthened by including better markers of differentiation status and additional controls or orthogonal approaches.

      We are thankful to the two reviewers and editors for their kind feedback and for highlighting the impact of NF-kB-independent IkBa function in stabilizing naïve pluripotency.

      In order to address reviewer’s comments, we will perform further analysis of differentiation trajectories, as well as a deeper comparison of the epigenetic features in our IkBa-KO mESCs with the Serum/LIF and 2i/LIF conditions. Moreover, we recognize that some sentences need to be modified to soften our conclusions in terms of effects on block in the naïve state or the global epigenetic effects, as the reviewers pointed out.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study probes the role of the NF-κB inhibitor IκBa in the regulation of pluripotency in mouse embyronic stem cells (mESCs). It follows from previous work that identified a chromatin-specific role for IκBa in the regulation of tissue stem cell differentiation. The work presented here shows that a fraction of IκBa specifically associates with chromatin in pluripotent stem cells. Using three Nfkbia-knockout lines, the authors show that IκBa ablation impairs the exit from pluripotency, with embryonic bodies (an in vitro model of mESC multi-lineage differentiation) still expressing high levels of pluripotency markers after sustained exposure to differentiation signals. The maintenance of aberrant pluripotency gene expression under differentiation conditions is accompanied by pluripotency-associated epigenetic profiles of DNA methylation and histone marks. Using elegant separation of function mutants identified in a separate study, the authors generate versions of IκBa that are either impaired in histone/chromatin binding or NF-κB binding. They show that the provision of the WT IκBa, or the NF-κB-binding mutant can rescue the changes in gene expression driven by loss of IκBa, but the chromatin-binding mutant can not. Thus the study identifies a chromatin-specific, NF-κB-independent role of IκBa as a regulator of exit from pluripotency.

      Strengths:

      The strengths of the manuscript lie in: (a) the use of several orthogonal assays to support the conclusions on the effects of exit from pluripotency; (b) the use of three independent clonal Nfkbia-KO mESC lines (lacking IκBa), which increase confidence in the conclusions; and (c) the use of separation of function mutants to determine the relative contributions of the chromatin-associated and NF-κB-associated IκBa, which would otherwise be very difficult to unpick.

      Weaknesses:

      In this reviewer's view, the term "differentiation" is used inappropriately in this manuscript. The data showing aberrant expression of pluripotency markers during embryoid body formation are supported by several lines of evidence and are convincing. However, the authors call the phenotype of Nfkbia-KO cells a "differentiation impairment" while the data on differentiation markers are not shown (beyond the fact that H3K4me1, marking poised enhancers, is reduced in genes underlying GO processes associated with differentiation and organ development). Data on differentiation marker expression from the transcriptomic and embryoid body immunofluorescent experiments, for example, should be at hand without the need to conduct many more experiments and would help to support the conclusions of the study or make them more specific. The lack of probing the differentiation versus pluripotency genes may be a missed opportunity in gaining in-depth understanding of the phenotype associated with loss of the chromatin-associated function of IκBa.

      Specific answer to weaknesses for Reviewer 1:

      We have data showing the lack of expression of specific differentiation markers that we will add to the manuscript. Moreover, we will also globally analyse differentiation markers in our transcriptomic data to have a more accurate description of the phenotype.

      Reviewer #2 (Public review):

      Summary:

      This manuscript investigates the role of IκBα in regulating mouse embryonic stem cell (ESC) pluripotency and differentiation. The authors demonstrate that IκBα knockout impairs the exit from the naïve pluripotent state during embryoid body differentiation. Through mechanistic studies using various mutants, they show that IκBα regulates ESC differentiation through chromatin-related functions, independent of the canonical NF-κB pathway.

      Strengths:

      The authors nicely investigate the role of IκBα in pluripotency exit, using embryoid body formation and complementing the phenotypic analysis with a number of genome-wide approaches, including transcriptomic, histone marks deposition, and DNA methylation analyses. Moreover, they generate a first-of-its-kind mutant set that allows them to uncouple IκBα's function in chromatin regulation versus its NF-κB-related functions. This work contributes to our understanding of cellular plasticity and development, potentially interesting a broad audience including developmental biologists, chromatin biology researchers, and cell signaling experts.

      Weaknesses:

      - The study's main limitation is the lack of crucial controls using bona fide naïve cells across key experiments, including DNA methylation analysis, gene expression profiling in embryoid bodies, and histone mark deposition. This omission makes it difficult to evaluate whether the observed changes in IκBα-KO cells truly reflect naïve pluripotency characteristics.

      - Several conclusions in the manuscript require a more measured interpretation. The authors should revise their statements regarding the strength of the pluripotency exit block, the extent of hypomethylation, and the global nature of chromatin changes.

      - From a methodological perspective, the manuscript would benefit from additional orthogonal approaches to strengthen the knockout findings, which may be influenced by clonal expansion of ES cells.

      Overall, this study makes an important contribution to the field. However, the concerns raised regarding controls, data interpretation, and methodology should be addressed to strengthen the manuscript and support the authors' conclusions.

      Specific answer to weaknesses for Reviewer 2:

      - As the reviewer pointed out, we have not performed all the analysis by comparing with cells in 2i LIF since our initial study was focused on Serum LIF and differentiation. However, it was the transcriptome analysis in Serum LIF which showed that KO cells resembled naïve ES cells in 2i LIF by GSEA. We have repeated key experiments with all conditions (Figure 1B, 1D, Figure 3C and 3), but we do not think that repeating all ‘omics’ experiments with 2i LIF conditions will add important information. Nevertheless, we will analyze different chromatin data (DNA methylation and different histone post-translational modifications) from previously published works in 2i/LIF and Serum/LIF and compare them with our IκBα-WT and IκBα-KO mESCs to better confirm the stabilization of the ground state pluripotency in IκBα-KO mESCs under Serum/LIF conditions.

      - We agree about reducing the strength of the pluripotency exit block, extend of hypomethylation and the global nature of chromatin changes. There are many changes in the chromatin that we are trying to better characterize by HiC in ongoing studies that are out of the scope of this manuscript.

      We have performed studies in 3 different IkBa KO and WT clones. In addition, the reconstitution studies with IkBa separation-of-function (SOF) mutants with differential effect after expressing the NFkB binding form (IkBaDChrom) or the chromatin binding form (IkBaDNFkB) also support the robustness of this phenotype.

    1. eLife Assessment

      This paper reports the analysis of coevolutionary patterns and dynamical information for identifying functionally relevant sites. These findings are considered important due to the broad utility of the unified framework and network analysis capable of revealing communities of key residues that go beyond the residue-pair concept. The data is solid, and the results are clearly presented.

    2. Reviewer #1 (Public review):

      Summary:<br /> As reported above, this paper by Xu et al reports on a new method to combine the analysis of coevolutionary patterns with dynamic profiles to identify functionally important residues and reveal correlations between binding sites.

      Strengths:<br /> In general, coevolutionary analysis and MD analysis are carried out separately and while there have been attempts to compare the information provided by the two, no unified framework exists. Here, the authors convincingly demonstrate that integrating signals from Dynamics and coevolution gives information that substantially overcomes the one provided by either method in isolation. While other methods are useful, they do not capture how dynamics is fundamental to define function and thus sculpts coevolution, via the 3D structure of the protein. At the same time, the authors demonstrate how coevolution in turn also influences internal dynamics. The Networks they rebuild unveil information at an even higher level: the model starts pairwise but through network representation the authors arrive to community analysis, reporting on interaction patterns that are larger than simple couples.

      Weaknesses:<br /> The authors should<br /> -Make an effort in suggesting/commenting the limits of applicability of their method;<br /> -Expand discussion on how DyNoPy compares to other methods;<br /> -Dynamic is not essential in all systems (structural proteins): The authors may want to comment on possible strategies they would use for other systems where their framework may not be suitable/applicable.

    3. Reviewer #2 (Public review):

      Summary:<br /> Authors introduced a computational framework, DyNoPy, that integrates residue coevolution analysis with molecular dynamics (MD) simulations to identify functionally important residues in proteins. DyNoPy identifies key residues and residue-residue coupling to generate an interaction graph and attempts to validate using two clinically relevant β-lactamases (SHV-1 and PDC-3).

      Strengths:<br /> DyNoPy could not only show clinically relevance of mutations but also predict new potential evolutionary mutations. Authors have provided biologically relevant insights into protein dynamics which can have potential applications in drug discovery and understanding molecular evolution.

      Weaknesses:<br /> Although DyNoPy could show the relevance of key residues in active and non-active site residues, no experiments have been performed to validate their predictions. In addition, they should compare their method with conventional techniques and show how their method could be different.

      An explanation of "communities" divided in the work and how these communities are relevant to the article should be provided. In addition, choice of collective variables and their relevance in residue coupling movement is also not very well explained. Dynamics cross correlation map can also be a good method for understanding the residue movements and can explain the residue-residue coupling, it is not explained how DyNoPy is different from the conventional methods or can perform better.

      In the sentence "DyNoPy identified eight significant communities of strongly coupled residues within SHV-1 (Supporting Fig. S4A)" I could not find a clear description of eight significant communities.

      Again the description of communities is not clear to me in the following sentence "Detailed description of the other three communities is provided in the supporting information (Fig. S6)."

      In the sentence "N170 acts as an intermediary between N136 and E166". Kindly cite the reference figure to show N179 as intermediate residue.

      Please be careful with the numbers. In the sentence "These residues not only interact with each other directly but are also indirectly coupled via 21 other residues." I could count 22 other residues and not 21.

      In the sentence "Unlike other substitution sites that are adjacent to the active site, R205 is situated more than 16 Å away from catalytic serine S70". Please add this label somewhere in the figure.

      Please cite a reference in the sentence "This indicates that mutations on G238 would result in an alteration on protein catalytic function, as well as an increased flexibility of the protein, which strongly aligns with previous finding."

    4. Reviewer #3 (Public review):

      Summary:<br /> In this paper, Xu, Dantu and coworkers report a protocol for analyzing coevolutionary and dynamical information to identify a subset of communities that capture functionally relevant sites in beta-lactamases.

      Strengths:<br /> The combination of coevolutionary information and metrics from MD simulations is interesting for capturing functionally relevant sites, which can have implications in the fields of drug discovery but also in protein design.

      Weaknesses:<br /> The combination of coevolutionary information and metrics from MD simulations is not new as other protocols have been proposed along the years (the current version of the paper neglects some of them, see below), and there are a few parameters of the protocol that, in my opinion, should be better analyzed and discussed.

      (1) As mentioned, the introduction of the paper lacks some important publications in the field of using graph theory to represent important interaction networks extracted from MD simulations (DOI: 10.1002/pro.4911), and also combining MD data with MSA to identify functionally relevant sites for enzyme design (doi: 10.1021/acscatal.4c04587, 10.1093/protein/gzae005).<br /> (2) The matrix used to apply graph theory (J_ij) is built from summing the scaled coevolution and degree of correlation values. The alpha and beta weights are defined, and the authors mention that alpha is set to 0.5, thus beta as well to fulfil with the alpha + beta = 1. Why a value of 0.5 has been selected? How this affects the overall results and conclusions extracted? The finding that many catalytically relevant residues are identified in the communities is not surprising given that such sites usually present a high conservation score.<br /> (3) Another important point that needs further explanation is the selection of the relevant descriptor of protein dynamics. In this study two different strategies have been used (one more global the other more local), but more details should be provided regarding their choice. What is the best strategy according to the authors? Why not using the same strategy for both related systems? The obtained results using one methodology or the other will have a large impact on the dynamical score. Another related point is: what is the impact of the MD simulation length, how the MSA is generated and number of sequences used for MSA construction?

    1. eLife Assessment

      Based on the perceived low efficacy of current therapies targeted to FGFR2 in gastric cancer (GC), the authors investigate an approach which combines SHP2 inhibition with existing FGFR2 inhibitors. The data were largely collected and analysed using solid and validated methodology. There is some useful data regarding combination therapy in a new clinical cohort, which supports previous studies that have reported the potential of targeting RTKs together with phosphatases.

    2. Reviewer #1 (Public review):

      The manuscript entitled "Blocking SHP2 1 benefits FGFR2 inhibitor and overcomes its resistance in 2 FGFR2-amplified gastric cancer" by Zhang, et al., reports that FGFR2 was amplification in 6.2% (10/161) of gastric cancer samples and that dual blocking SHP2 and FGFR2 enhanced the effects of FGFR2 inhibitor (FGFR2i) in FGFR2-amplified GC both in vitro and in vivo via suppressing RAS/ERK and PI3K/AKT pathways. Furthermore, the authors also showed that SHP2 blockade suppressed PD-1 expression and promoted IFN-γ secretion of CD8+ 46 T cells, enhancing the cytotoxic functions of T cells. Thus, the authors concluded that dual blocking SHP2 and FGFR2 is a compelling strategy for treatment of FGFR2-amplified gastric cancer. Although the finding is interesting, the finding that FGFR2 is amplified in gastric cancer and that FGFR inhibitors have some effect on treating gastric cancer is not novel. The data quality is not high, and the effects of double inhibitions are not significant. It appears that the conclusions are largely overstatement, the supporting data is weak and not compelling.

      The data in Figure 1 is not novel, similar data has been reported elsewhere.

      It is unclear why the two panels in Fig 2a and 2b can not be integrated into one panel, which will make it easier to compare the activities.

      The synergetic effects of azd4547 and shp099 are not significant in Fig 2e and 2f, as well as in Fig. 3g and fig. 4f

      Data in Fig. 5 is weak and can be removed. It is unclear why FGFR inhibitor has some activities toward t cells since t cells do not express FGFR.

    3. Reviewer #2 (Public review):

      Summary:

      This manuscript reports the application of a combined targeted therapeutic approach to gastric cancer treatment. The RTK, FGFR2 and the phosphatase, SHP2 are targeted with existing drugs; AZD457 and SHP099 respectively. Having shown increased mRNA levels of FGFR2 and SHP2 in a patient population and highlighted the issue of resistance to single therapies the combination of inhibitors is shown to reduce cancer-related signalling in two gastric cell lines. The efficacy of the dual therapy is further demonstrated in a single patient case study and mouse xenograft models. Finally, the rationale for SHP2 inhibition is shown to be linked to immune response.

      Strengths:

      The data is generally well presented and the study invokes a novel patient data set which could have wider value. The study provides additional evidence to support the combined therapeutic approach of RTK and phosphatase inhibition.

      Weaknesses:

      Combined therapy approaches targeting RTKs and SHP2 have been widely reported. Indeed, SHP099 in combination with FGFR inhibitors has been shown to overcome adaptive resistance in FGFR-driven cancers. Furthermore, the inhibition of SHP2 has been documented to have important implications in both targeting proliferative signalling as well as immune response. Thus, it is difficult to see novelty or a significant scientific advance in this manuscript. Although the data is generally well presented, there is inconsistency in the interpretation of the experimental outcomes from ex vivo, patient and mouse systems investigated. In addition, the study provides only minor or circumstantial understanding of the dual mechanism.

      Using data from a 161 patient cohort FGFR2 was identified as displaying amplification of FGFR2 in ~6% with concomitant elevation of mRNA of patients which correlated with PTPN11 (SHP2) mRNA expression. The broader context of this data is of value and could add a different patient demographic to other data on gastric cancer. However, there is no detail on patient stratification or prior therapeutic intervention.

      In SNU16 and KATOIII cells the combined therapy is shown to be effective and appears to be correlated with increased apoptotic effects (i.e. not immune response).

      Fig 2E suggests that the combined therapy in SNU16 cells is a little better than FGFR2-directed AZD457 inhibitor alone, particularly at the higher dose.

      The individual patient case study described via Fig 3 suggests efficacy of the combined therapy (at very high dosage), however, the cell biopsies only show reduced phosphorylation of ERK, but not AKT. This is at odds with the ex vivo cell-based assays. Thus, it is not clear how relevant this study is.

      The mouse xenograft study shows a convincing reduction in tumor mass/volume and clear reduction in pAKT, whilst pERK remains largely unaffected by the combined therapeutic approach. This is in conflict with the previous data which seems to show the opposite effect. In all, the impact of the dual therapy is unclear with respect to the two pathways mediated by ERK and AKT.

      Finally, the authors demonstrate the impact of SHP2 on PD-1 expression and propose that the SHP099/AZD4547 combination therapy significantly induces the production of IFN-γ in CD8+ T cells. This part of the study is unconvincing and would benefit from the investigation of the tumor micro-environment to assess T cell infiltration.

    4. Reviewer #3 (Public review):

      Summary:

      Fibroblast growth factor receptor 2 (FGFR2) is a receptor tyrosine kinase that can be amplified in gastric cancer and serves as a potential therapeutic target for this patient population. However, targeting FGFR2 has shown limited efficacy. Thus, this study seeks to identify additional molecules that can be effectively targeted in FGFR2 amplified gastric cancer, with a focus on Src homology region 2-containing protein tyrosine phosphatase 2 (SHP2). The authors first demonstrate that 6% of gastric cancer patients in a cohort of human patient samples exhibit FGFR2 amplification. Furthermore, they demonstrate that FGFR2 mRNA expression is positively correlated with PTPN11 gene expression (which is the gene that encodes the SHP2 protein). Using human gastric cancer cell lines with amplified FGFR2, the authors then test the effects of combining the FGFR inhibitor AZD4547 with the SHP2 inhibitor SHP099 on tumor cell death and signaling molecules. They demonstrate that combining the two inhibitors is more effective at tumor cell killing and reducing activation of downstream signaling pathways than either inhibitor alone. In further studies, the authors obtained gastric cancer cells with FGFR2 amplification from a patient that was treated with FGFR2 inhibitor. While this patient initially showed a partial response, the patient ultimately progressed, demonstrating resistance to FGFR2 inhibition. Following isolation of tumor cells from the patient's ascites, the authors demonstrate that these cells are sensitive to the combination treatment of AZD4547 and SHP099. Further studies were performed using a xenograft model using athymic nude mice in which the combination of SHP099 and AZD4547 were found to reduce tumor growth more significantly than either treatment alone. Finally, the authors demonstrate using an in vitro culture model that this combination treatment enhances T cell mediated cytotoxicity. The authors conclude that targeting FGFR2 and SHP2 represents a potential combination strategy in gastric patients with FGFR2 amplification.

      Strengths:

      The authors demonstrate that FGFR2 amplification positively correlates with PTPN11 in human gastric cancer samples, providing rationale for combination therapies. Furthermore, convincing data are provided demonstrating that targeting both FGFR and SHP2 is more effective than targeting either pathway alone using in vitro and in vivo models. The use of cells derived from a gastric cancer patient that progressed following treatment with an FGFR inhibitor is also a strength. The findings from this study support the conclusion that SHP2 inhibitors enhance the efficacy of FGFR-targeted therapies in cancer patients. This study also suggests that targeting SHP2 may also be an effective strategy for targeting cancers that are resistant to FGFR-targeted therapies.

      Weaknesses:

      The main caveat with these studies is the lack of an immune competent model with which to test the finding that this combination therapy enhances T cell cytotoxicity in vivo. Discussing this limitation within the context of these findings and future directions for this work, particularly since the combination therapy appears to work quite well without the presence of T cells in the environment, would be beneficial.

    1. eLife Assessment

      In this manuscript, the authors present useful findings demonstrating that the RNA modification enzyme Mettl5 regulates sleep in Drosophila. Through transcriptome- and proteome-wide analyses, the authors identified downstream targets affected in heterozygous mutants and proposed that Mettl5 regulates the translation and degradation of clock genes to maintain normal sleep function. However, the mechanisms by which Mettl5 achieves these functions, and whether they are direct or indirect, remain incomplete and would benefit from further analysis.

    2. Reviewer #1 (Public review):

      Summary:

      Here the authors attempted to test whether the function of Mettl5 in sleep regulation was conserved in drosophila, and if so, by which molecular mechanisms. To do so they performed sleep analysis, as well as RNA-seq and ribo-seq in order to identify the downstream targets. They found that the loss of one copy of Mettl5 affects sleep and that its catalytic activity is important for this function. Transcriptional and proteomic analyses show that multiple pathways were altered, including the clock signaling pathway and the proteasome. Based on these changes the authors propose that Mettl5 modulate sleep through regulation of the clock genes, both at the level of their production and degradation.

      Strengths:

      The phenotypical consequence of the loss of one copy of Mettl5 on sleep function is clear and well-documented.

      Weaknesses:

      The imaging and molecular parts are less convincing.<br /> - The colocalization of Mettl5 with glial and neuronal cells is not very clear<br /> - The section on gene ontology analysis is long and confusing<br /> - Among all the pathways affected the focus on proteosome sounds like cherry picking. And there is no experiment demonstrating its impact in the Mettl5 phenotype<br /> - The ribo seq shows some changes at the level of translation efficiency but there is no connection with the Mettl5 phenotypes. In other words, how the increased usage of some codons impact clock signalling. Are the genes enriched for these codons?<br /> - A few papers already demonstrated the role of Mettl5 in translation, even at the structural level (Rong et al, Cell reports 2020) and this was not commented by the authors. In Peng et al, 2022 the authors show that the m6A bridges the 18S rRNA with RPL24. Is this conserved in Drosophila?<br /> - The text will require strong editing and the authors should check and review extensively for improvements to the use of English.

      Conclusion

      Despite the effort to identify the underlying molecular defects following the loss of Mettl5 the authors felt short in doing so. Some of the results are over-interpreted and more experiments will be needed to understand how Mettl5 controls the translation of its targets. References to previous works was poorly commented.

    3. Reviewer #2 (Public review):

      Summary:

      The authors define the m6A methyltransferase Mettl5 as a novel sleep-regulatory gene that contributes to specific aspects of Drosophila sleep behaviors (i.e., sleep drive and arousal at early night; sleep homeostasis) and propose the possible implication of Mettl5-dependent clocks in this process. The model was primarily based on the assessment of sleep changes upon genetic/transgenic manipulations of Mettl5 expression (including CRISPR-deletion allele); differentially expressed genes between wild-type vs. Mettl5 mutant; and interaction effects of Mettl5 and clock genes on sleep. These findings exemplify how a subclass of m6A modifications (i.e., Mettl5-dependent m6A) and possible epi-transcriptomic control of gene expression could impact animal behaviors.

      Strengths:

      Comprehensive DEG analyses between control and Mettl5 mutant flies reveal the landscape of Mettl5-dependent gene regulation at both transcriptome and translatome levels. The molecular/genetic features underlying Mettl5-dependent gene expression may provide important clues to molecular substrates for circadian clocks, sleep, and other physiology relevant to Mettl5 function in Drosophila.

      Weaknesses:

      While these findings indicate the potential implication of Mettl5-dependent gene regulation in circadian clocks and sleep, several key data require substantial improvement and rigor of experimental design and data interpretation for fair conclusions. Weaknesses of this study and possible complications in the original observations include but are not limited to:

      (1) Genetic backgrounds in Mettl5 mutants: the heterozygosity of Mettl5 deletion causes sleep suppression at early night and long-period rhythms in circadian behaviors. The transgenic rescue using Gal4/UAS may support the specificity of the Mettl5 effects on sleep. However, it does not necessarily exclude the possibility that the Mettl5 deletion stocks somehow acquired long-period mutation allelic to other clock genes. Additional genetic/transgenic models of Mettl5 (e.g., homozygous or trans-heterozygous mutants of independent Mettl5 alleles; Mettl5 RNAi etc.) can address the background issue and determine 1) whether sleep suppression tightly correlates with long-period rhythms in Mettl5 mutants; and 2) whether Mettl5 effects are actually mapped to circadian pacemaker neurons (e.g., PDF- or tim-positive neurons) to affect circadian behaviors, clock gene expression, and synaptic plasticity in a cell-autonomous manner and thereby regulate sleep. Unfortunately, most experiments in the current study rely on a single genetic model (i.e., Mettl5 heterozygous mutant).

      (2) Gene expression and synaptic plasticity: gene expression profiles and the synaptic plasticity should be assessed by multiple time-point analyses since 1) they display high-amplitude oscillations over the 24-h window and 2) any phase-delaying mutation (e.g., Mettl5 deletion) could significantly affect their circadian changes. The current study performed a single time-point assessment of circadian clock/synaptic gene expression, misleading the conclusion for Mettl5 effects. Considering long-period rhythms in Mettl5 mutant clocks, transcriptome/translatome profiles in Mettl5 cannot distinguish between direct vs. indirect targets of Mettl5 (i.e., gene regulation by the loss of Mettl5-dependent m6A vs. by the delayed circadian phase in Mettl5 mutants). 

      (3) The text description for gene expression profiling and Mettl5-dependent gene regulation was very detailed, yet there is a huge gap between gene expression profiling and sleep/behavioral analyses. The model in Figure 5 should be better addressed and validated.

    4. Reviewer #3 (Public review):

      Xiaoyu Wu and colleagues examined the potential role in sleep of a Drosophila ribosomal RNA methyltransferase, mettl5. Based on sleep defects reported in CRISPR generated mutants, the authors performed both RNA-seq and Ribo-seq analyses of head tissue from mutants and compared to control animals collected at the same time point. While these data were subjected to a thorough analysis, it was difficult to understand the relative direction of differential expression between the two genotypes. In any case, a major conclusion was that the mutant showed altered expression of circadian clock genes, and that the altered expression of the period gene in particular accounted for the sleep defect reported in the mettl5 mutant. As noted above, a strength of this work is its relevance to a human developmental disorder as well as the transcriptomic and ribosomal profiling of the mutant. However, there are numerous weaknesses in the manuscript, most of which stem from misinterpretation of the findings, some methodological approaches, and also a lack of method detail provided. The authors seemed to have missed a major phenotype associated with the mettl5 mutant, which is that it caused a significant increase in period length, which was apparent even in a light: dark cycle. Thus the effect of the mutant on clock gene expression more likely contributed to this phenotype than any associated with changes in sleep behavior.

    1. eLife Assessment

      This manuscript describes a method using EM polyclonal epitope mapping to help elucidate endogenous antibodies. Overall the work described is interesting and the contribution will be of use to the field that is expected to only increase in impact and value over time. The significance of the work is considered valuable and the strength of evidence to support its findings is considered solid.

    2. Reviewer #1 (Public review):

      Summary:

      The paper addresses the problem of optimising the mapping of serum antibody responses against a known antigen. It uses the croEM analysis of polyclonal Fabs to antibody genes, with the ultimate aim of getting complete and accurate antibody sequences. The method, commonly termed EMPEM, is becoming increasingly used to understand responses in convalescent sera and optimisation of the workflows and provision of openly available tools is of genuine value to a growing number of people.

      The authors do not address the experimental aspects of the methods and do not present novel computational tools, rather they use a series of established computational methods to provide workflows that simplify the interpretation of the EM map in terms of the sequences of dominant antibodies.

      Strengths:

      The paper is well-written and clearly argued. The tests constructed seem appropriate and fair and demonstrate that the workflow works pretty well. For a small subset (~17%) of the EMPEM maps analysed the workflow was able to get convincing assignments of the V-genes.

      Weaknesses:

      The AI methods used are not a substitute for high quality data and at present very few of the results obtained from EMPEM will be of sufficient quality to robustly assign the sequence of the antibody. However, rather more are likely to be good enough, especially in combination with MS data, to provide a pretty good indication of the V-gene family.

    3. Reviewer #2 (Public review):

      In this manuscript, the authors seek to demonstrate that it is possible to sequence antibody variable domains from cryoEM reconstructions in combination with bottom-up LC-MSMS. In particular, they extract de novo sequences from single particle-cryo-EM-derived maps of antibodies using the "deep-learning tool ModelAngelo", which are run through the program Stitch to try to select the top scoring V-gene and construct a placeholder sequence for the CDR3 of both the heavy and light chain of the antibody under investigation. These reconstructed variable domains are then used as templates to guide the assembly of de novo peptides from LC-MS/MS data to improve the accuracy of the candidate sequence.

      Using this approach the authors claim to have demonstrated that "cryoEM reconstructions of monoclonal antigen-antibody complexes may contain sufficient information to accurately narrow down candidate V-genes and that this can be integrated with proteomics data to improve the accuracy of candidate sequences".

      WhiIe the approach is clearly a work in progress, the manuscript should made easier to understand for the general reader. Indeed, I had a hard time understanding the workflow until I got to Fig. 3. So re-ordering the figures, for example, may be helpful in this regard.

      It would be useful to provide additional concrete examples where the described workflow would assist in the elucidation of CDR3's, in cases where this isn't already known. (In the benchmark dataset from the Electron Microscopy Data Bank, all the antibodies and Fabs are presumably known, as is the case for the monoclonal antibody CR3022). I am having difficulty envisioning how one would prepare samples from actual plasma samples that would be appropriate for single particle cryo-EM and MS data on dominant antibodies of interest. In my experience, most of these samples tend to be quite complex mixtures. So additional discussion of this point would be helpful.

    1. eLife Assessment

      This fundamental work presents two clinically relevant BMP4 mutations that contribute to vertebrate development. The convincing evidence supports that the site-specific cleavage at the BMP4 pro-domain precisely regulates its function and provides mechanistic insight into how homodimers and heterodimers behave differently. The work will be of broad interest to researchers working on growth factor signaling mechanisms and vertebrate development.

    2. Reviewer #1 (Public review):

      Summary:

      The authors demonstrate that two human preproprotein human mutations in the BMP4 gene cause a defect in proprotein cleavage and BMP4 mature ligand formation, leading to hypomorphic phenotypes in mouse knock-in alleles and in Xenopus embryo assays.

      Strengths:

      They provide compelling biochemical and in vivo analyses supporting their conclusions, showing the reduced processing of the proprotein and concomitant reduced mature BMP4 ligand protein from impressively mouse embryonic lysates. They perform excellent analysis of the embryo and post-natal phenotypes demonstrating the hypomorphic nature of these alleles. Interesting phenotypic differences between the S91C and E93G mutants are shown with excellent hypotheses for the differences. Their results support that BMP4 heterodimers act predominantly throughout embryogenesis whereas BMP4 homodimers play essential roles at later developmental stages.

      Weaknesses:

      A control of BMP7 alone in the Xenopus assays seems important to exclude BMP7 homodimer activity in these assays.

      The Discussion could be strengthened by more in-depth explanations of how BMP4 homodimer versus heterodimer signaling is supported by the results, so that readers do not have to think it all through themselves. Similarly, a discussion of why the S91C mutant has a stronger phenotype than E93G early in the Discussion would be helpful or least mention that it will be addressed later.

    3. Reviewer #2 (Public review):

      Summary:

      Kim et al. report that two disease mutations in proBMP4, Ser91Cys and Glu93Gly, which disrupt the Ser91 FAM20C phosphorylation site, block the activation of proBMP4 homodimers. Consequently, analysis of DMZ explants from Xenopus embryos expressing the proBMP4 S91C or E93G mutants showed reduced pSmad1 and tbxt1 expression. The block in BMP4 activity caused by the mutations could be overcome by co-expression of BMP7, suggesting that the missense mutations selectively affect the activity of BMP4 homodimers but not BMP4/7 heterodimers. The expert amphibian tissue transplant studies were extended to in vivo studies in Bmp4S91C/+ and Bmp4E93G/+ mice, demonstrating the impact of these mutations on embryonic development, particularly in female mice, in line with patient studies. Finally, studies in MEFs revealed that the mutations did not affect proBMP4 glycosylation or ER-to-Golgi transport but appeared to inhibit the furin-dependent cleavage of proBMP4 to BMP4. Based on these findings and AI (AlphaFold) modeling of proBMP4, the authors speculate that pSer91 influences access of furin to its cleavage site at Arg289AlaLysArg292.

      Strengths:

      The Xenopus and mouse studies are valuable and elegantly describe the impact of the S91C and E93G disease mutations on BMP signaling and embryonic development.

      Weaknesses:

      The interpretation of how the mutations may disturb the furin-mediated cleavage of proBMP4 is underdeveloped and does not consider all of their data. Understanding how pS91 influences the furin-dependent cleavage at Arg292 seems to be the crux of this work and thus warrants more consideration. Specifically:

      (1) Figure S1 may be significantly more informative than implied. The authors report that BMP4S91D activates pSmad1 only incrementally better than S91C and much less than WT BMP4. However, Fig. S1B does not support the conclusion on page 7 (numbering beginning with title page); "these findings suggest that phosphorylation of S91 is required to generate fully active BMP4 homodimers". The authors rightly note that the S91C change likely has manifold effects beyond inhibiting furin cleavage. The E93G change may also affect proBMP4 beyond disturbing FAM20C phosphorylation. Additional mutation analyses would strengthen the work.

      (2) These findings in Figure S1 are potentially significant because they may inform how proBMP4 is protected from cleavage during transit through the TGN and entry into peripheral cellular compartments. Intriguing modeling studies in Figure 6 suggest that pSer91 is proximal to the furin cleavage site. Based on their presentation, pSer91 may contact Arg289, the critical P4 residue at the furin site. If so, might that suggest how pS91 may prevent furin cleavage, thus explaining why the S91D mutation inhibits processing as presented, and possibly how proBMP4 processing is delayed until transit to distal compartments (perhaps activated by a change in the endosomal microenvironment or a Ser91 phosphatase)? Have the authors considered or ruled out these possibilities? In addition to additional mutation analyses of the FAM20C site, moving the discussion of this model to an "Ideas and Speculation" subsection may be warranted.

      (3) The lack of an in vitro protease assay to test the effect of the S91 mutations on furin cleavage is problematic.

    4. Reviewer #3 (Public review):

      Summary:

      The authors describe important new biochemical elements in the synthesis of a class of critical developmental signaling molecules, BMP4. They also present a highly detailed description of developmental anomalies in mice bearing known human mutations at these specific elements.

      Strengths:

      Exceptionally detailed descriptions of pathologies occurring in mutant mice. Novel findings regarding the interaction of propeptide phosphorylation and convertase cleavage, both of which will move the field forward. Provocative hypothesis regarding furin access to cleavage sites, supported by Alphafold predictions.

      Weaknesses:

      Figure 6A presents two testable models for pre-release access of furin to cleavage sites since physical separation of enzyme from substrate only occurs in one model; could immunocytochemistry resolve?

    1. eLife Assessment

      This useful study introduces the peptidisc-TPP approach as a promising solution to challenges in membrane proteomics, enabling thermal proteome profiling in a detergent-free system. While the concept is innovative and holds significant potential, the demonstration of its utility and validation remains incomplete. The method presents a strong foundation for broader applications in identifying physiologically and pharmacologically relevant membrane protein-ligand interactions.

    2. Reviewer #1 (Public review):

      Summary:

      The idea is appealing, but the authors have not sufficiently demonstrated the utility of this approach.

      Strengths:

      Novelty of the approach, potential implications for discovering novel interactions

      Weaknesses:

      The Duong had introduced their highly elegant peptidisc approach several years ago. In this present work, they combine it with thermal proteome profiling (TPP) and attempt to demonstrate the utility of this combination for identifying novel membrane protein-ligand interactions.<br /> While I find this idea intriguing, and the approach potentially useful, I do not feel that the authors had sufficiently demonstrated the utility of this approach.<br /> My main concern is that no novel interactions are identified and validated. For the presentation of any new methodology, I think this is quite necessary.<br /> In addition, except for MsbA, no orthogonal methods are used to support the conclusions, and the authors rely entirely of quantifying rather small differences in abundances using either iBAQ or LFQ.<br /> Furthermore, the reported changes in abundances are solely based on iBAQ or LFQ analysis. This must be supported by a more quantitative approach such as SILAC or labeled peptides<br /> In summary, I think this story requires a stronger and broader demonstration of the ability of peptidisc-TPP to identify novel physiologically/pharmacologically relevant interactions.

    3. Reviewer #2 (Public review):

      Summary:

      The membrane mimetic thermal proteome profiling (MM-TPP) presented by Jandu et al. seems to be a useful way to minimize the interference of detergents in efficient mass spectrometry analysis of membrane proteins. Thermal proteome profiling is a mass spectrometric method that measures binding of a drug to different proteins in a cell lysate by monitoring thermal stabilization of the proteins because of the interaction with the ligands that are being studied. This method has been underexplored for membrane proteome because of the inefficient mass spectrometric detection of membrane proteins and because of the interference from detergents that are used often for membrane protein solubilization.

      Strengths:

      In this report the binding of ligands to membrane protein targets has been monitored in crude membrane lysates or tissue homogenates exalting the efficacy of the method to detect both intended and off-target binding events in a complex physiologically relevant sample setting.

      The manuscript is lucidly written and the data presented seems clear. The only insignificant grammatical error I found was that the 'P' in the word peptidisc is not capitalized in the beginning of the methods section "MM-TPP profiling on membrane proteomes". The clear writing made it easy to understand and evaluate what has been presented. Kudos to the authors.

      Weaknesses:

      While this is a solid report and a promising tool for analyzing membrane protein drug interactions, addressing some of the minor caveats listed below could make it much more impactful.

      The authors claim that MM-TPP is done by "completely circumventing structural perturbations invoked by detergents". This may not be entirely accurate, because before reconstitution of the membrane proteins in peptidisc, the membrane fractions are solubilized by 1% DDM. The solubilization and following centrifugation step lasts at least for 45 min. It is less likely that all the structural perturbations caused by DDM to various membrane proteins and their transient interactions become completely reversed or rescued by peptidisc reconstitution. In the introduction, the authors make statements such as "..it is widely acknowledged that even mild detergents can disrupt protein structures and activities, leading to challenges in accurately identifying drug targets.." and "[peptidisc] libraries are instrumental in capturing and stabilizing IMPs in their functional states while preserving their interactomes and lipid allosteric modulators...'. These need to be rephrased, as it has been shown by countless studies that even with membrane protein suspended in micelles robust ligand binding assays and binding kinetics have been performed leading to physiologically relevant conclusions and identification of protein-protein and protein-ligand interactions.

      If the method involves detergent solubilization, for example using 1% DDM, it is a bit disingenuous to argue that 'interactomes and lipid allosteric modulators' characterized by low-affinity interactions will remain intact or can be rescued upon detergent removal. Authors should discuss this or at least highlight the primary caveat of the peptidisc method of membrane protein reconstitution - which is that it begins with detergent solubilization of the proteome and does not completely circumvent structural perturbations invoked by detergents.

      It would also be important to test detergents that are even milder than 1% DDM and ones which are harsher than 1% DDM to show that this method of reconstitution can indeed rescue the perturbations to the structure and interactions of the membrane protein done by detergents during solubilization step. Based on the methods provided, it appears that the final amount of detergent in peptidisc membrane protein library was 0.008%, which is ~150 uM. The CMC of DDM depending on the amount of NaCl could be between 120-170 uM. Perhaps, to completely circumvent the perturbations from detergents other methods of detergent-free solubilization such as using SMA polymers and SMALP reconstitution could be explored for a comparison. Moreover, a comparison of the peptidisc reconstitution with detergent-free extraction strategies, such as SMA copolymers, could lend more strength to the presented method.

      Cross-verification of the identified interactions, and subsequent stabilization or destabilizations, should be demonstrated by other in vitro methods of thermal stability and ligand binding analysis using purified protein to support the efficacy of the MM-TPP method. An example cross-verification using SDS-PAGE, of the well-studied MsbA, is shown in Figure 2. In a similar fashion, other discussed targets such as, BCS1L, P2RX4, DgkA, Mao-B, and some un-annotated IMPs shown in supplementary figure 3 that display substantial stabilization or destabilization should be cross-verified.

    1. eLife Assessment

      This valuable study addresses a gap in our understanding of how the size of the attentional field is represented within the visual cortex. The evidence supporting the role of visual cortical activity is solid, based on a novel modeling analysis of fMRI data. The results will be of interest to psychologists and cognitive neuroscientists.

    2. Reviewer #1 (Public review):

      The authors conducted an fMRI study to investigate the neural effects of sustaining attention to areas of different sizes. Participants were instructed to attend to alphanumeric characters arranged in a circular array. The size of attention field was manipulated in four levels, ranging from small (18 deg) to large (162 deg). They used a model-based method to visualize attentional modulation in early visual cortex V1 to V3, and found spatially congruent modulations of the BOLD response, i.e., as the attended area increased in size, the neural modulation also increased in size in the visual cortex. They suggest that this result is a neural manifestation of the zoom-lens model of attention and that the model-based method can effectively reconstruct the neural modulation in the cortical space.

      The study is well-designed with sophisticated and comprehensive data analysis. The results are robust and show strong support for a well-known model of spatial attention, the zoom-lens model. Overall, I find the results interesting and useful for the field of visual attention research. I have questions about some aspects of the results and analysis as well as the bigger picture.

      (1) It appears that the modulation in V1 is weaker than V2 and V3 (Fig 2). In particular, the width modulation in V1 is not statistically significant (Fig 5). This result seems a bit unexpected. Given the known RF properties of neurons in these areas, in particular, smaller RF in V1, one might expect more spatially sensitive modulation in V1 than V2/V3. Some explanations and discussions would be helpful. Relatedly, one would also naturally wonder if this method can be applied to other extrastriate visual areas such as V4 and what the results look like.

      (2) I'm a bit confused about the angular error result. Fig 4 shows that the mean angular error is close to zero, but Fig 5 reports these values to be about 30-40 deg. Why the big discrepancy? Is it due to the latter reporting absolute errors? It seems reporting the overall bias is more useful than absolute value.

      (3) A significant effect is reported for amplitude in V3 (line 78), but the graph in Fig 5 shows hardly any difference. Please confirm the finding and also explain the directionality of the effect if there is indeed one.

      (4) The purpose of the temporal interval analysis is rather unclear. I assume it has to do with how much data is needed to recover the cortical modulation and hence how dynamic a signal the method can capture. While the results make sense (i.e., more data is better), there is no obvious conclusion and/or interpretation of its meaning.

      (5) I think it would be useful for the authors to make a more explicit connection to previous studies in this literature. In particular, two studies seem particularly relevant. First, how do the present results relate to those in Muller et al (2003, reference 37), which also found a zoom-lens type of neural effects. Second, how does the present method compare with spatial encoding model in Sprague & Serences (2013, reference 56), which also reconstructs the neural modulation of spatial attention. More discussions of these studies will help put the current study in the larger context.

      (6) Fig 4b, referenced on line 123, does not exist.

    3. Reviewer #2 (Public review):

      Summary:

      The study in question utilizes functional magnetic resonance imaging (fMRI) to dynamically estimate the locus and extent of covert spatial attention from visuocortical activity. The authors aim to address an important gap in our understanding of how the size of the attentional field is represented within the visual cortex. They present a novel paradigm that allows for the estimation of the spatial tuning of the attentional field and demonstrate the ability to reliably recover both the location and width of the attentional field based on BOLD responses.

      Strengths:

      (1) Innovative Paradigm: The development of a new approach to estimate the spatial tuning of the attentional field is a significant strength of this study. It provides a fresh perspective on how spatial attention modulates visual perception.<br /> (2) Refined fMRI Analysis: The use of fMRI to track the spatial tuning of the attentional field across different visual regions is methodologically rigorous and provides valuable insights into the neural mechanisms underlying attentional modulation.<br /> (3) Clear Presentation: The manuscript is well-organized, and the results are presented clearly, which aids in the reader's comprehension of the complex data and analyses involved.

      Weaknesses:

      (1) Lack of Neutral Cue Condition: The study does not include a neutral cue condition where the cue width spans 360{degree sign}, which could serve as a valuable baseline for assessing the BOLD response enhancements and diminishments in both attended and non-attended areas.<br /> (2) Clarity on Task Difficulty Ratios: The explicit reasoning for the chosen letter-to-number ratios for various cue widths is not detailed. Ensuring clarity on these ratios is crucial, as it affects the task difficulty and the comparability of behavioral performance across different cue widths. It is essential that observed differences in behavior and BOLD signals are attributable solely to changes in cue width and not confounded by variations in task difficulty.

    4. Reviewer #3 (Public review):

      Summary:

      In this report, the authors tested how manipulating the contiguous set of stimuli on the screen that should be used to guide behavior - that is, the scope of visual spatial attention - impacts the magnitude and profile of well-established attentional enhancements in visual retinotopic cortex. During fMRI scanning, participants attended to a cued section of the screen for blocks of trials and performed a letter vs digit discrimination task at each attended location (and judged whether the majority of characters were letters/digits). Importantly, the visual stimulus was identical across attention conditions, so any observed response modulations are due to top-down task demands rather than visual input. The authors employ population receptive field (pRF) models, which are used to sort voxel activation with respect to the location and scope of spatial attention and fit a Gaussian-like function to the profile of attentional enhancement from each region and condition. The authors find that attending to a broader region of space expands the profile of attentional enhancement across the cortex (with a larger effect in higher visual areas), but does not strongly impact the magnitude of this enhancement, such that each attended stimulus is enhanced to a similar degree. Interestingly, these modulations, overall, mimic changes in response properties caused by changes to the stimulus itself (increase in contrast matching the attended location in the primary experiment). The finding that attentional enhancement primarily broadens, but does not substantially weaken in most regions, is an important addition to our understanding of the impact of distributed attention on neural responses, and will provide meaningful constraints to neural models of attentional enhancement.

      Strengths:

      - Well-designed manipulations (changing location and scope of spatial attention), and careful retinotopic/pRF mapping, allow for a robust assay of the spatial profile of attentional enhancement, which has not been carefully measured in previous studies<br /> - Results are overall clear, especially concerning width of the spatial region of attentional enhancement, and lack of clear and consistent evidence for reduction in the amplitude of enhancement profile<br /> - Model-fitting to characterize spatial scope of enhancement improves interpretability of findings

      Weaknesses:

      - Task difficulty seems to vary as a function of spatial scope of attention, with varying ratios of letters/digits across spatial scope conditions, which may complicate interpretations of neural modulation results<br /> - Some aspects of analysis/data sorting are unclear (e.g., how are voxels selected for analyses?)<br /> - While the focus of this report is on modulations of visual cortex responses due to attention, the lack of inclusion of results from other retinotopic areas (e.g. V3AB, hV4, IPS regions like IPS0/1) is a weakness<br /> - Additional analyses comparing model fits across amounts of data analyzed suggest the model fitting procedure is biased, with some parameters (e.g., FWHM, error, gain) scaling with noise.

    5. Author response:

      We thank the three reviewers for their insightful feedback. We look forward to addressing the raised concerns in a revised version of the manuscript. There were a few common themes among the reviews that we will briefly touch upon now, and we will provide more details in the revised manuscript. 

      First, the reviewers asked for the reasoning behind the task ratios we implemented for the different attentional width conditions. The different ratios were selected to be as similar as possible given the size and spacing of our stimuli (aside from the narrowest cue width of one bin, the ratios for the others were 0.67, 0.60, and 0.67). As Figure 1b shows, task accuracy showed small and non-monotonic changes across the three larger cue widths, dissociable from the monotonic pattern seen for the model-estimated width of the attentional field. Furthermore, prior work has indicated that there is a relationship between task difficulty and the overall magnitude of the BOLD response, however we don’t suspect that this will influence the width of the modulation. How task difficulty influences the BOLD response is an important topic, and we hope that future work will investigate this relationship more directly.   

      Second, reviewers expressed interest in the distribution of spatial attention in higher visual areas. In our study we focus only on early visual regions (V1-V3). This was primarily driven by pragmatic considerations, in that we only have retinotopic estimates for our participants in these early visual areas. Our modeling approach is dependent on having access to the population receptive field estimates for all voxels, and while the main experiment was scanned using whole brain coverage, retinotopy was measured in a separate session using a field of view only covering the occipital cortex.  

      Lastly, we appreciate the opportunity to clarify the purpose of the temporal interval analysis. The reviewer is correct in assuming we set out to test how much data is needed to recover the cortical modulation and how dynamic a signal the method can capture. This analysis does show that more data provided more reliable estimates. The more important finding, however, is that the model was still able to recover the location and width of the attentional cue at shorter timescales of as few as two TRs. This has implications for the potential applicability of our approach to paradigms that involve more dynamic adaptation of the attentional field.

    1. eLife Assessment

      Understanding bacterial growth mechanisms can potentially help uncover novel drug targets that are crucial for maintaining cellular viability, particularly for bacterial pathogens. In this important study, the authors investigate the role of mycobacterial Wag31 in lipid and peptidoglycan biosynthesis. A detailed analysis of Wag31 domain architecture revealed a role in membrane tethering, more specifically, the N-terminal and C-terminal domains appear to display distinct functional roles therein. Whilst the data presented are of use, the experimental evidence is currently incomplete and does not yet fully support the conclusions made.

    2. Reviewer #1 (Public review):

      This a comprehensive study that sheds light on how Wag31 functions and localises in mycobacterial cells. A clear link to interactions with CL is shown using a combination of microscopy in combination with fusion fluorescent constructs, and lipid specific dyes. Furthermore, studies using mutant versions of Wag31 shed light on the functionalities of each domain in the protein. My concerns/suggestions for the manuscript are minor:

      (1) Ln 130. A better clarification/discussion is required here. It is clear that both depletion and overexpression have an effect on levels of various lipids, but subsequent descriptions show that they affect different classes of lipids.<br /> (2) The pulldown assays results are interesting, but links are tentative.<br /> (3) The authors may perhaps like to rephrase claims of effects lipid homeostasis, as my understanding is that lipid localisation rather than catabolism/breakdown is affected.

    3. Reviewer #2 (Public review):

      Summary:

      Kapoor et. al. investigated the role of the mycobacterial protein Wag31 in lipid and peptidoglycan synthesis and sought to delineate the role of the N- and C- terminal domains of Wag31. They demonstrated that modulating Wag31 levels influences lipid homeostasis in M. smegmatis and cardiolipin (CL) localisation in cells. Wag31 was found to preferentially bind CL-containing liposomes, and deleting the N-terminus of the protein significantly decreased this interaction. Novel interactions between Wag31 and proteins involved in lipid metabolism and cell wall synthesis were identified, suggesting that Wag31 recruits proteins to the intracellular membrane domain by direct interaction.

      Strengths:

      (1) The importance of Wag31 in maintaining lipid homeostasis is supported by several lines of evidence.<br /> (2) The interaction between Wag31 and cardiolipin, and the role of the N-terminus in this interaction was convincingly demonstrated.

      Weaknesses:

      (1) MS experiments provide some evidence for novel protein-protein interactions, however, the pull-down experiments are lacking a valid negative control.<br /> (2) The role of the N-terminus in the protein-protein interaction has not been ruled out.

    4. Reviewer #3 (Public review):

      Summary:

      This manuscript describes the characterization of mycobacterial cytoskeleton protein Wag31, examining its role in orchestrating protein-lipid and protein-protein interactions essential for mycobacterial survival. The most significant finding is that Wag31, which directs polar elongation and maintains the intracellular membrane domain, was revealed to have membrane tethering capabilities.

      Strengths:

      The authors provided a detailed analysis of Wag31 domain architecture, revealing distinct functional roles: the N-terminal domain facilitates lipid binding and membrane tethering, while the C-terminal domain mediates protein-protein interactions. Overall, this study offers a robust and new understanding of Wag31 function.

      Weaknesses:

      The following major concerns should be addressed.

      • Authors use 10-N-Nonyl-acridine orange (NAO) as a marker for cardiolipin localization. However, given that NAO is known to bind to various anionic phospholipids, how do the authors know that what they are seeing is specifically visualizing cardiolipin and not a different anionic phospholipid? For example, phosphatidylinositol is another abundant anionic phospholipid in mycobacterial plasma membrane.

      • Authors' data show that the N-terminal region of Wag31 is important for membrane tethering. The authors' data also show that the N-terminal region is important for sustaining mycobacterial morphology. However, the authors' statement in Line 256 "These results highlight the importance of tethering for sustaining mycobacterial morphology and survival" requires additional proof. It remains possible that the N-terminal region has another unknown activity, and this yet-unknown activity rather than the membrane tethering activity drives the morphological maintenance. Similarly, the N-terminal region is important for lipid homeostasis, but the statement in Line 270, "the maintenance of lipid homeostasis by Wag31 is a consequence of its tethering activity" requires additional proof. The authors should tone down these overstatements, or provide additional data to support their claims.

      • Authors suggest that Wag31 acts as a scaffold for the IMD (Fig. 8). However, Meniche et. al. has shown that MurG as well as GlfT2, two well-characterized IMD proteins, do not colocalize with Wag31 (DivIVA) (https://doi.org/10.1073/pnas.1402158111). IMD proteins are always slightly subpolar while Wag31 is located to the tip of the cell. Therefore, the authors' biochemical data cannot be easily reconciled with microscopic observations in the literature. This raises a question regarding the validity of protein-protein interaction shown in Figure 7. Since this pull-down assay was conducted by mixing E. coli lysate expressing Wag31 and Msm lysate expression Wag31 interactors like MurG, it is possible that the interactions are not direct. Authors should interpret their data more cautiously. If authors cannot provide additional data and sufficient justifications, they should avoid proposing a confusing model like Figure 8 that contradicts published observations.

    5. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      This a comprehensive study that sheds light on how Wag31 functions and localises in mycobacterial cells. A clear link to interactions with CL is shown using a combination of microscopy in combination with fusion fluorescent constructs, and lipid specific dyes. Furthermore, studies using mutant versions of Wag31 shed light on the functionalities of each domain in the protein. My concerns/suggestions for the manuscript are minor:

      (1) Ln 130. A better clarification/discussion is required here. It is clear that both depletion and overexpression have an effect on levels of various lipids, but subsequent descriptions show that they affect different classes of lipids.

      We thank the reviewer for the comments. We will improve Ln130 in the manuscript. The lipid classes that get impacted by the depletion of Wag31 vs overexpression are different. Wag31 is an adaptor protein that interacts with proteins of the ACCase complex (Meniche et al., 2014; Xu et al., 2014) that synthesize fatty acid precursors and regulate their activity (Habibi Arejan et al., 2022).

      The varied response to lipid homeostasis could be attributed to a change in the stoichiometry of these interactions with Wag31. While Wag31 depletion would prevent such interactions from occurring and might affect lipid synthesis that directly depends on Wag31-protein partner interactions, its overexpression would lead to promiscuous interactions and a change in the stoichiometry of native interactions, ultimately modulating lipid synthesis pathways.

      (2) The pulldown assays results are interesting, but links are tentative.

      The interactome of Wag31 was identified through the immunoprecipitation of Flag-tagged Wag31 complemented at an integrative locus in Wag31 mutant background to avoid overexpression artifacts. We used Msm::gfp expressing an integrative copy (at L5 locus) of FLAG-GFP as a control to subtract non-specific interactions. The experiment was performed in biological triplicates, and interactors that appeared in all replicates were selected for further analysis. Although we identified more than 100 interactors of Wag31, we analyzed only the top 25 hits, with a PSM cut-off ≥18 and unique peptides≥5. Additionally, two of Wag31's established interactors, AccD5 and Rne, were among the top five hits, thus validating our data.

      Though we agree that the interactions can either be direct or through a third partner, the fact that we obtained known interactors of Wag31 makes us believe these interactions are genuine. Moreover, we performed pulldown experiments for validation by mixing E. coli lysates expressing His-Wag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer contained 1% Triton X100, eliminating all non-specific and indirect interactions.  However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. We will describe this caveat in the revised manuscript. 

      (3) The authors may perhaps like to rephrase claims of effects lipid homeostasis, as my understanding is that lipid localisation rather than catabolism/breakdown is affected.

      In this manuscript, we are trying to convey that Wag31 is a spatiotemporal regulator of lipid metabolism. It is a peripheral protein that is hooked to the membrane via Cardiolipin and forms a scaffold at the poles, which helps localize several enzymes involved in lipid metabolism.

      Homeostasis is the process by which an organism maintains a steady-state of balance and stability in response to changes.  Depletion of Wag31 not only results in delocalisation of lipids in intracellular lipid inclusions but also leads to changes in the levels of various lipid classes. Advancement in the field of spatial biology underscores the importance of native localization of various biological molecules crucial for maintaining a steady-cell of the cell. Hence, we have used the word “homeostasis” to describe both the changes observed in lipid metabolism.

      Reviewer #2 (Public review):

      Summary

      Kapoor et. al. investigated the role of the mycobacterial protein Wag31 in lipid and peptidoglycan synthesis and sought to delineate the role of the N- and C- terminal domains of Wag31. They demonstrated that modulating Wag31 levels influences lipid homeostasis in M. smegmatis and cardiolipin (CL) localisation in cells. Wag31 was found to preferentially bind CL-containing liposomes, and deleting the N-terminus of the protein significantly decreased this interaction. Novel interactions between Wag31 and proteins involved in lipid metabolism and cell wall synthesis were identified, suggesting that Wag31 recruits proteins to the intracellular membrane domain by direct interaction.

      Strengths:

      (1) The importance of Wag31 in maintaining lipid homeostasis is supported by several lines of evidence.

      (2) The interaction between Wag31 and cardiolipin, and the role of the N-terminus in this interaction was convincingly demonstrated.

      Weaknesses:

      (1) MS experiments provide some evidence for novel protein-protein interactions. However, the pull-down experiments lack a valid negative control.

      We thank the reviewer for the comments. We will include a valid negative control in the experiment. We would choose ~2 mycobacterial proteins that are not a part of our interactome study and perform a similar pull-down experiment with them and a positive control (known interactor of Wag31).

      (2) The role of the N-terminus in the protein-protein interaction has not been ruled out.

      Previously, we attempted to express the N-terminal (1-60 aa) and the C-terminal (60-212 aa) proteins in various mycobacterial shuttle vectors to perform MS/MS experiments. Despite numerous efforts, neither was expressed with the N/C-terminal FLAG tag nor without any tag in episomal or integrative vectors due to the instability of the protein. Eventually, we successfully expressed the C-terminal Wag31 with an N and C-terminal hexa-His tag. However, this expression was not sufficient or stable enough for us to perform Ni affinity pull-down experiments for mass spectrometry.  The N-terminal of Wag31 could not be expressed in M. smegmatis even with N and C-terminal Hexa-His tags.

      To rule out the role of the N-terminal in mediating protein-protein interactions, we plan to attempt to express N-terminal of Wag31with N and C-terminal hexa-His tag in E. coli. If this clone successfully expresses in E. coli, we will perform pull-down experiments as described in Figure 7.

      Reviewer #3 (Public review):

      Summary:

      This manuscript describes the characterization of mycobacterial cytoskeleton protein Wag31, examining its role in orchestrating protein-lipid and protein-protein interactions essential for mycobacterial survival. The most significant finding is that Wag31, which directs polar elongation and maintains the intracellular membrane domain, was revealed to have membrane tethering capabilities.

      Strengths:

      The authors provided a detailed analysis of Wag31 domain architecture, revealing distinct functional roles: the N-terminal domain facilitates lipid binding and membrane tethering, while the C-terminal domain mediates protein-protein interactions. Overall, this study offers a robust and new understanding of Wag31 function.

      Weaknesses:

      The following major concerns should be addressed.

      • Authors use 10-N-Nonyl-acridine orange (NAO) as a marker for cardiolipin localization. However, given that NAO is known to bind to various anionic phospholipids, how do the authors know that what they are seeing is specifically visualizing cardiolipin and not a different anionic phospholipid? For example, phosphatidylinositol is another abundant anionic phospholipid in mycobacterial plasma membrane.

      We thank the reviewer for the comments. Despite its promiscuous binding to other anionic phospholipids, 10-N-Nonyl-acridine orange is widely used to stain Cardiolipin and determine its localisation in bacterial cells and mitochondria of eukaryotes (Garcia Fernandez et al., 2004; Mileykovskaya & Dowhan, 2000; Renner & Weibel, 2011).  This is because it has a stronger affinity for Cardiolipin than other anionic phospholipids with the affinity constant being 2 × 10<sup>6</sup> M<sup>−1</sup> for Cardiolipin association and 7 × 10<sup>4</sup> M<sup>−1</sup> for that of phosphatidylserine and phosphatidylinositol association (Petit et al., 1992). Additionally, there is not yet another stain available for detecting Cardiolipin. Our protein-lipid binding assays suggest that Wag31 preferentially binds to Cardiolipin over other anionic phospholipids (Fig. 4b), hence it is likely that the majority of redistribution of NAO fluorescence that we observe might be contributed by Cardiolipin mislocalization due to altered Wag31 levels, with smaller degree of NAO redistribution intensity coming indirectly from other anionic phospholipids displaced from the membrane due to the loss of membrane integrity and cell shape changes due to Wag31.

      • Authors' data show that the N-terminal region of Wag31 is important for membrane tethering. The authors' data also show that the N-terminal region is important for sustaining mycobacterial morphology. However, the authors' statement in Line 256 "These results highlight the importance of tethering for sustaining mycobacterial morphology and survival" requires additional proof. It remains possible that the N-terminal region has another unknown activity, and this yet-unknown activity rather than the membrane tethering activity drives the morphological maintenance. Similarly, the N-terminal region is important for lipid homeostasis, but the statement in Line 270, "the maintenance of lipid homeostasis by Wag31 is a consequence of its tethering activity" requires additional proof. The authors should tone down these overstatements or provide additional data to support their claims.

      We agree with the reviewer that there exists a possibility for another function of the N-terminal that may contribute to sustaining mycobacterial physiology and survival. We would revise our statements in the paper to accurately reflect the data. Results shown suggest that the tethering activity of the N-terminal region may contribute to mycobacterial morphology and survival. However, additional functions of this region can’t be ruled out. Similarly, the maintenance of lipid homeostasis by Wag31 may be associated with its tethering activity, although other mechanisms could also contribute to this process. 

      • Authors suggest that Wag31 acts as a scaffold for the IMD (Fig. 8). However, Meniche et. al. has shown that MurG as well as GlfT2, two well-characterized IMD proteins, do not colocalize with Wag31 (DivIVA) (https://doi.org/10.1073/pnas.1402158111). IMD proteins are always slightly subpolar while Wag31 is located to the tip of the cell. Therefore, the authors' biochemical data cannot be easily reconciled with microscopic observations in the literature. This raises a question regarding the validity of protein-protein interaction shown in Figure 7. Since this pull-down assay was conducted by mixing E. coli lysate expressing Wag31 and Msm lysate expression Wag31 interactors like MurG, it is possible that the interactions are not direct. Authors should interpret their data more cautiously. If authors cannot provide additional data and sufficient justifications, they should avoid proposing a confusing model like Figure 8 that contradicts published observations.

      In the literature, MurG and GlfT2 have been shown to have polar localization (Freeman et al., 2023; Hayashi et al., 2016; Kado et al., 2023), and two groups have shown slightly sub-polar localization of MurG (García-Heredia et al., 2021; Meniche et al., 2014). Additionally, (Freeman et al., 2023) they showed SepIVA to be a spatio-temporal regulator of MurG. MS/MS analysis of Wag31 immunoprecipitation data yielded both MurG and SepIVA to be interactors of Wag31 (Fig. 3). Given Wag31 also displays polar localisation, it likely associates with the polar MurG. However, since a sub-polar localization of MurG has also been reported, it is possible that they do not interact directly, and another protein mediates their interaction. We will modify the model proposed in Fig. 8 based on the above.

      We agree that for validation of interaction, we performed pulldown experiments by mixing E. coli lysates expressing His-Wag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer containing 1% Triton X100, which eliminates all non-specific and indirect interactions.  However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. We will describe this caveat in the revised manuscript and propose a model reflecting our results.

      References:

      Freeman, A. H., Tembiwa, K., Brenner, J. R., Chase, M. R., Fortune, S. M., Morita, Y. S., & Boutte, C. C. (2023). Arginine methylation sites on SepIVA help balance elongation and septation in Mycobacterium smegmatis. Mol Microbiol, 119(2), 208-223. https://doi.org/10.1111/mmi.15006

      Garcia Fernandez, M. I., Ceccarelli, D., & Muscatello, U. (2004). Use of the fluorescent dye 10-N-nonyl acridine orange in quantitative and location assays of cardiolipin: a study on different experimental models. Anal Biochem, 328(2), 174-180. https://doi.org/10.1016/j.ab.2004.01.020

      García-Heredia, A., Kado, T., Sein, C. E., Puffal, J., Osman, S. H., Judd, J., Gray, T. A., Morita, Y. S., & Siegrist, M. S. (2021). Membrane-partitioned cell wall synthesis in mycobacteria. eLife, 10. https://doi.org/10.7554/eLife.60263

      Habibi Arejan, N., Ensinck, D., Diacovich, L., Patel, P. B., Quintanilla, S. Y., Emami Saleh, A., Gramajo, H., & Boutte, C. C. (2022). Polar protein Wag31 both activates and inhibits cell wall metabolism at the poles and septum. Front Microbiol, 13, 1085918. https://doi.org/10.3389/fmicb.2022.1085918

      Hayashi, J. M., Luo, C. Y., Mayfield, J. A., Hsu, T., Fukuda, T., Walfield, A. L., Giffen, S. R., Leszyk, J. D., Baer, C. E., Bennion, O. T., Madduri, A., Shaffer, S. A., Aldridge, B. B., Sassetti, C. M., Sandler, S. J., Kinoshita, T., Moody, D. B., & Morita, Y. S. (2016). Spatially distinct and metabolically active membrane domain in mycobacteria. Proc Natl Acad Sci U S A, 113(19), 5400-5405. https://doi.org/10.1073/pnas.1525165113

      Kado, T., Akbary, Z., Motooka, D., Sparks, I. L., Melzer, E. S., Nakamura, S., Rojas, E. R., Morita, Y. S., & Siegrist, M. S. (2023). A cell wall synthase accelerates plasma membrane partitioning in mycobacteria. eLife, 12, e81924. https://doi.org/10.7554/eLife.81924

      Meniche, X., Otten, R., Siegrist, M. S., Baer, C. E., Murphy, K. C., Bertozzi, C. R., & Sassetti, C. M. (2014). Subpolar addition of new cell wall is directed by DivIVA in mycobacteria. Proc Natl Acad Sci U S A, 111(31), E3243-3251. https://doi.org/10.1073/pnas.1402158111

      Mileykovskaya, E., & Dowhan, W. (2000). Visualization of phospholipid domains in Escherichia coli by using the cardiolipin-specific fluorescent dye 10-N-nonyl acridine orange. J Bacteriol, 182(4), 1172-1175. https://doi.org/10.1128/JB.182.4.1172-1175.2000

      Petit, J. M., Maftah, A., Ratinaud, M. H., & Julien, R. (1992). 10N-nonyl acridine orange interacts with cardiolipin and allows the quantification of this phospholipid in isolated mitochondria. Eur J Biochem, 209(1), 267-273. https://doi.org/10.1111/j.1432-1033.1992.tb17285.x

      Renner, L. D., & Weibel, D. B. (2011). Cardiolipin microdomains localize to negatively curved regions of Escherichia coli membranes. Proc Natl Acad Sci U S A, 108(15), 6264-6269. https://doi.org/10.1073/pnas.1015757108

      Xu, W. X., Zhang, L., Mai, J. T., Peng, R. C., Yang, E. Z., Peng, C., & Wang, H. H. (2014). The Wag31 protein interacts with AccA3 and coordinates cell wall lipid permeability and lipophilic drug resistance in Mycobacterium smegmatis. Biochem Biophys Res Commun, 448(3), 255-260. https://doi.org/10.1016/j.bbrc.2014.04.116

    1. eLife Assessment

      This valuable study combines massively parallel reporter assays and regression analysis to identify sequence features in untranslated regions contributing to the stability of in vitro transcribed mRNA delivered to cells. The strength of evidence presented is solid, although some points about half-life measurements and the relevance of identified sequence features to native transcript stability will inform future discussion surrounding the present study. Taken together, the work will be of interest to a broad swath of colleagues studying post-transcriptional gene regulation and especially to those using massively parallel reporter assays.

    2. Reviewer #1 (Public review):

      In the manuscript by Su et al., the authors present a massively parallel reporter assay (MPRA) measuring the stability of in vitro transcribed mRNAs carrying wild-type or mutant 5' or 3' UTRs transfected into two different human cell lines. The goal presented at the beginning of the manuscript was to screen for effects of disease-associated point mutations on the stability of the reporter RNAs carrying partial human 5' or 3' UTRs. However, the majority of the manuscript is dedicated to identifying sequence components underlying the differential stability of reporter constructs. This analysis showed that UA dinucleotides are the most predictive feature of RNA stability in both cell lines and both UTRs.

      The effect of AU rich elements (AREs) on RNA stability is well established in multiple systems, and the present study confirms this general trend, but points out variability in the consequence of seemingly similar motifs on RNA stability. For example, the authors report that a long stretch of Us has extreme opposite effects on RNA stability depending on whether it is preceded by an A (strongly destabilizing) or followed by an A (strongly stabilizing). While the authors interpretation of a context-dependence of the effect is certainly well-founded, it seems counterintuitive that the preceding or following A would be the (only) determining factor. This points to a generally reductionist approach taken by the authors in the analysis of the data and in their attempt to dissect the contribution of "AU rich sequences" to RNA stability, with a general tendency to reduce the size and complexity of the features (e.g. to dinucleotides). While this certainly increases the statistical power of the analysis due to the number of occurrences of these motifs, it limits the interpretability of the results. How do UA dinucleotides per se contribute to destabilizing the RNA, both in 5' and 3' UTRs, but (according to limited data presented) not in coding sequences? What is the mechanism? RBPs binding to UA dinucleotide containing sequences are suggested to "mask" the destabilizing effect, thereby leading to a more stable RNA. Gain of UA dinucleotides is reported to have a destabilizing effect, but again no hypothesis is provided as to the underlying molecular mechanism. In addition to reducing the motif length to dinucleotides, the notion of "context dependence" is used in a very narrow sense.

      The present MPRA measures the effect of UTR sequences in one specific reporter context and using one experimental approach (following the decay of in vitro transcribed and transfected RNAs). While this method certainly has its merits compared to other approaches, it also comes with some caveats: RNA is delivered naked, without bound RBPs and no nuclear history, e.g. of splicing (no EJCs), editing and modifications. Therefore, it remains to be seen whether UA dinucleotide frequency is a substantial factor in determining the half-lives of endogenous mRNAs.

      The authors conclude their study with a meta-analysis of genes with increased UA dinucleotides in 5' and 3'UTRs, showing that specific functional groups are overrepresented among these genes. In addition, they provide evidence for an effect of disease-associated UTR mutations on endogenous RNA stability. While these elements link back to the original motivation of the study (screening for effects of point mutations in 5' and 3' UTRs), they provide only a limited amount of additional insights.

      In summary, this manuscript presents an interesting addition to the long-standing attempts at dissecting the sequence basis of RNA stability in human cells. The analysis is in general comprehensive and sound; however, it remains unclear to what extent the findings can be generalized beyond the method and the experimental system used here.

      Comments on revisions:

      Parts of my original comments have been adequately addressed by the reviewers.<br /> After reading the revised manuscript and the rebuttal, my main concern is related to the figure comparing the half-lives as measured in the two different cell lines that was included in the response to reviewer 2, but not in the revised manuscript. The complete lack of correlation between the half-lives of the 3'UTR library measured in the two cell lines is concerning. While variability and cell type-specific effects can be expected, some principles should be the same (such as the effect of UA dinucleotides that the authors report), leading to at least some correlation.<br /> In addition, it is unclear to me why the half-lives measured for the two libraries in HEK cells are shifted (median ln(t 1/2)=6-7 for the 5'UTR library and ln(t 1/2)=4-4.5 for the 3'UTR library), but not in SH.

      I feel that this figure contains important information that should be included in the final manuscript.

    3. Reviewer #2 (Public review):

      Summary of goals:

      Untranslated regions are key cis-regulatory elements that control mRNA stability, translation, and translocation. Through interactions with small RNAs and RNA binding proteins, UTRs form complex transcriptional circuitry that allows cells to fine-tune gene expression. Functional annotation of UTR variants has been very limited, and improvements could offer insights into disease relevant regulatory mechanisms. The goals were to advance our understanding of the determinants of UTR regulatory elements and characterize the effects of a set of "disease-relevant" UTR variants.

      Strengths:

      The use of a massively parallel reporter assay allowed for analysis of a substantial set (6,555 pairs) of 5' and 3' UTR fragments compiled from known disease associated variants. Two cell types were used.

      The findings confirm previous work about the importance of AREs, which helps show validity and adds some detailed comparisons of specific AU-rich motif effects in these two cell types.

      Using a Lasso regression, TA-dinucleotide content is identified as a strong regulator of RNA stability in a context dependent manner based on GC content and presence of RNA binding protein binding motifs. The findings have potential importance, drawing attention to a UTR feature that is not well characterized.

      The use of complementary datasets, including from half-life analyses of RNAs and from random sequence library MRPA's, is a useful addition and supports several important findings. The finding the TA dinucleotides have explanatory power separate from (and in some cases interacting with) GC content is valuable.

      The functional enrichment analysis suggests some new ideas about how UTRs may contribute to regulation of certain classes of genes.

      Weaknesses:

      In this section, original reviewer comments about the initial submission and the responses of the authors are listed together with new reviewer responses to the authors:

      Reviewer original comment 1: It is difficult to understand how the calculations for half-life were performed. The sequencing approach measures the relative frequency of each sequence at each time point (less stable sequences become relatively less frequent after time 0, whereas more stable sequences become relatively more frequent after time 0). Since there is no discussion of whether the abundance of the transfected RNA population is referenced to some external standard (e.g., housekeeping RNAs), it is not clear how absolute (rather than relative) half-lives were determined.

      Author response: [The authors showed the equations used to calculate half lives based on read counts.] They stated that "The absolute abundance was not required for the half-life calculation."

      Reviewer response to authors: The methods section states that DESeq2 was used to normalize read counts. DESeq2 normalization assumes that levels of most RNAs are not different between samples. That assumption is not valid here, since RNAs in the library are introduced into cells at time 0 and all RNAs decrease over time. If DESeq2 is applied without modification to normalize across timepoints, normalized reads from less stable RNAs will decrease over time (as expected) but normalized reads from more stable RNAs will increase. Can the authors please clarify in the methods how the read counts were normalized to account for this issue?

      Reviewer original comment 2: Fig. S1A and B are used to assess reproducibility. They show that read counts at a given time point correlate well across replicate experiments. However, this is not a good way to assess reproducibility or accuracy of the measurements of t1/2 are. (The major source of variability in read counts in these plots - especially at early time points - is likely starting abundance of each RNA sequence, not stability.) This creates concerns about how well the method is measuring t1/2. Also creating concern is the observation that many RNAs are associated with half-lives that are much longer than the time points analyzed in the study. For example, based upon Figure S1 and Table S1 correctly, the median t1/2 for the 5' UTR library in HEK cells appears to be >700 minutes. Given that RNA was collected at 30, 75, and 120 minutes, accurate measurements of RNAs with such long half lives would seem to be very difficult.

      Author response: ... The calculation of the half-life involves first determining the decay constant 𝜆, which represents a constant rate of decay. Since 𝜆 is a constant, it is possible to accurately calculate it without needing data over the entire decay range. Our experimental design considers this by selecting appropriate time points to ensure a reliable estimation of 𝜆, and thus, the half-life. To determine the most suitable time points, we conducted preliminary experiments using RT-PCR. These experiments indicated that 30, 75, and 120 minutes provided an effective range for capturing the decay dynamics of the transcripts.

      Reviewer response to author comments: Based on Fig. S1D, for 3' UTRs in both cell types and for 5' UTRs in SH-SY5Y cells, median t1/2 is in the range of ~30 to 90 minutes (corresponding to ln t1/2 = 3.5 to 4.5). Measuring RNAs at 30, 75, and 120 minutes would therefore be a good choice for these cases, However, median t1/2 in HEK cells appears to be ~600 minutes (corresponding to ln t1/2 ~6.4) for HEK cells. For t1/2 of 600 minutes, RNA levels at the final time point (120 minutes) would be 90% of the those at the first time point (30 minutes), which illustrates why the method would need to be able to reliably capture very small changes in RNA abundance to accurately measure t1/2 for transcripts with half-lives much longer than 120 minutes. As suggested in our original review, this concern could be addressed by showing the correlation of half-lives across replicates for the 5' and 3' UTR libraries in both cell types. Alternatively, the authors could show other measures of reproducibility for the half-life measurements across replicates. This requires no additional experimentation and can be done using the data from replicate runs shown in Fig. S1A and B. We remain concerned that for sequences with very long half-lives, extrapolating the half-life from small changes between 30 and 120 minutes will lead to imprecise measurements.

      Reviewer original comment 3: There is no direct comparison of t1/2 between the two cell types studied for the full set of sequences studied. This would be helpful in understanding whether the regulatory effects of UTRs are generally similar across cell lines (as has been shown in some previous studies) or whether there are fundamental differences. The distribution of t1/2's is clearly quite different in the two cell lines, but it is important to know if this reflects generally slow RNA turnover in HEK cells or whether there are a large number of sequence-specific effects on stability between cell lines. A related issue is that it is not clear whether the relatively small number of significant variant effects detected in HEK cells versus SH-SY5Y cells is attributable to real biological differences between cell types or to technical issues (many fewer read counts and much longer half lives in HEK cells).

      Author response: For both cell lines, we selected oligonucleotides with R2 > 0.5 and mean squared error (MSE) < 1 for analysis when estimating half-life (λ) by linear regression. This selection criterion was implemented to minimize the effect of experimental noise. After quality control, we selected common UTRs and compared the RNA half-lives of the two cell lines using a scatter plot. The figure below shows that RNA half-lives are quite different between the cell lines, with a moderate similarity observed in the 5' UTRs (R = 0.21), while the correlation in the 3' UTRs is non-significant. Despite the low correlation of mRNA half-life between the two cell lines, UA-dinucleotide and UA-rich sequences consistently emerge as the most significant destabilizing features, suggesting a shared regulatory mechanism across diverse cellular environments.

      Reviewer response to author comments: We appreciate that the authors shared this additional analysis of the data. We believe that this is an important finding and that the additional figure showing correlations of half-lives across cell types should be included in the manuscript or supplement. Discussion of this result in the manuscript would also be useful for readers. This result is surprising to us since we would have expected that widely expressed RNA-binding proteins would have led to more similar effects between the two cell types, as previously found using other approaches (e.g., studies of 3' UTR effects in MPRAs). It would also be appropriate to discuss that differences seen between the two cell types indicate that caution is warranted when trying to generalize the results of this study to other cell types.

      Reviewer original comment 4 has been addressed adequately in the revised manuscript.

      Appraisal and impact:

      Reviewer original comment 1: The work adds to existing studies that previously identified sequence features, including AREs and other RNA binding protein motifs, that regulate stability and puts a new emphasis on the role of "TA" (better "UA") dinucleotides. It is not clear how potential problems with the RNA stability measurements discussed above might influence the overall conclusions, which may limit the impact unless these can be addressed.

      It is difficult to understand whether the importance of TA dinucleotides is best explained by their occurrence in a related set of longer RBP binding motifs (see Fig 5J, these motifs may be encompassed by the "WWWWWW cluster") or whether some other explanation applies. Further discussion of this would be helpful. Does the LASSO method tend to collapse a more diverse set of longer motifs that are each relatively rare compared to the dinucleotide? It remains unclear whether TA dinucleotides are associated with less stability independent of the presence of the known larger WWWWWWW motif. As noted above, the importance of TA dinucleotides in the HEK experiments appears to be less than is implied in the text.

      Author response: To ensure the representativeness of the features entered into the LASSO model, we pre-selected those with an occurrence greater than 10% among all UTRs. There is no evidence to support a preference for dinucleotides by LASSO. To address whether the destabilizing effect of UA dinucleotides is part of the broader WWWWWW motif, we divided UA dinucleotides into two groups: those within the WWWWWW motif and those outside of it. Specifically, we divided UTRs into two categories: 'at least one UA within a WWWWWW motif' and 'no UA within a WWWWWW motif,' and visualized the results using a boxplot. As shown in [figures provided to the reviewers], the destabilizing trend still remains for UA dinucleotides outside of the WWWWWW motif, although the effect appears to be more pronounced when UA is within the WWWWWW motif. This suggests that while UA dinucleotides have a destabilizing effect independently, their impact is amplified when they are part of the broader WWWWWW motif.

      Reviewer response to authors: These are useful additional analyses, and we suggest that the additional figure and discussion should be included in the manuscript/supplement so that readers can benefit from them.

      Reviewer original comment 2: The inclusion of more than a single cell type is an acknowledgement of the importance of evaluating cell type-specific effects. The work suggests a number of cell type-specific differences, but due to technical issues (especially with the HEK data, as outlined above) and the use of only two cell lines, it is difficult to understand cell type effects from the work.

      The inclusion of both 3' and 5' UTR sequences distinguishes this work from most prior studies in the field. Contrasting the effects of these regions on stability is of interest, although the role of these UTRs (especially the 5' UTR) in translational regulation is not assessed here.

      Author response: We examined the role of UTR and UTR variants in translation regulation using polysome profiling. By both univariate analysis and an elastic regression model, we identified motifs of short repeated sequences, including SRSF2 binding sites, as mutation hotspots that lead to aberrant translation. Furthermore, these polysome-shifting mutations had a considerable impact on RNA secondary structures, particularly in upstream AUG-containing 5' UTRs. Integrating these features, our model achieved high accuracy (AUROC > 0.8) in predicting polysome-shifting mutations in the test dataset. Additionally, metagene analysis indicated that pathogenic variants were enriched at the upstream open reading frame (uORF) translation start site, suggesting changes in uORF usage underlie the translation deficiencies caused by these mutations. Illustrating this, we demonstrated that a pathogenic mutation in the IRF6 5' UTR suppresses translation of the primary open reading frame by creating a uORF. Remarkably, site-directed ADAR editing of the mutant mRNA rescued this translation deficiency. Because the regulation of translation and stability does not converge, we illustrate these two mechanisms in two separate manuscripts (this one and doi.org/10.1101/2024.04.11.589132).

      Reviewer response to authors: This is useful context. No further comment.

    4. Reviewer #3 (Public review):

      Summary:

      In their manuscript titled "Multiplexed Assays of Human Disease‐relevant Mutations Reveal UTR Dinucleotide Composition as a Major Determinant of RNA Stability" the authors aim to investigate the effect of sequence variations in 3'UTR and 5'UTRs on the stability of mRNAs in two different human cell lines.

      To do so, the authors use a massively parallel reporter assay (MPRA). They transfect cells with a set of mRNA reporters that contain sequence variants in their 3' or 5' UTRs, which were previously reported in human diseases. They follow their clearance from cells over time relative to the matching non-variant sequence. To analyze their results, they define a set of factors (RBP and miRNA binding sites, sequence features, secondary structure etc.) and test their association with differences in mRNA stability. For features with a significant association, they use clustering to select a subset of factors for LASSO regression and identify factors that affect mRNA stability.<br /> They conclude that the TA dinucleotide content of UTRs is the strongest destabilizing sequence feature. Within that context, elevated GC content and protein binding can protect susceptible mRNAs from degradation. They also show that TA dinucleotide content of UTRs affects native mRNA stability and that it is associated with specific functional groups. Finally, they link disease associated sequence variants with differences in mRNA stability of reporters.

      Strengths:

      (1) This work introduces a different MPRA approach to analyze the effect of genetic variants. While previous works in tissue culture use DNA transfections that require normalization for transcription efficiency, here the mRNA is directly introduced into cells at fixed amounts, allowing a more direct view of the mRNA regulation.

      (2) The authors also introduce a unique analysis approach, which takes into account multiple factors that might affect mRNA stability. This approach allows them to identify general sequence features that affect mRNA stability beyond specific genetic variants, and reach important insights on mRNA stability regulation. Indeed, while the conclusions to genetic variants identified in this work are interesting, the main strength of the work involves general effect of sequence features rather than specific variants.

      (3) The authors provide adequate support for their claims and validate their analysis using both their reporter data and native genes. For the main feature identified, TA di-nucleotides, they perform follow-up experiments with modified reporters that further strengthen their claims, and also validate the effect on native cellular transcripts (beyond reporters), demonstrating its validity also within native scenarios.

      (4) The work provides a broad analysis of mRNA stability, across two mRNA regulatory segments (3'UTR and 5'UTR) and is performed in two separate cell-types. Comparison between two different cell-types is adequate, and the results demonstrate, as expected, the dependence of mRNA stability on the cellular context. Analysis of 3'UTR and 5'UTR regulatory effects also shows interesting differences and similarities between these two regulatory regions.

      Weaknesses:

      In their revised manuscripts, the authors successfully address many of the weaknesses raised in the original review, including the effect of possible confounding effects, and additional methodology details. Notably, two of the issues raised in the original report, have only been partially addressed in the revision.

      (1) The analysis and regression models built in this work are not thoroughly investigated relative to native genes within cells.<br /> While using MPRAs indeed allows to isolate regulatory effects that are less influential in-vivo, the resulting effects still provide some regulatory function in-vivo. The goal of such an analysis would not be to demonstrate the predictive power of the models, or to make any claims regarding using these models to fully explain or predict the stability of native transcripts. Clearly, additional more prominent factors could function in controlling endogenous RNA stability.<br /> Instead, the goal of such an investigation is to simply assess the fraction of in-vivo regulation that the factors identified in this work contribute in native contexts, and what is the relative contribution of the phenomena captured by the well-controlled MPRA study.<br /> This reviewer believes that even if the effects identified by the current MPRA study only contribute a small fraction of in-vivo variation, an analysis that aim to estimate what this fraction is, will be very relevant to this study for several reasons. First, in order to appreciate the results of this study within their in-vivo context. Second, in light of the questions raised as motivation for this study, and particularly the need to identify the effect of disease-associated 3'UTR variants, which clearly have an in-vivo effect.

      (2) Methodology validation can be performed with simulated data (generated in-silico by the authors) to provide an independent support for the ability of the current methodology to correctly extract regulatory effects from the data.

    5. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      In the manuscript by Su et al., the authors present a massively parallel reporter assay (MPRA) measuring the stability of in vitro transcribed mRNAs carrying wild-type or mutant 5' or 3' UTRs transfected into two different human cell lines. The goal presented at the beginning of the manuscript was to screen for effects of disease-associated point mutations on the stability of the reporter RNAs carrying partial human 5' or 3' UTRs. However, the majority of the manuscript is dedicated to identifying sequence components underlying the differential stability of reporter constructs. This shows that TA dinucleotides are the most predictive feature of RNA stability in both cell lines and both UTRs.

      The effect of AU rich elements (AREs) on RNA stability is well established in multiple systems, and the present study confirms this general trend but points out variability in the consequence of seemingly similar motifs on RNA stability. For example, the authors report that a long stretch of Us has extreme opposite effects on RNA stability depending on whether it is preceded by an A (strongly destabilizing) or followed by an A (strongly stabilizing). While the authors interpretation of a context- dependence of the effect is certainly well-founded, it seems counterintuitive that the preceding or following A would be the (only) determining factor. This points to a generally reductionist approach taken by the authors in the analysis of the data and in their attempt to dissect the contribution of "AU rich sequences" to RNA stability, with a general tendency to reduce the size and complexity of the features (e.g. to dinucleotides). While this certainly increases the statistical power of the analysis due to the number of occurrences of these motifs, it limits the interpretability of the results. How do TA dinucleotides per se contribute to destabilizing the RNA, both in 5' and 3' UTRs, but (according to limited data presented) not in coding sequences? What is the mechanism? RBPs binding to TA dinucleotide containing sequences are suggested to "mask" the destabilizing effect, thereby leading to a more stable RNA. Gain of TA dinucleotides is reported to have a destabilizing effect, but again no hypothesis is provided as to the underlying molecular mechanism. In addition to reducing the motif length to dinucleotides, the notion of "context dependence" is used in a very narrow sense; especially when focusing on simple and short motifs, a more extensive analysis of the interdependence of these features (beyond the existing analysis of the relationship between TA- diNTs and GC content) could potentially reveal more of the context dependence underlying the seemingly opposite behavior of very similar motifs.

      (We have used UA instead of TA, as per the reviewer's suggestion)

      The contribution of coding region sequence to RNA stability has been extensively discussed (For example: doi.org/10.1016/j.molcel.2022.03.032; doi.org/10.1186/s13059-020-02251-5; doi.org/10.15252/embr.201948220; doi.org/10.1371/journal.pone.0228730; doi.org/10.7554/eLife.45396). While UA content at the third codon position (wobble position) has been implicated as a pro-degradation signal, codon optimality has emerged as the most prominent determinant for RNA stability. This indicates that the role of coding regions in RNA stability differs from that of UTRs due to the involvement of translation elongation. We did not intend to suggest that UA-dinucleotides in UTRs and coding regions have the same effect. 

      To ensure the representativeness of the features entered into the LASSO model, we pre-selected those with an occurrence greater than 10% among all UTRs. As a result, while motifs with very low occurrences were excluded from the analysis, there is no evidence to indicate a preference for dinucleotides by the LASSO model.

      We hypothesize that UA-dinucleotide may recruit endonucleases RNase A family, whose catalytic pockets exhibit a strong bias for UA dinucleotide (doi.org/10.1016/j.febslet.2010.04.018). Structures or protein bindings that block this recognition might stabilize RNAs. To gain further insight into the motif interactions, we investigated the interactions between UA and other 15 dinucleotides through more detailed analyses. We conducted a linear regression analysis investigating interactions between UA and the other 15 dinucleotides. The formula used below includes UA:

      , where all 𝛽 terms represent the regression coefficients, and , , and represent the number of UA dinucleotides, the number of other dinucleotides (other than UA), and the GC content of the i<sup>th</sup> UTR, respectively, and 𝜖<sub>i</sub> denotes the error term. For each dinucleotide, we tested the significance of 𝛽<sub>UAxGC%</sub> and 𝛽<sub>UAxDiNT</sub>, and compared their p-values using a quantile-quantile (QQ) plot. Author response image 1 shows that the interaction effect of UA dinucleotides with GC% is much more significant than interactions with the other 15 dinucleotides, as indicated by the inflated QQ plot of p-values. This suggests that GC content is a more critical contextual factor influencing UA dinucleotides' impact on RNA stability.

      Author response image 1.

      The present MPRAs measures the effect of UTR sequences in one specific reporter context and using one experimental approach (following the decay of in vitro transcribed and transfected RNAs). While this approach certainly has its merits compared to other approaches, it also comes with some caveats: RNA is delivered naked, without bound RBPs and no nuclear history, e.g. of splicing (no EJCs), editing and modifications. One way to assess the generalizability of the results as well as the context dependence of the effects is to perform the same analysis on existing datasets of RNA stability measurements obtained through other methods (e.g. transcription inhibition). Are TA dinucleotides universally the most predictive feature of RNA half-lives?

      Our system studies the stability control of RNA synthesized in vitro and delivered into human cells. While we did not intend to generalize our conclusions to endogenous RNAs, our approach contributes to the understanding of in vitro synthesized RNA used for cellular expression, such as in vaccines. It is known that endogenous RNAs undergo very different regulation. The most prominent factors controlling endogenous RNA stability are the density of splice junctions and the length of UTRs (doi.org/10.1186/s13059-022-02811-x; doi.org/10.1186/s12915-021-00949-x). To decipher the sequence regulation, these factors are controlled in our experiments. Therefore, we do not expect the dinucleotide features found by our approach to be generalized as the most predictive feature of RNA half-life in vivo. 

      The authors conclude their study with a meta-analysis of genes with increased TA dinucleotides in 5' and 3'UTRs, showing that specific functional groups are overrepresented among these genes. In addition, they provide evidence for an effect of disease-associated UTR mutations on endogenous RNA stability. While these elements link back to the original motivation of the study (screening for effects of point mutations in 5' and 3' UTRs), they provide only a limited amount of additional insights.

      We utilized the Taiwan Biobank to investigate whether mutations significantly affecting RNA stability also impact human biochemical measurements. Our findings indicate that these mutations indeed have a significant effect on various biochemical indices. This highlights the importance of our study, as it bridges basic science with potential applications in precision medicine. By linking specific UTR mutations with measurable changes in biochemical indices, our research underscores the potential for these findings to inform targeted medical interventions in the future.

      In summary, this manuscript presents an interesting addition to the long-standing attempts at dissecting the sequence basis of RNA stability in human cells. The analysis is in general very comprehensive and sound; however, at times the goal of the authors to find novelty and specificity in the data overshadows some analyses. One example is the case where the authors try to show that TA-dinucleotides and GC content are decoupled and not merely two sides of the same coin.

      They claim that the effect of TA dinucleotides is different between high- and low-GC content contexts but do not control for the fact that low GC-content regions naturally will contain more TA dinucleotides and therefore the effect sizes and the resulting correlation between TA-diNT rate and stability will be stronger (Fig. 5A). A more thorough analysis and greater caution in some of the claims could further improve the credibility of the conclusions.

      Low GC content implies a higher UA content but does not directly equate to a high UA-dinucleotide ratio. For instance, the sequence AUUGAACCUU has a lower GC content (0.3) compared to UAUAGGCCGC (0.6), yet it also has a lower UA-dinucleotide ratio (0 vs. 0.22). To address this concern more rigorously, we performed a stratified analysis based on UA-diNT rate. As shown in our Fig. S7C, even after stratifying by UA- dinucleotide ratio (upper panel high UA- dinucleotide ratio / lower panel low UA- dinucleotide ratio), we still observe that the destabilizing effect of UA is stronger in the low GC content group.

      Reviewer #2 (Public Review):

      Summary of goals:

      Untranslated regions are key cis-regulatory elements that control mRNA stability, translation, and translocation. Through interactions with small RNAs and RNA binding proteins, UTRs form complex transcriptional circuitry that allows cells to fine-tune gene expression. Functional annotation of UTR variants has been very limited, and improvements could offer insights into disease relevant regulatory mechanisms. The goals were to advance our understanding of the determinants of UTR regulatory elements and characterize the effects of a set of "disease-relevant" UTR variants.

      Strengths:

      The use of a massively parallel reporter assay allowed for analysis of a substantial set (6,555 pairs) of 5' and 3' UTR fragments compiled from known disease associated variants. Two cell types were used.

      The findings confirm previous work about the importance of AREs, which helps show validity and adds some detailed comparisons of specific AU-rich motif effects in these two cell types.

      Using a Lasso regression, TA-dinucleotide content is identified as a strong regulator of RNA stability in a context dependent manner based on GC content and presence of RNA binding protein binding motifs. The findings have potential importance, drawing attention to a UTR feature that is not well characterized.

      The use of complementary datasets, including from half-life analyses of RNAs and from random sequence library MRPA's, is a useful addition and supports several important findings. The finding the TA dinucleotides have explanatory power separate from (and in some cases interacting with) GC content is valuable.

      The functional enrichment analysis suggests some new ideas about how UTRs may contribute to regulation of certain classes of genes.

      Weaknesses:

      It is difficult to understand how the calculations for half-life were performed. The sequencing approach measures the relative frequency of each sequence at each time point (less stable sequences become relatively less frequent after time 0, whereas more stable sequences become relatively more frequent after time 0). Since there is no discussion of whether the abundance of the transfected RNA population is referenced to some external standard (e.g., housekeeping RNAs), it is not clear how absolute (rather than relative) half-lives were determined.

      We estimated decay constant λ and half-life (t<sub>1/2</sub>) by the following equations:

      where C<sub>i(t)</sub> and C<sub>i(t=0)</sub> are read count values of the ith replicate at time points 𝑡 and 0 (see also Methods). The absolute abundance was not required for the half-life calculation. 

      Fig. S1A and B are used to assess reproducibility. They show that read counts at a given time point correlate well across replicate experiments. However, this is not a good way to assess reproducibility or accuracy of the measurements of t1/2 are. (The major source of variability in read counts in these plots - especially at early time points - is likely the starting abundance of each RNA sequence, not stability.) This creates concerns about how well the method is measuring t1/2. Also creating concern is the observation that many RNAs are associated with half-lives that are much longer than the time points analyzed in the study. For example, based upon Figure S1 and Table S1 correctly, the median t1/2 for the 5' UTR library in HEK cells appears to be >700 minutes. Given that RNA was collected at 30, 75, and 120 minutes, accurate measurements of RNAs with such long half lives would seem to be very difficult.

      We estimated the half-life based on the following equations:

      where C<sub>i(t)</sub> and C<sub>i(t=0)</sub> are read count values of the ith replicate at time points 𝑡 and 0 (see also Methods). The calculation of the half-life involves first determining the decay constant 𝜆, which represents a constant rate of decay. Since 𝜆 is a constant, it is possible to accurately calculate it without needing data over the entire decay range. Our experimental design considers this by selecting appropriate time points to ensure a reliable estimation of 𝜆, and thus, the half-life. To determine the most suitable time points, we conducted preliminary experiments using RT-PCR.

      These experiments indicated that 30, 75, and 120 minutes provided an effective range for capturing the decay dynamics of the transcripts.

      There is no direct comparison of t1/2 between the two cell types studied for the full set of sequences studied. This would be helpful in understanding whether the regulatory effects of UTRs are generally similar across cell lines (as has been shown in some previous studies) or whether there are fundamental differences. The distribution of t1/2's is clearly quite different in the two cell lines, but it is important to know if this reflects generally slow RNA turnover in HEK cells or whether there are a large number of sequence-specific effects on stability between cell lines. A related issue is that it is not clear whether the relatively small number of significant variant effects detected in HEK cells versus SH-SY5Y cells is attributable to real biological differences between cell types or to technical issues (many fewer read counts and much longer half lives in HEK cells).

      For both cell lines, we selected oligonucleotides with R<sup>2</sup> > 0.5 and mean squared error (MSE) < 1 for analysis when estimating half-life (λ) by linear regression. This selection criterion was implemented to minimize the effect of experimental noise. After quality control, we selected common UTRs and compared the RNA half-lives of the two cell lines using a scatter plot. Author response image 2 shows that RNA half-lives are quite different between the cell lines, with a moderate similarity observed in the 5' UTRs (R = 0.21), while the correlation in the 3' UTRs is non-significant.

      Author response image 2.

      Despite the low correlation of mRNA half-life between the two cell lines, UA-dinucleotide and UA-rich sequences consistently emerge as the most significant destabilizing features, suggesting a shared regulatory mechanism across diverse cellular environments.

      The general assertion is made in many places that TA dinucleotides are the most prominent destabilizing element in UTRs (e.g., in the title, the abstract, Fig. 4 legend, and on p. 12). This appears to be true for only one of the two cell lines tested based on Fig. 3.

      UA-dinucleotides and other UA-rich sequences exhibit similar effects on RNA stability, as illustrated in Fig. S5A-C. In two cell lines, UA-dinucleotide and WWWWWW sequences were representatives of the same stability-affecting cluster. While the impact of UA-dinucleotides can be generalized, we have rephrased some statements for clarification to avoid any potential misunderstanding. For examples: 

      Abstract: “...We found that UA dinucleotides and UA-rich motifs are the most prominent destabilizing element.“

      p.10: “UA dinucleotides and UA-rich motifs are the most common and effective RNA destabilizing factor” 

      Figure 4: “The UTR UA dinucleotides and UA-rich motifs are the most common and influential RNA destabilizing factor.”

      Appraisal and impact:

      The work adds to existing studies that previously identified sequence features, including AREs and other RNA binding protein motifs, that regulate stability and puts a new emphasis on the role of "TA" (better "UA") dinucleotides. It is not clear how potential problems with the RNA stability measurements discussed above might influence the overall conclusions, which may limit the impact unless these can be addressed.

      It is difficult to understand whether the importance of TA dinucleotides is best explained by their occurrence in a related set of longer RBP binding motifs (see Fig 5J, these motifs may be encompassed by the "WWWWWW cluster") or whether some other explanation applies. Further discussion of this would be helpful. Does the LASSO method tend to collapse a more diverse set of longer motifs that are each relatively rare compared to the dinucleotide? It remains unclear whether TA dinucleotides are associated with less stability independent of the presence of the known larger WWWWWWW motif. As noted above, the importance of TA dinucleotides in the HEK experiments appears to be less than is implied in the text.

      To ensure the representativeness of the features entered into the LASSO model, we pre-selected those with an occurrence greater than 10% among all UTRs. There is no evidence to support a preference for dinucleotides by LASSO. To address whether the destabilizing effect of UA dinucleotides is part of the broader WWWWWW motif, we divided UA dinucleotides into two groups: those within the WWWWWW motif and those outside of it. Specifically, we divided UTRs into two categories: 'at least one UA within a WWWWWW motif' and 'no UA within a WWWWWW motif,' and visualized the results using a boxplot. As shown in Author response image 3, the destabilizing trend still remains for UA dinucleotides outside of the WWWWWW motif, although the effect appears to be more pronounced when UA is within the WWWWWW motif. This suggests that while UA dinucleotides have a destabilizing effect independently, their impact is amplified when they are part of the broader WWWWWW motif.

      Author response image 3.

      The inclusion of more than a single cell type is an acknowledgement of the importance of evaluating cell type-specific effects. The work suggests a number of cell type-specific differences, but due to technical issues (especially with the HEK data, as outlined above) and the use of only two cell lines, it is difficult to understand cell type effects from the work.

      The inclusion of both 3' and 5' UTR sequences distinguishes this work from most prior studies in the field. Contrasting the effects of these regions on stability is of interest, although the role of these UTRs (especially the 5' UTR) in translational regulation is not assessed here.

      We examined the role of UTR and UTR variants in translation regulation using polysome profiling. By both univariate analysis and an elastic regression model, we identified motifs of short repeated sequences, including SRSF2 binding sites, as mutation hotspots that lead to aberrant translation. Furthermore, these polysome-shifting mutations had a considerable impact on RNA secondary structures, particularly in upstream AUG-containing 5’ UTRs. Integrating these features, our model achieved high accuracy (AUROC > 0.8) in predicting polysome-shifting mutations in the test dataset. Additionally, metagene analysis indicated that pathogenic variants were enriched at the upstream open reading frame (uORF) translation start site, suggesting changes in uORF usage underlie the translation deficiencies caused by these mutations. Illustrating this, we demonstrated that a pathogenic mutation in the IRF6 5’ UTR suppresses translation of the primary open reading frame by creating a uORF. Remarkably, site-directed ADAR editing of the mutant mRNA rescued this translation deficiency. Because the regulation of translation and stability does not converge, we illustrate these two mechanisms in two separate manuscripts (this one and doi.org/10.1101/2024.04.11.589132).

      Reviewer #3 (Public Review):

      Summary:

      In their manuscript titled "Multiplexed Assays of Human Disease‐relevant Mutations Reveal UTR

      Dinucleotide Composition as a Major Determinant of RNA Stability" the authors aim to investigate the effect of sequence variations in 3'UTR and 5'UTRs on the stability of mRNAs in two different human cell lines.

      To do so, the authors use a massively parallel reporter assay (MPRA). They transfect cells with a set of mRNA reporters that contain sequence variants in their 3' or 5' UTRs, which were previously reported in human diseases. They follow their clearance from cells over time relative to the matching non-variant sequence. To analyze their results, they define a set of factors (RBP and miRNA binding sites, sequence features, secondary structure etc.) and test their association with differences in mRNA stability. For features with a significant association, they use clustering to select a subset of factors for LASSO regression and identify factors that affect mRNA stability.

      They conclude that the TA dinucleotide content of UTRs is the strongest destabilizing sequence feature. Within that context, elevated GC content and protein binding can protect susceptible mRNAs from degradation. They also show that TA dinucleotide content of UTRs affects native mRNA stability, and that it is associated with specific functional groups. Finally, they link disease associated sequence variants with differences in mRNA stability of reporters.

      Strengths:

      This work introduces a different MPRA approach to analyze the effect of genetic variants. While previous works in tissue culture use DNA transfections that require normalization for transcription efficiency, here the mRNA is directly introduced into cells at fixed amounts, allowing a more direct view of the mRNA regulation.

      The authors also introduce a unique analysis approach, which takes into account multiple factors that might affect mRNA stability. This approach allows them to identify general sequence features that affect mRNA stability beyond specific genetic variants, and reach important insights on mRNA stability regulation. Indeed, while the conclusions to genetic variants identified in this work are interesting, the main strength of the work involve general effect of sequence features rather than specific variants.

      The authors provide adequate supports for their claims, and validate their analysis using both their reporter data and native genes. For the main feature identified, TA di-nucleotides, they perform follow-up experiments with modified reporters that further strengthen their claims, and also validate the effect on native cellular transcripts (beyond reporters), demonstrating its validity also within native scenarios.

      The work provides a broad analysis of mRNA stability, across two mRNA regulatory segments (3'UTR and 5'UTR) and is performed in two separate cell-types. Comparison between two different cell-types is adequate, and the results demonstrate, as expected, the dependence of mRNA stability on the cellular context. Analysis of 3'UTR and 5'UTR regulatory effects also shows interesting differences and similarities between these two regulatory regions.

      Weaknesses:

      (1) The authors fail to acknowledge several possible confounding factors of their MPRA approach in the discussion.

      First, while transfection of mRNA directly into cells allows to avoid the need to normalize for differences in transcription, the introduction of naked mRNA molecules is different than native cellular mRNAs and could introduce biases due to differences in mRNA modifications, protein associations etc. that may occur co-transcriptionally.

      Second, along those lines, the authors also use in-vitro polyadenylation. The length of the polyA tail of the transfected transcripts could potentially be very different than that of native mRNAs and also affect stability.

      The transcripts used in our study were polyadenylated in vitro with approximately 100 nucleotides 

      (Fig. S1C), similar to the polyA tail lengths typically observed in vivo (dx.doi.org/10.1016/j.molcel.2014.02.007).  Additionally, these transcripts were capped to emulate essential mRNA characteristics and to minimize immune responses in recipient cells. This design allows us to study RNA decay for in vitro-synthesized RNA delivered into human cells, akin to RNA vaccines, but it does not necessarily extend to endogenous RNAs. As mentioned, endogenous RNAs undergo nuclear processing and are decorated by numerous trans factors, resulting in distinct regulatory mechanisms. We therefore provided a more discussion on these differences and their implications in the revised manuscript: “However, while our approach effectively assesses the stability of synthesized RNA in human cells, it may not fully capture the decay dynamics of nuclear-synthesized RNA, which can be influenced by endogenous modifications and trans-acting RNA binding factors. (p. 18)”

      (2) The analysis approach used in this work for identifying regulatory features in UTRs was not previously used. As such, lack of in-depth details of the methodology, and possibly also more general validation of the approach, is a drawback in convincing the reader in the validity of this approach and its results.

      In particular, a main point that is not addressed is how the authors decide on the set of "factors" used in their analysis? As choosing different sets of factors might affect the results of the analysis. 

      In our study, we employed the calculation of the Variance Inflation Factor (VIF) as a basis for selecting variables. This well-established method is widely used to detect variables with high collinearity, thus ensuring the robustness and reliability of our analysis. By identifying and excluding highly collinear variables, we aimed to minimize multicollinearity and improve the accuracy of our regression models. For more detailed information on the use of VIF in regression analysis, please refer to Akinwande, M., Dikko, H., and Samson, A. (2015). Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. Open Journal of Statistics, 5, 754-767. doi: 10.4236/ojs.2015.57075. We have included the method details in the revised manuscript (p. 28) :”… to avoid multicollinearity caused by similar features that perturb feature selection, all features were clustered using single-linkage hierarchical clustering with the distance metric defined as one minus the absolute value of the Spearman correlation coefficient. We cut the tree at a specific height, and the feature that had the greatest influence on RNA stability, which was examined using a simple linear regression model, was selected to be the representative of each cluster. Then we calculated the variance inflation factor (VIF) value of the representative features. The VIFs were obtained by the following linear model and equations:

      where and are the estimated value of the jth feature and the value of the kth feature of the ith UTR (note that the kth feature is a feature other than the jth feature), and are the intercept and the regression coefficients of the linear model that regressed the jth feature on the other remaining features, and is the mean level of the jth feature of all UTRs.”

      For example, the choice to use 7-mer sequences within the factors set is not explained, particularly when almost all motifs that are eventually identified (Figure 3B-E) are shorter.

      The known RBP motifs are primarily 6-mer. To explore the possibility of discovering novel motifs that could significantly impact our model, we started with 7-mer sequences. However, our analysis revealed that including these additional variables did not improve the explanatory power of the model; instead, it reduced it. Consequently, our final model focuses on motifs shorter than 7-mer. We explained the motif selections in the revised manuscript (p. 9): “Given our discovery that the effect of AREs is heavily dependent on sequence content, we decided to further explore the effects of other sequence elements, i.e., beyond known regulatory motifs, in more detail. Since most reported RBP motifs are 6-mers, we initiated a search for novel motifs by analyzing the presence of all 7-mers in our massively parallel reporter assay (MPRA) library, correlating their occurrence with mRNA half-life.”

      In addition, the authors do not perform validations to demonstrate the validity of their approach on simulated data or well-established control datasets. Such analysis would be helpful to further convince the reader in the usefulness and robustness of the analysis.

      We acknowledge the importance of validating our approach on simulated data or well-established control datasets to demonstrate its robustness and reliability. However, to the best of our knowledge, there are currently no well-established control datasets available that perfectly correspond to our specific study context. Despite this, we will continue to search for any relevant datasets that could be utilized for this purpose in future work. This effort will help to further reinforce the confidence in our methodology and its findings.

      (3) The analysis and regression models built in this work are not thoroughly investigated relative to native genes within cells. The effect of sequence "factors" on native cellular transcripts' stability is not investigated beyond TA di-nucleotides, and it is unclear to what degree do other predicted factors also affect native transcripts.

      Our system studies the stability control of RNA synthesized in vitro and delivered into human cells. While we validated the UTR UA-dinucleotide effect in vivo, we did not intend to conclude that this is the most influential regulation for endogenous RNAs. It is known that endogenous RNAs undergo very different regulation. The most prominent factors controlling endogenous RNA stability are the density of splice junctions and the length of UTRs (doi.org/10.1186/s13059-022-02811-x; doi.org/10.1186/s12915-021-00949-x). To decipher the sequence regulation, we controlled for these factors in our experiments. Therefore, we acknowledge that several endogenous features, which were excluded by our approach, may serve as predictive features of RNA half-life in vivo. 

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Specific comments:  

      Some references are missing, e.g for the sentence:

      Please see the response below.

      "Similarly, point mutation of the GFPT1 3' UTR results in congenital myasthenic syndrome." (p5)

      The reference has been added to the text:

      Dusl, M., Senderek, J., Muller, J. S., Vogel, J. G., Pertl, A., Stucka, R., Lochmuller, H., David, R., & Abicht, A. (2015). A 3'-UTR mutation creates a microRNA target site in the GFPT1 gene of patients with congenital myasthenic syndrome. Human Molecular Genetics, 24(12), 34183426. https://doi.org/10.1093/hmg/ddv090 

      "...but there have been no systematic assessments of the explicit effects of variants of both UTRs on stability regulation." (not true in the current phrasing; e.g. PMIDs 32719458, 36156153, 34849835)

      These references have been added to the text. However, we have to point out that these studies do not focus on the effects of the disease-relevant variants. To clarify, we modified the sentence to "... systematic assessments of the explicit effects of disease-relevant variants in both UTRs on stability regulation are still absent."

      "Multiple approaches have revealed AREs as exerting a destabilizing effect on RNA stability (Barreau et al., 2005). (p8)

      The reference has been added to the text:

      Barreau, C., Paillard, L., & Osborne, H. B. (2005). AU-rich elements and associated factors: are there unifying principles? Nucleic Acids Research, 33(22), 7138-7150. https://doi.org/10.1093/nar/gki1012 

      "This effect is specific, as such ratios in the coding region are inconsequential." (p12)

      This refers to our findings of Fig. 4G and Supplemental Fig. S5F.

      What are the sequences at the 5' and 3'UTR without insertion of a library? 5'UTR library (especially in SH) has much longer half-life compared to 3'utr library (Fig S1D).

      There is no designed 5’UTR of the 3’UTR library, only the Kozak sequence derived from the pEGFPC1 vector. This may partially underlie the shorter half-life of the 3’ UTR library.

      Fig2A: What are the units? "half-life (log)" Do the numbers correspond to log10(min)?

      It represents ln (min). To clarify, we now use ‘ln t<sub>1/2</sub> (min)’ in all figures.

      Fig 2 and 3: This was done only on the wild-type sequences? Or all tested sequences together, wt and mut?

      It was done only on the wild-type sequences. To clarify, we modified the text to “we examined the effect of AREs on RNA stability of the ref alleles according to specific sequence content….(p.8)” and “We considered as many factors as possible to explain the half-life of our ref UTR libraries,…. (p.9)”. ‘ref’ stands for reference.

      "Furthermore, to avoid collinearity confounding our model, e.g., the effects of very similar factors (such as 'AA' and 'AAA' sequences), we clustered the factors according to their properties, and then only one representative factor from within a cluster (i.e., the one with the highest correlation to halflife within a cluster) was subjected to LASSO regression": Given the observed context dependence, e.g. in the case of poly-U stretches: Isn't this clustering leading to similar/identical motifs with different context being grouped together (such as polyU preceded by an A (strongly destabilizing, according to Fig 2B) or followed by one (strongly stabilizing, according to Fig 2B), resulting in ignoring the context or using one potential outcome while a motif from the same cluster can have the opposite effect?

      Thank you very much for pointing this out. To determine if considering different contextual effects within each feature cluster would enhance model performance, we modified our feature selection by choosing both the feature with the largest positive and the largest negative effect on RNA half-life in Step III of Figure 3A. We then split the data into a 2:1 training and testing set and repeated this process 100 times. Model performance was evaluated using mean average error (MAE), root mean squared error (RMSE), and adjusted R-squared. From Author response image 4, we observed no significant improvement in model performance using this new approach. Notably, in the SH-SY5Y 5' UTR model, our original method even outperformed the modified one, with statistically lower MAE and RMSE and a higher adjusted R-squared. Therefore, we believe our current approach remains appropriate.

      Author response image 4.

      "Overall, motifs that are at least two nucleotides long proved critical for RNA stability, supporting the sequence specificity of the decay process." Unclear why this supports the "sequence specificity"

      No monomers were selected as an explanatory factor. On the contrary, specific sequence combinations and order are important for the regulation. These findings suggest sequence-specific recognition for the decay process.

      Fig3: The same features were used in both cell lines? If yes: Since they were selected for their highest correlation with half-life, how was a common set chosen? If no: problematic to compare.

      Thank you for your question regarding feature selection across cell lines. Initially, the features were collected uniformly for both cell lines. However, subsequent feature selection steps were cell-type specific, focusing on identifying features with the greatest impact on RNA half-life in each context. This approach allows us to still compare model performance and discuss the similarities and differences in selected features across cell types. By maintaining a consistent starting point, we ensure that any observed differences reflect cell-specific regulatory dynamics.

      uORFs were not used as features?

      Thank you for pointing this out. At the beginning of our study, we investigated the impact of Kozak sequence strength (categorized as weak, moderate, strong, or optimal) on RNA half-life. However, we found that this feature performed poorly in predicting RNA stability, and as a result, we decided not to include upstream open reading frames (uORFs) or Kozak sequences in our subsequent analyses.

      Experimental reproducibility: Only correlations between replicates for the same time point is shown, but no comparison between time points or between decay rates. How reproducible were the paired differences between mut/wt?

      The decay rate was calculated by modeling the slope of a linear regression of all time points. Therefore, there is only one decay rate associated with a genotype. To rule out inconsistent data, we excluded any regression with a mean square error greater than 1, as this indicates a poor fit of the data points. 

      Fig 7C/p17: This does not establish a "causal relationship" as the authors claim.

      We agree with the reviewer’s suggestion. We have modified the text on p.17 to “to establish a correlation between UTR variants and health outcomes,…..”

      In the discussion, the authors claim that TA-diNTs are not only an opposite of the GC percentage and base this on Fig 5A.

      Fig 5A: The range of TA-diNTs is naturally much higher in the low GC group. To make the high and low GC content comparable (as the authors aim to do), the correlation should be assessed for the same range of TA dint in both cases.

      To address this concern more rigorously, we performed a stratified analysis based on UA-diNT rate. As shown in our Fig. S7C, even after stratifying by UA- dinucleotide ratio (upper panel high UA- dinucleotide ratio / lower panel low UA- dinucleotide ratio), we still observe that the destabilizing effect of UA is stronger in the low GC content group.

      Supplemental Figure S7. Interplay of GC content and TA dinucleotide on stability regulation, related to Figure 5. (C) Stratifications of both TA dinucleotide ratio and GC content showed that the destabilizing effect of TA dinucleotide is the most prominent under conditions of low TA dinucleotide ratio and low GC content. The same trend was observed for 5’ UTR (left) and 3’ UTR (right).

      The injection of in vitro transcribed and polyA/capped RNA certainly has advantages over other methods, but delivering naked mRNA without nuclear history might also lead to artifacts. The caveats of the approach should be discussed more extensively.

      We appreciate the suggestion and have hence added the following in the Discussion (p.18): “However, while our approach effectively assesses the stability of synthesized RNA in human cells, it may not fully capture the decay dynamics of nuclear-synthesized RNA, which can be influenced by endogenous modifications and trans-acting RNA binding factors.”

      "We unexpectedly identified many crucial regulatory features in 5' UTRs." Why was this unexpected?

      We initially thought the 3’ UTR would play a major role in stability regulation. To avoid confusion, we have removed the word ‘unexpected’ from the text (p. 20): "We identified many crucial regulatory features in 5' UTRs."

      "...a massively parallel reporter assay in which coding regions and human 5'/3' UTRs with diseaserelevant mutations were generated in vitro and then directly transfected into human cell lines to assess their decay patterns by next‐generation sequencing": also coding regions?

      Thanks for the question. Indeed, the coding region was not synthesized together with the UTR library. Therefore, we modified the text of p. 6 to “…we developed a massively parallel reporter assay in which human 5’/3’ UTRs with disease-relevant mutations were generated in vitro, ligated with the enhanced green fluorescence protein (EGFP) coding region, and then directly transfected into human cell lines to assess their decay patterns by next-generation sequencing.”

      Reviewer #2 (Recommendations For The Authors):

      Nomenclature: When discussing RNA sequences, "U" should be used in place of "T" (e.g., "UA dinucleotide").

      We have replaced the RNA sequence “T” with “U” of the text and figures.

      Abstract: "We examined the RNA degradation patterns mediated by the UTR library in multiple cell lines" - It would be clearer to state that two cell lines (rather than multiple) were used.

      We appreciate the suggestion. We have modified the abstract as suggested: “We examined the RNA degradation patterns mediated by the UTR library in two cell lines…"

      The manuscript refers to "wild-type (WT) and mutant (mt) alleles." (p. 7 and elsewhere). It would be better to use "reference" instead of "wild type" given that these are human populations.

      We appreciate the suggestion. All instances of ‘wild-type’ or ‘WT’ in the text and figures have been replaced with ‘reference’ or ‘ref’.

      In the introduction, it is stated that traditional MPRAs "cannot differentiate the effect of the UTRs on transcription, stability and, in some cases, even protein production, greatly limiting scientific interpretation." This is confusing, since these assays can and have been used in association with both RNA decay measurements and measurements of reporter protein levels that allow assessment of effects on stability and protein production (including in the cited references).

      We reason that the RNA steady-state level (e.g., sequencing the overall RNA normalized to DNA) or protein steady-state level (e.g., detecting the fluorescence signal) does not precisely reveal the decay kinetics of the RNA. Steady-state level is a result of production and decay, both of which UTRs contribute to. Similarly, the protein level is not a perfect estimate of the RNA decay.

      To clarify, we have modified the introduction (p. 5) to “Nevertheless, because the steady-state level is a result of production and decay, these approaches cannot differentiate the effect of the UTRs on transcription, stability and, in some cases, even protein production, greatly limiting scientific interpretation.” 

      Adding raw and normalized read count data from individual experiments (e.g., to Table S1) would make it more likely for others to use this dataset to address additional questions.

      All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE217518 (reviewer token snspaakujtsdpcv).

      The manuscript would benefit from further clarification about model selection. Additional details regarding how the features were clustered, and the actual clusters themselves should be included.

      It should be discussed why Lasso was chosen vs Ridge or Elastic Net, in the context of handling multicollinearity. Often, data is subsetted for training and validation, and model performance metrics are presented.

      Thank you for pointing out the need for further clarification on model selection. The features were clustered using single-linkage hierarchical clustering with the distance metric defined as one minus the absolute value of the Spearman correlation coefficient (this information has been added to the manuscript on p. 28: “…to avoid multicollinearity caused by similar features that perturb feature selection, all features were clustered using single-linkage hierarchical clustering with the distance metric defined as one minus the absolute value of the Spearman correlation coefficient.”). The resulting feature clusters are available in Supplemental Table S3. 

      Regarding model selection, we chose LASSO over ridge and elastic net primarily for feature selection, as ridge does not perform feature selection. Elastic net is essentially a hybrid of ridge regression and LASSO regularization, but we opted for LASSO for its simplicity and effectiveness in selecting a sparse set of important features.

      We also performed a 2:1 training and testing set analysis and have included these details in the manuscript. Model performance metrics, including correlation coefficient between observed and predicted values in the testing set, mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared, are provided in new Supplemental Table S4.

      Recommend reviewing and correcting verb tenses in the methods section.

      We appreciate the reviewer’s suggestion. We have corrected verb tenses in the methods section, which includes “The UTRs were defined by NCBI RefSeq and ENCODE V27. (p.21)”, “The variant was placed in the middle of the sequence….(p.22)”, and “eCLIP signals with value < 1 or p value > 0.05 were removed. (p.26)”

      Please add information about which cell type(s) are being used in each of the figure legends (e.g., in Figs. 2B and 5).

      We appreciate the reviewer’s suggestion. We have added the cell type information in the figure legends: “Figure 2…. (B) The ten most influential AREs in terms of RNA stability in SH-SY5Y cells.” And “Figure 5…..(A) MPRA data of SH-SY5Y cells stratified according to the GC content (GC%) of UTRs.”

      Recommend review of axis labels and consistency in formatting the log(half-lives) and including the base of the log and the time unit (minutes). Even better, converting axis labels from log minutes to minutes would make this easier to understand.

      Thank you for the suggestion regarding axis labels and consistency. We have unified the half-life label to ‘ln t<sub>1/2</sub> (min)’ in all figures. We chose not to convert the axis from logarithmic minutes to minutes because the original scale is highly skewed, which would hinder clear data visualization.

      The discussion refers to Figure 1D but Figure 1 only has A-C

      Thank you for pointing out this mistake. ‘Fig. 1D’ has been changed to ‘Fig. 1B’ in the text (p. 7 and p. 20).

      The analyses in Fig. 2 are interpreted as demonstrating that AREs destabilize RNAs. These analyses are examining associations, so it would be more appropriate to say that AREs are associated with destabilization (since it is formally possible that other sequences that are present in these UTR fragment cause destabilization). A similar issue arises on p. 10: "TA dinucleotides alone can negatively regulate RNA stability, with a Pearson's correlation coefficient of ‐0.287 for 5' UTRs and ‐0.377 for 3' UTRs (Fig. 4A,C)." This is an association and does not establish causation. Again on p. 17: "We identified several SNPs in UTRs that induce aberrant RNA expression and/or protein expression (Supplemental Table S7)." These may be causal but may simply be in LD with other variants that are causal.

      We agree that the association observed is not proven to be causal. Therefore, we modified the text as suggested: 

      “AUUUA/AUUA-containing AREs are associated with RNA destabilization.” (p. 8)

      “UA dinucleotides alone present a negative correlation with RNA stability, with a Pearson’s correlation coefficient of -0.287 for 5’ UTRs and -0.377 for 3’ UTRs.”  (p.10)

      “We identified several SNPs in UTRs that correlated with aberrant RNA expression and/or protein expression.”  (p. 17)

      Figure 4C is important in that it examines whether variant sequences that differ in a manner that changes the number of dinucleotide repeats affect stability. Please show the number (not just the percentage) of sequences in each category.

      Thank you for your insightful comment. We believe the figure you referred to is Figure 4E. We have updated the figure to include the number of sequences in each category.

      Figure 6A and B: The horizontal axes appear to be misaligned since the dotted vertical lines do not cross at 0. ?

      The dotted vertical lines represent the genomic background of the UA-diNT ratio. To clarify it, we have modified the legend to: “Figure 6……(A) The top ten biological processes for which the 5’ UTR UA-dinucleotide ratio most significantly deviated from the genomic background (dashed line).”

      It may be helpful to state what the dashed and solid lines represent on Figure 6 E/F. Please correct spelling of "Biological" in 6E.

      As per the reviewer’s suggestions, we have modified the legend of Figure 6 to: “………..(E) Biological processes for RNAs in which the UA-dinucleotide ratios of both 5’ and 3’ UTRs are significantly different from the genomic background (dashed lines). (F) Molecular functions for RNAs in which the UA-dinucleotide ratios of both 5’ and 3’ UTRs are significantly different from the genomic background (dashed lines). The thin solid lines represent the standard deviation of the UAdinucleotide ratio within the gene group.” 

      In addition, the spelling of “Biological” in Fig. 6E has been corrected.

      Reviewer #3 (Recommendations For The Authors):

      I have 3 points that I think could improve science and its presentation within the manuscript.

      (1) Most importantly, how well do LASSO regression models predict the stability of native transcripts? Such analysis can also be useful for comparison between two different cell-types. How well does the regression model learned (on reporters) within one cell-type predict mRNA stability (of reporters and native genes) in this cell-type and in the other cell-type? Similarly, models can also help to analyze the effects of 5'UTR and 3'UTR sequences on mRNA stability. In particular, how well does the regression model of each separate regulatory sequence (3'UTR or 5'UTR) is able to predict the stability of native genes in the cell? Can the predictions be improved by combining both 3'UTR and 5'UTR sequence features within the regression models?

      The decay model for native transcripts has been established in prior research (doi.org/10.1186/s13059-022-02811-x; doi.org/10.1186/s12915-021-00949-x), which indicates that exon junction density and transcript length are the primary determinants of RNA stability. Based on these findings, we designed the MPRA with fixed length and without splicing to focus on the contribution of primary sequences. We validated the destabilizing effect of UA dinucleotide on endogenous RNAs (Fig. 4G and Supplemental Fig. S5F) but do not recommend using our model to fully explain or predict the stability of native transcripts.

      To assess the model's cross-cell type predictive performance for RNA half-life, we employed the Regression Error Characteristic (REC) curve (Bi & Bennett, 2003). Similar to the receiver operating characteristic (ROC) curve, the REC curve illustrates the trade-off between error tolerance and accuracy, with better performance indicated by curves trending toward the upper left. We also computed the Area Over the Curve (AOC) as a performance metric, where lower values indicate better predictive ability. From Author response image 5, the REC curves reveal that cross-cell type prediction performance is suboptimal. The y-axis represents prediction accuracy, while the x-axis denotes error tolerance for the natural logarithm of RNA half-life (ln(𝑡<sub>1/2</sub>), in minutes).

      Author response image 5.

      In response to the suggestion of combining 5' and 3' UTR sequence features in the regression model, we believe this approach may not be ideal. As shown in Figure S1D, the distribution of RNA half-lives between 5' and 3' UTRs is significantly different, reflecting their distinct regulatory roles. Additionally, the base composition differs, with 5' UTRs having a higher GC content compared to 3' UTRs. Combining these datasets would likely make the origin of the sequence (5' or 3' UTR) the most predictive feature, thereby reducing the model's interpretability. Furthermore, our MPRA results, derived from separate 5’ or 3’ UTR library, do not support a combined model, further suggesting this approach may not be suitable with our data.

      The conclusions regarding genetic variants are interesting, yet the main strength of the work involves identifying general sequence features that affect mRNA stability rather than specific variants. I wonder if the authors have considered to shift the focus of the motivation part to reflect that?

      We appreciated the reviewer’s suggestion. We have revised the abstract and introductions to emphasize the general UTR regulation. Here is the revised abstract:

      UTRs contain crucial regulatory elements for RNA stability, translation and localization, so their integrity is indispensable for gene expression. Approximately 3.7% of genetic variants associated with diseases occur in UTRs, yet a comprehensive understanding of UTR variant functions remains limited due to inefficient experimental and computational assessment methods. To systematically evaluate the effects of UTR variants on RNA stability, we established a massively parallel reporter assay on 6,555 UTR variants reported in human disease databases. We examined the RNA degradation patterns mediated by the UTR library in two cell lines, and then applied LASSO regression to model the influential regulators of RNA stability. We found that UA dinucleotides and UA-rich motifs are the most prominent destabilizing element. Gain of UA dinucleotide outlined mutant UTRs with reduced stability. Studies on endogenous transcripts indicate that high UA-dinucleotide ratios in UTRs promote RNA degradation. Conversely, elevated GC content and protein binding on UA dinucleotides protect high-UA RNA from degradation. Further analysis reveals polarized roles of UA-dinucleotide-binding proteins in RNA protection and degradation. Furthermore, the UA-dinucleotide ratio of both UTRs is a common characteristic of genes in innate immune response pathways, implying a coordinated stability regulation through UTRs at the transcriptomic level. We also demonstrate that stability-altering UTRs are associated with changes in biobank-based health indices, underscoring the importance of precise UTR regulation for wellness. Our study highlights the importance of RNA stability regulation through UTR primary sequences, paving the way for further exploration of their implications in gene networks and precision medicine.

      Plots presenting correlations (e.g., Figure 4A, 4C) are more informative when plotted as density plots (i.e., using colorscale to show density of the dots at each part of the plot).

      We greatly appreciate the reviewer's insightful suggestion regarding the use of density plots for presenting correlations. We have modified Figures 4A and 4C in the revised manuscript to implement density plotting. The updated figures now utilize a colorscale that highlights areas of high and low data density.

    1. eLife Assessment

      This important study enhances our understanding of the foraging behaviour of aerial insectivorous birds. Using solid methodology, the authors have collected extensive data on bird movements and prey availability, which in turn provide support for the main claim of the study. The work will be of broad interest to behavioural ecologists.

    2. Reviewer #1 (Public review):

      This study tests whether Little Swifts exhibit optimal foraging, which the data seem to indicate is the case. This is unsurprising as most animals would be expected to optimize the energy income : expenditure ratio, however it hasn't been explicitly quantified before the way it was in this manuscript.

      The major strength of this work is the sheer volume of tracking data and the accuracy of those data. The ATLAS tracking system really enhanced this study and allowed for pinpoint monitoring of the tracked birds. These data could be used to ask and answer many questions beyond just the one tested here.

      The major weakness of this work lies in the sampling of insect prey abundance at a single point on the landscape, 6.5 km from the colony. This sampling then requires the authors to work under the assumption that prey abundance is simultaneously even across the study region. It may be fair to say that prey populations might be correlated over space but are not equal. It is uncertain whether other aspects of the prey data are problematic. For example, the radar only samples insects at 50m or higher from the ground - how often do Little Swifts forage under 50m high?

      The finding that Little Swifts forage optimally is indeed supported by the data, notwithstanding some of the shortcomings in the prey abundance data. The authors achieved their aims and the results support their conclusions.

      At its centre, this work adds to our understanding of Little Swift foraging and extends to a greater understanding of aerial insectivores in general. While unsurprising that Little Swifts act as optimal foragers, it is good to have quantified this and show that the population declines observed in so many aerial insectivores are not necessarily a function of inflexible foraging habits. Further, the methods used in this research have great potential for other work. For example, the ATLAS system poses some real advantages and an exciting challenge to existing systems, like MOTUS. The radar that was used to quantify prey abundance also presents exciting possibilities if multiple units could be deployed to get a more spatially-explicit view.

      To improve the context of this work, it is worth noting that this research goes into much further depth than any previous studies on a similar topic in several flycatcher and swallow species. A further justification is posited that this research is needed due to dramatic insect population declines, however, the magnitude and extent of such declines are fiercely debated in the literature.

    3. Reviewer #2 (Public review):

      Summary:

      Bloch et al. studied the relationships between aerial foragers (lesser swifts) tracked using an automated radio telemetry system (Atlas) and their prey (flying insects) monitored using a small vertical-looking radar (BirdScan MR1). The aim of the study was to check whether swifts optimise their foraging according to the abundance of their prey. The results provide evidence that small swifts can increase their foraging rate when aerial insect abundance is high, but found no correlation between insect abundance and flight energy expenditure.

      Key points:

      This study fills gaps in fundamental knowledge of prey-predator dynamics in the air. It describes the coincidence between the abundance of flying insects and the characteristics derived from monitoring individual swifts.

      Weaknesses:

      The paper uses assumptions largely derived from optimal foraging theory, but mixes up the form of natural selection: parental energy, parental survival (predation risk), nestling foraging and reproductive success. The results are partly inconsistent, and confounding factors (e.g., the brooding phase versus the nestling phase) remained ignored. In conclusion, the analyses performed are insufficient to rigorously assess whether lesser swifts are optimising their foraging beyond making shorter foraging trips.

      The filters applied to the monitoring data are necessary but may strongly influence the characteristics derived based on maximum or mean values. Sensitivity tests or the use of characteristics that are less dependent on extreme values could provide more robust results.

    4. Author response:

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

      Reviewer #1 (Recommendations for the authors):

      I am generally satisfied with the authors' revisions and response to my previous comments. I have amended my previous review.

      We thank Reviewer #1 for his valuable comments and suggestions, which improved this manuscript.

      Thank you for considering the comments in your revised version. I still feel a strong mismatch between the claims of optimal foraging behaviour and the results with little compelling evidence.

      On terminology: MTR means Migration Traffic Rates. The authors responded that in their study, MTR is defined as Movement traffic rates. I have two problems with this definition: i) it creates confusion in the literature on the definition of MTR, ii) a traffic inherently describes a movement, and this pleonasm is not necessary.

      We revised the acronyms in this article, replacing MTR with MoTR to clearly distinguish between Migration Traffic Rate (MTR) and Movement Traffic Rate (MoTR).

      Minimal size of insects: Please detail radar settings (power sent, STC; detection thresholds). These parameters define the minimal size of the detected animals.

      We added the following paragraph to provide additional information regarding the radar's detection capabilities:

      " with decreasing detection probability at increasing altitudes. The detection threshold, defined by the STC setting, was 93 dBm, and the transmit power was 25 kW."

    1. eLife Assessment

      In this valuable study, Li et al., set out to understand the mechanisms of audiovisual temporal recalibration - the brain's ability to adjust to the latency differences that emerge due to different (distance-dependent) transduction latencies of auditory and visual signals - through psychophysical measurements and modeling. The analysis and specification of a formal model for this process provide convincing evidence to supports a role for causal inference in recalibration.

    2. Reviewer #1 (Public review):

      This study asks whether the phenomenon of crossmodal temporal recalibration, i.e. the adjustment of time perception by consistent temporal mismatches across the senses, can be explained by the concept of multisensory causal inference. In particular they ask whether the explanation offered by causal inference better explains temporal recalibration better than a model assuming that crossmodal stimuli are always integrated, regardless of how discrepant they are.

      The study is motivated by previous work in the spatial domain, where it has been shown consistently across studies that the use of crossmodal spatial information is explained by the concept of multisensory causal inference. It is also motivated by the observation that the behavioral data showcasing temporal recalibration feature nonlinearities that, by their nature, cannot be explained by a fixed integration model (sometimes also called mandatory fusion).

      To probe this the authors implemented a sophisticated experiment that probed temporal recalibration in several sessions. They then fit the data using the two classes of candidate models and rely model criteria to provide evidence for their conclusion. The study is sophisticated, conceptually and technically state-of-the-art and theoretically grounded. The data clearly support the authors conclusions.

      I find the conceptual advance somewhat limited. First, by design the fixed integration model cannot explain data with a nonlinear dependency on multisensory discrepancy, as already explained in many studies on spatial multisensory perception. Hence, it is not surprising that the causal inference model better fits the data. Second, and again similar to studies on spatial paradigms, the causal inference model fails to predict the behavioral data for large discrepancies. The model predictions in Figure 5 show the (expected) vanishing recalibration for large delta, while the behavioral data don't' decay to zero. Either the range of tested SOAs is too small to show that both the model and data converge to the same vanishing effect at large SOAs, or the model's formula is not the best for explaining the data. Again, the studies using spatial paradigms have the same problem, but in my view this poses the most interesting question here.

      In my view there is nothing generally wrong with the study, it does extend the 'known' to another type of paradigm. However, it covers little new ground on the conceptual side.<br /> On that note, the small sample size of n=10 is likely not an issue, but still it is on the very low end for this type of study.

      Comments on revision:

      The revision has addressed most of these points and makes for a much stronger contribution. The issue of sample size remains.

    3. Reviewer #2 (Public review):

      Summary:

      Li et al.'s goal is to understand the mechanisms of audiovisual temporal recalibration. This is an interesting challenge that the brain readily solves in order to compensate for real-world latency differences in the time of arrival of audio/visual signals. To do this they perform a 3-phase recalibration experiment on 9 observers that involves a temporal order judgment (TOJ) pretest and posttest (in which observers are required to judge whether an auditory and visual stimulus were coincident, auditory leading or visual leading) and a conditioning phase in which participants are exposed to a sequence of AV stimuli with a particular temporal disparity. Participants are required to monitor both streams of information for infrequent oddballs, before being tested again in the TOJ, although this time there are 3 conditioning trials for every 1 TOJ trial. Like many previous studies, they demonstrate that conditioning stimuli shift the point of subjective simultaneity (pss) in the direction of the exposure sequence.

      These shifts are modest - maxing out at around -50 ms for auditory leading sequences and slightly less than that for visual leading sequences. Similar effects are observed even for the longest offsets where it seems unlikely listeners would perceive the stimuli as synchronous (and therefore under a causal inference model you might intuitively expect no recalibration, and indeed simulations in Figure 5 seem to predict exactly that which isn't what most of their human observers did). Overall I think their data contribute evidence that a causal inference step is likely included within the process of recalibration.

      Strengths:

      The manuscript performs comprehensive testing over 9 days and 100s of trials and accompanies this with mathematical models to explain the data. The paper is reasonably clearly written and the data appear to support the conclusions.

      Comments on revision:

      In the revised manuscript the authors incorporate an alternative model (the asynchrony contingent model), and demonstrate that the causal inference model still out performs this. They provide additional analysis with Bayes factors to perform model comparisons, and provide significant individual subject data in the supplementary materials. Overall they have addressed most of the key points that my original review raised, including a demonstration of the conditions under which recalibration effects do not delay to zero over long delays. The number of subjects remains rather low, but at least we can now appreciate the heterogeneity within them. I still have some reservations about the magnitude of the conceptual advance that this study makes.

    4. Reviewer #3 (Public review):

      Summary:

      Li et al. describe an audiovisual temporal recalibration experiment in which participants perform baseline sessions of ternary order judgments about audiovisual stimulus pairs with various stimulus-onset asynchronies (SOAs). These are followed by adaptation at several adapting SOAs (each on a different day), followed by post-adaptation sessions to assess changes in psychometric functions. The key novelty is the formal specification and application/fit of a causal-inference model for the perception of relative timing, providing simulated predictions for the complete set of psychometric functions both pre and post adaptation.

      Strengths:

      (1) Formal models are preferable to vague theoretical statements about a process, and prior to this work, certain accounts of temporal recalibration (specifically those that do not rely on a population code) had only qualitative theoretical statements to explain how/why the magnitude of recalibration changes non-linearly with the stimulus-onset asynchrony of the adaptor.<br /> (2) The experiment is appropriate, the methods are well described, and the average model prediction is a good match to the average data (Figure 4). Conclusions are supported by the data and modelling.<br /> (3) The work should be impactful. There seems a good chance that this will become the go-to modelling framework for those exploring non population-code accounts of temporal recalibration (or comparing them with population-code accounts).<br /> (4) Key issues for the generality of the model, such as recalibration asymmetries reported by other authors that are inconsistent with those reported here, are thoughtfully discussed.

      Weaknesses:

      (1) Models are not compared using a gold-standard measure such as leave-one-out cross validation. However, this is legitimate given lengthy model fitting times, and a sensible approximation is presented.<br /> (2) The model misses in a systematic way for the psychometric functions of some participants/conditions. In addition to misses relating to occasional failures to estimate the magnitude of recalibration, some of the misses are because all functions are only permitted to shift in central tendency (whereas some participants show changes better characterized at one or both decision criteria). Given the fact that the modelling in general embraces individual differences, it might have been worth allowing different kinds of change for different participants. However, this is not really critical for the central concern (changes in the magnitude of recalibration for different adaptors) and there is a limit to how much can be done along these lines without making the model too flexible to test.<br /> (3) As a minor point, the model relies on simulation, which may limit its take-up/application by others in the field (although open access code will be provided).

    5. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      This study asks whether the phenomenon of crossmodal temporal recalibration, i.e. the adjustment of time perception by consistent temporal mismatches across the senses, can be explained by the concept of multisensory causal inference. In particular, they ask whether the explanation offered by causal inference better explains temporal recalibration better than a model assuming that crossmodal stimuli are always integrated, regardless of how discrepant they are.

      The study is motivated by previous work in the spatial domain, where it has been shown consistently across studies that the use of crossmodal spatial information is explained by the concept of multisensory causal inference. It is also motivated by the observation that the behavioral data showcasing temporal recalibration feature nonlinearities that, by their nature, cannot be explained by a fixed integration model (sometimes also called mandatory fusion).

      To probe this the authors implemented a sophisticated experiment that probed temporal recalibration in several sessions. They then fit the data using the two classes of candidate models and rely on model criteria to provide evidence for their conclusion. The study is sophisticated, conceptually and technically state-of-the-art, and theoretically grounded. The data clearly support the authors’ conclusions.

      I find the conceptual advance somewhat limited. First, by design, the fixed integration model cannot explain data with a nonlinear dependency on multisensory discrepancy, as already explained in many studies on spatial multisensory perception. Hence, it is not surprising that the causal inference model better fits the data.

      We have addressed this comment by including an asynchrony-contingent model, which is capable of predicting the nonlinearity of recalibration effects by employing a heuristic approximation of the causal-inference process (Fig. 3). We also updated the previous competitor model with a more reasonable asynchrony-correction model as the baseline of model comparison, which assumes recalibration aims to restore synchrony whenever the sensory measurement of SOA indicates an asynchrony. The causal-inference model outperformed both models, as indicated by model evidence (Fig. 4A). Furthermore, model predictions show that the causal-inference model more accurately captures recalibration at large SOAs at both the group (Fig. 4B) and the individual levels (Fig. S4).

      Second, and again similar to studies on spatial paradigms, the causal inference model fails to predict the behavioral data for large discrepancies. The model predictions in Figure 5 show the (expected) vanishing recalibration for large delta, while the behavioral data don’t decay to zero. Either the range of tested SOAs is too small to show that both the model and data converge to the same vanishing effect at large SOAs, or the model's formula is not the best for explaining the data. Again, the studies using spatial paradigms have the same problem, but in my view, this poses the most interesting question here.

      We included an additional simulation (Fig. 5B) to show that the causal-inference model can predict non-zero recalibration for long adapter SOAs, especially in observers with a high common-cause prior and low sensory precision. This ability to predict a non-zero recalibration effect even at large SOA, such as 0.7 s, is one key feature of the causal-inference model that distinguishes it from the asynchrony-contingent model.

      In my view there is nothing generally wrong with the study, it does extend the 'known' to another type of paradigm. However, it covers little new ground on the conceptual side.

      On that note, the small sample size of n=10 is likely not an issue, but still, it is on the very low end for this type of study.

      This study used a within-subject design, which included 3 phases each repeated in 9 sessions, totaling 13.5 hours per participant. This extensive data collection allows us to better constrain the model for each participant. Our conclusions are based on the different models’ ability to fit individual data.

      Reviewer #2 (Public Review):

      Summary:

      Li et al.’s goal is to understand the mechanisms of audiovisual temporal recalibration. This is an interesting challenge that the brain readily solves in order to compensate for real-world latency differences in the time of arrival of audio/visual signals. To do this they perform a 3-phase recalibration experiment on 9 observers that involves a temporal order judgment (TOJ) pretest and posttest (in which observers are required to judge whether an auditory and visual stimulus were coincident, auditory leading or visual leading) and a conditioning phase in which participants are exposed to a sequence of AV stimuli with a particular temporal disparity. Participants are required to monitor both streams of information for infrequent oddballs, before being tested again in the TOJ, although this time there are 3 conditioning trials for every 1 TOJ trial. Like many previous studies, they demonstrate that conditioning stimuli shift the point of subjective simultaneity (pss) in the direction of the exposure sequence.

      These shifts are modest - maxing out at around -50 ms for auditory leading sequences and slightly less than that for visual leading sequences. Similar effects are observed even for the longest offsets where it seems unlikely listeners would perceive the stimuli as synchronous (and therefore under a causal inference model you might intuitively expect no recalibration, and indeed simulations in Figure 5 seem to predict exactly that which isn't what most of their human observers did). Overall I think their data contribute evidence that a causal inference step is likely included within the process of recalibration.

      Strengths:

      The manuscript performs comprehensive testing over 9 days and 100s of trials and accompanies this with mathematical models to explain the data. The paper is reasonably clearly written and the data appear to support the conclusions.

      Weaknesses:

      While I believe the data contribute evidence that a causal inference step is likely included within the process of recalibration, this to my mind is not a mechanism but might be seen more as a logical checkpoint to determine whether whatever underlying neuronal mechanism actually instantiates the recalibration should be triggered.

      We have addressed this comment by replacing the fixed-update model with an asynchrony-correction model, which assumes that the system first evaluates whether the measurement of SOA is asynchronous, thus indicating a need for recalibration (Fig. 3). If it does, it shifts the audiovisual bias by a proportion of the measured SOA. We additionally included an asynchrony-contingent model, which is capable of replicating the nonlinearity of recalibration effects by a heuristic approximation of the causal-inference process.

      Model comparisons indicate that the causal-inference model of temporal recalibration outperforms both alternative models (Fig. 4A). Furthermore, the model predictions demonstrate that the causal-inference model more accurately captures recalibration at large SOAs at both the group level (Fig. 4B) and individual level (Fig. S4).

      The authors’ causal inference model strongly predicts that there should be no recalibration for stimuli at 0.7 ms offset, yet only 3/9 participants appear to show this effect. They note that a significant difference in their design and that of others is the inclusion of longer lags, which are unlikely to originate from the same source, but don’t offer any explanation for this key difference between their data and the predictions of a causal inference model.

      We added further simulations to show that the causal-inference model can predict non-zero recalibration also for longer adapter SOAs, especially in observers with a large common-cause prior (Fig. 5A) and low sensory precision (Fig. 5B). This ability to predict a non-zero recalibration effect even at longer adapter SOAs, such as 0.7 s, is a key feature of the causal-inference model that distinguishes it from the asynchrony-contingent model.

      I’m also not completely convinced that the causal inference model isn’t ‘best’ simply because it has sufficient free parameters to capture the noise in the data. The tested models do not (I think) have equivalent complexity - the causal inference model fits best, but has more parameters with which to fit the data. Moreover, while it fits ‘best’, is it a good model? Figure S6 is useful in this regard but is not completely clear - are the red dots the actual data or the causal inference prediction? This suggests that it does fit the data very well, but is this based on predicting held-out data, or is it just that by having more parameters it can better capture the noise? Similarly, S7 is a potentially useful figure but it's not clear what is data and what are model predictions (what are the differences between each row for each participant; are they two different models or pre-test post-test or data and model prediction?!).

      I'm not an expert on the implementation of such models but my reading of the supplemental methods is that the model is fit using all the data rather than fit and tested on held-out data. This seems problematic.

      We recognize the risk of overfitting with the causal-inference model. We now rely on Bayesian model comparisons, which use model evidence for model selection. This method automatically incorporates a penalty for model complexity through the marginalization over the parameter space (MacKay, 2003).

      Our design is not suitable for cross-validation because the model-fitting process is computationally intensive and time-consuming. Each fit of the causal-inference model takes approximately 30 hours, and multiple fits with different initial starting points are required to rule out that the parameter estimates correspond to local minima.

      I would have liked to have seen more individual participant data (which is currently in the supplemental materials, albeit in a not very clear manner as discussed above).

      We have revised Supplementary Figures S4-S6 to show additional model predictions of the recalibration effect for individual participants, and participants’ temporal-order judgments are now shown in Supplement Figure S7. These figures confirm the better performance of the causal-inference model.

      The way that S3 is described in the text (line 141) makes it sound like everyone was in the same direction, however, it is clear that 2 /9 listeners show the opposite pattern, and 2 have confidence intervals close to zero (albeit on the -ve side).

      We have revised the text to clarify that the asymmetry occurs in both directions and is idiosyncratic (lines 168-171). We summarized the distribution of the individual asymmetries of the recalibration effect across visual-leading and auditory-leading adapter SOAs in Supplementary Figure S2.

      Reviewer #3 (Public Review):

      Summary:

      Li et al. describe an audiovisual temporal recalibration experiment in which participants perform baseline sessions of ternary order judgments about audiovisual stimulus pairs with various stimulus-onset asynchronies (SOAs). These are followed by adaptation at several adapting SOAs (each on a different day), followed by post-adaptation sessions to assess changes in psychometric functions. The key novelty is the formal specification and application/fit of a causal-inference model for the perception of relative timing, providing simulated predictions for the complete set of psychometric functions both pre and post-adaptation.

      Strengths:

      (1) Formal models are preferable to vague theoretical statements about a process, and prior to this work, certain accounts of temporal recalibration (specifically those that do not rely on a population code) had only qualitative theoretical statements to explain how/why the magnitude of recalibration changes non-linearly with the stimulus-onset asynchrony of the adapter.

      (2) The experiment is appropriate, the methods are well described, and the average model prediction is a fairly good match to the average data (Figure 4). Conclusions may be overstated slightly, but seem to be essentially supported by the data and modelling.

      (3) The work should be impactful. There seems a good chance that this will become the go-to modelling framework for those exploring non-population-code accounts of temporal recalibration (or comparing them with population-code accounts).

      (4) A key issue for the generality of the model, specifically in terms of recalibration asymmetries reported by other authors that are inconsistent with those reported here, is properly acknowledged in the discussion.

      Weaknesses:

      (1) The evidence for the model comes in two forms. First, two trends in the data (non-linearity and asymmetry) are illustrated, and the model is shown to be capable of delivering patterns like these. Second, the model is compared, via AIC, to three other models. However, the main comparison models are clearly not going to fit the data very well, so the fact that the new model fits better does not seem all that compelling. I would suggest that the authors consider a comparison with the atheoretical model they use to first illustrate the data (in Figure 2). This model fits all sessions but with complete freedom to move the bias around (whereas the new model constrains the way bias changes via a principled account). The atheoretical model will obviously fit better, but will have many more free parameters, so a comparison via AIC/BIC or similar should be informative

      In the revised manuscript, we switched from AIC to Bayesian model selection, which approximates and compares model evidence. This method incorporates a strong penalty for model complexity through marginalization over the parameter space (MacKay, 2003).

      We have addressed this comment by updating the former competitor model into a more reasonable version that induces recalibration only for some measured SOAs and by including another (asynchrony-contingent) model that is capable of predicting the nonlinearity and asymmetry of recalibration (Fig. 3) while heuristically approximating the causal inference computations. The causal-inference model outperformed the asynchrony-contingent model, as indicated by model evidence (Fig. 4A). Furthermore, model predictions show that the causal-inference model more accurately captures recalibration at large SOAs at both the group (Fig. 4B) and the individual level (Fig. S4).

      (2) It does not appear that some key comparisons have been subjected to appropriate inferential statistical tests. Specifically, lines 196-207 - presumably this is the mean (and SD or SE) change in AIC between models across the group of 9 observers. So are these differences actually significant, for example via t-test?

      We statistically compared the models using Bayes factors (Fig. 4A). The model evidence for each model was approximated using Variational Bayesian Monte Carlo. Bayes factors provided strong evidence in support of the causal-inference model relative to the other models.

      (3) The manuscript tends to gloss over the population-code account of temporal recalibration, which can already provide a quantitative account of how the magnitude of recalibration varies with adapter SOA. This could be better acknowledged, and the features a population code may struggle with (asymmetry?) are considered.

      We simulated a population-code model to examine its prediction of the recalibration effect for different adapter SOAs (lines 380–388, Supplement Section 8). The population-code model can predict the nonlinearity of recalibration, i.e., a decreasing recalibration effect as the adapter SOA increases. However, to capture the asymmetry of recalibration effects across auditory-leading and visual-leading adapter stimuli, we would need to assume that the auditory-leading and visual-leading SOAs are represented by neural populations with unequal tuning curves.

      (4) The engagement with relevant past literature seems a little thin. Firstly, papers that have applied causal inference modeling to judgments of relative timing are overlooked (see references below). There should be greater clarity regarding how the modelling here builds on or differs from these previous papers (most obviously in terms of additionally modelling the recalibration process, but other details may vary too). Secondly, there is no discussion of previous findings like that in Fujisaki et al.’s seminal work on recalibration, where the spatial overlap of the audio and visual events didn’t seem to matter (although admittedly this was an N = 2 control experiment). This kind of finding would seem relevant to a causal inference account.

      References:

      Magnotti JF, Ma WJ and Beauchamp MS (2013) Causal inference of asynchronous audiovisual speech. Front. Psychol. 4:798. doi: 10.3389/fpsyg.2013.00798

      Sato, Y. (2021). Comparing Bayesian models for simultaneity judgement with different causal assumptions. J. Math. Psychol., 102, 102521.

      We have revised the Introduction and Discussion to better situate our study within the existing literature. Specifically, we have incorporated the suggested references (lines 66–69) and provided clearer distinctions on how our modeling approach builds on or differs from previous work on causal-inference models, particularly in terms of modeling the recalibration process (lines 75–79). Additionally, we have discussed findings that might contradict the assumptions of the causal-inference model (lines 405–424).

      (5) As a minor point, the model relies on simulation, which may limit its take-up/application by others in the field.

      Upon acceptance, we will publicly share the code for all models (simulation and parameter fitting) to enable researchers to adapt and apply these models to their own data.

      (6) There is little in the way of reassurance regarding the model’s identifiability and recoverability. The authors might for example consider some parameter recovery simulations or similar.

      We conducted a model recovery for each of the six models described in the main text and confirmed that the asynchrony-contingent and causal-inference models are identifiable (Supplement Section 11). Simulations of the asynchrony-correction model were sometimes best fit by causal-inference models, because the latter behaves similarly when the prior of a common cause is set to one.

      We also conducted a parameter recovery for the winning model, the causal-inference model with modality-specific precision (Supplement Section 13).

      Key parameters, including audiovisual bias  , amount of auditory latency noise  , amount of visual latency noise  , criterion, lapse rate  showed satisfactory recovery performance. The less accurate recovery of  is likely due to a tradeoff with learning rate  .

      (7) I don't recall any statements about open science and the availability of code and data.

      Upon acceptance of the manuscript, all code (simulation and parameter fitting) and data will be made available on OSF and publicly available.

      Recommendations for the authors:

      Reviewing Editor (Recommendations For The Authors):

      In addition to the comments below, we would like to offer the following summary based on the discussion between reviewers:

      The major shortcoming of the work is that there should ideally be a bit more evidence to support the model, over and above a demonstration that it captures important trends and beats an account that was already known to be wrong. We suggest you:

      (1) Revise the figure legends (Figure 5 and Figure 6E).

      We revised all figures and figure legends.

      (2) Additionally report model differences in terms of BIC (which will favour the preferred model less under the current analysis);

      We now base the model comparison on Bayesian model selection, which approximates and compares model evidence. This method incorporates a strong penalty for model complexity through marginalization over the parameter space (MacKay, 2003).

      (3) Move to instead fitting the models multiple times in order to get leave-one-out estimates of best-fitting loglikelihood for each left-out data point (and then sum those for the comparison metric).

      Unfortunately, our design is not suitable for cross-validation methods because the model-fitting process is computationally intensive and time-consuming. Each fit of the causal-inference model takes approximately 30 hours, and multiple fits with different initial starting points are required to rule out local minima.

      (4) Offering a comparison with a more convincing model (for example an atheoretical fit with free parameters for all adapters, e.g. as suggested by Reviewer 3.

      We updated the previous competitor model and included an asynchrony-contingent model, which is capable of predicting the nonlinearity of recalibration (Fig. 3). The causal-inference model still outperformed the asynchrony-contingent model (Fig. 4A). Furthermore, model predictions show that only the causal-inference model captures non-zero recalibration effects for long adapter SOAs at both the group level (Fig. 4B) and individual level (Figure S4).

      Reviewer #1 (Recommendations For The Authors):

      A larger sample size would be better.

      This study used a within-subject design, which included 9 sessions, totaling 13.5 hours per participant. This extensive data collection allows us to better constrain the model for each participant. Our conclusions are based on the different models’ ability to fit individual data rather than on group statistics.

      It would be good to better put the study in the context of spatial ventriloquism, where similar model comparisons have been done over the last ten years and there is a large body of work to connect to.

      We now discuss our model in relation to models of cross-modal spatial recalibration in the Introduction (lines 70–78) and Discussion (lines 324–330).

      Reviewer #2 (Recommendations For The Authors):

      Previous authors (e.g. Yarrow et al.,) have described latency shift and criterion change models as providing a good fit of experimental data. Did the authors attempt a criterion shift model in addition to a shift model?

      We have considered criterion-shift variants of our atheoretical recalibration models in Supplement Section 1. To summarize the results, we varied two model assumptions: 1) the use of either a Gaussian or an exponential measurement distribution, and 2) recalibration being implemented either as a shift of bias or a criterion. We fit each model variant separately to the ternary TOJ responses of all sessions. Bayesian model comparisons indicated that the bias-shift model with exponential measurement distributions best captured the data of most participants.

      Figure 4B - I'm not convinced that the modality-independent uncertainty is anything but a straw man. Models not allowed to be asymmetric do not show asymmetry? (the asymmetry index is irrelevant in the fixed update model as I understand it so it is not surprising the model is identical?).

      We included the assumption that temporal uncertainty might be modality-independent for several reasons. First, there is evidence suggesting that a central mechanism governs the precision of temporal-order judgments (Hirsh & Sherrick, 1961), indicating that precision is primarily limited by a central mechanism rather than the sensory channels themselves. Second, from a modeling perspective, it was necessary to test whether an audio-visual temporal bias alone, i.e., assuming modality-independent uncertainty, could introduce asymmetry across adapter SOAs. Additionally, most previous studies implicitly assumed symmetric likelihoods, i.e., modality-independent latency noise, by fitting cumulative Gaussians to the psychometric curves derived from 2AFC-TOJ tasks (Di Luca et al., 2009; Fujisaki et al., 2004; Harrar & Harris, 2005; Keetels & Vroomen, 2007; Navarra et al., 2005; Tanaka et al., 2011; Vatakis et al., 2007, 2008; Vroomen et al., 2004).

      Why does a zero SOA adapter shift the pss towards auditory leading? Is this a consequence of the previous day’s conditioning - it’s not clear from the methods whether all listeners had the same SOA conditioning sequence across days.

      The auditory-leading recalibration effect for an adapter SOA of zero has been consistently reported in previous studies (e.g., Fujisaki et al., 2004; Vroomen et al., 2004). This effect symbolizes the asymmetry in recalibration. This asymmetry can be explained by differences across modalities in the noisiness of the latencies (Figure 5C) in combination with audiovisual temporal bias (Figure S8).

      We added details about the order of testing to the Methods section (lines 456–457).

      Reviewer #3 (Recommendations For The Authors):

      Abstract

      “Our results indicate that human observers employ causal-inference-based percepts to recalibrate cross-modal temporal perception” Your results indicate this is plausible. However, this statement (basically repeated at the end of the intro and again in the discussion) is - in my opinion - too strong.

      We have revised the statement as suggested.

      Intro and later

      Within the wider literature on relative timing perception, the temporal order judgement (TOJ) task refers to a task with just two response options. Tasks with three response options, as employed here, are typically referred to as ternary judgments. I would suggest language consistent with the existing literature (or if not, the contrast to standard usage could be clarified).

      Ref: Ulrich, R. (1987). Threshold models of temporal-order judgments evaluated by a ternary response task. Percept. Psychophys., 42, 224-239.

      We revised the term for the task as suggested throughout the manuscript.

      Results, 2.2.2

      “However, temporal precision might not be due to the variability of arrival latency.” Indeed, although there is some recent evidence that it might be.

      Ref: Yarrow, K., Kohl, C, Segasby, T., Kaur Bansal, R., Rowe, P., & Arnold, D.H. Neural-latency noise places limits on human sensitivity to the timing of events. Cognition, 222, 105012 (2022).

      We included the reference as suggested (lines 245–248).

      Methods, 4.3.

      Should there be some information here about the order of adaptation sessions (e.g. random for each observer)?

      We added details about the order of testing to the Methods section (lines 456–457).

      Supplemental material section 1.

      Here, you test whether the changes resulting from recalibration look more like a shift of the entire psychometric function or an expansion of the psychometric function on one side (most straightforwardly compatible with a change of one decision criterion). Fine, but the way you have done this is odd, because you have introduced a further difference in the models (Gaussian vs. exponential latency noise) so that you cannot actually conclude that the trend towards a win for the bias-shift model is simply down to the bias vs. criterion difference. It could just as easily be down to the different shapes of psychometric functions that the two models can predict (with the exponential noise model permitting asymmetry in slopes). There seems to be no reason that this comparison cannot be made entirely within the exponential noise framework (by a very simple reparameterization that focuses on the two boundaries rather than the midpoint and extent of the decision window). Then, you would be focusing entirely on the question of interest. It would also equate model parameters, removing any reliance on asymptotic assumptions being met for AIC.

      We revised our exploration of atheoretical recalibration models. To summarize the results, we varied two model assumptions: 1) the use of either a Gaussian or an exponential measurement distribution, and 2) recalibration being implemented either as a shift of the cross-modal temporal bias or as a shift of the criterion. We fit each model separately to the ternary TOJ responses of all sessions. Bayesian model comparisons indicated that the bias-shift model with exponential measurement distributions best described the data of most participants.

      References

      Di Luca, M., Machulla, T.-K., & Ernst, M. O. (2009). Recalibration of multisensory simultaneity:

      cross-modal transfer coincides with a change in perceptual latency. Journal of Vision, 9(12), Article 7.

      Fujisaki, W., Shimojo, S., Kashino, M., & Nishida, S. ’ya. (2004). Recalibration of audiovisual simultaneity. Nature Neuroscience, 7(7), 773–778.

      Harrar, V., & Harris, L. R. (2005). Simultaneity constancy: detecting events with touch and vision. Experimental Brain Research. Experimentelle Hirnforschung. Experimentation Cerebrale, 166(3-4), 465–473.

      Hirsh, I. J., & Sherrick, C. E., Jr. (1961). Perceived order in different sense modalities. Journal of Experimental Psychology, 62(5), 423–432.

      Keetels, M., & Vroomen, J. (2007). No effect of auditory-visual spatial disparity on temporal recalibration. Experimental Brain Research. Experimentelle Hirnforschung. Experimentation Cerebrale, 182(4), 559–565.

      MacKay, D. J. (2003). Information theory, inference and learning algorithms.https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=201b835c3f3a3626ca07b e68cc28cf7d286bf8d5

      Navarra, J., Vatakis, A., Zampini, M., Soto-Faraco, S., Humphreys, W., & Spence, C. (2005). Exposure to asynchronous audiovisual speech extends the temporal window for audiovisual integration. Brain Research. Cognitive Brain Research, 25(2), 499–507.

      Tanaka, A., Asakawa, K., & Imai, H. (2011). The change in perceptual synchrony between auditory and visual speech after exposure to asynchronous speech. Neuroreport, 22(14), 684–688.

      Vatakis, A., Navarra, J., Soto-Faraco, S., & Spence, C. (2007). Temporal recalibration during asynchronous audiovisual speech perception. Experimental Brain Research. Experimentelle Hirnforschung. Experimentation Cerebrale, 181(1), 173–181.

      Vatakis, A., Navarra, J., Soto-Faraco, S., & Spence, C. (2008). Audiovisual temporal adaptation of speech: temporal order versus simultaneity judgments. Experimental Brain Research. Experimentelle Hirnforschung. Experimentation Cerebrale, 185(3), 521–529.

      Vroomen, J., Keetels, M., de Gelder, B., & Bertelson, P. (2004). Recalibration of temporal order perception by exposure to audio-visual asynchrony. Brain Research. Cognitive Brain Research, 22(1), 32–35.

    1. eLife Assessment

      This valuable study presents the design of a new device for using high-density electrophysiological probes ('Neuropixels') in freely moving rodents. The evidence demonstrating the system's versatility and ability to record high-quality extracellular data in both mice and rats is compelling. This study will be of significant interest to neuroscientists performing chronic electrophysiological recordings.

    2. Reviewer #1 (Public review):

      Summary:

      In this manuscript by Bimbard et al., a new method to perform stable recordings over long periods of time with neuropixels as well as the technical details on how the electrodes can be explanted for a follow up reuse is provided. I think the description of all parts of the method are very clear, and the validation analyses (n of units per day over time, RMS over recording days...) are very convincing. I however missed a stronger emphasis on why this could provide a big impact on the ephys community, by enabling new analyses, new behavior correlation studies or neurophysiological mechanisms across temporal scales that were previously inaccessible with high temporal resolution (i.e. not with imaging).

      Strengths:

      Open source method. Validation across laboratories. Across species (mice and rats) demonstration of its use and in different behavioral conditions (head-fixed and freely moving). The implant offers a major advance compared to previous methods and that will help the community generate richer datasets.

      Weaknesses:

      None noted.

    3. Reviewer #2 (Public review):

      Summary:

      This work by Bimbard et al., introduces a new implant for Neuropixels probes. While Neuropixels probes have critically improved and extended our ability to record the activity of a large number of neurons with high temporal resolution, the use of these expensive devices in chronic experiments has so far been hampered by the difficulty of safely implanting them and, importantly, to explant and reuse them after conclusion of the experiment. The authors present a newly designed two-part implant, consisting of a docking and a payload module, that allows for secure implantation and straightforward recovery of the probes. The implant is lightweight, making it amenable for use in mice and rats, and customizable. The authors provide schematics and files for printing of the implants, which can be easily modified and adapted to custom experiments by researchers with little to no design experience. Importantly, the authors demonstrate the successful use of this implant across multiple use cases, in head-fixed and freely moving experiments, in mice and rats, with different versions of Neuropixels probes and across 8 different labs. Taken together, the presented implants promise to make chronic Neuropixels recordings and long-term studies of neuronal activity significantly easier and attainable for both current and future Neuropixels users.

      Strengths:<br /> - The implants have been successfully tested across 8 different laboratories, in mice and rats, in head-fixed and freely moving conditions and have been adapted in multiple ways for a number of distinct experiments.<br /> - Implants are easily customizable and authors provide a straightforward approach for customization across multiple design dimensions even for researchers not experienced in design.<br /> - The authors provide clear and straightforward descriptions of the construction, implantation and explant of the described implants.<br /> - The split of the implant into a docking and payload module makes reuse even in different experiments (using different docking modules) easy.<br /> - The authors demonstrate that implants can be re-used multiple times and still allow for high-quality recordings.<br /> - The authors show that the chronic implantations allow for the tracking of individual neurons across days and weeks (using additional software tracking solutions), which is critical for a large number of experiments requiring the description of neuronal activity, e.g. throughout learning processes.<br /> - The authors show that implanted animals can even perform complex behavioral tasks, with no apparent reduction in their performance.

    4. Reviewer #3 (Public review):

      Summary:

      In this manuscript, Bimbard and colleagues describe a new implant apparatus called "Apollo Implant", which should facilitate recording in freely moving rodents (both mice and rats) using Neuropixels probes. The authors collected data from both mice and rats, they used 3 different versions of Neuropixels, multiple labs have already adopted this method, which is impressive. They openly share their CAD designs and surgery protocol to further facilitate the adaptation of their method.

      Strengths:

      Overall, the "Apollo Implant" is easy to use and adapt, as it has been used in other laboratories successfully and custom modifications are already available. The device is reproducible using common 3D printing services and can be easily modified thanks to its CAD design (the video explaining this is extremely helpful). The weight and price are amazing compared to other systems for rigid silicon probes allowing a wide range of use of the "Apollo Implant".

      Weaknesses:

      The "Apollo Implant" can only handle Neuropixels probes. It cannot hold other widely used and commercially available silicon probes. Certain angles and distances may be better served by 2 implants.

    5. Author response:

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

      Reviewer 1 (Public Review):

      Summary:

      In this manuscript by Bimbard et al., a new method to perform stable recordings over long periods of time with neuropixels, as well as the technical details on how the electrodes can be explanted for follow-up reuse, is provided. I think the description of all parts of the method is very clear, and the validation analyses (n of units per day over time, RMS over recording days...) are very convincing. I however missed a stronger emphasis on why this could provide a big impact on the ephys community, by enabling new analyses, new behavior correlation studies, or neurophysiological mechanisms across temporal scales.

      Strengths:

      Open source method. Validation across laboratories. Across species (mice and rats) demonstration of its use and in different behavioral conditions (head-fixed and freely moving).

      Weaknesses:

      Weak emphasis on what can be enabled with this new method that didn't exist before.

      We thank the reviewer for highlighting the limited discussion around scientific impact. Our implant has several advantages which combine to make it much more accessible than previous solutions. This enables a variety of recording configurations that would not have been possible with previous designs, facilitating recordings from a wider range of brain regions, animals, and experimental setups. In short, there are three key advances which we now emphasise in the manuscript:

      Adaptability: The CAD files can be readily adapted to a wide range of configurations (implantation depth, angle, position of headstage, etc.). Labs have already modified the design for their needs, and re-shared with the community (Discussion, Para 5).

      Weight: Because of the lightweight design, experimenters can i) perform complex and demanding freely moving tasks as we exemplify in the manuscript, and ii) implant female and water restricted mice while respecting animal welfare weight limitations (Flexible design, Para 1).

      Cost: At ~$10, our implant is significantly cheaper than published alternatives, which makes it affordable to more labs and means that testing modifications is cost-effective (Discussion, Para 4).

      Reviewer 1 (Recommendations For The Authors):

      - Differences between mice and rats seem very significant. Although this is probably not surprising, I suggest that the authors comment on this to make it clear to anyone trying to use in different species that are not quantified in the main figures.

      The reviewer is correct—there are qualitative differences between mice and rats, particularly with respect to the unit median amplitude. We have added a comment in the discussion to highlight these inter-species variations (Discussion, Para 7)

      - Another comment that would be useful to have would be how to tackle the problem of tracking the same neuron across days. Even if currently impossible, it could be useful to provide discussion along those lines as to where future improvements (either in hardware or software) can be made.

      We thank the reviewer for highlighting this. Figure. 5 does show data from tracking the same neuron across days (and even months). We have modified the language to make this clear.

      Reviewer 2 (Public Review):  

      Summary:

      This work by Bimbard et al., introduces a new implant for Neuropixels probes. While Neuropixels probes have critically improved and extended our ability to record the activity of a large number of neurons with high temporal resolution, the use of these expensive devices in chronic experiments has so far been hampered by the difficulty of safely implanting them and, importantly, to explant and reuse them after conclusion of the experiment. The authors present a newly designed two-part implant, consisting of a docking and a payload module, that allows for secure implantation and straightforward recovery of the probes. The implant is lightweight, making it amenable for use in mice and rats, and customizable. The authors provide schematics and files for printing of the implants, which can be easily modified and adapted to custom experiments by researchers with little to no design experience. Importantly, the authors demonstrate the successful use of this implant across multiple use cases, in head-fixed and freely moving experiments, in mice and rats, with different versions of Neuropixels probes, and across 8 different labs. Taken together, the presented implants promise to make chronic Neuropixel recordings and long-term studies of neuronal activity significantly easier and attainable for both current and future Neuropixels users.

      Strengths:

      The implants have been successfully tested across 8 different laboratories, in mice and rats, in headfixed and freely moving conditions, and have been adapted in multiple ways for a number of distinct experiments.

      Implants are easily customizable and the authors provide a straightforward approach for customization across multiple design dimensions even for researchers not experienced in design.

      The authors provide clear and straightforward descriptions of the construction, implantation, and explant of the described implants.

      The split of the implant into a docking and payload module makes reuse even in different experiments (using different docking modules) easy.

      The authors demonstrate that implants can be re-used multiple times and still allow for high-quality recordings.

      The authors show that the chronic implantations allow for the tracking of individual neurons across days and weeks (using additional software tracking solutions), which is critical for a large number of experiments requiring the description of neuronal activity, e.g. throughout learning processes.

      The authors show that implanted animals can even perform complex behavioral tasks, with no apparent reduction in their performance.

      Weaknesses:

      While implanted animals can still perform complex behavioral tasks, the authors describe that the implants may reduce the animals' mobility, as measured by prolonged reaction times. However, the presented data does not allow us to judge whether this effect is specifically due to the presented implant or whether any implant or just tethering of the animals per se would have the same effects.

      The reviewer is correct: some of the differences in mouse reaction time could be due to the tether rather than the implant. As these experiments were also performed in water-restricted female mice with the heavier Neuropixels 1.0 implant, our data represent the maximal impact of the implant, and we have highlighted this point in the revision (Freely behaving animals, Para 2).  

      While the authors make certain comparisons to other, previously published approaches for chronic implantation and re-use of Neuropixels probes, it is hard to make conclusive comparisons and judge the advantages of the current implant. For example, while the authors emphasize that the lower weight of their implant allows them to perform recordings in mice (and is surely advantageous), the previously described, heavier implants they mention (Steinmetz et al., 2021; van Daal et al., 2021), have also been used in mice. Whether the weight difference makes a difference in practice therefore remains somewhat unclear.

      The reviewer is correct: without a direct comparison, we cannot be certain that our smaller, lighter implant improves behavioural results (although this is supported by the literature, e.g. Newman et al, 2023). However, the reduced weight of our implant is critical for several laboratories represented in this manuscript due to animal welfare requirements. Indeed, in van Daal et al the authors “recommend a [mouse] weight of >25 g for implanting Neuropixels 1.0 probes.” This limit precludes using (the vast majority of) female mice, or water-restricted animals. Conversely, our implant can be routinely used with lighter, water-restricted male and female mice. We emphasised this point in the revision (Discussion, Para 2).

      The non-permanent integration of the headstages into the implant, while allowing for the use of the same headstage for multiple animals in parallel, requires repeated connections and does not provide strong protection for the implant. This may especially be an issue for the use in rats, requiring additional protective components as in the presented rat experiments.

      We apologise for not clarifying the various headstage holder options in the manuscript and we have now addressed this in the revision (Freely behaving animals, Para 1&2). Our repository has headstage holder designs (in the XtraModifications/Mouse_FreelyMoving folder). This allows leaving the headstage on the implant, and thus minimize the number of connections (albeit increasing the weight for the mouse). Indeed, mice recorded while performing the task described in our manuscript had the head-stage semi-permanently integrated to the implant, and we now highlight this in the revision (Freely behaving animals, Para 1).

      Reviewer 2 (Recommendations For The Authors): 

      The description of the different versions of the head-stage holders should be more clear, listing also advantages/disadvantages of the different solutions. It would be also useful if the authors could comment on the use of these head-stage holders in rats, since they do not seem to offer much protection.

      We thank the reviewer for this point, and we have added notes to the manuscript to clarify the various advantages of the different headstage-holders, and that the headstage can be permanently attached to the implant (Freely behaving animals, Para 1&2). This is the primary advantage of these solutions compared with the minimal implant—at the expense of increasing the implant weight.  

      The reviewer’s concerns regarding the lack of protection for implants in rats is well-placed, and we now emphasise that these experiments benefited from the additional protection of an external 3D casing, which is likely critical for use in larger animals (Freely behaving animals, Para 1).

      While re-used probes seem to show similar yields across multiple uses (Figure 4C), it seems as if there is a much higher variability of the yield for probes that are used for the first (maybe also second) time. There are probes with much higher than average yields, but it seems none of the re-used probes show such high yields. Is this a real effect? Is this because the high-yield probes happened to have not been used multiple times? Is there an analysis the authors could provide to reduce the concern that yields may generally be lower for re-used probes/that there are no very high yields for re-used probes?

      We understand the reviewer’s concern with respect to Figure 4C, however, the re-use of any given probe was determined only by the experimental needs of the project. It is therefore not possible that there is a relationship between probes selected for re-use and unit-yield. We now specify this in the revised legend of Figure 4C. This variability (and the consistency in yield across uses) likely stems from differences between labs, brain region, and implantation protocol.

      The authors claim that a 'large fraction' of units could be tracked for the entire duration of the experiment (Figure 5A,B). They mention in the discussion that quantification can be found in a different manuscript (van Beest et al., 2023), but this should also be quantified here in at least some more detail, also for other animals in addition to the one mouse which was recorded for ~100 days. What fraction can be held for different durations? What is the average holding time, etc.?

      We agree with the reviewer, and have now added new panels quantifying the probability and reliability of tracking a neuron across days (Figure 5E-F). We also comment on the change in tracking probability across time, and its variability across recordings (Stability, Para 4).

      Reviewer 3 (Public Reviews):

      Summary:

      In this manuscript, Bimbard and colleagues describe a new implant apparatus called "Apollo Implant", which should facilitate recording in freely moving rodents (mice and rats) using Neuropixels probes. The authors collected data from both mice and rats, they used 3 different versions of Neuropixels, multiple labs have already adopted this method, which is impressive. They openly share their CAD designs and surgery protocol to further facilitate the adaptation of their method.

      Strengths:

      Overall, the "Apollo Implant" is easy to use and adapt, as it has been used in other laboratories successfully and custom modifications are already available. The device is reproducible using common 3D printing services and can be easily modified thanks to its CAD design (the video explaining this is extremely helpful). The weight and price are amazing compared to other systems for rigid silicon probes allowing a wide range of use of the "Apollo Implant".

      Weaknesses:

      The "Apollo Implant" can only handle Neuropixels probes. It cannot hold other widely used and commercially available silicon probes. Certain angles and distances are not possible in their current form (distance between probes 1.8 to 4mm, implantation depth 2-6.5 mm, or angle of insertion up to 20 degrees).

      As we now discuss in the manuscript (Discussion, Para 4), one implant accommodating the diversity of the existing probes is beyond the scope of this project. However, because the design is adaptable, groups should be able to modify the current version of the implant to adapt to their electrodes’ size and format (and can highlight any issues in the Github “Discussions” area).

      With Neuropixels, the current range of depths covers practically all trajectories in the mouse brain. In rats, where deeper penetrations may be useful, the experimenter can attach the probe at a lower point in the payload module to expose more of the shank. We now specify this in the Github repository.  

      We have now extended the range of inter-probe distances from a maximum of 4 mm to 6.5 mm. Distances beyond this may be better served by 2 implants, and smaller distances could be achieved by attaching two probes on the same side of the docking module. These points are now specified in the revised manuscript (Flexible design, Para 2).

      Reviewer 3 (Recommendations For The Authors):

      I have only a few questions and suggestions:

      Is it possible to create step-by-step instructions for explantation (similar to Figure-1 with CAD schematics)? You mention that payload holder is attached to a micromanipulator, but it is unclear how this is achieved. How was the payload secured with a screw (which screw)? My understanding is that as you turn the screw in the payload holder, it will grab onto the payload module from both sides, but the screw is not in contact with the payload module, correct? I found the screw type on your GitHub, but it would be great if you could add a bill of materials in a table format, so readers don't have to jump between GitHub and article.

      We have now added a bill of materials to the revised manuscript (Implant design and materials, Para 2), although up-to-date links are still provided on the Github repository due to changing availability.

      What happens if you do a dual probe implant and cannot avoid blood vessels in one or both of the craniotomies due to the pre-defined geometry? Is this a frequent issue? How can you overcome this during the surgery?

      Blood vessels can be difficult to avoid in some cases, but we are typically able to rotate/reposition the probes to solve this issue. In some cases, with 4-shank probes, the blood vessel can be positioned between individual probe shanks. We now detail this in the revised manuscript (Assembly and implantation, Para 3).

      I assume if the head is not aligned (line-332) the probe can break during recovery. Have you experienced this during explanation?

      As we now specify in the manuscript (Explantation, Para 2), we are careful when explanting the probe to avoid this issue, and due to the flexibility of the shanks, it does not appear to be a major concern.

      Why did you remove the UV glue (line 435)? How can you level the skull? I assume you have covered bregma and lambda in the first surgery which can create an uneven surface to measure even after you remove the UV glue.

      We thank the reviewer for highlighting this omission from the methods. We now explain (Implantation, Carandini-Harris laboratory) that the UV-glue is completely removed during the second surgery, and the skull is cleaned and scored. This improves the adhesion of the dental cement, and allows for reliable levelling of the skull.

      In line 112 you mentioned that the number of recorded neurons was stable; however, you found a 3% mean decrease in unit count per day (line 120). Stability is great until day 10 (in Figure 4A), but it deteriorates quickly after that. I think it would help readers if you could add the mean{plus minus}SEM of recorded units in the text for days 1-10, days 11-50, and days 51-100 (using the data from Figure 4A).

      We have now added Supplementary Figure 4 to show unit count across bins of days, and a corresponding comment in the text (Stability, Para 2).

      A full survey of the probe (Figure 4B) means that you recorded neuronal activity across 4-5000 channels (depending on how many channels were in the brain). While it is clear that a full probe survey can reduce the number of animals needed for a study, it is also clear in this figure that by day 25 you can record ~300 neurons on 4000 channels. It would be great to discuss this in the discussion and give a balanced view of the long-term stability of these recordings.

      Overall, keeping a large number of units for a long time still remains a challenge. Here, we could record on average 85 neurons per bank during the first 10 days, and then only 45 after 50 days. It is important to note that our quantification averages across all banks recorded, including those in a ventricle or partly outside of the brain. Thus, our results represent a lower estimate of the total neurons recorded. Our new Supplementary Figure 4 helps to highlight the diversity of neuron number recorded per animal. Further improvements in surgical techniques and spike sorting will likely improve stability further and we have now added this comment in the manuscript (Stability, Para 2). For example, we observed excellent stability in a mouse where the craniotomy was stabilized with KwikSil (Supplementary Figure 5).

      The RMS value was around 20 uV in some of the recordings, and according to Figure 4G it is around 16 uV on average. Is it safe to accept putative single units with 20 uV amplitudes, when the baseline noise level is this close to the spike peak-to-peak amplitude?

      On average, less than 1% of the units selected using all the other metrics except the amplitude had an amplitude below 30 µV, and 2.6% below 50 µV. Increasing the threshold to 30 µV, or even 50 µV, did not affect the results. We have now added this comment in the Methods (Data processing, Para 3).

      Can you add the waveform and ISIH of the example unit from day 106 to Figure 5?

      We have now added 4 units tracked up to day 106 in Figure 5.  

      Could you move Supplementary Figure 3A to Figure 4? The number of units is more valuable information than the RMS noise level. I understand that you don't have such a nice coverage of all the days as in Figure 3 and 4, but you might be able to group for the first 3 days and the last 3 days (and if data is available, the middle 3 days) as a boxplot. The goal would be for the reader to be able to see whether there is any change in the number of single units over time.

      We agree with the reviewer, the number of units is more valuable. We had included this information in Figure 4A-F, but we have made edits to the text to make it clearer that this is what is being shown. The data from Figure 3A is already contained within Figure 4, but in 3A the data is separated by individual labs.

      Product numbers are missing in multiple places: line-285 (screw), line-288 (screw), line-290 (screw), line-309 (manipulator), line-374 (gold pin and silver wire), line-384 (Mill-Max), line-394 (silver wire), and many more. It would be great if you could add all these details, so people can replicate your protocol.

      We thank the reviewer for highlighting this, and we have added details of screw thread-size and length to relevant parts of the manuscript, although any type of screw can be used. Similarly, other components are non-specific (e.g. multiple silver-wire diameters were used across labs), so we have not included specific product numbers for general consumer items (like screws and silver wires) to avoid indicating that a specific part must be purchased.

      While it is great to see lab-specific methods, I am not sure in their current form it helps to understand the protocol better. The information is conveyed in different ways (I assume these were written by different people), in different orders, and in different depths (some mention probe implant location relative to bregma and midline, some don't). There are many different glues, epoxies, cement, wires, and pins. I would recommend rewriting these methods sections under a unified template, so it is easier to follow.

      We thank the reviewer for this suggestion and we have rewritten this section of the methods accordingly. We now use a template structure to simplify the comparisons between labs: the same template is used for each lab in each section (payload module assembly, implantation, and data acquisition).

      Line-307: why is a skull screw optional for grounding? What did you use for ground and reference if not a ground screw?

      We now specify in the manuscript that during head-fixed experiments, the animal’s headplate can be used for grounding, and combined with internal referencing provided by the Neuropixels, yielded lownoise recordings (Implantation protocol, Methods).

    1. eLife Assessment

      The manuscript provides important new insights into the mechanisms of statistical learning in early human development, showing that statistical learning in neonates occurs robustly and is not limited to linguistic features but occurs across different domains. The evidence is convincing and the findings are highly relevant for researchers working in several domains, including developmental cognitive neuroscience, developmental psychology, linguistics, and speech pathology.

    2. Reviewer #1 (Public review):

      Summary:

      Parsing speech into meaningful linguistic units is a fundamental yet challenging task that infants face while acquiring the native language. Computing transitional probabilities (TPs) between syllables is a segmentation cue well-attested since birth. In this research, the authors examine whether newborns compute TPs over any available speech feature (linguistic and non-linguistic), or whether by contrast newborns favor computation of TPs over linguistic content over non-linguistic speech features such as speaker voice. Using EEG and the artificial language learning paradigm, they record the neural responses of two groups of newborns presented with speech streams in which either phonetic content or speaker voice are structured to provide TPs informative of word boundaries, while the other dimension provides uninformative information. They compare newborns' neural responses to these structured streams to their processing of a stream in which both dimensions vary randomly. After the random and structured familiarization streams, the newborns are presented with (pseudo)words as defined by their informative TPs, as well as partwords (that is, sequences that straddle a word boundary), extracted from the same streams. Analysis of the neural responses show that while newborns neural activity entrained to the syllabic rate (2 Hz) when listening to the random and structured streams, it additionally entrained at the word rate (4 Hz) only when listening to the structured streams, finding no differential response between the streams structured around voice or phonetic information. Newborns showed also different neural activity in response to the words and part words. In sum, the study reveals that newborns compute TPs over linguistic and non-linguistic features of speech, these are calculated independently, and linguistic features do not lead to a processing advantage.

      Strengths:

      This interesting research furthers our knowledge of the scope of the statistical learning mechanism, which is confirmed to be a general-purpose powerful tool that allows humans to extract patterns of co-occurring events while revealing no apparent preferential processing for linguistic features. To answer its question, the study combines a highly replicated and well-established paradigm, i.e. the use of an artificial language in which pseudowords are concatenated to yield informative TPs to word boundaries, with a state-of-the-art EEG analysis, i.e. neural entrainment. The sample size of the groups is sufficient to ensure power, and the design and analysis are solid and have been successfully employed before.

      Weaknesses:

      There are no significant weaknesses to signal in the manuscript. However, in order to fully conclude that there is no obvious advantage for the linguistic dimension in neonates, future studies should pit both dimensions against each other, to determine whether statistical learning weighs linguistic and non-linguistic features equally, or whether phonetic content is preferentially processed.

      To sum up, the authors achieved their central aim of determining whether TPs are computed over both linguistic and non-linguistic features, and their conclusions are supported by the results. This research is important for researchers working on language and cognitive development, and language processing, as well as for those working on cross-species comparative approaches.

      Comments on revisions:

      The authors have addressed my suggestions. I have no further comments.

    3. Reviewer #2 (Public review):

      Summary:

      The manuscript investigates to what degree neonates show evidence for statistical learning from regularities in streams of syllables, either with respect to phonemes or with respect to speaker identity. Using EEG, the authors found evidence for both, stronger entrainment to regularities as well as ERP differences in response to violations of previously introduced regularities. In addition, violations of phoneme regularities elicited an ERP pattern which the authors argue might index a precursor of the N400 response in older children and adults.

      Strengths:

      All in all, this is a very convincing paper, which uses a clever manipulation of syllable streams to target the processing of different features. The combination of neural entrainment and ERP analysis allows for the assessment of different processing stages, and implementing this paradigm in a comparably large sample of neonates is impressive.

      Weaknesses

      The authors addressed all the concerns I previously raised well and I have no further comments.

    4. Reviewer #3 (Public review):

      Summary:

      This study is focused on testing whether statistical learning (a mechanism for parsing the speech signal into smaller chunks) preferentially operates over certain features of the speech at birth in humans. The features under investigation are phonetic content and speaker identity. Newborns are tested in an EEG paradigm in which they are exposed to a long stream of syllables. In Experiment 1, newborns are familiarized with a sound stream that comprises regularities (transitional probabilities) over syllables (e.g., "pe" followed by "tu" in "petu" with 1.0 probability) while the voices uttering the syllables remain random. In Experiment 2, newborns are familiarized with the same sound stream but, this time, the regularities are built over voices (e.g., "green voice" followed by "red voice" with 1.0 probability) while the concatenation of syllables stays random. At the test, all newborns listened to duplets (individual chunks) that either matched or violated the structure of the familiarization. In both experiments, newborns showed neural entrainment to the regularities implemented in the stream, but only the duplets defined by transitional probabilities over syllables (aka word forms) elicited a N400 ERP component. These results suggest that statistical learning operates in parallel and independently on different dimensions of the speech already at birth and that there seems to be an advantage for processing statistics defining word forms rather than voice patterns.

      Strengths:

      This paper presents an original experimental design that combines two types of statistical regularities in a speech input. The design is robust and appropriate for EEG with newborns. I appreciated the clarity of the Methods section. There is also a behavioral experiment with adults that acts like a control study for newborns. The research question is interesting, and the results add new information about how statistical learning works at the beginning of postnatal life, and on which features of the speech. The figures are clear and helpful in understanding the methods, especially the stimuli and how the regularities were implemented.

      Weaknesses:

      I appreciated how the authors addressed my previous comments and concerns. I am satisfied with the changes made by the authors. I believe the paper reads much better. Also, the adjustment to the theoretical framework suits well.

    5. Author response:

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

      We thank the three reviewers for their positive comments and useful suggestions. We have implemented most of the reviewers’ recommendations and hope the manuscript is clearer now.

      The main modifications are:

      - A revision of the introduction to better explain what Transitional Probabilities are and clarify the rationale of the experimental design

      - A revision of the discussion

      - To tune down and better explain the interpretation of the different responses between duplets after a stream with phonetic or voice regularities (possibly an N400).

      - To better clarify the framing of statistical learning as a universal learning mechanism that might share computational principles across features (or domains).

      Below, we provide detailed answers to each reviewer's point.

      Response to Reviewer 1:

      There are no significant weaknesses to signal in the manuscript. However, in order to fully conclude that there is no obvious advantage for the linguistic dimension in neonates, it would have been most useful to test a third condition in which the two dimensions were pitted against each other, that is, in which they provide conflicting information as to the boundaries of the words comprised in the artificial language.

      This last condition would have allowed us to determine whether statistical learning weighs linguistic and non-linguistic features equally, or whether phonetic content is preferentially processed.

      We appreciate the reviewers' suggestion that a stream with conflicting information would provide valuable insights. In the present study, we started with a simpler case involving two orthogonal features (i.e., phonemes and voices), with one feature being informative and the other uninformative, and we found similar learning capacities for both. Future work should explore whether infants—and humans more broadly—can simultaneously track regularities in multiple speech features. However, creating a stream with two conflicting statistical structures is challenging. To use neural entrainment, the two features must lead to segmentation at different chunk sizes so that their effects lead to changes in power/PLV at different frequencies—for instance, using duplets for the voice dimension and triplets for the linguistic dimension (or vice versa). Consequently, the two dimensions would not be directly comparable within the same participant in terms of the number of distinguishable syllables/voices, memory demand, or SNR given the 1/F decrease in amplitude of background EEG activity. This would involve comparisons between two distinct groups counter-balancing chunk size and linguistic non-linguistic dimension. Considering the test phase, words for one dimension would have been part-words for the other dimension. As we are measuring differences and not preferences, interpreting the results would also have been difficult. Additionally, it may be difficult to find a sufficient number of clearly discriminable voices for such a design (triplets imply 12 voices). Therefore, an entirely different experimental paradigm would need to be developed.

      If such a design were tested, one possibility is that the regularities for the two dimensions are calculated in parallel, in line with the idea that the calculation of statistical regularities is a ubiquitous implicit mechanism (see Benjamin et al., 2024, for a proposed neural mechanism). Yet, similar to our present study, possibly only phonetic features would be used as word candidates. Another possibility is that only one informative feature would be explicitly processed at a time due to the serial nature of perceptual awareness, which may prioritise one feature over the other.

      We added one sentence in the discussion stating that more research is needed to understand whether infants can track both regularities simultaneously (p.13, l.270 “Future work could explore whether they can simultaneously track multiple regularities.”).

      Note: The reviewer’s summary contains a typo: syllabic rate (4 Hz) –not 2 Hz, and word rate (2 Hz) –not 4 Hz.

      Response to Reviewer 2:

      N400: I am skeptical regarding the interpretation of the phoneme-specific ERP effect as a precursor of the N400 and would suggest toning it down. While the authors are correct in that infant ERP components are typically slower and more posterior compared to adult components, and the observed pattern is hence consistent with an adult N400, at the same time, it could also be a lot of other things. On a functional level, I can't follow the author's argument as to why a violation in phoneme regularity should elicit an N400, since there is no evidence for any semantic processing involved. In sum, I think there is just not enough evidence from the present paradigm to confidently call it an N400.

      The reviewer is correct that we cannot definitively determine the type of processing reflected by the ERP component that appears when neonates hear a duplet after exposure to a stream with phonetic regularities. We interpreted this component as a precursor to the N400, based on prior findings in speech segmentation tasks without semantic content, where a ~400 ms component emerged when adult participants recognised pseudowords (Sander et al., 2002) or during structured streams of syllables (Cunillera et al., 2006, 2009). Additionally, the component we observed had a similar topography and timing to those labelled as N400 in infant studies, where semantic processing was involved (Parise et al., 2010; Friedrich & Friederici, 2011).

      Given our experimental design, the difference we observed must be related to the type of regularity during familiarisation (either phonemes or voices). Thus, we interpreted this component as reflecting lexical search— a process which could be triggered by a linguistic structure but which would not be relevant to a non-linguistic regularity such as voices. However, we are open to alternative interpretations. In any case, this difference between the two streams reveals that computing regularities based on phonemes versus voices does not lead to the same processes.

      We revised the abstract (p.2, l.33) and the discussion of this result (p.15, l.299), toning them down. We hope the rationale of the interpretation is clearer now, as is the fact that it is just one possible interpretation of the results.

      Female and male voices: Why did the authors choose to include male and female voices? While using both female and male stimuli of course leads to a higher generalizability, it also introduces a second dimension for one feature that is not present for this other (i.e., phoneme for Experiment 1 and voice identity plus gender for Experiment 2). Hence, couldn't it also be that the infants extracted the regularity with which one gender voice followed the other? For instance, in List B, in the words, one gender is always followed by the other (M-F or F-M), while in 2/3 of the part-words, the gender is repeated (F-F and M-M). Wouldn't you expect the same pattern of results if infants learned regularities based on gender rather than identity?

      We used three female and three male voices to maximise acoustic variability. The streams were synthesised using MBROLA, which provides a limited set of artificial voices. Indeed, there were not enough French voices of acceptable quality, so we also used two Italian voices (the phonemes used existed in both Italian and French).

      Voices differ in timbre, and female voices tend to be higher pitched. However, it is sometimes difficult to categorise low-pitched female voices and high-pitched male voices. Given that gender may be an important factor in infants' speech perception (newborns, for instance, prefer female voices at birth), we conducted tests to assess whether this dimension could have influenced our results.

      We report these analyses in SI and referred to them in the methods section (p.25, l.468 “We performed post-hoc tests to ensure that the results were not driven by a perception of two voices: female and male (see SI).”).

      We first quantified the transitional probabilities matrices during the structured stream of Experiment 2, considering that there are only two types of voices: Female and Male.

      For List A, all transition probabilities are equal to 0.5 (P(M|F), P(F|M), P(M|M), P(F|F)), resulting in flat TPs throughout the stream (see Author response image 1, top). Therefore, we would not expect neural entrainment at the word rate (2 Hz), nor would we anticipate ERP differences between the presented duplets in the test phase.

      For List B, P(M|F)=P(F|M)=0.66 while P(M|M)=P(F|F)=0.33. However, this does not produce a regular pattern of TP drops throughout the stream (see Author response image 1, bottom). As a result, strong neural entrainment at 2 Hz was unlikely, although some degree of entrainment might have occasionally occurred due to some drops occurring at a 2 Hz frequency. Regarding the test phase, all three Words and only one Part-word presented alternating patterns (TP=0.6). Therefore, the difference in the ERPs between Words and Part- words in List B might be attributed to gender alternation.

      However, it seems unlikely that gender alternation alone explains the entire pattern of results, as the effect is inconsistent and appears in only one of the lists. To rule out this possibility, we analysed the effects in each list separately.

      Author response image 1.

      Transition probabilities (TPs) across the structured stream in Experiment 2, considering voices processed by gender (Female or Male). Top: List A. Bottom: List B.

      We computed the mean activation within the time windows and electrodes of interest and compared the effects of word type and list using a two-way ANOVA. For the difference between Words and Part-words over the positive cluster, we observed a main effect of word type (F(1,31) = 5.902, p = 0.021), with no effects of list or interactions (p > 0.1). Over the negative cluster, we again observed a main effect of word type (F(1,31) = 10.916, p = 0.0016), with no effects of list or interactions (p > 0.1). See Author response image 2.

      Author response image 2:

      Difference in ERP voltage (Words – Part-words) for the two lists (A and B); W=Words; P=Part-Words,

      We conducted a similar analysis for neural entrainment during the structured stream on voices. A comparison of entrainment at 2 Hz between participants who completed List A and List B showed no significant differences (t(30) = -0.27, p = 0.79). A test against zero for each list indicated significant entrainment in both cases (List A: t(17) = 4.44, p = 0.00036; List B: t(13) = 3.16, p = 0.0075). See Author response image 3.

      Author response image 3.

      Neural entrainment at 2Hz during the structured stream of Experiment 2 for Lists A and B.

      Words entrainment over occipital electrodes: Do you have any idea why the duplet entrainment effect occurs over the electrodes it does, in particular over the occipital electrodes (which seems a bit unintuitive given that this is a purely auditory experiment with sleeping neonates).

      Neural entrainment might be considered as a succession of evoked response induced by the stream. After applying an average reference in high-density EEG recordings, the auditory ERP in neonates typically consists of a central positivity and a posterior negativity with a source located at the electrical zero in a single-dipole model (i.e. approximately in the superior temporal region (Dehaene-Lambertz & Dehaene, 1994). In adults, because of the average reference (i.e. the sum of voltages is equal to zero at each time point) and because the electrodes cannot capture the negative pole of the auditory response, the negativity is distributed around the head. In infants, however, the brain is higher within the skull, allowing for a more accurate recording of the negative pole of the auditory ERP (see Figure 4 for the location of electrodes in an infant head model).

      Besides the posterior electrodes, we can see some entrainment on more anterior electrodes that probably corresponds to the positive pole of the auditory ERP.

      We added a phrase in the discussion to explain why we can expect phase-locked activity in posterior electrodes (p.14, l.277: “Auditory ERPs, after reference-averaged, typically consist of a central positivity and posterior negativity”).

      Author response image 4:

      International 10–20 sensors' location on the skull of an infant template, with the underlying 3-D reconstruction of the grey-white matter interface and projection of each electrode to the cortex. Computed across 16 infants (from Kabdebon et al, Neuroimage, 2014). The O1, O2, T5, and T6 electrodes project lower than in adults.

      Response to Reviewer 3:

      (1) While it's true that voice is not essential for language (i.e., sign languages are implemented over gestures; the use of voices to produce non-linguistic sounds, like laughter), it is a feature of spoken languages. Thus I'm not sure if we can really consider this study as a comparison between linguistic and non-linguistic dimensions. In turn, I'm not sure that these results show that statistical learning at birth operates on non-linguistic features, being voices a linguistic dimension at least in spoken languages. I'd like to hear the authors' opinions on this.

      On one hand, it has been shown that statistical learning (SL) operates across multiple modalities and domains in human adults and animals. On the other hand, SL is considered essential for infants to begin parsing speech. Therefore, we aimed to investigate whether SL capacities at birth are more effective on linguistic dimensions of speech, potentially as a way to promote language learning.

      We agree with the reviewer that voices play an important role in communication (e.g., for identifying who is speaking); however, they do not contribute to language structure or meaning, and listeners are expected to normalize across voices to accurately perceive phonemes and words. Thus, voices are speech features but not linguistic features. Additionally, in natural speech, there are no abrupt voice changes within a word as in our experiment; instead, voice changes typically occur on a longer timescale and involve only a limited number of voices, such as in a dialogue. Therefore, computing regularities based on voice changes would not be useful in real-life language learning. We considered that contrasting syllables and voices was an elegant way to test SL beyond its linguistic dimension, as the experimental paradigm is identical in both experiments.

      We have rephrased the introduction to make this point clearer. See p.5, l.88-92: “To test this, we have taken advantage of the fact that syllables convey two important pieces of information for humans: what is being said and who is speaking, i.e. linguistic content and speaker’s identity. While statistical learning…”.

      Along the same line, in the Discussion section, the present results are interpreted within a theoretical framework showing statistical learning in auditory non-linguistic (string of tones, music) and visual domains as well as visual and other animal species. I'm not sure if that theoretical framework is the right fit for the present results.

      (2) I'm not sure whether the fact that we see parallel and independent tracking of statistics in the two dimensions of speech at birth indicates that newborns would be able to do so in all the other dimensions of the speech. If so, what other dimensions are the authors referring to?

      The reviewer is correct that demonstrating the universality of SL requires testing additional modalities and acoustic dimensions. However, we postulate that SL is grounded in a basic mechanism of long-term associative learning, as proposed in Benjamin et al. (2024), which relies on a slow decay in the representation of a given event. This simple mechanism, capable of operating on any representational output, accounts for many types of sequence learning reported in the literature (Benjamin et al., in preparation).

      We have revised the discussion to clarify this theoretical framework.

      In p.13, l.264: “This mechanism might be rooted in associative learning processes relying on the co- existence of event representations driven by slow activation decays (Benjamin et al., 2024). ”

      In p., l. 364: “Altogether, our results show that statistical learning works similarly on different speech features in human neonates with no clear advantage for computing linguistically relevant regularities in speech. This supports the idea that statistical learning is a general learning mechanism, probably operating on common computational principles across neural networks (Benjamin et al., 2024)…”.

      (3) Lines 341-345: Statistical learning is an evolutionary ancient learning mechanism but I do not think that the present results are showing it. This is a study on human neonates and adults, there are no other animal species involved therefore I do not see a connection with the evolutionary history of statistical learning. It would be much more interesting to make claims on the ontogeny (rather than philogeny) of statistical learning, and what regularities newborns are able to detect right after birth. I believe that this is one of the strengths of this work.

      We did not intend to make claims about the phylogeny of SL. Since SL appears to be a learning mechanism shared across species, we use it as a framework to suggest that SL may arise from general operational principles applicable to diverse neural networks. Thus, while it is highly useful for language acquisition, it is not specific to it.

      We have removed the sentence “Statistical learning is an evolutionary ancient learning mechanism.”, and replaced it by (p.18, l.364) “Altogether, our results show that statistical learning works similarly on different speech features in human neonates with no clear advantage for computing linguistically relevant regularities in speech.” We now emphasise in the discussion that infants compute regularities on both features and propose that SL might be a universal learning mechanism sharing computational principles (Benjamin et al., 2024) (see point 2).

      (4) The description of the stimuli in Lines 110-113 is a bit confusing. In Experiment 1, e.g., "pe" and "tu" are both uttered by the same voice, correct? ("random voice each time" is confusing). Whereas in Experiment 2, e.g., "pe" and "tu" are uttered by different voices, for example, "pe" by yellow voice and "tu" by red voice. If this is correct, then I recommend the authors to rephrase this section to make it more clear.

      To clarify, in Experiment 1, the voices were randomly assigned to each syllable, with the constraint that no voice was repeated consecutively. This means that syllables within the same word were spoken by different voices, and each syllable was heard with various voices throughout the stream. As a result, neonates had to retrieve the words based solely on syllabic patterns, without relying on consistent voice associations or specific voice relationships.

      In Experiment 2, the design was orthogonal: while the syllables were presented in a random order, the voices followed a structured pattern. Similar to Experiment 1, each syllable (e.g., “pe” and “tu”) was spoken by different voices. The key difference is that in Experiment 2, the structured regularities were applied to the voices rather than the syllables. In other words, the “green” voice was always followed by the “red” voice for example but uttered different syllables.

      We have revised the description of the stimuli and the legend of Figure 1 to clarify these important points.

      See p.6, l. 113: “The structure consisted of the random concatenation of three duplets (i.e., two-syllable units) defined only by one of the two dimensions. For example, in Experiment 1, one duplet could be petu with each syllable uttered by a random voice each time they appear in the stream (e.g pe is produced by voice1 and tu by voice6 in one instance and in another instance pe is produced by voice3 and tu by

      voice2). In contrast, in Experiment 2, one duplet could be the combination [voice1- voice6], each uttering randomly any of the syllables.”

      p.20, l. 390 (Figure 1 legend): “For example, the two syllables of the word “petu” were produced by different voices, which randomly changed at each presentation of the word (e.g. “yellow” voice and “green” voice for the first instance, “blue” and “purple” voice for the second instance, etc..). In Experiment 2, the statistical structure was based on voices (TPs alternated between 1 and 0.5), while the syllables changed randomly (uniform TPs of 0.2). For example, the “green” voice was always followed by the “red” voice, but they were randomly saying different syllables “boda” in the first instance, “tupe” in the second instance, etc... “

      (5) Line 114: the sentence "they should compute a 36 x 36 TPs matrix relating each acoustic signal, with TPs alternating between 1/6 within words and 1/12 between words" is confusing as it seems like there are different acoustic signals. Can the authors clarify this point?

      Thank you for highlighting this point. To clarify, our suggestion is that neonates might not track regularities between phonemes and voices as separate features. Instead, they may treat each syllable-voice combination as a distinct item—for example, "pe" spoken by the "yellow" voice is one item, while "pe" spoken by the "red" voice is another. Under this scenario, there would be a total of 36 unique items (6 syllables × 6 voices), and infants would need to track regularities between these 36 combinations.

      We have modified this sentence in the manuscript to make it clearer.

      See p.7, l. 120: “If infants at birth compute regularities based on a neural representation of the syllable as a whole, i.e. comprising both phonetic and voice content, this would require computing a 36 × 36 TPs matrix relating each token.”

      Reviewer #1 (Recommendations for the authors):

      (1) The acronym TP should be spelled out, and a brief description of the fact that dips in TPs signal boundaries while high TPs signal a cohesive unit could be useful for non-specialist readers.

      We have added it at the beginning of the introduction (lines 52-60)

      (2) p.5, l.76: "Here, we aimed to further characterise the characteristics of this mechanism...". I suggest this is rephrased as "to further characterise this mechanism".

      We have changed it as suggested by the reviewer (now p.5, l.81)

      (3) p.9, l.172: "[...] this contribution is unlikely since the electrodes differ from the electrodes, showing enhanced word-rate activity at 2 Hz."

      It is unclear which electrodes differ from which electrodes. I figure that the authors mean that the electrodes showing stronger activity at 2 Hz differ from those showing it at 4 Hz, but the sentence could use rephrasing.

      This part has been rephrased (p.9, l.177-181)

      (4) p.10, l.182: "[...] the entrainment during the first minute of the structure stream [… ]".

      Structured stream.

      It has been corrected (p.10, l.190)

      (5) p.12, l.234: "we compared STATISTICAL LEARNING"

      Why the use of capitals?

      This was an error and it was corrected (p.12, l.242).

      (6) p.15, l.298: "[...] suggesting that such negativity might be related to semantic."

      The sentence feels incomplete. To semantics? To the processing of semantic information?

      The phrase has been corrected (p.15, l.314). Additionally, the discussion of the posterior negativity observed for duplets after familiarisation with a stream with regularities over phonemes has been rephrased (p.15, l.)

      (7) Same page, l.301: "3-mo-olds" 3-month-olds.

      It has been corrected (now in p.16, l.333)

      (8) Same page, l.307: "(see also (Bergelson and Aslin, 2017)" (see also Bergelson and Aslin, 2017).

      It has been corrected (now in p.17, l.340)

      (9) Same page, l.310: "[...] would be considered as possible candidate" As possible candidates.

      This has been rephrased and corrected (now in p.17, l.343)

      Reviewer #2 (Recommendations for the authors):

      (1) Figure 2: The authors mention a "thick orange line", which I think should be a "thick black line".

      We are sorry for this. It has been corrected.

      (2) Ln 166: Should be Figure 2C rather than 3C.

      It has been corrected (now in p.9, l.173)

      (3) Figure 4 is not referenced in the manuscript.

      We referred to it now on p. 12, l.236

    1. eLife Assessment

      This study presents a valuable finding on how the interplay between transcription factors SOX2 and OCT4 establishes the pluripotency network in early mouse embryos. The evidence supporting the claims of the authors is solid, although inclusion of additional omics data would further strengthen the study. The work will be of interest to biologists working on embryonic development and gene regulation.

    2. Reviewer #1 (Public review):

      Summary:

      Numerous mechanism and structural studies reported the cooperative role of Oct4 and Sox2 during the establishment of pluripotency during reprogramming. Due to the difficulty in sample collection and RNA-seq with low-number cells, the precise mechanisms remain in early embryos. This manuscript reported the role of OCT4 and SOX2 in mouse early embryos using knockout models with low-input ATAC-seq and RNA-seq. Compared to the control, chromatin accessibility and transcriptome were affected when Oct4 and Sox2 were deleted in early ICM. Specifically, decreased ATAC-seq peaks showed enrichment of Motifs of TF such as OCT, SOX, and OCT-SOX, indicating their importance during early development. Moreover, by deep analysis of ATAC-seq and RNA-seq data, they found Oct4 and Sox2 target enhancer to activate their downstream genes. In addition, they also uncovered the role of OS during development from the morula to ICM, which provided the scientific community with a more comprehensive understanding.

      Strengths:

      On the whole, the manuscript is innovative, and the conclusions of this paper are mostly well supported by data.

      Weaknesses:

      Major Points:<br /> (1) In Figure 1, a more detailed description of the knockout strategy should be provided to clarify itself. The knockout strategy in Fig1 is somewhat obscure, such as how is OCT4 inactivated in Oct4mKO2 heterozygotes. As shown in Figure 1, the exon of OCT4 is not deleted, and its promoter is not destroyed. Therefore, how does OCT4 inactivate to form heterozygotes?<br /> (2) Is ZP 3-Cre expressed in the zygotes? Is there any residual protein?<br /> (3) What motifs are enriched in the rising ATAC-seq peaks after knocking out of OCT4 and SOX2?<br /> (4) The ordinate of Fig4c is lost.<br /> (5) Signals of H3K4me1, H3K27ac, and so on are usually used to define enhancers, and the loci of enhancers vary greatly in different cells. In the manuscript, the authors defined ATAC-seq peaks far from the TSS as enhancers. The definition in this manuscript is not strictly an enhancer.<br /> (6) If Oct4 and Sox2 truly activate sap 30 and Uhrf 1, what effect does interfering with both genes have on gene expression and chromatin accessibility?

      Comments on revisions:

      The authors have addressed my concerns so I am fine with revision in principle.

    3. Reviewer #2 (Public review):

      In this manuscript, Hou et al. investigate the interplay between OCT4 and SOX2 in driving the pluripotent state during early embryonic lineage development. Using knockout (KO) embryos, the authors specifically analyze the transcriptome and chromatin state within the ICM-to-EPI developmental trajectory. They emphasize the critical role of OCT4 and the supportive function of SOX2, along with other factors, in promoting embryonic fate. Although the paper presents high-quality data, several key claims are not well-supported, and direct evidence is generally lacking.

      Comments on revisions:

      The authors have addressed many of the concerns raised in the initial review and provided alternative analytical approaches to address the relevant questions in this revision. Some of these are useful; however, they have not fully addressed one critical point.<br /> In my original critique, I noted that the maternal KO might not be suitable as a control, given that there is no significant phenotypic difference between the maternal-only KO and the maternal-zygotic KO. While we did not dispute the molecular differences presented in Figure 2, so how the authors conclude in the Response "embryos with a maternal KO or zygotic heterozygous KO of Oct4 or Sox2 show no noticeable ... molecular difference (Figure 2-figure supplement 4A)"? The authors should recheck whether this is a typographical error or a valid statement.

      Additionally, I recommend the removal of phrases such as "absolutely priority" and "pivotal" throughout the manuscript, as these terms are overly assertive without sufficient supporting evidence.

    4. Author response:

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

      Public Reviews: 

      Reviewer #1 Comments on revisions: 

      The authors have addressed my concerns so I am fine with revision in principle.

      Thank you for taking the time to review our work and for your thoughtful feedback. We’re glad to hear that your concerns have been addressed.

      Reviewer #2 Comments on revisions:

      The authors have addressed many of the concerns raised in the initial review and provided alternative analytical approaches to address the relevant questions in this revision. Some of these are useful; however, they have not fully addressed one critical point. 

      In my original critique, I noted that the maternal KO might not be suitable as a control, given that there is no significant phenotypic difference between the maternal-only KO and the maternal-zygotic KO. While we did not dispute the molecular differences presented in Figure 2, so how the authors conclude in the Response "embryos with a maternal KO or zygotic heterozygous KO of Oct4 or Sox2 show no noticeable ... molecular difference (Figure 2-figure supplement 4A)"? The authors should recheck whether this is a typographical error or a valid statement. 

      Additionally, I recommend the removal of phrases such as "absolutely priority" and "pivotal" throughout the manuscript, as these terms are overly assertive without sufficient supporting evidence.

      We sincerely appreciate the reviewer’s feedback and would like to take this opportunity to provide further clarification, as there might have been a misunderstanding.

      We respectfully disagree with the reviewer’s statement that “there is no significant phenotypic difference between the maternal-only KO and the maternal-zygotic KO.” Based on privious publications, there is clear evidence that maternal-zygotic KO embryos exhibit significant defects: they fail to form a healthy primitive endoderm, are unable to give rise to embryonic stem cells (ESCs) in vitro, and die shortly after implantation (Frum et al., Dev Cell 2013; Wu et al., Nat Cell Biol 2013; Le Bin et al., Development 2014; Wicklow et al., PLoS Genet 2014). In contrast, maternal-only KO embryos develop as healthy as wild-type (WT) embryos and do not display any of these phenotypic abnormalities. We believe that this distinction validates our use of maternal KO embryos as proper controls in our experiments. 

      To address the reviewer’s concerns and ensure clarity, we have also revised the following statement in the manuscript.

      Original manuscript: “Mouse embryos with a maternal KO or zygotic heterozygous KO of either factor show no noticeable phenotype or molecular difference (Figure 2-figure supplement 4A) (Avilion et al., 2003; Frum et al., 2013; Kehler et al, 2004; Nichols et al., 1998; Wicklow et al., 2014; Wu et al., 2013).” 

      Revised manuscript: “Maternal KO embryos (circles in Figure 2—figure supplement 4A) clustered together with wildtype embryos (triangles and squares) in the PCA analysis, consistent with previous studies reporting no observable phenotype in maternal KO embryos (Avilion et al., 2003; Frum et al., 2013; Kehler et al, 2004; Nichols et al., 1998; Wicklow et al., 2014; Wu et al., 2013).”

      While we acknowledge the potential for using maternal-only KO controls to underestimate differences between control and KO samples, we believe this approach does not introduce false positives in our RNA-seq and ATAC-seq experiments, only the possibility of more conservative conclusions. This minimizes the risk of overestimating the molecular impact.

      We appreciate the reviewer’s recommendation regarding the use of overly assertive terms. Upon careful review of the manuscript and response letter, we could not find instances of the term “absolutely priority.” However, we do use the term “pivotal” and would prefer to retain it as we believe it accurately reflects the importance of the findings presented in our manuscript.

      Thank you for your thoughtful comments and suggestions! We hope this response clarifies our rationale and addresses the concerns.

      ---

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

      Public Reviews:

      Reviewer #1 (Public review)

      Summary:

      Numerous mechanism and structural studies reported the cooperative role of Oct4 and Sox2 during the establishment of pluripotency during reprogramming. Due to the difficulty in sample collection and RNA-seq with low-number cells, the precise mechanisms remain in early embryos. This manuscript reported the role of OCT4 and SOX2 in mouse early embryos using knockout models with low-input ATAC-seq and RNA-seq. Compared to the control, chromatin accessibility and transcriptome were affected when Oct4 and Sox2 were deleted in early ICM. Specifically, decreased ATAC-seq peaks showed enrichment of Motifs of TF such as OCT, SOX, and OCT-SOX, indicating their importance during early development. Moreover, by deep analysis of ATAC-seq and RNA-seq data, they found Oct4 and Sox2 target enhancer to activate their downstream genes. In addition, they also uncovered the role of OS during development from the morula to ICM, which provided the scientific community with a more comprehensive understanding.

      Strengths:

      On the whole, the manuscript is innovative, and the conclusions of this paper are mostly well supported by data, however, there are some issues that need to be addressed.

      Weaknesses:

      Major Points:

      (1) In Figure 1, a more detailed description of the knockout strategy should be provided to clarify itself. The knockout strategy in Fig1 is somewhat obscure, such as how is OCT4 inactivated in Oct4mKO2 heterozygotes. As shown in Figure 1, the exon of OCT4 is not deleted, and its promoter is not destroyed. Therefore, how does OCT4 inactivate to form heterozygotes?

      Thank you for helping clarify this. We will add a detailed description of the knockout strategy in the legends for Figure 1A and 1B, as shown below. Note that the same strategy was used by Nichols et al (Cell, 1998).

      Figure 1A. Schemes of mKO2-labeled Oct4 KO (Oct4<sup>mKO2</sup>) and Oct4<sup>flox</sup> alleles. In the Oct4<sup>mKO2</sup> allele, a PGK-pac∆tk-P2A-mKO2-pA cassette was inserted 3.6 kb upstream of the Oct4 transcription start site (TSS) and a promoter-less FRT-SA-IRES-hph-P2A-Venus-pA cassette was inserted into Oct4 intron 1. The inclusion of a stop codon followed by three sets of polyadenylation signal sequences (pA) after the Venus cassette ensures both transcriptional and translational termination, effectively blocking the expression of Oct4 exons 2–5.

      Figure 1B. Schemes of EGFP-labeled Sox2 KO (Sox2<sup>EGFP</sup>) and Sox2 <sup>flox</sup> alleles. In the Sox2 Sox2<sup>EGFP</sup> allele, the 5’ untranslated region (UTR), coding sequence and a portion of the 3’ UTR of Sox2 were deleted and replaced with a PGK-EGFP-pA cassette. Notably, 1,023 bp of the Sox2 3’UTR remain intact.

      (2) Is ZP3-Cre expressed in the zygotes? Is there any residual protein?

      This is indeed a very important issue. Here is why we think we are on the safe side. ZP3 is specifically expressed in growing oocytes, thus making ZP3-Cre a widely used tool for deleting maternally inherited alleles. When we crossed Oct4<sup>flox/flox</sup>; ZP3-Cre<sup>-</sup>_females with _Oct4<sup>flox/flox</sup>; ZP3-Cre<sup>+</sup> males, we got ZP3-Cre<sup>+</sup> Oct4<sup>flox/flox</sup> but no Oct4<sup> flox/∆</sup> or Oct4<sup> ∆/∆</sup> pups, suggesting that the paternally inherited ZP3-Cre allele is not functionally active in zygotes, which is consistent with reports from other researchers (e.g. Frum, et al., Dev Cell 2013; Wu, et al., Nat Cell Biol 2013).

      (3) What motifs are enriched in the rising ATAC-seq peaks after knocking out of OCT4 and SOX2?

      The enriched motifs in the rising ATAC-seq peaking in Oct4 KO and Sox2 KO ICMs are the GATA, TEAD, EOMES and KLF motifs, as shown in Figure 4A and Figure supplement 7.

      (4) The ordinate of Fig4c is lost.

      Thank you for pointing this out. The y-axis is average normalized signals (reads per million-normalized pileup signals). We will add it in the revised version.

      (5) Signals of H3K4me1, H3K27ac, and so on are usually used to define enhancers, and the loci of enhancers vary greatly in different cells. In the manuscript, the authors defined ATAC-seq peaks far from the TSS as enhancers. The definition in this manuscript is not strictly an enhancer.

      Thank you for this insightful comment. We analyzed the published H3K27ac ChIP-seq data of mouse ICM at 94-96 h post hCG (B. Liu, et al., Nat Cell Biol 2024) to assess the enrichment of H3K27ac around our ATAC-seq peaks. Unfortunately, the data quality is poor, e.g., inconsistent across replicates (Author response image 1A), and shows little enrichment around the well-defined enhancers (Author response image 1B). Nevertheless, as we admit not all the distal ATAC-seq peaks or open chromatin regions are enhancers, we have replaced “enhancers” with “open chromatin regions”, “ATAC-seq peaks” or “putative enhancers”.

      Author response image 1.

      Analysis of the published H3K27ac ChIP-seq dataset of mouse ICM at 94-96 h post hCG (B. Liu, et al., Nat Cell Biol 2024). A. ChIP-seq profiles of H3K27ac over the decreased, unchanged and increased ATAC-seq peaks in our Oct4-KO late ICMs. To exclude spurious peaks, only strong unchanged peaks (57,512 out of 142,096) were used in the analysis. B. IGV tracks displaying ATAC-seq and H3K27ac ChIP-seq profiles around Dppa3 and Oct4. Red boxes mark the known OCT-SOX enhancers.

      (6) If Oct4 and Sox2 truly activate sap 30 and Uhrf 1, what effect does interfere with both genes have on gene expression and chromatin accessibility?

      This is indeed an interesting question. Unfortunately, we have not conducted this specific experiment, so we do not have direct results. However, Sap30 is a key component of the mSin3A corepressor complex, while Uhrf1 regulates the establishment and maintenance of DNA methylation. Both proteins are known to function as repressors. Therefore, we hypothesize that interfering with these two genes could alleviate repression of some genes, such as trophectoderm markers, similar to what we have observed in Oct4 KO and Sox2 KO ICMs.

      Reviewer #2 (Public review):

      In this manuscript, Hou et al. investigate the interplay between OCT4 and SOX2 in driving the pluripotent state during early embryonic lineage development. Using knockout (KO) embryos, the authors specifically analyze the transcriptome and chromatin state within the ICM-to-EPI developmental trajectory. They emphasize the critical role of OCT4 and the supportive function of SOX2, along with other factors, in promoting embryonic fate. Although the paper presents high-quality data, several key claims are not well-supported, and direct evidence is generally lacking.

      Major Points:

      (1) Although the authors claim that both maternal KO and maternal KO/zygotic hetero KO mice develop normally, the molecular changes in these groups appear overestimated. A wildtype control is recommended for a more robust comparison. (a complementary comment from the reviewer: “Both maternal KO and maternal-zygotic KO in this study exhibited phenotypic consistency but molecular disparity. Specifically, both KO and control groups could develop normally; however, their chromatin landscapes and transcriptomic profiles showed different. This raises the question of whether the molecular differences are real. We suggest that inclusion of a completely wild-type control group would make the comparison more robust.”)

      Thank you for your feedback as this point was obviously not clear in the manuscript. Here is our explanation: Mouse embryos with a maternal KO or zygotic heterozygous KO of Oct4 or Sox2 show no noticeable phenotype or molecular difference (Figure 2-figure supplement 4A) (Avilion et al., 2003; Frum et al., 2013; Kehler et al, 2004; Nichols et al., 1998; Wicklow et al., 2014; Wu et al., 2013). We have clarified this point in the revised manuscript.

      (2) The authors assert that OCT4 and SOX2 activate the pluripotent network via the OCT-SOX enhancer. However, the definition of this enhancer is based solely on proximity to TSSs, which is a rough approximation. Canonical enhancers are typically located in intronic and intergenic regions and marked by H3K4me1 or H3K27ac. Re-analyzing enhancer regions with these standards could be beneficial. Additionally, the definitions of "close to" or "near" in lines 183-184 are unclear and not defined in the legends or methods.

      Thank you for this insightful and helpful comment. As stated in the response to Reviewer #1’s point (5), we have replaced “enhancers” with “open chromatin regions”, “ATAC-seq peaks” or “putative enhancers”.

      The definition of "close to" or "near" in lines 183-184 is in the legend of Figure 2E and Methods. In the GSEA analysis, Ensembl protein-coding genes with TSSs located within 10 kb of ATAC-seq peak centers were included, so that some of the intronic ATAC-seq peaks were taken into consideration. We have also added the information in the main text of the revised manuscript.

      (3) There is no evidence that the decreased peaks/enhancers could be the direct targets of Oct4 and Sox2 throughout this manuscript. Figures 2 and 4 show only minimal peak annotations related to OCT and SOX motifs, and there is a lack of chromatin IP data. Therefore, claims about direct targets are not substantiated and should be appropriately revised.

      Yes indeed, you have a point. In Figure Supplement 3C, we analyzed the published Sox2 CUT&RUN data from E4.5 ICMs (Li et al., Science, 2023), which demonstrates that the reduced ATAC-seq peaks in our Sox2 KO ICMs are enriched with the Sox2 CUT&RUN signals. Unfortunately, we did not to find similar published data for Oct4 in embryos. We have removed the statement indicating that these are the direct targets in the revised manuscript.

      (4) Lines 143-146 lack direct data to support the claim. Actually, the main difference in cluster 1, 11 and 3, 8, 14 is whether the peak contains OCT-SOX motif. However, the reviewer cannot get any information of peaks activated by OCT4 rather than SOX2 in cluster 1, 11.

      Thank you for the comment that we hope we can clarify.

      Lines 143-146 are: “Notably, the peaks activated by Oct4 but not by Sox2 in the ICM tended to be already open at the morula stage (Figure 2B, clusters 1 and 11), whereas those dependent on both Oct4 and Sox2 became open in the ICM (Figure 2B, clusters 3, 8 and 14).”

      We agree with you that clusters 3/8/14 are more enriched in OCT-SOX motifs than clusters 1/11. However, this is consistent with our observation that accessibility of peaks in clusters 1 and 11 relies mainly on Oct4, while accessibility in clusters 3, 8, 14 depends on both Oct4 and Sox2. But maybe the term “activate” is misleading. We have rephrased the text as below:

      “Notably, compared to the peaks that depend on Oct4 but not Sox2 (Figure 2B, clusters 1 and 11), those reliant on both Oct4 and Sox2 show greater enrichment of the OCT-SOX motif (Figure 2B, clusters 3, 8 and 14). The former group was generally already open in the morula, while the latter group only became open in the ICM. “

      Minor Points:

      (1) Lines 153-159: The figure panel does not show obvious enrichment of SOX2 signals or significant differences in H3K27ac signals across clusters, thus not supporting the claim.

      We hope to be able to explain this.

      Line 153-159 refer to two datasets:  Figure Supplement 3C and 3D.

      In Figure Supplement 3C, the average plots above the heatmaps show that the decreased ATAC-seq peaks (the indigo lines) have higher enrichment with Sox2 CUT&RUN signals than the increased or unchanged peaks (the yellow and light blue lines, respectively).

      In Figure Supplement 3D, the average plots indicate that H3K27ac signals around the center of the decreased ATAC-seq peaks (the indigo line) show higher enrichment compared to the unaltered and decreased groups (the light blue and yellow lines, respectively). Notably, H3K27ac enrichment appears slightly offset from the central nucleosome-free regions.

      (2) Lines 189-190: The term "identify" is overstated for the integrative analysis of RNA-seq and ATAC-seq, which typically helps infer TF targets rather than definitively identifying them.

      You are right. We have replaced “identify” with “infer” in the revised manuscript.

      (3) The Discussion is lengthy and should be condensed.

      We have shortened the discussion in the revised manuscript.

    1. eLife Assessment

      This analysis of the formation of the oral-aboral body axis in cnidarians, the sister group of bilaterians, is a significant and fundamental contribution to the field of Wnt signalling and planar cell polarity. The evidence supporting the conclusions is compelling and has the potential to contribute to a deeper understanding of the origin and evolution of Wnt signalling in metazoans. These findings will be of broad interest to developmental and evolutionary biologists.

    2. Reviewer #1 (Public review):

      Summary:

      This noteworthy paper examines the role of planar cell polarity and Wnt signalling in the body axis formation of the hydrozoan Clytia. In contrast to the freshwater polyp Hydra or the sea anemone Nematostella, Clytia represents a cnidarian model system with a complete life cycle (planula-polyp-medusa). In this species, classical experiments have demonstrated that a global polarity is established from the oral end of the embryos (Freeman, 1981). Prior research has demonstrated that Wnt3 plays a role in the formation of the oral organiser in Clytia and other cnidarians, acting in an autocatalytic feedback loop with β-catenin. However, the question of whether and to what extent an oral-aboral gradient of Wnt activity is established remained unanswered. This gradient is thought to control both tissue differentiation and tissue polarity. The planar cell polarity (PCP) pathway has been linked to this polarity, although it is generally considered to be β-catenin independent.

      The authors have conducted a series of sophisticated experiments utilising morpholinos, mRNA microinjection, and immunofluorescent visualisation of PCP. The objective of these experiments was to address the function of Wnt3, β-catenin, and PCP core proteins in the coordination of the global polarity of Clytia embryos. The authors conclude that PCP plays a role in regulating polarity along the oral-aboral axis of embryos and larvae. This offers a conceivable explanation for how polarity information is established and distributed globally during Clytia embryogenesis, with implications for our understanding of axis formation in cnidarians and the evolution of Wnt signalling in general. While the experiments are well-designed and executed, there are some criticisms, questions, or suggestions that should be addressed.

      Comments:

      Beautiful and solid experiments to clarify the role of canonical Wnt signalling and PCP core factors in coordinating planar cell polarity. However, there are also several points that should be addressed.

      (1) Wnt3 cue and global PCP. PCP has been described in detail in a previous paper on Clytia (Momose et al, 2012): its orientation along the oral-aboral body axis (ciliary basal body positioning studies), and its function in directional polarity during gastrulation (Stbm-, Fz1-, and Dsh-MO experiments). I wonder if this part could be shortened. What is new, however, are the knockdown and Wnt3-mRNA rescue experiments, which provide a deeper insight into the link between Wnt3 function in the blastopore organiser as a source or cue for axis formation. These experiments demonstrate that the Wnt3 knockdown induces defects equivalent to PCP factor knockdown, but can be rescued by Wnt3-mRNA injection, even at a distance of 200 µm away from the Wnt-positive area. The experimental set-up of these new molecular experiments follows in important aspects those of Freeman's experiments of 1981 (who in turn was motivated to re-examine Teissier's work of 1931/1933 ...). Freeman did not use the term "global polarity" but the concept of an axis-inducing source and a long-range tissue polarity can be traced back to both researchers.

      (2) PCP propagation and β-catenin. The central but unanswered question in this study focuses on the interaction between Wnt3 and PCP and the propagation of PCP. Wnt3 has been described in cnidarians but also in vertebrates and insects as a canonical Wnt interacting with β-catenin in an autocatalytic loop. The surprising result of this study is that the action of Wnt3 on PCP orientation is not inhibited in the presence of a dominant-negative form of CheTCF (dnTCF) ruling out a potential function of β-catenin in PCP. This was supported by studies with constitutively active β-catenin (CA-β-cat) mRNA which was unable to restore PCP coordination nor elongation of Wnt3-depleted embryos but did restore β-catenin-dependent gastrulation. Based on these data, the authors conclude that Wnt3 has two independent roles: Wnt/β-catenin activation and initial PCP orientation (two-step model for PCP formation). However, the molecular basis for the interaction of Wnt3 with the PCP machinery and how the specificity of Wnt3 for both pathways is regulated at the level of Wnt-receiving cells (Fz-Dsh) remain unresolved. Also, with respect to PCP propagation, there is no answer with respect to the underlying mechanisms. The authors found that PCP components are expressed in the mid-blastula stage, but without any further indication of how the signal might be propagated, e.g., by a wavefront of local cell alignment. Here, it is necessary to address the underlying possible cellular interactions more explicitly.

      (3) The proposed two-step model for PCP formation has important evolutionary implications in that it excludes the current alternate model according to which a long-range Wnt3-gradient orients PCP ("Wnt/β-catenin-first"). Nevertheless, the initial PCP orientation by Wnt3 - as proposed in the two-step model - is not explained at all on the molecular level. Another possible, but less well-discussed and studied option for linking Wnt3 with PCP action could be the role of other Wnt pathways. The authors present compelling evidence that Wnt3 is the most highly expressed Wnt in Clytia at all stages of development. The authors convincingly show that Wnt3 is the most highly expressed Wnt in Clytia at all stages of development (Figure S1). However, Wnt7 is also more highly expressed, which makes it a candidate for signal transduction from canonical Wnts to PCP Wnts. An involvement of Wnt7 in PCP regulation has been described in vertebrates (http://dx.doi.org/10.1016/j.celrep.2013.12.026). This would challenge the entire discussion and speculation on the evolutionary implications according to which PCP Wnt signaling comes first (PCP-first scenario") and canonical Wnt signaling later in metazoan evolution.

      (4) The discussion, including Figure 6, is strongly biased towards the traditional evolutionary scenario postulating a choanzoan-sponge ancestry of metazoans. Chromosome-linkage data of pre-metazoans and metazoans (Schulz et al., 2023; https://doi.org/10 (1038/s41586-023-05936-6) now indicate a radically different scenario according to which ctenophores represent the ancestral form and are sister to sponges, cnidarians and bilaterians (the Ctenophora-sister hypothesis). This has also implications for the evolution of Wnt signalling, as discussed in the recent Nature Genetics Review by Holzem et al. (2024) (https://doi.org/10.1038/s41576-024-00699-w). Furthermore, it calls into question the hypothesis of a filter-feeding multicellular gastrula-like ancestor as proposed by Haeckel (Maegele et al., 2023). These papers have not yet been referenced, but they would provide a more robust discussion.

    3. Reviewer #2 (Public review):

      Summary:

      Canonical Wnt signaling has previously been shown to be responsible for correct patterning of the oral-aboral axis as well as germ layer formation in several cnidarians. In the post-gastrula stage, the planula larvae are not only elongated, they have a specific swimming direction due to the decentralized cellular positioning and slanted anchoring of the cilia. This in turn is in most other animals the result of a Wnt-Planar-cell polarity pathway. This paper by Uveira et al investigates the role of Wnt3 signaling in serving as a local cue for the PCP pathway which then is responsible for the orientation of the cilia and elongation of the planula larva of the hydrozoan Clytia hemisphaerica. Wnt3 was shown before to activate the canonical pathway via ß-catenin and to act as an axial organizer. The authors provide compelling evidence for this somewhat unusual direct link between the pathways through the same signaling molecule, Wnt3. In conclusion, they propose a two-step model: (1) local orientation by Wnt3 secretion and (2) global propagation by the PCP pathway over the whole embryo.

      Strengths:

      In a series of elegant and also seemingly sophisticated experiments, they show that Wnt3 activates the PCP pathway directly, as it happens in the absence of canonical Wnt signaling (e.g. through co-expression of dnTCF). Conversely, constitutive active ß-catenin was not able to rescue PCP coordination upon Wnt3 depletion, yet restored gastrulation. This uncouples the effect of Wnt3 on axis specification and morphogenetic movements from the elongation via PCP. Through transplantation of single blastomeres providing a local source of Wnt3, they also demonstrate the reorganization of cellular polarity immediately adjacent to the Wnt3-expressing cell patch. These transplantation experiments also uncover that mechanical cues can also trigger polarization, suggesting a mechanotransduction or direct influence on subcellular structures, e.g. actin fiber orientation.

      This is a beautiful and elegant study addressing an important question. The results have significant implications also for our understanding of the evolutionary origin of axis formation and the link of these two ancient pathways, which in most animals are controlled by distinct Wnt ligands and Frizzled receptors. The quality of the data is stunning and the paper is written in a clear and succinct manner. This paper has the potential to become a widely cited milestone paper.

      Weaknesses:

      I can not detect any major weaknesses. The work only raises a few more follow-up questions, which the authors are invited to comment on.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      The manuscript by Oleh et al. uses in vitro electrophysiology and compartmental modeling (via NEURON) to investigate the expression and function of HCN channels in mouse L2/3 pyramidal neurons. The authors conclude that L2/3 neurons have developmentally regulated HCN channels, the activation of which can be observed when subjected to large hyperpolarizations. They further conclude via blockade experiments that HCN channels in L2/3 neurons influence cellular excitability and pathway-specific EPSP kinetics, which can be neuromodulated. While the authors perform a wide range of slice physiology experiments, concrete evidence that L2/3 cells express functionally relevant HCN channels is limited. There are serious experimental design caveats and confounds that make drawing strong conclusions from the data difficult. Furthermore, the significance of the findings is generally unclear, given modest effect sizes and a lack of any functional relevance, either directly via in vivo experiments or indirectly via strong HCN-mediated changes in known operations/computations/functions of L2/3 neurons.

      Specific points:

      (1) The interpretability and impact of this manuscript are limited due to numerous methodological issues in experimental design, data collection, and analysis. The authors have not followed best practices in the field, and as such, much of the data is ambiguous and/or weak and does not support their interpretations (detailed below). Additionally, the authors fail to appropriately explain their rationale for many of their choices, making it difficult to understand why they did what they did. Furthermore, many important references appear to be missing, both in terms of contextualizing the work and in terms of approach/method. For example, the authors do not cite Kalmbach et al 2018, which performed a directly comparable set of experiments on HCN channels in L2/3 neurons of both humans and mice. This is an unacceptable omission. Additionally, the authors fail to cite prior literature regarding the specificity or lack thereof of Cs+ in blocking HCN. In describing a result, the authors state "In line with previous reports, we found that L2/3 PCs exhibited an unremarkable amount of sag at 'typical' current commands" but they then fail to cite the previous reports.

      We thank the reviewer for the thorough examination of our manuscript; however, we disagree with many of the raised concerns for several reasons, as detailed here:

      To address the lack of certain citations, we would like to emphasize that in the introduction section, we did initially focus on the several decades-long line of investigation into the HCN channel content of layer 2/3 pyramidal cells (L2/3 PCs), where there has undoubtedly been some controversy as to their functional contribution. We did not explicitly cite papers that claimed to find no/little HCN channels/sag- although this would be a significant list of publications from some excellent investigators, as methods used may have differed from ours leading to different interpretations. Simply stated, unless one was explicitly looking for HCN in L2/3 PCs, it might go unobserved. However, we now addressed this more clearly in the revision:

      Just to take one example: in the publication mentioned by the reviewer (Kalmbach et al 2018), the investigators did not carry out voltage clamp or dynamic clamp recordings, as we did in our work here. Furthermore, the reported input resistance values in the aforementioned paper were far above other reports in mice (Routh et al. 2022, Brandalise et al 2022, Hedrick et al 2012; which were similar to our findings here), suggesting that recordings in Kalmbach were carried out at membrane potentials where HCN activation may be less available (Routh, Brager and Johnston 2022).

      Another reason for some mixed findings in the field is undoubtedly due to the small/nonexistent sag in L2/3 current clamp recordings (in mice). We also observed a very small sag, which can be explained by the following:  The ‘sag’ potential is a biphasic voltage response emerging from a relatively fast passive membrane response and a slower Ih activation. In L2/3 PCs, hyperpolarization-activated currents are apparently faster than previously described, and are located proximally (Figure 2 & Figure 5). Therefore, their recruitment in mouse L2/3 PCs is on a similar timescale to the passive membrane response, resulting in a more monophasic response. We now include a more full set of citations in the updated introduction section, to highlight the importance of HCN channels in L2/3 PCs in mice (and other species).

      The justification for using cesium (i.e., ‘best practices’) is detailed below.

      (2) A critical experimental concern in the manuscript is the reliance on cesium, a nonspecific blocker, to evaluate HCN channel function. Cesium blocks HCN channels but also acts at potassium channels (and possibly other channels as well). The authors do not acknowledge this or attempt to justify their use of Cs+ and do not cite prior work on this subject. They do not show control experiments demonstrating that the application of Cs+ in their preparation only affects Ih. Additionally, the authors write 1 mM cesium in the text but appear to use 2 mM in the figures. In later experiments, the authors switch to ZD7288, a more commonly used and generally accepted more specific blocker of HCN channels. However, they use a very high concentration, which is also known to produce off-target effects (see Chevaleyre and Castillo, 2002). To make robust conclusions, the authors should have used both blockers (at accepted/conservative concentrations) for all (or at least most) experiments. Using one blocker for some experiments and then another for different experiments is fraught with potential confounds.

      To address the concerns regarding the usage of cesium to block HCN channels, we would like to state that neither cesium nor ZD-7288 are without off-target effects, however in our case the potential off-target effects of external cesium were deemed less impactful, especially concerning AP firing output experiments. Extracellular cesium has been widely accepted as a blocker of HCN channels (Lau et al. 2010, Wickenden et al. 2009, Rateau and Ropert 2005, Hemond et al. 2009, Yang et al. 2015, Matt et al. 2010). However, it is well known to act on potassium channels as well at higher concentrations, which has been demonstrated with intracellular and extracellular application (Puil et al. 1981, Fleidervish et al. 2008, Williams et al. 1991, 2008).

      Although we initially performed ‘internal’ control experiments to ensure the cesium concentration was unlikely to greatly block voltage gated K+ channels during our recordings, we recognize these were not included in the original manuscript. These are detailed as follows: during our recordings cesium had no significant effect on action potential halfwidth, ruling out substantial blocking of potassium channels, nor did it affect any other aspects of suprathreshold activity (now reported in results, page 4 - line 113). Furthermore, we observed similar effects on passive properties (resting membrane potential, input resistance) following ZD-7288 as with cesium, which we now also updated in our figures (Supplementary Figure 1). We did acknowledge that ZD-7288 is a widely accepted blocker of HCN, and for this reason we carried out some of our experiments using this pharmacological agent instead of cesium.

      On the other hand, ZD-7288 suffers from its own side effects, such as potential effects on sodium channels (Wu et al. 2012) and calcium channels (Sánchez-Alonso et al. 2008, Felix et al. 2003). As our aim was to provide functional evidence for the importance of HCN channels, we initially deemed these potential effects unacceptable in experiments where AP firing output (e.g., in cell-attached experiments) was measured. Nonetheless, in new experiments now included here, we found the effects of ZD and cesium on AP output were similar as shown in new Supplemental Figure 1.

      Many experiments were supported by complementary findings using external cesium and ZD-7288. For example, the effect of ZD-7288 on EPSPs was confirmed by similar synaptic stimulation experiments using cesium. This is important, as synaptic inputs of L2/3 PCs are modulated by both dendritic sodium (Ferrarese et al. 2018) and calcium channels (Landau 2022), therefore the application of ZD-7288 alone may have been difficult to interpret in isolation. We thank the reviewer for bringing up this important point.

      (3) A stronger case could be made that HCN is expressed in the somatic compartment of L2/3 cells if the authors had directly measured HCN-isolated currents with outside-out or nucleated patch recording (with appropriate leak subtraction and pharmacology). Whole-cell voltage-clamp in neurons with axons and/or dendrites does not work. It has been shown to produce erroneous results over and over again in the field due to well-known space clamp problems (see Rall, Spruston, Williams, etc.). The authors could have also included negative controls, such as recordings in neurons that do not express HCN or in HCN-knockout animals. Without these experiments, the authors draw a false equivalency between the effects of cesium and HCN channels, when the outcomes they describe could be driven simply by multiple other cesium-sensitive currents. Distortions are common in these preparations when attempting to study channels (see Williams and Womzy, J Neuro, 2011). In Fig 2h, cesium-sensitive currents look too large and fast to be from HCN currents alone given what the authors have shown in their earlier current clamp data. Furthermore, serious errors in leak subtraction appear to be visible in Supplementary Figure 1c. To claim that these conductances are solely from HCN may be misleading.

      We disagree with the argument that “Whole-cell voltage-clamp in neurons with axons and/or dendrites does not work”. Although this method is not without its confounds (i.e. space clamp), it is still a useful initial measure as demonstrated countless times in the literature. However, the reviewer is correct that the best approach to establish the somatodendritic distribution of ion channels is by direct somatic and dendritic outside-out patches. Due to the small diameter of L2/3 PC dendrites, these experiments haven’t been carried out yet in the literature for any other ion channel either to our knowledge. Mapping this distribution electrophysiologically may be outside the scope of the current manuscript, but it was hard for us to ignore the sheer size of the Cs<sup>+</sup> sensitive hyperpolarizing currents in whole cell. Thus, we will opt to report this data.

      Also, we should point out that space clamp-related errors manifest in the overestimation of frequency-dependent features, such as activation kinetics, and underestimation of steady-state current amplitudes. The activation time constant of our measured currents are somewhat faster than previously reported; reducing major concerns regarding space clamp errors. Furthermore, we simply do not understand what “too large… to be from HCN currents” means. Our voltage-clamp measured currents are similar to previously reported HCN currents (Meng et al. 2011, Li 2011, Zhao et al. 2019, Yu et al. 2004, Zhang et al. 2008, Spinelli et al. 2018, Craven et al. 2006, Ying et al. 2012, Biel et al. 2009).

      Furthermore, we should point out that our measured currents activated at hyperpolarized voltages, had the same voltage dependence as HCN currents, did not show inactivation, influenced both input resistance and resting membrane potential, and are blocked by low concentration extracellular cesium. Each of these features would point to HCN.

      (4) The authors present current-clamp traces with some sag, a primary indicator of HCN conductance, in Figure 2. However, they do not show example traces with cesium or ZD7288 blockade. Additionally, the normalization of current injected by cellular capacitance and the lack of reporting of input resistance or estimated cellular size makes it difficult to determine how much current is actually needed to observe the sag, which is important for assessing the functional relevance of these channels. The sag ratio in controls also varies significantly without explanation (Figure 6 vs Figure 7). Could this variability be a result of genetically defined subgroups within L2/3? For example, in humans, HCN expression in L2/3 varies from superficial and deep neurons. The authors do not make an effort to investigate this. Regardless of inconsistencies in either current injection or cell type, the sag ratio appears to be rather modest and similar to what has already been reported previously in other papers.

      We thank the reviewer for pointing out that our explanation for the modest sag ratio might have not been sufficient to properly understand why this measurement cannot be applied to layer 2/3 pyramidal cells. Briefly: sag potential emerges from a relatively (compared to I<sub>h</sub>) fast passive membrane response and a slower HCN recruitment. The opposing polarity and different timescales of these two mechanisms results in a biphasic response called “sag” potential. However, if the timescale of these two mechanisms is similar, the voltage response is not predicted to be biphasic. We have shown that hyperpolarization activated currents in our preparations are fast and proximal, therefore they are recruited during the passive response (see Figure 2g.). This means that although a substantial amount of HCN currents are activated during hyperpolarization, their activation will not result in substantial sag. Therefore, sag ratio measurement is not necessarily applicable to approximate the HCN content of mouse L2/3 PCs. We would like to emphasize that sag ratio measurements are correct in case of other cell types (i.e. L5 and CA1 PCs_,_ and our aim is not to discredit the method, but rather to show that it cannot be applied similarly in the case of mouse L2/3 PCs.

      Our own measurements, similar to others in the literature show that L2/3 PCs exhibit modest sag ratios, however, this does not mean that HCN is not relevant. I<sub>h</sub> activation in L2/3 PCs does not manifest in large sag potential but rather in a continuous distortion of steady-state responses (Figure 2b.). The reviewer is correct that L2/3 PCs are non-homogenous, therefore we sampled along the entire L2/3 axis. This yielded some potential variability in our results (i.e., passive properties); yet we did not observe any cells where hyperpolarizing-activated/Cs<sup>+</sup>-sensitive currents could not be resolved. As structural variability of L2/3 cells does result in variability in cellular capacitance, we compensated for this variability by injecting cellular capacitance-normalized currents. Our measured cellular capacitances were in accordance with previously published values, in the range of 50-120 pF. Therefore, the injected currents were not outside frequently used values. Together, we would like to state that whether substantial sag potential is present or not, initial estimates of the HCN content for each L2/3 PC should be treated with caution.

      (5) In the later experiments with ZD7288, the authors measured EPSP half-width at greater distances from the soma. However, they use minimal stimulation to evoke EPSPs at increasingly far distances from the soma. Without controlling for amplitude, the authors cannot easily distinguish between attenuation and spread from dendritic filtering and additional activation and spread from HCN blockade. At a minimum, the authors should share the variability of EPSP amplitude versus the change in EPSP half-width and/or stimulation amplitudes by distance. In general, this kind of experiment yields much clearer results if a more precise local activation of synapses is used, such as dendritic current injection, glutamate uncaging, sucrose puff, or glutamate iontophoresis. There are recording quality concerns here as well: the cell pictured in Figure 3a does not have visible dendritic spines, and a substantial amount of membrane is visible in the recording pipette. These concerns also apply to the similar developmental experiment in 6f-h, where EPSP amplitude is not controlled, and therefore, attenuation and spread by distance cannot be effectively measured. The outcome, that L2/3 cells have dendritic properties that violate cable theory, seems implausible and is more likely a result of variable amplitude by proximity.

      To resolve this issue, we made a supplementary figure showing elicited amplitudes, which showed no significant distance dependence and minimal variability (new Supplementary Figure 6). We thank the reviewer for suggesting an amplitude-halfwidth comparison control (now included as new Supplementary Figure 6).). To address the issue of the non-visible spines, we would like to note that these images are of lower magnification and power to resolve them. The presence of dendritic spines was confirmed in every recorded pyramidal cell observed using 2P microscopy at higher magnification.

      We would like to emphasize that although our recordings “seemingly” violated the cable theory, this is only true if we assume a completely passive condition. As shown in our manuscript, cable theory was not violated, as the presence of NMDA receptor boosting explained the observed ‘non-Rallian’ phenomenon.

      (6) Minimal stimulation used for experiments in Figures 3d-i and Figures 4g-h does not resolve the half-width measurement's sensitivity to dendritic filtering, nor does cesium blockade preclude only HCN channel involvement. Example traces should be shown for all conditions in 3h; the example traces shown here do not appear to even be from the same cell. These experiments should be paired (with and without cesium/ZD). The same problem appears in Figure 4, where it is not clear that the authors performed controls and drug conditions on the same cells. 4g also lacks a scale bar, so readers cannot determine how much these measurements are affected by filtering and evoked amplitude variability. Finally, if we are to believe that minimal stimulation is used to evoke responses of single axons with 50% fail rates, NMDA receptor activation should be minimal to begin with. If the authors wish to make this claim, they need to do more precise activation of NMDA-mediated EPSPs and examine the effects of ZD7288 on these responses in the same cell. As the data is presented, it is not possible to draw the conclusion that HCN boosts NMDA-mediated responses in L2/3 neurons.

      As stated in the figure legends, the control and drug application traces are from the same cell, both in figure 3 and figure 4, and the scalebar is not included as the amplitudes were normalized for clarity. We have address the effects of dendritic filtering above in answer (5), and cesium blockade above in answer (2). To reiterate, dendritic filtering alone cannot explain our observations, and cesium is often a better choice for blocking HCN channels compared to ZD-7288, which blocks sodium channels as well.

      When an excitatory synaptic signal arrives onto a pyramidal cell in typical conditions, neurotransmitter sensitive receptors transmit a synaptic current to the dendritic spine. This dendritic spine is electrically isolated by the high resistance of the spine neck and due to the small membrane surface of the spine, the synaptic current can elicit remarkably large voltage changes. These voltage changes can be large enough to depolarize the spine close to zero millivolts upon even single small inputs (Jayant et al. 2016). Therefore, to state that single inputs arriving to dendritic spines cannot be large enough to recruit NMDA receptor activation is incorrect. This is further exemplified by the substantial literature showing ‘miniature’ NMDA recruitment via stochastic vesicle release alone.

      (7) The quality of recordings included in the dataset has concerning variability: for example, resting membrane potentials vary by >15-20 mV and the AP threshold varies by 20 mV in controls. This is indicative of either a very wide range of genetically distinct cell types that the authors are ignoring or the inclusion of cells that are either unhealthy or have bad seals.

      Although we are aware of the diversity of L2/3 PCs, resolving further layer depth differences is outside the scope of our current manuscript. However, as shown in Kalmbech et al, resting membrane potential can greatly vary (>15-20 mV) in L2/3 PCs depending on distance from pia. We acknowledge that the variance in AP threshold is large and could be due to genetically distinct cell types.

      (8) The authors make no mention of blocking GABAergic signaling, so it must be assumed that it is intact for all experiments. Electrical stimulation can therefore evoke a mixture of excitatory and inhibitory responses, which may well synapse at very different locations, adding to interpretability and variability concerns.

      We thank the reviewer for pointing out our lack of detail regarding the GABAergic signaling blocker SR 95531. We did include this drug in our recordings of (50Hz stim.) signal summation, so GABAergic responses did not contaminate our recordings. We now included this information in the results section (page 5) and the methods section (page 15)

      (9) The investigation of serotonergic interaction with HCN channels produces modest effect sizes and suffers the same problems as described above.

      We do not agree with the reviewer that 50% drop in neuronal AP firing responses (Figure 7b) was a modest effect size. Thus, we opted to keep this data in the manuscript.

      (10) The computational modeling is not well described and is not biologically plausible. Persistent and transient K channels are missing. Values for other parameters are not listed. The model does not seem to follow cable theory, which, as described above, is not only implausible but is also not supported by the experimental findings.

      The model was downloaded from the Cell Type Database from the Allen Institute, with only minor modifications including the addition of dendritic HCN channels and NDMA receptors- which were varied along a wide parameter space to find a ‘best fit’ to our observations. These additions were necessary to recapitulate our experimental findings. We agree the model likely does not fully recapitulate all aspects of the dendrites, which as we hope to convey in this manuscript, are not fully resolved in mouse L2/3 PCs. This is a previously published neuronal model, and despite its potential shortcomings, is one among a handful of open-source neuronal models of a fully reconstructed L2/3 PC.

      Reviewer #2 (Public Review):

      Summary:

      This paper by Olah et al. uncovers a previously unknown role of HCN channels in shaping synaptic inputs to L2/3 cortical neurons. The authors demonstrate using slice electrophysiology and computational modeling that, unlike layer 5 pyramidal neurons, L2/3 neurons have an enrichment of HCN channels in the proximal dendrites. This location provides a locus of neuromodulation for inputs onto the proximal dendrites from L4 without an influence on distal inputs from L1. The authors use pharmacology to demonstrate the effect of HCN channels on NMDA-mediated synaptic inputs from L4. The authors further demonstrate the developmental time course of HCN function in L2/3 pyramidal neurons. Taken together, this a well-constructed investigation of HCN channel function and the consequences of these channels on synaptic integration in L2/3 pyramidal neurons.

      Strengths:

      The authors use careful, well-constrained experiments using multiple pharmacological agents to asses HCN channel contributions to synaptic integrations. The authors also use a voltage clamp to directly measure the current through HCN channels across developmental ages. The authors also provide supplemental data showing that their observation is consistent across multiple areas of the cerebral cortex.

      Weaknesses:

      The gradient of the HCN channel function is based almost exclusively on changes in EPSP width measured at the soma. While providing strong evidence for the presence of HCN current in L2/3 neurons, there are space clamp issues related to the use of somatic whole-cell voltage clamps that should be considered in the discussion.

      We thank the reviewer for pointing out our careful and well-constrained experiments and for making suggestions. The potential effects of space clamp errors are detailed in the extended explanations under Reviewer 1, Specific points (3).

      Reviewer #3 (Public Review):

      Summary:

      The authors study the function of HCN channels in L2/3 pyramidal neurons, employing somatic whole-cell recordings in acute slices of visual cortex in adult mice and a bevy of technically challenging techniques. Their primary claim is a non-uniform HCN distribution across the dendritic arbor with a greater density closer to the soma (roughly opposite of the gradient found in L5 PT-type neurons). The second major claim is that multiple sources of long-range excitatory input (cortical and thalamic) are differentially affected by the HCN distribution. They further describe an interesting interplay of NMDAR and HCN, serotonergic modulation of HCN, and compare HCN-related properties at 1, 2 and 6 weeks of age. Several results are supported by biophysical simulations.

      Strengths:

      The authors collected data from both male and female mice, at an age (6-10 weeks) that permits comparison with in vivo studies, in sufficient numbers for each condition, and they collected a good number of data points for almost all figure panels. This is all the more positive, considering the demanding nature of multi-electrode recording configurations and pipette-perfusion. The main strength of the study is the question and focus.

      Weaknesses:

      Unfortunately, in its present form, the main claims are not adequately supported by the experimental evidence: primarily because the evidence is indirect and circumstantial, but also because multiple unusual experimental choices (along with poor presentation of results) undermine the reader's confidence. Additionally, the authors overstate the novelty of certain results and fail to cite important related publications. Some of these weaknesses can be addressed by improved analysis and statistics, resolving inconsistent data across figures, reorganizing/improving figure panels, more complete methods, improved citations, and proofreading. In particular, given the emphasis on EPSPs, the primary data (for example EPSPs, overlaid conditions) should be shown much more.

      However, on the experimental side, addressing the reviewer's concerns would require a very substantial additional effort: direct measurement of HCN density at different points in the dendritic arbor and soma; the internal solution chosen here (K-gluconate) is reported to inhibit HCN; bath-applied cesium at the concentrations used blocks multiple potassium channels, i.e. is not selective for HCN (the fact that the more selective blocker ZD7288 was used in a subset of experiments makes the choice of Cs+ as the primary blocker all the more curious); pathway-specific synaptic stimulation, for example via optogenetic activation of specific long-range inputs, to complement / support / verify the layer-specific electrical stimulation.

      We thank the reviewer for their very careful examination of our manuscript and helpful suggestions. We addressed the concerns raised in the review and presented more raw traces in our figures. Although direct dendritic HCN mapping measurements are outside the scope of the current manuscript due to the morphological constraints presented by L2/3 PCs (which explains why no other full dendritic nonlinearity distribution has been described in L2/3 PCs with this method), we nonetheless supplemented our manuscript with additional suggested experiments as suggested. For example, we included the excellent suggestion of pathway-specific optogenetic stimulation to further validate the disparate effect of HCN channels for distal and proximal inputs. We agree that ZD-7288 is a widely accepted blocker of HCN channels. However, the off-target effects on sodium channels may have significantly confounded our measurements of AP output using extracellular stimulation. Therefore, we chose low concentration cesium as the primary blocker for those experiments, but now validated several other Cs<sup>+</sup>-based results with ZD-7288 as well.

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      I have some issues that need clarification or correction.

      (1) On page 3, line 90, the authors state "We found that bath application of Cs+ (1mM)..." but the methods and Figure 1 state "2mM Cs+". Please check and correct.

      Correct, typo corrected.

      (2) Related to Cs+ application, the methods state that "CsMeSO4 (2mM) was bath applied..." Is this correct? CsMeSO4 is typically used intracellularly while CsCl is used extracellularly. If so, please justify. If not, please correct.

      It is correct. The justification for not using CsCl selectively extracellularly is that introducing intracellular chloride ions can significantly alter basic biophysical properties, unrelated to the cesium effect. However, no similar distinction has been made for CsMeSO4, which would exclude the use of this drug extracellularly.

      (3) The authors normalize the current injections by cell capacitance (pA/pF). Was this done because there is a significant variance in cell morphology? A bit of justification for why the authors chose to normalize the current injection this way would help. If there is significant variation in cell capacitance across cells (or developmental ages), the authors could also include these data.

      Indeed, we choose to normalize current injection to cellular capacitance due to the markedly different morphology of deep and superficial L2/3 PCs. Deeper L2/3 PCs have a pronounced apical branch, closely resembling other pyramidal cell types such as L5 PCs, while superficial L2/3 PC lack a thick main apical branch and instead are equipped with multiple, thinner apical dendrites. This morphological variation would yield an inherent bias in several of the reported measurements, therefore we corrected for it by normalizing current injection to cellular capacitance, similar to our previous recent publications (Olah, Goettemoeller et al., 2022, Goettemoeller et al. 2024, Kumar et al. 2024).

      (4) On page 15, line 445, the section heading is "PV cell NEURON modeling". Is this a typo? The models are of L2/3 pyramidal neurons, correct?  

      Correct, typo corrected.

      (5) Figures 3F and 3I are plots of the voltage integral for different inputs before and after Cs+. The y-axis label units are "pA*ms". This should be "mV*ms" for a voltage integral.  

      Correct, typo corrected.

      (6) On page 9, line 273, the text reads "Voltage clamp experiments revealed that the rectification of steady-state voltage responses to hyperpolarizing current injection was amplified with 5-CT (Fig. 7c)". Both the text and Figure 7C describe current clamp, not voltage clamp, recordings. Please check and correct.

      Correct, typo corrected.

      (7) Figure 2i looks to be a normalized conductance vs voltage (i.e. activation) plot. The y-axis shows 0-1 but the units are in nS. Is that a coincidence or an error?

      Correct, typo corrected.

      Reviewer #3 (Recommendations For The Authors):

      This is your paper. My comments are my own opinion, I don't expect you to agree or to respond. But I hope that what I wrote below will help you to understand my perspective.

      Please pardon my directness (and sheer volume) in this section - I have a lot of notes/thoughts and hope you may find some of them helpful. My high-level comments are unfortunately rather critical, and in (small) part that is because I encountered too many errors/typos/ambiguities in figures, legend, and text. I expect many would be caught with good proofreading, but uncorrected caused confusion on my part, or an inability to interpret your figures with confidence, given some ambiguity.

      The paper reads a bit like patchwork - likely a result of many "helpful" reviewers who came before me. Consider starting with and focusing on the synaptic findings, expanding the number of figures and panels dedicated to that, showing example traces for all conditions, and giving yourself the space to portray these complex experiments and results. While I'm not a fan of a large number of supplemental figures, I feel you could move the "extra" results to the supplementals to improve the focus and get right to the meat of it.

      For me, the main concern is that the evidence you present for the non-uniform HCN distribution is rather indirect. Ideally, I'd like to see patch recordings from various dendritic locations (as others have done in rats, at least; I'm not sure if L2/3 mice have had such conductance density measurements made in basal and apical dendrites). Otherwise, perhaps optical mapping, either functional or via staining. I also mention some concerns about the choice of internal and cesium. More generally, I want to see more primary data (traces), in particular for the big synaptic findings (non-uniform, L1-vs-L4 differences, NMDAR).

      We thank the reviewer for the helpful suggestions. Indeed, direct patch clamp recording is widely considered to be the best method to identify dendritic ion channel distribution, however, we choose an in silico approach instead, for several reasons. Undoubtedly, one of the main reasons to omit direct dendritic recordings was that due to the uniquely narrow apical dendrites this method is extremely challenging, with no previous examples in the literature where isolated dendritic outside-out patch recordings were achieved from this cell type. However, there are theoretical considerations as well. In primates, it has been demonstrated that HCN1 channels are concentrated on dendritic spines (Datta et al., 2023) therefore direct outside-out recordings are not adequate in these circumstances. In future experiments we could directly target L2/3 PC dendrites for outside out recordings in order to resolve dendritic nonlinearity distribution, although a cell-attached methodology may be better suited due to the HCN biophysical properties being closely regulated by intracellular signaling pathways.

      The introduction and Figures 1 and 2 are not so interesting and not entirely accurate: L2/3 do not have "abundant" HCN, nor is there an actual controversy about whether they have HCN. It's been clear (published) for years that they have about the same as all other non-PT neocortical pyramidal neurons (see e.g. Larkum 2007; Sheets 2011). Your own Figure 1A has a logarithmic scale and shows L2/3 as having the lowest expression (?) of all pyramidals and roughly 10x lower than L5 PT, but the text says "comparable", which is misleading.

      We thank the reviewer for this comment. Although there are sporadic reports in the literature about the HCN content of L2/3 PCs, most of these publications arrive to the same conclusion from the negligible sag potential (as the mentioned Larkum et al., 2007 publication); namely that L2/3 PCs do not contain significant amount of HCN channels. We have shown with voltage and current clamp recordings that this assumption is false, as sag potential is not a reliable indicator of HCN content in L2/3 PCs. With the term “controversial” we aimed to highlight the different conclusions of functional investigations (e.g. Sheets et al., 2011) and sag potential recordings (e.g. Larkum et al., 2007), regarding the importance of HCN channels in L2/3 PCs.

      Non-uniform HCN with distal lower density has already been published for a (rare) pyramidal neuron in CA1 (Bullis 2007), similar to what you found in L2/3, and different from the main CA1 population.

      We thank the reviewer for this suggestion. We have now included the mentioned citation in the introduction section (page 3).

      Express sag as a ratio or percentage, consistently. Figure out why in Figure 7 the average sag ratio is 0.02 while in Fig. S1 it is 0.07 (for V1) - that is a massive difference.

      The calculation of sag ratio is consistent across the manuscript (at -6pA.pF), except for experiments depicted in Fig. 7 where sag ratio was calculated from -2pA/pF steps. Explanation below:

      Sag should be measured at a common membrane potential, with each neuron receiving a current pulse appropriate to reach that potential. Your approach of capacitance-based may allow for the same, but it is not clear which responses are used to calculate a single sag value per cell (as in Figure 2d).

      Thank you, we now included this info in the methods section. Sag potential was measured at the -6 pA/pF step peak voltage, except for Fig. 7 as noted above. We have now included this discrepancy detail in the methods section (page 14 ). These recordings in Fig. 7 took significantly longer than any other recording in the manuscript, as it took a considerable time to reach steady-state response from 5-CT application. -6pA/pF is a current injection in the range of 400-800 pA, which was proven to be too severe for continued application in cells after more than an hour of recording. Accordingly, we decided to lower the hyperpolarizing current step in these recordings. The absolute value of sag is thus different in Fig. 7, but nonetheless the 5-CT effect was still significant. Notably, we probably wouldn’t have noticed the small sag in L2/3 here (and thus the entire study), save for the fact that we looked at -6pA/pF to begin.

      In a paper focused on HCN, I would have liked to see resonance curves in the passive characterization.

      We thank the reviewer for the suggestion. Resonance curves can indeed provide useful insights into the impact of HCN on a cell’s physiological behavior, however, these experiments are outside the scope of our current manuscript as without in vivo recordings, resonance curves do not contribute to the manuscript in our opinion.

      How did you identify L2/3? Did you target cells in L2 or L3 or in the middle, or did you sample across the full layer width for each condition? A quantitative diagram showing where you patched (soma) and where you stimulated (L1, L4) with actual measurements, would be helpful (supplemental perhaps). You mention in the text that some L2/3 don't have a tuft, suggesting some variability in morphology - some info on this would be useful, i.e. since you did fill at least some of the neurons (eg 3A), how similar/different are the dendritic arbors?

      We sampled the entire L2/3 region during our recordings. It has been published that deep and superficial L2/3  PCs are markedly different in their morphology, and a recent publication (Brandelise et al. 2023) has even separated these two subpopulations to broad-tufted and slender tufted pyramidal cells, which receive distinct subcortical inputs. Although this differentiation opens exciting avenues for future research, examining potential layer gradients in our dataset would warrant significantly higher sample numbers and is currently out of the scope of our manuscript.

      Distal vs proximal: this could use more clarification, considering how central it is to your results. What about a synapse on a basal dendrite, but 150 or 200 um from the soma, is that considered proximal? Is the distance to the soma you report measured along the 3D dendrite, along the 2D dendrite, as a straight line to the soma, or just relative to some layers or cortical markers? (I apologize if I missed this).

      We thank the reviewer for pointing out the missing description in the results section. We have amended this oversight (p15).  Furthermore, although deeper L3 PCs have characteristic apical and basal dendritic branches, when recordings were made from more superficial L2 cells, a large portion of their dendrites extended radially, which made their classification ambiguous. Therefore, we did not use “apical” and “basal” terminology in the paper to avoid confusion. Distances were measured along the 3D reconstructed surface of the recovered pyramidal cells. This information is now included in the methods.

      Line 445, "PV cell NEURON modeling" ... hmm. Everyone re-uses methods sections to some degree, but this is not confidence-inspiring, and also not from a proofreading perspective.

      We have corrected the typo.

      It seems that you constructed a new HCN NEURON mechanism when several have been published/reviewed already. Please explain your reasons or at least comment on the differences.

      There are slight differences in our model compared to previously published models. Nevertheless, we took a previously published HCN model as a base (Gasparini et al, 2004), and created our own model to fit our whole-cell voltage clamp recordings.

      Bath-applied Cs+ can change synaptic transmission (in the hippocampus; Chevaleyre 2002). But also ZD7288 has some such effects. Also, see (Harris 1995) for a Cs+ and ZD7288 comparison. As well as (Harris 1994) for more Cs+ side-effects (it broadens APs, etc). Bath-applied blockers may affect both long-range and local synapses in your recordings, via K-channels or perhaps presynaptic HCN (though I am aware of your Fig. 1e). Since you can do intracellular perfusion, you could apply ZD7288 postsynaptically (Sheets 2011), an elegant solution.

      We thank the reviewer for the suggestion. We were aware of the potential presynaptic effects of cesium (i.e., presynaptic Kv or other channel effects) and did measure PPR after cesium application (Fig. 1h), noting no effect. At Cs<sup>+</sup> concentrations used here, we now also include new data in the results showing no effect on somatically recorded AP waveform (i.e., representative of a Kv channel effect). As stated earlier for reviewer 1, we now performed additional experiments using either cesium or ZD-7288 for comparison (e.g., see updated Fig. 1; Supplementary Figure 1; Fig. 3b-e). Intracellular ZD re-perfusion is an elegant solution which we will absolutely consider in future experiments.

      K-Gluconate is reported to inhibit Ih (Velumian 1997), consider at least some control experiments with a different internal for the main synaptic finding - maybe you'll find no big change ...

      We thank the reviewer for the suggestion. Although K-Gluconate can inhibit HCN current, the use of this intracellular solution is often used in the literature to measure this current (Huang & Trussel 2014). We have chosen this intracellular solution to improve recording stability.  

      (Biel 2009) is a very comprehensive HCN review, you may find it useful.

      We thank the reviewer for bringing this to our attention, we have now included the citation in the introduction.

      "Hidden" in your title seems too much.

      We changed the title to more accurately describe our findings and removed ‘hidden’.

      While I'm glad you didn't record at room temperature, the choice of 30C seems a bit unfortunate - if you go to the trouble to heat the bath, why not at least 34C, which is reasonably standard as an approximation for physiological temperature?

      We thank the reviewer for pointing this out. The choice of 30C was made to approach physiological temperature levels, while preserving the slices for extended amounts of time which is a standard approach. Future experiments in vivo be performed to further understand the naturalistic relevance at ~37C.

      Line 506: do you mean "Hz" here? It's not a frequency, is it? I think it's a unitless ratio?

      Correct, we have amended the typo.

      Line 95: you have not shown that HCN is "essential" for "excess" AP firing.

      We have corrected the phrasing, we agree.

      Fig. 2b,c: is this data from a single example neuron, maybe the same neuron as in 2a? Or from all recorded neurons pooled?

      The data is from several recorded cells pooled.

      Fig. 3 (important figure):

      Why did you not use a paired test for panels e and f? You have the same number of neurons for each condition and the expectation is that you record each neuron in control and then in cesium condition, which would be a paired comparison. Or did you record only 1 condition per neuron?

      This figure presents your main finding (in my opinion). You should show examples of the synaptic responses, i.e. raw traces, for each condition and panel, and overlaid in such a way that the reader can immediately see the relevant comparison - it's worth the space it requires.

      We thank the reviewer for the suggestions. Traces are only overlaid in the paper when they come from the same cell. For Fig. 3d-i, EPSPs in every neuron were evoked in 2-3 different locations (i.e., 1-2 ‘L4’ locations for Type-I and Type-II synapses, and one ‘L1’ location in each) with the same stimulation pipette and one pharmacological condition per cell. Therefore two-sample t-test were used since the control and cesium conditions came from separate cells (i.e., separate observations). This was necessary, as we can never assume that the stimulating electrode can return back to the same synapse after moving it. We were not comfortable with showing overlaid traces from different cells, however, we did show representative traces from control and the Cs<sup>+</sup> conditions in Fig. 3h. Complementary ZD-7288 experiments can be found on panel b and c, where we did perform within-cell pharmacology (and thus used paired t-tests) from one stimulation area/cell. We hope these complementary experiments increase overall confidence as neither pharmacological approach is 100% without off-target effects. We now also included more overlaid traces where appropriate (i.e., Fig. 3b, and in the new  Fig. 3k experiments using within-cell pharmacology comparisons). We do realize these complementary approaches could cause confusion to the reader, and have now done our best to make the slightly different approaches in this Figure clearer in the results section.

      Consider repeating at least some of these critical experiments with ZD7288 instead of Cs+ (and not K-gluc), or even with ZD7288 pipette perfusion, if it's technically feasible here.

      We thank the reviewer for the suggestions. Although many of our recordings using Cs<sup>+</sup> already had complementary experiments (such as synaptic experiments Figure 3e vs Figure 3b), we recognize the need to extend the manuscript with more ZD-7288 experiments. We have now extended Figure 1 with three panels (Figure 1 c,d,e), which recapitulates a fundamental finding, the change in overall excitability upon HCN channel blockade, using ZD-7288 as well.

      Fig. 3a, why show a schematic (and weirdly scaled) stimulating electrode? Don't you have a BF photo showing the actual stimulating electrode, which you could trace to scale or overlay? Could you use this panel to indicate what counts as "distal" and what as "proximal", visually?

      The stimulating electrode was unfortunately not filled with florescent materials, therefore it was not captured during the z-stack.

      Fig. 3b: is the y-axis labeled correctly? A "100% change" would mean a doubling, but based on the data points here I think y=100% means "no change"?

      The scale is labeled correctly, 100% means doubling.

      Fig. 3b, c: again, show traces representing distal and proximal, not just one example (without telling us how far it was). And use those traces to illustrate the half-width measurement, which may be non-trivial.

      We have extended Figure 3b with an inset showing the effect of ZD-7288 on a proximal stimulating site. The legend now includes additional information indicating stimulating location 28 µm away from the soma in control conditions (black trace) and upon Z-7288 application (green trace).  

      Line 543, 549: it seems you swapped labels "h" and "i"?

      Typo corrected.

      Fig. 4b: to me, MK-801 only *partially* blocks amplification, but in the text L198 you write "abolish".

      We thank the reviewer for pointing this out. Indeed, there are several other subthreshold mechanisms that are still intact after pipette perfusion, which can cause amplification. We have now clarified this in the text (p7).

      Fig. 4e,f: what is the message? Uniform NMDAR? The red asterisk in (e) is at a proximal/distal ratio of roughly 1. I don't understand the meaning of the asterisk (the legend is too basic) and I'm surprised to see a ratio of 1 as the best fit, and also that the red asterisk is at a dendritic distance of 0 um in (f). This could use more explanation (if you feel it's relevant).

      We thank the reviewer for pointing this out. We have now included a better explanation in the results and figure legend. We have also updated the figure to make it clearer and added model traces in Fig. 4f, which correspond to example data from slices in Fig. 4g (both green). The graph suggests nonuniform, proximally abundant NMDA distribution. The color coding corresponds to the proximal EPSP halfwidth divided by distal EPSP halfwidth. It is true that the dendritic distance ‘center’ was best-fit very close to the soma, but also note the dispersion (distribution) half-width was >150mm, so there is quite a significant dendritic spread despite the proximal bias prediction. Based on this model there is likely NMDA spread throughout the entire dendrite, but biased proximally. Naturally, future work will need to map this at the spine level so this is currently an oversimplification. Nonetheless, a proximal NMDA bias was necessary to recapitulate findings from Fig. 3, and additional slice recordings in Fig. 4 were consistent with this interpretation.

      Fig. 4g: I feel your choice of which traces to overlay is focusing on the wrong question. As the reader, what I want to see here is an overlay of all 4 conditions for one pathway. If this is a sequential recording in a single cell (Cs, Cs+MK801, wash out Cs, MK801), then the overlay would be ideal and need not be scaled. Otherwise, you can scale it. But the L1/L4 comparison does not seem appropriate to me. I find myself trying to imagine what all the dark lines would look like overlaid, and all the light lines overlaid separately. Also, the time axis is missing from this panel. Consider a subtraction of traces (if appropriate).

      In these recordings, all EPSPs cells were measured using a stimulating electrode that was moved between L1 and L4 (only once, to keep the exact input consistent) to measure the different inputs in a single neuron. In separate sets of experiments, the same method was used but in the presence of Cs<sup>+</sup>, Cs<sup>+</sup> + MK-801, or MK-801 alone. This was the most controlled method in our hands for this type of approach, as drug wash outs were either impractical or not possible.  Overlaying four traces would have presented a more cluttered image, and were not actually performed experimentally. As our aim was to resolve the proximal-distal halfwidth relationship, therefore we deemed the within-cell L1 vs. L4 comparison appropriate. We have nonetheless added model traces in Fig. 4f, which correspond to example data from slices in Fig. 4g (both green). The bar graphs should serve also serve to illustrate the input-specific  relationship- i.e., that the only time the L1 and L4 EPSP relationship was inverted was in the presence of Cs<sup>+</sup> (green bars) and that this effect was occluded with simultaneous MK-801 in the pipette (red bars).

      Line 579: should "hyperpolarized" be depolarized?

      Corrected

      Fig. 5a: it looks like the HCN density is high in the most basal dendrites (black curve above), then drops towards the soma, then rises again in the apicals (red curve). Is that indeed how the density was modeled? If so, this is completely at odds with the impression I received from reading your text and experimental data - there, "proximal" seems to mean where the L4 axons are, and "distal" seems to mean where the L1 axons are, in other words, high HCN towards the pia and low HCN towards the white matter. But this diagram suggests a biphasic hill-valley-hill distribution of HCN (meaning there is a second "distal" region below the soma). In that case, would the laterally-distant basal dendrites also be considered distal? How does the model implement the distribution - is it 1D, 2D or 3D? As you can probably tell, this figure raised more questions for me and made me wonder why I don't have a better understanding yet of your definitions.

      We thank the reviewer for pointing this out. We agree our initial cartoon of the parameter fitting procedure was not accurate and should have just been depicted a single ‘curve’. We have now simplified it to better demonstrate what the model is testing, and also made the terms more consistent and accurate. There is no ‘second’ region in the model. We hope this better illustrates it now. We also edited the legend to be clearer. Because the model description in Fig. 4d suffered from similar shortcomings, we also modified it accordingly as well as the figure legend there.

      Fig. 5b: why is the best fit at a proximal/distal ratio of 1, yet sigma is 50 um?

      Proximal/distal bias on this figure was fitted to 0.985 (prox/distal ratio) as we modeled control conditions, with intact NDMA and HCN channels,  which closely approximated the control recording comparisons.

      Fig. 6h, Line 662: "vs CsMeSO4 ... for putative LGN events" The panel shows proximal vs distal, not control vs Cs+. What's going on here?

      Typo corrected.

      Fig. 7e: the ctrl sag ratio here averages 0.02, while in Fig. S1 the average (for V1 and others) is about 0.07.  Please refer to our answer given to the previous question regarding sag ratio measurements. Briefly, recordings made with 5-CT application were made using a less severe, -2 pA/pF current injection to test seg responses. This more modest hyperpolarization activated less HCN channels, therefore the sag ratio is lower compared to previously reported datapoints.

      We have included this explanation in the methods section (page 14)

      Now hear you are using a paired test for this pharmacology, but you didn't previously (see my earlier comments/questions).

      Paired t-test were used for these experiments as these control and test datapoints came from the same cell. Cells were recorded in control conditions, and after drug application.

      Line 137: single-axon activation: but cortical axons make multi-synaptic contacts, at least for certain types of pre- and post-synaptic neurons, and (e.g. in L5-L5 pairs) those contacts can be distributed across the entire dendritic arbor. In other words, it's possible that when you stimulate in L1, you activate local axons, and the signal could then propagate to multiple synaptic contact locations, some being distal and some proximal. Maybe you have reasons to believe you're able to avoid this?

      We thank the reviewer for this question. Cortical axons often make distributed contacts, however, top-down and bottom-up pathways innervating L2/3 PCs are at least somewhat restricted to L2/3/L4 and L1, respectively (Shen et al. 2022, Sermet et al. 2019). Therefore, due to the lack evidence suggesting a heavily mixed topographical distribution for top-down and bottom-up inputs, we have reason to believe that L1 stimulation will result in mainly distal input recruitment, while L4 stimulation will mainly excite proximal dendritic regions. The resolution of our experiments was also improved by the minimal stimulation and visual guidance (subset of experiments) of the stimulation. Furthermore, new optogenetic experiments stimulating LGN and LM axons, which have been anatomically defined previously as biased to deeper layers and L1, respectively, were now also performed (Fig. 3j-l) with analogous cesium effects as our local electrical stimulation experiments. Future work using varying optogenetic stimulation parameters will expand on this.

      L140: "previous reports" ==> citation needed.

      We have inserted the citation needed.

      L149: "arriving to layer 1"; but I think earlier you noted that some or many L2/3 neurons lack a dendritic tuft; do they all nevertheless have dendrites in L1? Note that cortico-cortical long-range axons still need to pass through all cortical layers on their way up to L1.

      We thank the reviewer for the question. Although the more superficial L2/3 PCs lack distinct apical tuft, their dendrites reach the pia similarly to deeper L2/3 PCs. All of our recorded and post-hoc recovered cells had dendrites in L1, except in cases where they were clearly cut during the slicing procedure, which cells were occluded from the study.

      When you write "L4 axons" or "L4 inputs", do you specifically mean long-range thalamic axons? Or axons from local L4 neurons? What about axons in L4 that originate from L5 pyramidal neurons?

      In case of ‘L4’ axons, we cannot disambiguate these inputs a priori, as they are both part of the bottom-up pathway, and are possibly experimentally indistinguishable. Even with restricted opto LGN stimulation, disynaptic inputs via L4 PCs cannot be completely ruled out under our conditions. On the other hand, the probability of L5 PC axons to terminate on L2/3 PCs is exceedingly low (single reported connection out of 1145 potential connections; Hage et al. 2022). We did find two clearly different synaptic subpopulations (Supp. Fig 3) in L4- which was tempting to classify as one or the other. However we felt there was not enough evidence in the literature as well as our additional optogenetic experiments to make a classification on the source of these different L4 inputs. Thus we deemed them as Type-I or Type-II for now.

      Do you inject more holding current to compensate for the resting membrane potential when Cs+ or ZD7288 is in the bath?

      We thank the reviewer for the question. We did not inject a compensatory current, as we wanted to investigate the dual, physiologically relevant action of HCN channels (George et al. 2009)

      I'd like to see distributions (histograms) of L4 and L1 EPSP amplitudes, under control conditions and ideally also under HCN block.

      We have now extended the manuscript with a supplementary figure (Supplementary Figure 6) to show that EPSP peak was not distance dependent in control conditions, and there was no relationship between peak and halfwidth in our dataset.

      Line 186, custom pipette perfusion: why not use this for internal ZD7288, to make it cell-specific?

      We thank the reviewer for the question, this is a good point. In future work we will consider this when applicable. It is certainly a way to control for bath application confounds in many ways.

      L205: "recapitulate our experimental findings" - which findings do you mean? I think a bit of explanation/referencing would help.

      Corrected.

      Line 210: L4-evoked were narrower than L1-evoked: is this not expected based on filtering?

      We thank the reviewer for pointing this out, the word “Intriguingly” has been omitted.

      Line 231 and 235: "in L5 PCs" should be restricted to L5 PT-type PCs.

      We have corrected this throughout the manuscript.

      Neuromodulation, Fig. 7, L263-282: the neuromodulation finding is interesting. However, a bit like the developmental figure, it feels "tacked on" and the transition feels a bit awkward. I think you may want to discuss/cite more of the existing literature on neuromodulatory interactions with HCN (not just L2/3). Most importantly, what I feel is missing is a connection to your main finding, namely L1 and L4 inputs. Does serotonergic neuromodulation put L1 and L4 back on equal footing, or does it exaggerate the differences?

      We thank the reviewer for the question. We agree with the reviewer that Figure 7 does not give a complete picture about how the adult brain can capitalize on this channel distribution, as our intention was to show that HCN channels are not a stationary feature of L2/3 PC, but a feature which can be regulated developmentally and even in the adult brain via neuromodulation. In other words, the subthreshold NMDA boosting we observed can be gated by HCN, depending on developmental stage and/or neuromodulatory state of the system. We have now added some brief language to better introduce the transition and its relevance to the current study in the results (p8), and discussed the implications in the discussion section of the original manuscript.

      General comment: different types/sources of synapses may have different EPSP kinetics. I feel this is not mentioned/discussed adequately, considering your emphasis on EPSPs/HCN.

      See points above on input-specific synaptic diversity.

      Line 319/320: enriched distal HCN is found in L5 PT-type, not in all L5 PCs.

      Corrected

      L320: CA1 reportedly has a subset of pyramidal neurons that have higher proximal HCN than distal (I gave the citation above). In light of that, I think "unprecedented" is an overstatement.

      Corrected.

      Methods:

      L367: What form of anesthesia was used?

      Amended.

      Which brain areas, and how?

      Amended.

      Why did you first hold slices at 34C, but during recording hold at 30C?

      We held the slices at 34C to accelerate the degradation of superficial damaged parts of the slice, which is in line with currently used acute slice preparation methodologies, regardless of the subsequent recording temperature.

      Pipette resistance/tip size?

      Amended.

      Cell-attached recordings (L385): provide details of recordings. What was the command potential (fixed value, or did you adjust it per neuron by some criteria)?

      Amended.

      What type of stimulating electrode did you use? If glass, what solution is inside, and what tip size?

      We thank the reviewer for pointing these out, the specific points were added to the methods section.

      L392/393: you adjusted the holding (bias) current to sit at -80 mV. What were the range and max values of holding current? Was -80 mV the "raw" potential, or did it account for liquid junction? If you did not account for liquid junction potential, then would -80 in your hands effectively be between -95 and -90 mV? That seems unusually hyperpolarized.

      All cells were held with bias holding currents between -50 pA and 150 pA. To be clear, as mentioned below, we did not change the bias current after any drug applications. We did not correct for liquid junction potential, and cells were ‘held‘ with bias current at -80 mV as during our recordings, as 1) this value was apparently close to the RMP (i.e. little bias current needed at this voltage on average) (Fig. 2e) and 2) to keep consistent conditions across recordings. The uncorrected -80 mV is in the range of previously reported membrane potential values both in vivo and in vitro (Svoboda et al. 1999, Oswald et al. 2008, Luo et al. 2017), which found the (corrected) RMP to be below -80mV. Naturally this will not reflect every in vivo condition completely and further investigation using naturalistic conditions in the future are warranted.  

      Did you adjust the bias current during/after pharmacology?

      Bias current was not adjusted in order to resolve the effect on resting membrane potential.

      L398: sag calculation could use better explanation: how did you combine/analyze multiple steps from a single neuron when calculating sag? Did you choose one level (how) or did you average across step sizes or ...?

      Sag ratio was measured at -6 pA/pF current step except for one set of experiments in Fig. 7. Methods section was amended.

      L400, 401: 10 uM Alexa-594 or 30 um Alexa-594, which is correct?

      10 µM is correct, typo was corrected

      L445: "PV cell" seems like a typo?

      Typo is corrected.

      L450: "altered", please describe the algorithm or manual process.

      Alterations were made manually.

      L474: NDMA, typo.

      Typo is fixed.

      L474: "were adjusted", again please describe the process.

      Adjustments were made by a grid-search algorithm.

      Biel, M., Wahl-Schott, C., Michalakis, S., & Zong, X. (2009). Hyperpolarization-activated cation channels: from genes to function. Physiological reviews, 89(3), 847-885. https://journals.physiology.org/doi/full/10.1152/physrev.00029.2008 - (very comprehensive review of HCN)

      Bullis JB, Jones TD, Poolos NP. Reversed somatodendritic I(h) gradient in a class of rat hippocampal neurons with pyramidal morphology. J Physiol. 2007 Mar 1;579(Pt 2):431-43. doi: 10.1113/jphysiol.2006.123836. Epub 2006 Dec 21. PMID: 17185334; PMCID: PMC2075407. https://physoc.onlinelibrary.wiley.com/doi/full/10.1113/jphysiol.2006.123836 - (CA1 subset (PLPs) have a reversed HCN gradient; cell-attached patches, NMDAR)

      Velumian AA, Zhang L, Pennefather P, Carlen PL. Reversible inhibition of IK, IAHP, Ih, and ICa currents by internally applied gluconate in rat hippocampal pyramidal neurones. Pflugers Arch. 1997 Jan;433(3):343-50. doi: 10.1007/s004240050286. PMID: 9064651. https://link.springer.com/article/10.1007/s004240050286 - (K-Gluc internal inhibits HCN)

      Sheets, P. L., Suter, B. A., Kiritani, T., Chan, C. S., Surmeier, D. J., & Shepherd, G. M. (2011). Corticospinal-specific HCN expression in mouse motor cortex: I h-dependent synaptic integration as a candidate microcircuit mechanism involved in motor control. Journal of neurophysiology, 106(5), 2216-2231. https://journals.physiology.org/doi/full/10.1152/jn.00232.2011 - (L2/3 IT have same sag ratio as all other non-PT pyramidals, roughly 5% (vs 20% PT); intracellular ZD7288 used at 10 or 25 um)

      Harris NC, Constanti A. Mechanism of block by ZD 7288 of the hyperpolarization-activated inward rectifying current in guinea pig substantia nigra neurons in vitro. J Neurophysiol. 1995 Dec;74(6):2366-78. doi: 10.1152/jn.1995.74.6.2366. PMID: 8747199. https://journals.physiology.org/doi/abs/10.1152/jn.1995.74.6.2366 - (comparison Cs+ and ZD7288)

      Harris, N. C., Libri, V., & Constanti, A. (1994). Selective blockade of the hyperpolarization-activated cationic current (Ih) in guinea pig substantia nigra pars compacta neurones by a novel bradycardic agent, Zeneca ZM 227189. Neuroscience letters, 176(2), 221-225. https://www.sciencedirect.com/science/article/abs/pii/0304394094900876 - (Cs+ is not HCN-selective; it also broadens APs, reduces the AHP)

      Chevaleyre, V., & Castillo, P. E. (2002). Assessing the role of Ih channels in synaptic transmission and mossy fiber LTP. Proceedings of the National Academy of Sciences, 99(14), 9538-9543. https://pnas.org/doi/abs/10.1073/pnas.142213199 - (Cs+ blocks K channels, increases transmitter release; but also ZD7288 affects synaptic transmission)

      Thank you

    2. eLife Assessment

      In this valuable study the authors use electrophysiology in brain slices and computer modeling and suggest that layer 2/3 pyramidal neurons of the mouse cortex have functional HCN channels on the proximal apical dendrite which allows distinct processing of input at that location from the input to distal apical dendrites. The revisions improved the solid paper but some of the concerns were not addressed sufficiently and many of these concerns could be addressed by further revision.

    3. Reviewer #2 (Public review):

      Summary:

      This paper by Olah et al., uncovers a previously unknown role of HCN channels in shaping synaptic inputs to L2/3 cortical neurons. The authors demonstrate using slice electrophysiology and computational modeling that unlike layer 5 pyramidal neurons, L2/3 neurons have an enrichment of HCN channels in the proximal dendrites. This location provides a locus of neuromodulation for inputs onto the proximal dendrites from L4 without an influence on distal inputs from L1. the authors use pharmacology to demonstrate the effect of HCN channels on NMDA-mediated synaptic inputs from L4. The authors further demonstrate the developmental time course of HCN function in L2/3 pyramidal neurons. Taken together, this a well constructed investigation of HCN channel function and the consequences of these channels on synaptic integration in L2/3 pyramidal neurons.

      Strengths:

      The authors use careful, well-constrained experiments using multiple pharmacological agents to asses HCN channel contributions to synaptic integrations. The authors also use voltage-clamp to directly measure the current through HCN channels across developmental ages. The authors also provide supplemental data showing that their observation is consistent across multiple areas of the cerebral cortex.

      Weaknesses:

      The gradient of HCN channel function is based almost exclusively on changes in EPSP width measured at the soma. While providing strong evidence for the presence of HCN current in L2/3 neurons, there are space clamp issues related to the use of somatic whole-cell voltage clamp that should be considered in the discussion. One omission by the authors is related to cell morphology. They make a point of normalizing the current injections to cell capacitance to account for variability in neuronal morphology. It is not clear however, how, if at all, this variability would affect EPSP propagation and modulation by proximal HCN channels. This should at least be discussed. Also, if there is high variability in cell morphology, was this considered in the modeling experiments?

    4. Reviewer #3 (Public review):

      Summary:

      The authors study the function of HCN channels in L2/3 pyramidal neurons, employing somatic whole-cell recordings in acute slices of visual cortex in adult mice and a bevy of technically challenging techniques. Their primary claim is a non-uniform HCN distribution across the dendritic arbor with greater density closer to the soma (roughly opposite of the gradient found in L5 PT-type neurons). The second major claim is that multiple sources of long-range excitatory input (cortical and thalamic) are differentially affected by the HCN distribution. They further describe an interesting interplay of NMDAR and HCN, serotonergic modulation of HCN, and compare HCN-related properties at 1-, 2- and 6-weeks of age. Several results are accompanied by biophysical simulations.

      Strengths:

      The authors collected data from both male and female mice, at an age (6-10 weeks) that permits comparison with in vivo studies, in sufficient numbers for each condition, and they collected a good number of data points for almost all figure panels. This is all the more positive, considering the demanding nature of multi-electrode recording configurations and pipette-perfusion. The main strength of the study is the question and focus.

      Weaknesses:

      Unfortunately, in its present form, the main claims are not adequately supported by the experimental evidence: primarily because the evidence is indirect and circumstantial, but also because multiple unusual experimental choices (along with poor presentation of results) undermine the reader's confidence. Additionally, the authors overstate the novelty of certain results and fail to cite important related publications. Some of these weaknesses can be addressed by improved analysis, statistics, resolving inconsistent data across figures, reorganizing/improving figure panels, more complete methods, improved citations, and proofreading. In particular, given the emphasis on EPSPs, the primary data (example EPSPs, overlaid conditions) should be shown much more.

      However on the experimental side, addressing the reviewer's concerns would require a very substantial additional effort: direct measurement of HCN density at different points in the dendritic arbor and soma; the internal solution chosen here (K-gluconate) is reported to inhibit HCN; bath-applied cesium at the concentrations used blocks multiple potassium channels, i.e. is not selective for HCN (the authors have concerns about using the more selective blocker ZD7288, but did use it in a subset of experiments, some of which show quantitatively different results). In response to initial review, the authors performed pathway-specific synaptic stimulation, via optogenetic activation of specific long-range inputs - this approach is valuable and interesting, however the results are presented very minimally and only partially match those obtained by layer-specific electrical stimulation.

    1. eLife assessment

      This important and detailed study presents the most comprehensive view of the functional organization and requirements for a mother centriole's distal appendage in primary cilia assembly published to date. Crispr-knockouts and super-resolution microscopy analysis of the distal appendage proteins provides convincing evidence to support the claims of the authors. This work will be of high value to cell biologists and biophysicists working on the structure and function of the centrosome as well as human geneticists exploring ciliary pathology.

    2. Reviewer #1 (Public Review):

      In this work, Kanie and colleagues explored the composition, structure, and assembly hierarchy of distal appendage proteins. The microscopy was well executed and appropriately quantified. Importantly, the quality of individual antibodies was documented with a discussion of how this might complicate results. The hierarchy of assembly was established by careful quantification of assembly in an extensive set of knockout cell lines. This work will be of interest to cell biologists exploring organelle assembly as well as human geneticists trying to understand the clinical implications of mutations.

    3. Reviewer #2 (Public Review):

      Kanie et all have carried out a tour-de-force effort to further understand the hierarchy and function of centriole distal appendages in ciliogenesis. They made a thorough effort to understand the localization of all the known distal appendage proteins. To examine the distal appendage hierarchy, they used an automated analysis of centrosomal localization. It is not clear how this was quantified and pictures are not shown. They used CEP170, a marker for subdistal appendages, to define a mask around centrioles. It is not clear how the experiment was analyzed and normalized. The techniques used in this study cannot be compared with those commonly used in the field which normally include storm and other super-resolution techniques (which are less prone to artifacts) and correlated electron microscopy. Thus, it is not possible to make a head-to-head comparison. The lack of rescue experiments further weakens the conclusions of this paper.

    4. Reviewer #3 (Public Review):

      Distal appendages are multiprotein complexes that are only present on the mother centriole as a 9-fold symmetric structure that functions in ciliogenesis. How distal appendage proteins are organized and assembled still remains poorly understood. In this manuscript, Kanie et al. comprehensively analyzed the localizations of known and newly described distal appendage proteins using super-resolution microscopy. They investigated mechanisms associated with distal appendage assembly and their roles in the early stages of ciliogenesis in CRISPR-Cas9 knockout cells, which enabled a clearer investigation of these structures compared to previous RNAi depletion studies. These studies confirm previous findings for distal appendage protein ciliogenesis function and demonstrate the CEP83-SCLT1-CEP164-TTBK2 module is critical for both distal appendage assembly and the initiation of ciliogenesis. Notably, they find that CEP89 is dispensable for distal appendage assembly, but is needed for the recruitment of RAB34-positive ciliary vesicles to the mother centriole for ciliogenesis. Finally, this work introduces the application of single-molecule 3D super-resolution microscopy as a tool for interrogating the relationship between membranes and distal appendages. Overall this work extends our fundamental understanding of distal appendage structure/function in ciliogenesis.

      An interesting observation from this work is that CEP83 is found localized both at the innermost region and the outermost region of the distal appendages when detected by antibodies that recognize a different epitope of CEP83 (Figure 1A), suggesting a helical structure that could serve as a platform for distal appendage assembly. A previous study using STORM imaging also showed that another distal appendage protein CEP164 occupies a wider region of the distal appendages when using an antibody recognizing the N-terminal residues of Cep164 (M Bowler et al. 2019). Together these studies show the importance of evaluating the structure of distal appendage proteins and the challenges of using antibody detection to reveal distal appendage hierarchy.

      This work also highlights the potential differences in functional conclusions that can be drawn when comparing RNAi and CRISPR knockout depletion approaches. The latter which expectedly can lead to a more precise functional analysis of these small distal appendage structures, albeit with the potential for knockout cells to display compensatory regulation. Although not directly addressed in the text, the authors find that RPE-1 MYO5A knockout cells could ciliate which differs from a report by Wu et al. (2018). Furthermore, in the case of RAB34 knockout cells, the authors find CP110 removal from the mother centriole, while in previously published RAB34 KO studies this was not observed. In the case of the. RAB34 data a plausible explanation for the results given by the authors is that different assay conditions were used as was noted by the authors.

    1. eLife Assessment

      This important paper explores the impact of early life stress (ELS) on adult brain and behavior. The significance of the convincing findings are that they implicate regulation of non-neuronal cells in the development of brain and behavioral dysfunction associated with ELS. With an elegant combination of behavioral models, morphological and functional assessments using immunostaining, electrophysiology, and viral-mediated loss-of-function approaches, the authors report that astrocyte dysfunction plays a role in ELS responses. The work is of interest to a broad behavioral and cellular neuroscience audience.

    2. Reviewer #1 (Public review):

      Summary:

      The manuscript asks the question of whether astrocytes contribute to behavioral deficits triggered by early life stress. This question is tested by experiments that monitor the effects of early life stress on anxiety-like behaviors, long-term potentiation in the lateral amygdala, and immunohistochemistry of astrocyte-specific (GFAP, Cx43, GLT-1) and general activity (c-Fos ) markers. Secondarily, astrocyte activity in the lateral amygdala is impaired by viruses that suppress gap-junction coupling or reduce astrocyte Ca2+ followed by behavioral, synaptic plasticity, and c-Fos staining. Early life stress is found to reduce expression of GFAP, Cx43 and induce translocation of the glucocorticoid receptor to astrocytic nuclei. Both early life stress and astrocyte manipulations are found to result in generalization of fear to neutral auditory cues. All of the experiments are done well with appropriate statistics and control groups. The manuscript is very well-written and the data are presented clearly. The authors' conclusion that lateral amygdala astrocytes regulate amygdala-dependent behaviors is strongly supported by the data as is the conclusion that cellular and behavioral outcomes provoked by early life stress are similar to the outcomes provoked by astrocyte dysfunction. However, the extent to which early life stress requires astrocytes to generate these outcomes remains open to debate.

      Strengths:

      A strong combination of behavioral, electrophysiology, and immunostaining approaches is utilized and possible sex-differences in behavioral data are considered. The experiments clearly demonstrate that disruption of astrocyte networks or reduction of astrocyte Ca2+ provoke generalization of fear and impair long-term potentiation in lateral amygdala. The provocative finding that astrocyte dysfunction accounts for a subset of behavioral effects of early life stress (e.g. not elevated plus or distance traveled observations) is also perceived as a strength.

      Weaknesses:

      The main weakness is absence of direct evidence that behavioral and neuronal plasticity after early life stress can be attributed to astrocytes. It remains unknown what would happen if astrocyte activity were disrupted concurrently with early life stress or if changes in astrocyte Ca2+ could attenuate early life stress outcomes. As is, the only presented evidence that early life stress involves astrocytes is nuclear translocation of GR and downregulation of GFAP and Cx43 in Figure 3 which may or may not cause the reported astrocyte activity changes.

    3. Reviewer #2 (Public review):

      Summary:

      In this manuscript, Guayasamin et al. show that early-life stress (ELS) can induce a shift in fear generalisation in mice. They took advantage of a fear conditioning paradigm followed by a discrimination test and complement learning and memory findings with measurements for anxiety-like behaviors. Next, astrocytic dysfunction in the lateral amygdala was investigated at the cellular level by combining staining for c-Fos with astrocyte-related proteins. Changes in excitatory neurotransmission were observed in acute brains slices after ELS suggesting impaired communication between neurons and astrocytes. To confirm causality of astrocytic-neuronal dysfunction in behavioral changes, viral manipulations were performed in unstressed mice. Occlusion of functional coupling with a dominant negative construct for gap junction connexin 43 or reduction in astrocytic calcium with CalEx mimicked the behavioral changes observed after ELS suggesting that dysfunction of the astrocytic network underlies ELS-induced memory impairments.

      Strengths:

      Overall, this well written manuscript highlights a key role for astrocytes in regulating stress-induced behavioral and synaptic deficits in the lateral amygdala in the context of ELS. Results are innovative, and methodological approaches relevant to decipher the role of astrocytes in behaviors. As mentioned by the authors, non-neuronal cells are receiving increasing attention in the neuroscience, stress and psychiatry fields.

      Weaknesses:

      I did have several suggestions and comments that were addressed during the review process. I believe that it improved clarity and will increase the impact of the work.

    4. Reviewer #3 (Public review):

      Summary:

      The authors show that ELS induces a number of brain and behavioral changes in the adult lateral amygdala. These changes include enduring astrocytic dysfunction, and inducing astrocytic dysfunction via genetic interventions is sufficient to phenocopy the behavioral and neural phenotypes suggesting astrocyte dysfunction may play a causal role in ELS-associated pathologies.

      Strengths:

      A strength is the shift in focus to astrocytes to understand how ELS alters adult behavior.

      Weaknesses:

      The mechanistic links between some of the correlates - altered astrocytic function, changes in neural excitability and synaptic plasticity in the lateral amygdala and behavior - are underdeveloped.

      Comments on revisions:

      The authors have significantly improved the paper with the addition of new experimental data, analyses, and textual changes.

    5. Author response:

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

      eLife Assessment

      Early-life adversity or stress can enhance stress susceptibility by causing changes in emotion, cognition, and reward-seeking behaviors. This important manuscript highlights the involvement of lateral amygdala astrocytes in fear generalization and the associated synaptic plasticity, which are parallel to the effects of early life stress. With an elegant combination of behavioral models, morphological and functional assessments using immunostaining, electrophysiology, and viral-mediated loss-of-function approaches, the authors provide solid correlational and causal evidence that is consistent with the hypothesis that early life stress produces neural and behavioral dysfunction via perturbing lateral amygdala astrocytic function.

      We would like to thank the authors and editors for taking the time to review our work, and re-review it now. Also, we are grateful for this very positive assessment of our work. In this revised manuscript we made a strong effort to address comments made by all reviewers, providing clarification where required and new data to our manuscript in order to further support our observations.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The manuscript asks the question of whether astrocytes contribute to behavioral deficits triggered by early life stress. This question is tested by experiments that monitor the effects of early life stress on anxiety-like behaviors, long-term potentiation in the lateral amygdala, and immunohistochemistry of astrocyte-specific (GFAP, Cx43, GLT-1) and general activity (c-Fos ) markers. Secondarily, astrocyte activity in the lateral amygdala is impaired by viruses that suppress gap-junction coupling or reduce astrocyte Ca2+ followed by behavioral, synaptic plasticity, and c-Fos staining. Early life stress is found to reduce the expression of GFAP and Cx43 and to induce translocation of the glucocorticoid receptor to astrocytic nuclei. Both early life stress and astrocyte manipulations are found to result in the generalization of fear to neutral auditory cues. All of the experiments are done well with appropriate statistics and control groups. The manuscript is very well-written and the data are presented clearly. The authors' conclusion that lateral amygdala astrocytes regulate amygdala-dependent behaviors is strongly supported by the data. However, the extent to which astrocytes contribute to behavioral and neuronal consequences of early life stress remains open to debate.

      Strengths:

      A strong combination of behavioral, electrophysiology, and immunostaining approaches is utilized and possible sex differences in behavioral data are considered. The experiments clearly demonstrate that disruption of astrocyte networks or reduction of astrocyte Ca2+ provokes generalization of fear and impairs long-term potentiation in the lateral amygdala. The provocative finding that astrocyte dysfunction accounts for a subset of behavioral effects of early life stress (e.g. not elevated plus or distance traveled observations) is also perceived as a strength.

      Weaknesses:

      The main weakness is the absence of more direct evidence that behavioral and neuronal plasticity after early life stress can be attributed to astrocytes. It remains unknown what would happen if astrocyte activity were disrupted concurrently with early life stress or if the facilitation of astrocyte Ca2+ would attenuate early life stress outcomes. As is, the only evidence that early life stress involves astrocytes is nuclear translocation of GR and downregulation of GFAP and Cx43 in Figure 3 which may or may not provoke astrocyte Ca2+ or astrocyte network activity changes.

      We would like to thank the reviewer for their constructive feedback on our work. In the revised version we have added new experiments that further support a role of astrocytes in ELS-induced behavioural dysfunction. Specifically, we carried out two-photon calcium imaging in lateral amygdala astrocytes using viral overexpression of membrane tethered GCaMP6f. These experiments revealed a decrease in astrocyte calcium activity following ELS (Figure 4). Interestingly these data also showed an important number of sex differences (Figure 4 - Figure supplement 1).

      These new data allow us to strengthen the link between ELS-induced astrocyte hypofunction and behavioural changes. Indeed, we validated the impact of CalEx on astrocyte calcium activity in the lateral amygdala, again using two-photon microscopy, and show that CalEx resulted in an astrocyte calcium signature that very closely resembled that of ELS, i.e. reduced frequency and amplitude of events (Figure 5 - Figure supplement 2). As such, we feel like these data, while still correlative in nature, strengthen our findings and conclusion that astrocyte dysfunction alone is sufficient to recapitulate the effects of stress on excitability, synaptic function, and behaviour.

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, Guayasamin et al. show that early-life stress (ELS) can induce a shift in fear generalisation in mice. They took advantage of a fear conditioning paradigm followed by a discrimination test and complemented learning and memory findings with measurements for anxiety-like behaviors. Next, astrocytic dysfunction in the lateral amygdala was investigated at the cellular level by combining staining for c-Fos with astrocyte-related proteins. Changes in excitatory neurotransmission were observed in acute brains slices after ELS suggesting impaired communication between neurons and astrocytes. To confirm the causality of astrocytic-neuronal dysfunction in behavioral changes, viral manipulations were performed in unstressed mice. Occlusion of functional coupling with a dominant negative construct for gap junction connexin 43 or reduction in astrocytic calcium with CalEx mimicked the behavioral changes observed after ELS suggesting that dysfunction of the astrocytic network underlies ELS-induced memory impairments.

      Strengths:

      Overall, this well-written manuscript highlights a key role for astrocytes in regulating stress-induced behavioral and synaptic deficits in the lateral amygdala in the context of ELS. Results are innovative, and methodological approaches relevant to decipher the role of astrocytes in behaviors. As mentioned by the authors, non-neuronal cells are receiving increasing attention in the neuroscience, stress, and psychiatry fields.

      Weaknesses:

      I do have several suggestions and comments to address that I believe will improve the clarity and impact of the work. For example, there is currently a lack of information on the timeline for behavioral experiments, tissue collection, etc.

      We thank the reviewer for their kind comments and constructive feedback on our manuscript. We agree that certain aspects could have been made more clear and we have revised the manuscript and figures to be more explicit regarding timelines. Including the addition of timelines on figures and improved clarity in the text where possible. We have also addressed the private comments provided by the reviewers alluded to in this public review.

      Reviewer #3 (Public Review):

      Summary

      The authors show that ELS induces a number of brain and behavioral changes in the adult lateral amygdala. These changes include enduring astrocytic dysfunction, and inducing astrocytic dysfunction via genetic interventions is sufficient to phenocopy the behavioral and neural phenotypes. This suggests that astrocyte dysfunction may play a causal role in ELS-associated pathologies.

      Strengths:

      A strength is the shift in focus to astrocytes to understand how ELS alters adult behavior.

      Weaknesses:

      The mechanistic links between some of the correlates - altered astrocytic function, changes in neural excitability, and synaptic plasticity in the lateral amygdala and behaviour - are underdeveloped.

      We thank the reviewer for their comments. We are happy that they found our shift in focus towards astrocytes to be a strength of our work. Regarding mechanistic links being underdeveloped, we have attempted to address this by placing more effort into understanding the functional changes in astrocytes and how this relates to behaviour.

      To address this comment we have used two-photon calcium imaging to quantify the impact of ELS on astrocyte calcium activity. As such, the revised manuscript contains several new figures including a detailed characterisation of the effects of ELS on astrocyte calcium activity (Figure 4), including sex differences in naive and the effects of stress (Figure 4 - Figure supplement 1), and an important validation of the impact of CalEx on astrocyte calcium activity. CalEx mirrors the impact of stress on astrocyte calcium activity reducing the frequency and amplitude of individual events (Figure 5 - Figure supplement 2).

      Considering the strong overlap of the effects of ELS and CalEx on synapses, excitability, behaviour, and now astrocyte calcium activity, we hope that this added detail addresses some of the points highlighted by the reviewer.

      Recommendations for the authors:

      The reviewers all agree on one major issue for the authors to address. There is a bit of a lack of mechanistic linking between the astrocyte function and the early life stress and these data are more correlational than causal in nature. This could either be addressed by scaling back the data interpretation and title to be more reflective of the data at hand or if the authors would consider, doing the causal experiment of examining the manipulation of astrocyte activity following early life stress to see if this does influence the phenotype.

      We agree with reviewers on this issue and realise that we have overstated our findings somewhat. As an immediate fix, suggested by reviewers, we have changed the title to more closely align with our data stating that astrocyte dysfunction is “associated with” rather than “induces” as well as adjusting our interpretations.

      In addition to this one major comment, there are a list of minor comments that the authors should consider to improve the manuscript.

      (1) A major caveat is the lack of information on the timeline for behavioral experiments, tissue collection, etc. The authors mention "Mice between ages P45-70' but considering the developmental changes occurring between late adolescence and young adulthood, I recommend adding timelines on all Figures clearly indicating when behavioral tests were performed, and tissue collected for electrophysiology or immunostaining. With corticosterone (CORT) back at baseline at P70 vs a difference observed at P45 was this time point favored? It should be clarified throughout.

      We apologise for the lack of clarity on this and have added more timelines on figures.

      The age range favoured (p45-p70), relates to adolescence a time when latent psychiatric disorders tend to manifest in humans following early-life adversity. We have clarified this choice in the text.

      (2) Given the transient increase in corticosterone levels in early-life stress mice, peaking at P45 and declining to control levels by P70, it would be informative to know whether the reported behavioral and synaptic changes differ within this time window. This may not be doable in the current approach, but this should be addressed nonetheless. Furthermore, it wasn't clear why the increase in blood corticosterone was delayed. Was this expected? How does this relate to earlier work? Wouldn't it be expected to be elevated at P17 (end of ELS period)?

      We agree that this observation was very unexpected. Initially, we expected CORT to be elevated at P17, end of ELS period. We believe that low CORT levels during the ELS paradigm can be attributed to this paradigm coinciding with the stress hyporesponsive period (SHRP) which in rodents lasts until roughly postnatal day 14. During this period, mild stressors fail to elicit CORT responses. Considering our ELS paradigm lasts from P10-P17, there is a significant overlap with the SHRP.

      This point is now included in the discussion with several citations regarding this biological phenomenon, as well as other studies that report similar findings to our own, i.e. a delayed increase in blood corticosterone levels following early-life stress.

      (3) It is mentioned that behavioral tests were performed in both sexes with no sex differences observed. Were animals of both sexes also included in other experiments (ephys, immunostaining, blood CORT analysis)? Behavioral outcomes could be the same but underlying biological processes different. This is a topic that should be discussed. Identification of males vs females on graphs would be helpful.

      We apologise for not having provided this data in the previous version of the manuscript. In the revised manuscript we provide analysis of sex differences for our initial behavioural observations (Figure 2 - Figure supplement 1), c-Fos (Figure 2 - Figure supplement 2), for GFAP and Cx43 (Figure 3 - Figure supplement 1), calcium signalling (Figure 4 - Figure supplement 1), and for CalEx and dnCx43 experiments across behaviour (Figure 5 - Figure supplement 4) and c-Fos (Figure 5 - Figure supplement 5).

      (4) How long-lasting are the generalization phenotypes? Do they outlast the transient increase in blood corticosterone? Showing this would provide a more solid foundation for future explorations.

      The reviewers raise a very important point. It remains unclear as to how long these effects last and this is something we are keen to address in future studies, with careful experiments designed to explicitly test this question, as well as subsequent questions regarding whether long-lasting effects are due to impaired brain development or whether these effects emerge due to CORT changes, or other changes, or a combination of them all?

      As an aside, an additional manuscript from our lab (Depaauw-Holt et al. 2024 bioRxiv) which uses the same stressor but focuses on distinct brain regions and behaviours uses a prolonged time window in which the effects of stress are readily observable all the way to P90.

      So while we do provide the answers in this work, it is a really great idea that we would like to follow up subsequently.

      (5) With the ELS-induced change in locomotion, I would recommend presenting open field (center, periphery) and elevated plus maze (open, closed arms) data independently. It could also be interesting to analyze corner time in the open field as well as center time in the elevated plus maze.

      We now provide data for the open field and elevated plus maze as requested. Our findings remain unchanged, but we agree with the reviewer that this way of representing the data is more clear.

      (6) For Figure 2C, the ideal stats would be an ANOVA with CS (+/-) as a within-subject variable and treatment (naive/ELS) as a between-subjects variable. Then the best support for the generalization claim would be a CS x treatment interaction. I encourage the authors to do these stats. I note that this point is mitigated by the discrimination analysis presented in 2D (where they compare naive and ELS groups directly).

      We have carried out the analysis as requested and these data further support the notion of fear generalisation in ELS mice (Figure 2 - Figure supplement 2A, B). Additionally, the analyses are included in a supplementary table. We hope that we have understood correctly, and this figure accurately reflects the reviewer’s suggestion.

      (7) In Figure 2H, why not evaluate c-Fos levels after the discrimination test which is the main behavioral outcome? This statement in the Discussion should be modified if, as per my understanding, c-Fos was measured after the fear paradigm only "We find that both ELS and astrocyte dysfunction both enhance neuronal excitability, assessed by local c-Fos staining in the lateral amygdala following auditory discriminative fear conditioning. One interpretation of these data is that astrocytes might tune engram formation, with astrocyte dysfunction, genetically or after stress, increasing c-Fos expression resulting in a loss of specificity of the memory trace and generalisation of fear.'

      We agree that further evaluation of c-Fos levels following the discrimination test would be insightful. We honestly did not consider this time point in our initial experimental design, as we considered previous reports in the literature that investigated how the numbers of cells recruited to the engram (c-Fos density) could influence memory accuracy at a later time point. As such, investigating c-Fos levels following training was our initial target. We have modified the text to be more explicit in our experimental approach.

      This is nevertheless a fascinating point that we are keen to pursue in future studies.

      (8) Some thoughts on why dnCx43 suppression of astrocyte network activity is less effective at inducing fear generalization than CalEx suppression of astrocyte Ca2+ are warranted. One might predict that both manipulations should result in similar effects, as seen in fEPSP and cFos data in Figure 4.

      We agree that this is an interesting observation and the fact we did not observe the same behavioural phenotype despite fEPSP and c-Fos data to be the same is puzzling.

      Nevertheless, we do see increased fear generalisation in both dnCx43 and CalEx. We hypothesise that CalEx had a more profound effect due to the wide range of processes that are presumably affected by reduced astrocyte calcium activity, whereas blocking gap junction channels still leaves a large number of astrocyte functions intact.

      Overall, our conclusion is that behaviour is a more sensitive assay compared to the cellular phenotypes, which highlights the importance of answering these questions from multiple angles.

      (9) Ideally changes in functional coupling following the dnCx43 manipulation) should be shown here (line 169).

      We, unfortunately, did not directly evaluate functional coupling in dnCx43 mice in this manuscript. This would have been a useful experiment, but we rely on our previous data where we extensively characterised this tool (Murphy-Royal et al. 2020 Nat Comms).

      (10) It would be relevant to perform c-Fos staining with markers for astrocytes or neuronal cells. Is an increase in activity expected for both cell types?

      This is a fascinating question, given recent work on this topic showing that astrocytes can indeed express c-Fos and may be recruited into engrams. We analysed our existing tissue, we found that indeed astrocytes were labelled with c-Fos following our behavioural conditioning paradigm. Our data align with recent reports, and we demonstrate a small percentage of astrocytes expressing c-Fos (Figure 2 - figure supplement 3). This modest number of astrocytes expressing c-Fos is discussed in the text and placed into context of very recent papers that have been published since our submission to eLife.

      (11) Were the same mice subjected to behavior analysis than immunostaining?

      We generated separate cohorts of mice for immunostaining and behaviour, and have made this more clear in the text.

      (12) Language describing learning paradigm. The CS+ (line 73) isn't in itself aversive (and shouldn't be described as such). It acquires that value after pairing with the US (which is aversive).

      We agree that this is poorly worded and have modified the text from “aversive cue” to “conditioned cue”.

      (13) It is hard to appreciate the glucocorticoid receptor translocation with the images provided. Would it be possible to increase magnification or at least, provide small inserts at higher magnification?

      We have re-imaged our brain sections to get more detailed images. These are provided in revised manuscript (Figure 3)

      (14) For the viral injection experiment, for how long is the virus expressed before running behavior/recording/c-Fos staining? Is the age of the tested mice the same as Figures1-3 or they were injected at P45 and tested weeks later?

      We age-matched all mice for all experiments and tried to keep our experimental window as tight as possible (p45-70). All mice were injected at P25-30 in order to meet the experimental time window. To be more precise we have added timelines on all figures.

      (15) A validation of the virus is missing to confirm the reduction of Cx43 expression at mRNA and protein levels when compared to controls. A reference is provided but to my understanding age of the animals might be different.

      Here, I believe the reviewer is referring to dnCx43. In this experiment we used a viral approach to overexpress a non-functional connexin 43 protein (Murphy-Royal et al. 2020 Nat Comms). As such, a PCR or immuno against this protein would be expected to reveal higher expression levels. We have tried to clarify this approach in the text.

      It is true that we did not fully characterise this tool in the lateral amygdala which would have been useful but considering our extensive experience with this tool and in it’s development with our collaborators Baljit Khakh, Randy Stout, David Spray (see Murphy-Royal et al. 2020) we are confident in these data, despite the limitation of validation in this manuscript.

      (16) Same comment for the CalEx, a validation would be appreciated. Based on Yu et al. could a GCaMP6f virus be more appropriate as control?

      We agree this is an important experiment as our lab has not fully validated this tool in house (compared to dnCx43, which we previously validated).

      Importantly, we now have the capacity to do these experiments. Until very recently our two-photon microscope was not fully functional due to dodgy PMTs sent from the company we purchased our equipment from… Troubleshooting this issue took many months before we were convinced that we were not at fault and that the problem was the equipment.

      As such, mice were injected with both a membrane tethered GCaMP6f under the control of the short GFAP promoter - AAV2/5-gfaABC1D-lck-GCaMP6f and CalEx - AAV2/5-gfaABC1D-hPMCA2w/b-mCherry. Using this approach we were able to record calcium activity from CalEx positive and CalEx negative astrocytes in the same tissue (Figure 5 - figure supplement 2).

      We report that this approach does indeed reduce astrocyte calcium but does not entirely eliminate it. In fact, CalEx expressing astrocytes displayed similar calcium activity dynamics to that we observed following ELS. Together, this further strengthens our rationale to use CalEx in order to mimic the effects of stress on astrocytes, and determine downstream effects on excitability, synapses, and behaviour.

      (17) Have previous studies found ELS--> generalization phenotypes in adulthood? If so, these should be discussed in more detail. If not, perhaps this point can be made more explicit.

      This is a great point. After looking deeper into the literature in more depth we found an example of this in which ELS resulted in context fear generalisation in adult rats. This work is cited in the discussion in the context of our findings.

      (18) A paper by Krugers et al (Biol Psychiatry 2020) seems especially relevant (glucocorticoids, fear generalization, engram size) and should be discussed.

      Thank you for bringing this work to our attention. This is certainly important work that we had unfortunately overlooked. We have added a citation and discussed the manuscript Lesuis et al. Biol. Psychiatry 2021, which contains the data discussed in the conference proceeding by Krugers et al. Biol. Psychiatry 2020.

      Additionally, we added another great manuscript by Lesuis et al. recently published in Cell in which they investigated the cellular mechanisms by which acute stress results in fear generalisation via endocannabinoids.

      (19) Minor text revisions are necessary at lines 101 and 264 as well as p.5, line 58: "ratio" and p.10, line 128: "region of interest".

      Thank you for pointing out these typos and errors. We have corrected them.

      Editor's note:

      Should you choose to revise your manuscript, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript.

    1. eLife Assessment

      This important study combines the use of Fisher Kernels with Hidden Markov models aiming to improve brain-behaviour prediction. The evidence supporting the authors' conclusions is compelling, comparing brain-behaviour prediction accuracies across a range of different traits, including out of sample assessment. This work is timely and will be of interest to neuroscientists working on functional connectivity for brain-behaviour association.

    2. Reviewer #1 (Public review):

      Summary:

      The authors attempt to validate Fisher Kernels on the top of HMM as a way to better describe human brain dynamics at resting-state. The objective criterion was the better prediction of the proposed pipeline of the individual traits.

      Comments on revisions:

      The authors addressed adequately all my comments.

    3. Reviewer #3 (Public review):

      Summary:

      In this work, the authors use a Hidden Markov Model (HMM) to describe dynamic connectivity and amplitude patterns in fMRI data, and propose to integrate these features with the Fisher kernel to improve the prediction of individual traits. The approach is tested using a large sample of healthy young adults from the Human Connectome Project. The HMM-Fisher Kernel approach was shown to achieve higher prediction accuracy with lower variance on many individual traits compared to alternate kernels and measures of static connectivity. As an additional finding, the authors demonstrate that parameters of the HMM state matrix may be more informative in predicting behavioral/cognitive variables in this data compared to state-transition probabilities.

      Comments on revisions:

      The authors have now addressed my comments, and I believe this work will be an interesting contribution to the literature.

    4. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors attempt to validate Fisher Kernels on the top of HMM as a way to better describe human brain dynamics at resting state. The objective criterion was the better prediction of the proposed pipeline of the individual traits.

      Strengths:

      The authors analyzed rs-fMRI dataset from the HCP providing results also from other kernels.

      The authors also provided findings from simulation data.

      Weaknesses:

      (1) The authors should explain in detail how they applied cross-validation across the dataset for both optimization of parameters, and also for cross-validation of the models to predict individual traits.

      Indeed, there were details about the cross-validation for hyperparameter tuning and prediction missing. This problem was also raised by Reviewer #2. We have now rephrased this section in 4.4 and added details: ll. 804-813:

      “We used k-fold nested cross-validation (CV) to select and evaluate the models. We used 10 folds for both the outer loop (used to train and test the model) and the inner loop (used to select the optimal hyperparameters) such that 90% were used for training and 10% for testing. The optimal hyperparameters λ (and τ in the case of the Gaussian kernels) were selected using grid-search from the vectors λ=[0.0001,0.001,0.01,0.1,0.3,0.5,0.7,0.9,1] and . In both the outer and the inner loop, we accounted for family structure in the HCP dataset so that subjects from the same family were never split across folds (Winkler et al., 2015). Within the CV, we regressed out sex and head motion confounds, i.e., we estimated the regression coefficients for the confounds on the training set and applied them to the test set (Snoek et al., 2019).“ and ll. 818-820: “We generated the 100 random repetitions of the 10 outer CV folds once, and then used them for training and prediction of all methods, so that all methods were fit to the same partitions.”

      (2) They discussed throughout the paper that their proposed (HMM+Fisher) kernel approach outperformed dynamic functional connectivity (dFC). However, they compared the proposed methodology with just static FC.

      We would like to clarify that the HMM is itself a method for estimating dynamic (or time-varying) FC, just like the sliding window approach, see also Vidaurre, 2024 (https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00363/124983) for an overview of terminology.

      See also our response to Q3.

      (3) If the authors wanted to claim that their methodology is better than dFC, then they have to demonstrate results based on dFC with the trivial sliding window approach.

      We would like to be clear that we do not claim in the manuscript that our method outperforms other dynamic functional connectivity (dFC) approaches, such as sliding window FC. We have now made changes to the manuscript to make this clearer.

      First, we have clarified our use of the term “brain dynamics” to signify “time-varying amplitude and functional connectivity patterns” in this context, as Reviewer #2 raised the point that the former term is ambiguous (ll.33-35: “One way of describing brain dynamics are state-space models, which allow capturing recurring patterns of activity and functional connectivity (FC) across the whole brain.”).

      Second, our focus is on our method being a way of using dFC for predictive modelling, since there currently is no widely accepted way of doing this. One reason why dFC is not usually considered in prediction studies is that it is mathematically not trivial how to use the parameters from estimators of dynamic FC for a prediction. This includes the sliding window approach. We do not aim at comparing across different dFC estimators in this paper. To make these points clearer, we have revised the introduction to now say:

      Ll. 39-50:

      “One reason why brain dynamics are not usually considered in this context pertains to their representation: They are represented using models of varying complexity that are estimated from modalities such as functional MRI or MEG. Although there exists a variety of methods for estimating time-varying or dynamic FC (Lurie et al., 2019), like the commonly used sliding-window approach, there is currently no widely accepted way of using them for prediction problems. This is because these models are usually parametrised by a high number of parameters with complex mathematical relationships between the parameters that reflect the model assumptions. How to leverage these parameters for prediction is currently an open question.

      We here propose the Fisher kernel for predicting individual traits from brain dynamics, using information from generative models that do not assume any knowledge of task timings. We focus on models of brain dynamics that capture within-session changes in functional connectivity and amplitude from fMRI scans, in this case acquired during wakeful rest, and how the parameters from these models can be used to predict behavioural variables or traits. In particular, we use the Hidden Markov Model (HMM), which is a probabilistic generative model of time-varying amplitude and functional connectivity (FC) dynamics (Vidaurre et al., 2017).”

      Reviewer #2 (Public Review):

      Summary:

      The manuscript presents a valuable investigation into the use of Fisher Kernels for extracting representations from temporal models of brain activity, with the aim of improving regression and classification applications. The authors provide solid evidence through extensive benchmarks and simulations that demonstrate the potential of Fisher Kernels to enhance the accuracy and robustness of regression and classification performance in the context of functional magnetic resonance imaging (fMRI) data. This is an important achievement for the neuroimaging community interested in predictive modeling from brain dynamics and, in particular, state-space models.

      Strengths:

      (1) The study's main contribution is the innovative application of Fisher Kernels to temporal brain activity models, which represents a valuable advancement in the field of human cognitive neuroimaging.

      (2) The evidence presented is solid, supported by extensive benchmarks that showcase the method's effectiveness in various scenarios.

      (3) Model inspection and simulations provide important insights into the nature of the signal picked up by the method, highlighting the importance of state rather than transition probabilities.

      (4) The documentation and description of the methods are solid including sufficient mathematical details and availability of source code, ensuring that the study can be replicated and extended by other researchers.

      Weaknesses:

      (1) The generalizability of the findings is currently limited to the young and healthy population represented in the Human Connectome Project (HCP) dataset. The potential of the method for other populations and modalities remains to be investigated.

      As suggested by the reviewer, we have added a limitations paragraph and included a statement about the dataset: Ll. 477-481: “The fMRI dataset we used (HCP 1200 Young Adult) is a large sample taken from a healthy, young population, and it remains to be shown how our findings generalise to other datasets, e.g. other modalities such as EEG/MEG, clinical data, older populations, different data quality, or smaller sample sizes both in terms of the number of participants and the scanning duration”.

      We would like to emphasise that this is a methodological contribution, rather than a basic science investigation about cognition and brain-behaviour associations. Therefore, the method would be equally usable on different populations, even if the results vary.

      (2) The possibility of positivity bias in the HMM, due to the use of a population model before cross-validation, needs to be addressed to confirm the robustness of the results.

      As pointed out by both Reviewers #2 and #3, we did not separate subjects into training and test set before fitting the HMM. To address this issue, we have now repeated the predictions for HMMs fit only to the training subjects. We show that this has no effect on the results. Since this question has consequences for the Fisher kernel, we have also added simulations showing how the different kernels react to increasing heterogeneity between training and test set. These new results are added as results section 2.4 (ll. 376-423).

      (3) The statistical significance testing might be compromised by incorrect assumptions about the independence between cross-validation distributions, which warrants further examination or clearer documentation.

      We have now replaced the significance testing with repeated k-fold cross-validated corrected tests. Note that this required re-running the models to be able to test differences in accuracies on the level of individual folds, resulting in different plots throughout the manuscript and different statistical results. This does not, however, change the main conclusions of our manuscript.

      (4) The inclusion of the R^2 score, sensitive to scale, would provide a more comprehensive understanding of the method's performance, as the Pearson correlation coefficient alone is not standard in machine learning and may not be sufficient (even if it is common practice in applied machine learning studies in human neuroimaging).

      We have now added the coefficient of determination to the results figures.

      (5) The process for hyperparameter tuning is not clearly documented in the methods section, both for kernel methods and the elastic net.

      As mentioned above in the response to Reviewer #1, we have now added details about hyperparameter tuning for the kernel methods and the non-kernelised static FC regression models (see also Reviewer #1 comment 1): Ll.804-813: “We used k-fold nested cross-validation (CV) to select and evaluate the models. We used 10 folds for both the outer loop (used to train and test the model) and the inner loop (used to select the optimal hyperparameters) such that 90% were used for training and 10% for testing. The optimal hyperparameters  (and  in the case of the Gaussian kernels) were selected using grid-search from the vectors λ=[0.0001,0.001,0.01,0.1,0.3,0.5,0.7,0.9,1] and . In both the outer and the inner loop, we accounted for family structure in the HCP dataset so that subjects from the same family were never split across folds (Winkler et al., 2015). Within the CV, we regressed out sex and head motion confounds, i.e., we estimated the regression coefficients for the confounds on the training set and applied them to the test set (Snoek et al., 2019).” and ll. 818-820: “We generated the 100 random repetitions of the 10 outer CV folds once, and then used them for training and prediction of all methods, so that all methods were fit to the same partitions.”, as well as ll.913-917: “All time-averaged FC models are fitted using the same (nested) cross-validation strategy as described above (10-fold CV using the outer loop for model evaluation and the inner loop for model selection using grid-search for hyperparameter tuning, accounting for family structure in the dataset, and repeated 100 times with randomised folds).”

      (6) For the time-averaged benchmarks, a comparison with kernel methods using metrics defined on the Riemannian SPD manifold, such as employing the Frobenius norm of the logarithm map within a Gaussian kernel, would strengthen the analysis, cf. Jayasumana (https://arxiv.org/abs/1412.4172) Table 1, log-euclidean metric.

      We have now added the log-Euclidean Gaussian kernel proposed by the reviewer to the model comparisons. The additional model does not change our conclusions.

      (7) A more nuanced and explicit discussion of the limitations, including the reliance on HCP data, lack of clinical focus, and the context of tasks for which performance is expected to be on the low end (e.g. cognitive scores), is crucial for framing the findings within the appropriate context.

      We have now revised the discussion section and added an explicit limitations paragraph: Ll. 475-484:

      “We here aimed to show the potential of the HMM-Fisher kernel approach to leverage information from patterns of brain dynamics to predict individual traits in an example fMRI dataset as well as simulated data. The fMRI dataset we used (HCP 1200 Young Adult) is a large sample taken from a healthy, young population, and it remains to be shown how the exhibited performance generalises to other datasets, e.g. other modalities such as EEG/MEG, clinical data, older populations, different data quality, or smaller sample sizes both in terms of the number of participants and the scanning duration. Additionally, we only tested our approach for the prediction of a specific set of demographic items and cognitive scores; it may be interesting to test the framework in also on clinical variables, such as the presence of a disease or the response to pharmacological treatment.”

      (8) While further benchmarks could enhance the study, the authors should provide a critical appraisal of the current findings and outline directions for future research, considering the scope and budget constraints of the work.

      In addition to the new limitations paragraph (see previous comment), we have now rephrased our interpretation of the results and extended the outlook paragraph: Ll. 485-507:

      “There is growing interest in combining different data types or modalities, such as structural, static, and dynamic measures, to predict phenotypes (Engemann et al., 2020; Schouten et al., 2016). While directly combining the features from each modality can be problematic, modality-specific kernels, such as the Fisher kernel for time-varying amplitude and/or FC, can be easily combined using approaches such as stacking (Breiman, 1996) or Multi Kernel Learning (MKL) (Gönen & Alpaydın, 2011). MKL can improve prediction accuracy of multimodal studies (Vaghari et al., 2022), and stacking has recently been shown to be a useful framework for combining static and time-varying FC predictions (Griffin et al., 2024). A detailed comparison of different multimodal prediction strategies including kernels for time-varying amplitude/FC may may be the focus of future work.

      In a clinical context, while there are nowadays highly accurate biomarkers and prognostics for many diseases, others, such as psychiatric diseases, remain poorly understood, diagnosed, and treated. Here, improving the description of individual variability in brain measures may have potential benefits for a variety of clinical goals, e.g., to diagnose or predict individual patients’ outcomes, find biomarkers, or to deepen our understanding of changes in the brain related to treatment responses like drugs or non-pharmacological therapies (Marquand et al., 2016; Stephan et al., 2017; Wen et al., 2022; Wolfers et al., 2015). However, the focus so far has mostly been on static or structural information, leaving the potentially crucial information from brain dynamics untapped. Our proposed approach provides one avenue of addressing this by leveraging individual patterns of time-varying amplitude and FC, and it can be flexibly modified or extended to include, e.g., information about temporally recurring frequency patterns (Vidaurre et al., 2016).”

      Reviewer #3 (Public Review):

      Summary:

      In this work, the authors use a Hidden Markov Model (HMM) to describe dynamic connectivity and amplitude patterns in fMRI data, and propose to integrate these features with the Fisher Kernel to improve the prediction of individual traits. The approach is tested using a large sample of healthy young adults from the Human Connectome Project. The HMM-Fisher Kernel approach was shown to achieve higher prediction accuracy with lower variance on many individual traits compared to alternate kernels and measures of static connectivity. As an additional finding, the authors demonstrate that parameters of the HMM state matrix may be more informative in predicting behavioral/cognitive variables in this data compared to state-transition probabilities.

      Strengths:

      - Overall, this work helps to address the timely challenge of how to leverage high-dimensional dynamic features to describe brain activity in individuals.

      - The idea to use a Fisher Kernel seems novel and suitable in this context.

      - Detailed comparisons are carried out across the set of individual traits, as well as across models with alternate kernels and features.

      - The paper is well-written and clear, and the analysis is thorough.

      Potential weaknesses:

      - One conclusion of the paper is that the Fisher Kernel "predicts more accurately than other methods" (Section 2.1 heading). I was not certain this conclusion is fully justified by the data presented, as it appears that certain individual traits may be better predicted by other approaches (e.g., as shown in Figure 3) and I found it hard to tell if certain pairwise comparisons were performed -- was the linear Fisher Kernel significantly better than the linear Naive normalized kernel, for example?

      We have revised the abstract and the discussion to state the results more appropriately. For instance, we changed the relevant section in the abstract to (ll. 24-26):

      “We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models.”,

      and in the discussion, removing the sentence

      “resulting in better generalisability and interpretability compared to other methods”,

      and adding (given the revised statistical results) ll. 435-436:

      “though most comparisons were not statistically significant given the narrow margin for improvements.”

      In conjunction with the new statistical approach (see Reviewer #2, comment 3), we have now streamlined the comparisons. We explained which comparisons were performed in the methods ll.880-890:

      “For the main results, we separately compare the linear Fisher kernel to the other linear kernels, and the Gaussian Fisher kernel to the other Gaussian kernels, as well as to each other. We also compare the linear Fisher kernel to all time-averaged methods. Finally, to test for the effect of tangent space projection for the time-averaged FC prediction, we also compare the Ridge regression model to the Ridge Regression in Riemannian space. To test for effects of removing sets of features, we use the approach described above to compare the kernels constructed from the full feature sets to their versions where features were removed or reduced. Finally, to test for effects of training the HMM either on all subjects or only on the subjects that were later used as training set, we compare each kernel to the corresponding kernel constructed from HMM parameters, where training and test set were kept separate.“

      Model performance evaluation is done on the level of all predictions (i.e., across target variables, CV folds, and CV iterations) rather than for each of the target variables separately. That means different best-performing methods depending on the target variables are to be expected.

      - While 10-fold cross-validation is used for behavioral prediction, it appears that data from the entire set of subjects is concatenated to produce the initial group-level HMM estimates (which are then customized to individuals). I wonder if this procedure could introduce some shared information between CV training and test sets. This may be a minor issue when comparing the HMM-based models to one another, but it may be more important when comparing with other models such as those based on time-averaged connectivity, which are calculated separately for train/test partitions (if I understood correctly).

      The lack of separation between training and test set before fitting the HMM was also pointed out by Reviewer #2. We are addressing this issue in the new Results section 2.4 (see also our response to Reviewer #2, comment 2).

      Recommendations for the authors:

      The individual public reviews all indicate the merits of the study, however, they also highlight relatively consistent questions or issues that ought to be addressed. Most significantly, the authors ought to provide greater clarity surrounding the use of the cross-validation procedures they employ, and the use of a common atlas derived outside the cross-validation loop. Also, the authors should ensure that the statistical testing procedures they employ accommodate the dependencies induced between folds by the cross-validation procedure and give care to ensuring that the conclusions they make are fully supported by the data and statistical tests they present.

      Reviewer #1 (Recommendations For The Authors):

      Overall, the study is interesting but demands further improvements. Below, I summarize my comments:

      (1) The authors should explain in detail how they applied cross-validation across the dataset for both optimization of parameters, and also for cross-validation of the models to predict individual traits.

      How did you split the dataset for both parameters optimization, and for the CV of the prediction of behavioral traits?

      A review and a summary of various CVs that have been applied on the same dataset should be applied.

      We apologise for the oversight and have now added more details to the CV section of the methods, see our response to Reviewer #1 comment 1:

      In ll. 804-813:

      “We used k-fold nested cross-validation (CV) to select and evaluate the models. We used 10 folds for both the outer loop (used to train and test the model) and the inner loop (used to select the optimal hyperparameters) such that 90% were used for training and 10% for testing. The optimal hyperparameters  (and  in the case of the Gaussian kernels) were selected using grid-search from the vectors λ=[0.0001,0.001,0.01,0.1,0.3,0.5,0.7,0.9,1] and . In both the outer and the inner loop, we accounted for family structure in the HCP dataset so that subjects from the same family were never split across folds (Winkler et al., 2015). Within the CV, we regressed out sex and head motion confounds, i.e., we estimated the regression coefficients for the confounds on the training set and applied them to the test set (Snoek et al., 2019).“ and ll. 818-820: “We generated the 100 random repetitions of the 10 outer CV folds once, and then used them for training and prediction of all methods, so that all methods were fit to the same partitions.”

      (2) The authors should explain in more detail how they applied ICA-based parcellation at the group-level.

      A. Did you apply it across the whole group? If yes, then this is problematic since it rejects the CV approach. It should be applied within the folds.

      B. How did you define the representative time-source per ROI?

      A: How group ICA was applied was stated in the Methods section (4.1 HCP imaging and behavioural data), ll. 543-548:

      “The parcellation was estimated from the data using multi-session spatial ICA on the temporally concatenated data from all subjects.”

      We have now added a disclaimer about the divide between training and test set:

      “Note that this means that there is no strict divide between the subjects used for training and the subjects for testing the later predictive models, so that there is potential for leakage of information between training and test set. However, since this step does not concern the target variable, but only the preprocessing of the predictors, the effect can be expected to be minimal (Rosenblatt et al., 2024).”

      We understand that in order to make sure we avoid data leakage, it would be desirable to estimate and apply group ICA separately for the folds, but the computational load of this would be well beyond the constraints of this particular work, where we have instead used the parcellation provided by the HCP consortium.

      B: This was also stated in 4.1, ll. 554-559: “Timecourses were extracted using dual regression (Beckmann et al., 2009), where group-level components are regressed onto each subject’s fMRI data to obtain subject-specific versions of the parcels and their timecourses. We normalised the timecourses of each subject to ensure that the model of brain dynamics and, crucially, the kernels were not driven by (averaged) amplitude and variance differences between subjects.”

      (3) The authors discussed throughout the paper that their proposed (HMM+Fisher) kernel approach outperformed dynamic functional connectivity (dFC). However, they compared the proposed methodology with just static FC.

      A. The authors didn't explain how static and dFC have been applied.

      B. If the authors wanted to claim that their methodology is better than dFC, then they have to demonstrate results based on dFC with the trivial sliding window approach.

      C. Moreover, the static FC networks have been constructed by concatenating time samples that belong to the same state across the time course of resting-state activity.

      So, it's HMM-informed static FC analysis, which is problematic since it's derived from HMM applied over the brain dynamics.

      I don't agree that connectivity is derived exclusively from the clustering of human brain dynamics!

      D. A static approach of using the whole time course, and a dFC following the trivial sliding-window approach should be adopted and presented for comparison with (HMM+Fisher) kernel.

      We do not intend to claim our manuscript that our method outperforms other methods for doing dynamic FC. Indeed, we would like to be clear that the HMM itself is a method for capturing dynamic FC. Please see our responses to public review comments 2 and 3 by reviewer #1, copied below, which is intended to clear up this misunderstanding:

      We would like to clarify that the HMM is itself a method for estimating dynamic (or time-varying) FC, just like the sliding window approach, see also Vidaurre, 2024 (https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00363/124983) for an overview of terminology.

      We would like to be clear that we do not claim in the manuscript that our method outperforms other dynamic functional connectivity (dFC) approaches, such as sliding window FC. We have now made changes to the manuscript to make this clearer.

      First, we have clarified our use of the term “brain dynamics” to signify “time-varying amplitude and functional connectivity patterns” in this context, as Reviewer #2 raised the point that the former term is ambiguous.

      Second, our focus is on our method being a way of using dFC for predictive modelling, since there currently is no widely accepted way of doing this. One reason why dFC is not usually considered in prediction studies is that it is mathematically not trivial how to use the parameters from estimators of dynamic FC for a prediction. This includes the sliding window approach. We do not aim at comparing across different dFC estimators in this paper. To make these points clearer, we have revised the introduction to now say:

      Ll. 39-50:

      “One reason why brain dynamics are not usually considered in this context pertains to their representation: They are represented using models of varying complexity that are estimated from modalities such as functional MRI or MEG. Although there exists a variety of methods for estimating time-varying or dynamic FC (Lurie et al., 2019), like the commonly used sliding-window approach, there is currently no widely accepted way of using them for prediction problems. This is because these models are usually parametrised by a high number of parameters with complex mathematical relationships between the parameters that reflect the model assumptions. How to leverage these parameters for prediction is currently an open question.

      We here propose the Fisher kernel for predicting individual traits from brain dynamics, using information from generative models that do not assume any knowledge of task timings. We focus on models of brain dynamics that capture within-session changes in functional connectivity and amplitude from fMRI scans, in this case acquired during wakeful rest, and how the parameters from these models can be used to predict behavioural variables or traits. In particular, we use the Hidden Markov Model (HMM), which is a probabilistic generative model of time-varying amplitude and functional connectivity (FC) dynamics (Vidaurre et al., 2017).”

      To the additional points raised here:

      A: How static and dynamic FC have been estimated is explicitly stated in the relevant Methods sections 4.2 (The Hidden Markov Model), which explains the details of using the HMM to estimate dynamic functional connectivity; and 4.5 (Regression models based on time-averaged FC features), which explains how static FC was computed.

      B: We are not making this claim. We have now modified the Introduction to avoid further misunderstandings, as per ll. 33-36: “One way of describing brain dynamics are state-space models, which allow capturing recurring patterns of activity and functional connectivity (FC) across the whole brain.”

      C: This is not how static FC networks were constructed; we apologise for the confusion. We also do not perform any kind of clustering. The only “HMM-informed static FC analysis” is the static FC KL divergence model to allow for a more direct comparison with the time-varying FC KL divergence model, but we have included several other static FC models (log-Euclidean, Ridge regression, Ridge regression Riem., Elastic Net, Elastic Net Riem., and Selected Edges), which do not use HMMs. This is explained in Methods section 4.5.

      D: As explained above, we have included four (five in the revised manuscript) static approaches using the whole time course, and we do not claim that our method outperforms other dynamic FC models. We also disagree that using the sliding window approach for predictive modelling is trivial, as explained in the introduction of the manuscript and under public review comment 3.

      (4) Did you correct for multiple comparisons across the various statistical tests?

      All statistical comparisons have been corrected for multiple comparisons. Please find the relevant text in Methods section 4.4.1.

      (5) Do we expect that behavioral traits are encapsulated in resting-state human brain dynamics, and on which brain areas mostly? Please, elaborate on this.

      While this is certainly an interesting question, our paper is a methodological contribution about how to predict from models of brain dynamics, rather than a basic science study about the relation between resting-state brain dynamics and behaviour. The biological aspects and interpretation of the specific brain-behaviour associations are a secondary point and out of scope for this paper. Our approach uses whole-brain dynamics, which does not require selecting brain areas of interest.

      Reviewer #2 (Recommendations For The Authors):

      Beyond the general principles included in the public review, here are a few additional pointers to minor issues that I would wish to see addressed.

      Introduction:

      - The term "brain dynamics" encompasses a broad spectrum of phenomena, not limited to those captured by state-space models. It includes various measures such as time-averaged connectivity and mean EEG power within specific frequency bands. To ensure clarity and relevance for a diverse readership, it would be beneficial to adopt a more inclusive and balanced approach to the terminology used.

      The reviewer rightly points out the ambiguity of the term “brain dynamics”, which we use in the interest of readability. The HMM is one of several possible descriptions of brain dynamics. We have now included a statement early in the introduction to narrow this down:

      Ll. 32-35:

      “… the patterns in which brain activity unfolds over time, i.e., brain dynamics. One way of describing brain dynamics are state-space models, which allow capturing recurring patterns of activity and functional connectivity (FC) across the whole brain.”

      And ll. 503-507:

      “Our proposed approach provides one avenue of addressing this by leveraging individual patterns of time-varying amplitude and FC, as one of many possible descriptions of brain dynamics, and it can be flexibly modified or extended to include, e.g., information about temporally recurring frequency patterns (Vidaurre et al., 2016).”

      Figures:

      - The font sizes across the figures, particularly in subpanels 2B and 2C, are quite small and may challenge readability. It is advisable to standardize the font sizes throughout all figures to enhance legibility.

      We have slightly increased the overall font sizes, while we are generally following figure recommendations set out by Nature. The font sizes are the same throughout the figures.

      - When presenting performance comparisons, a horizontal layout is often more intuitive for readers, as it aligns with the natural left-to-right reading direction. This is not just a personal preference; it is supported by visualization best practices as outlined in resources like the NVS Cheat Sheet (https://github.com/GraphicsPrinciples/CheatSheet/blob/master/NVSCheatSheet.pdf) and Kieran Healy's book (https://socviz.co/lookatdata.html).

      We have changed all figures to use horizontal layout, hoping that this will ease visual comparison between the different models.

      - In the kernel density estimation (KDE) and violin plot representations, it appears that the data displays may be truncated. It is crucial to indicate where the data distribution ends. Overplotting individual data points could provide additional clarity.

      To avoid confusion about the data distribution in the violin plots, we have now overlaid scatter plots, as suggested by the reviewer. Overlaying the fold-level accuracies was not feasible (since this would result in ~1.5 million transparent points for a single figure), so we instead show the accuracies averaged over folds but separate for target variables and CV iterations. Only the newly added coefficient of determination plots had to be truncated, which we have noted in the figure legend.

      - Figure 3 could inadvertently suggest that time-varying features correspond to panel A and time-averaged features to panel B. To avoid confusion, consider reorganizing the labels at the bottom into two rows for clearer attribution.

      We have changed the layout of the time-varying and time-averaged labels in the new version of the plots to avoid this issue.

      Discussion:

      - The discussion on multimodal modeling might give the impression that it is more effective with multiple kernel learning (MKL) than with other methods. To present a more balanced view, it would be appropriate to rephrase this section. For instance, stacking, examples of which are cited in the same paragraph, has been successfully applied in practice. The text could be adjusted to reflect that Fisher Kernels via MKL adds to the array of viable options for multimodal modeling. As a side thought: additionally, a well-designed comparison between MKL and stacking methods, conducted by experts in each domain, could greatly benefit the field. In certain scenarios, it might even be demonstrated that the two approaches converge, such as when using linear kernels.

      We would like to thank the reviewer for the suggestion about the discussion concerning multimodal modelling. We agree that there are other relevant methods that may lead to interesting future work and have now included stacking and refined the section: ll. 487-494:

      “While directly combining the features from each modality can be problematic, modality-specific kernels, such as the Fisher kernel for time-varying amplitude and/or FC, can be easily combined using approaches such as stacking (Breiman, 1996) or Multi Kernel Learning (MKL) (Gönen & Alpaydın, 2011). MKL can improve prediction accuracy of multimodal studies (Vaghari et al., 2022), and stacking has recently been shown to be a useful framework for combining static and time-varying FC predictions (Griffin et al., 2024). A detailed comparison of different multimodal prediction strategies including kernels for time-varying amplitude/FC may be the focus of future work.”

      - The potential clinical applications of brain dynamics extend beyond diagnosis and individual outcome prediction. They play a significant role in the context of biomarkers, including pharmacodynamics, prognostic assessments, responder analysis, and other uses. The current discussion might be misinterpreted as being specific to hidden Markov model (HMM) approaches. For diagnostic purposes, where clinical assessment or established biomarkers are already available, the need for new models may be less pressing. It would be advantageous to reframe the discussion to emphasize the potential for gaining deeper insights into changes in brain activity that could indicate therapeutic effects or improvements not captured by structural brain measures. However, this forward-looking perspective is not the focus of the current work. A nuanced revision of this section is recommended to better reflect the breadth of applications.

      We appreciate the reviewer’s thoughtful suggestions regarding the discussion of potential clinical applications. We have included the suggestions and refined this section of the discussion: Ll. 495-507:

      “In a clinical context, while there are nowadays highly accurate biomarkers and prognostics for many diseases, others, such as psychiatric diseases, remain poorly understood, diagnosed, and treated. Here, improving the description of individual variability in brain measures may have potential benefits for a variety of clinical goals, e.g., to diagnose or predict individual patients’ outcomes, find biomarkers, or to deepen our understanding of changes in the brain related to treatment responses like drugs or non-pharmacological therapies (Marquand et al., 2016; Stephan et al., 2017; Wen et al., 2022; Wolfers et al., 2015). However, the focus so far has mostly been on static or structural information, leaving the potentially crucial information from brain dynamics untapped. Our proposed approach provides one avenue of addressing this by leveraging individual patterns of time-varying amplitude and FC, and it can be flexibly modified or extended to include, e.g., information about temporally recurring frequency patterns (Vidaurre et al., 2016).”

      Reviewer #3 (Recommendations For The Authors):

      - I wondered if the authors could provide, within the Introduction, an intuitive description for how the Fisher Kernel "preserves the structure of the underlying model of brain dynamics" / "preserves the mathematical structure of the underlying HMM"? Providing more background may help to motivate this study to a general audience.

      We agree that this would be helpful and have now added this to the introduction: Ll.61-67:

      “Mathematically, the HMM parameters lie on a Riemannian manifold (the structure). This defines, for instance, the relation between parameters, such as: how changing one parameter, like the probabilities of transitioning from one state to another, would affect the fitting of other parameters, like the states’ FC. It also defines the relative importance of each parameter; for example, how a change of 0.1 in the transition probabilities would not be the same as a change of 0.1 in one edge of the states’ FC matrices.”

      To communicate the intuition behind the concept, the idea was also illustrated in Figure 1, panel 4 by showing Euclidean distances as straight lines through a curved surface (4a, Naïve kernel), as opposed to the tangent space projection onto the curved manifold (4b, Fisher kernel).

      - Some clarifications regarding Figure 2a would be helpful. Was the linear Fisher Kernel significantly better than the linear Naive normalized kernel? I couldn't find whether this comparison was carried out. Apologies if I have missed it in the text. For some of the brackets indicating pairwise tests and their significance values, the start/endpoints of the bracket fall between two violins; in this case, were the results of the linear and Gaussian Fisher Kernels pooled together for this comparison?

      We have now streamlined the statistical comparisons and avoided plotting brackets falling between two violin plots. The comparisons that were carried out are stated in the methods section 4.4.1. Please see also our response to above to Reviewer #3 public review, potential weaknesses, point 1, relevant point copied below:

      In conjunction with the new statistical approach (see Reviewer #2, comment 3), we have now streamlined the comparisons. We explained which comparisons were performed in the methods ll.880-890:

      “For the main results, we separately compare the linear Fisher kernel to the other linear kernels, and the Gaussian Fisher kernel to the other Gaussian kernels, as well as to each other. We also compare the linear Fisher kernel to all time-averaged methods. Finally, to test for the effect of tangent space projection for the time-averaged FC prediction, we also compare the Ridge regression model to the Ridge Regression in Riemannian space. To test for effects of removing sets of features, we use the approach described above to compare the kernels constructed from the full feature sets to their versions where features were removed or reduced. Finally, to test for effects of training the HMM either on all subjects or only on the subjects that were later used as training set, we compare each kernel to the corresponding kernel constructed from HMM parameters, where training and test set were kept separate”.

      - The authors may wish to include, in the Discussion, some remarks on the use of all subjects in fitting the group-level HMM and the implications for the cross-validation performance, and/or try some analysis to ensure that the effect is minor.

      As suggested by reviewers #2 and #3, we have now performed the suggested analysis and show that fitting the group-level HMM to all subjects compared to only to the training subjects has no effect on the results. Please see our response to Reviewer #2, public review, comment 2.

      - The decision to use k=6 states was made here, and I wondered if the authors may include some support for this choice (e.g., based on findings from prior studies)?

      We have now refined and extended our explanation and rationale behind the number of states: Ll. 586-594: “The number of states can be understood as the level of detail or granularity with which we describe the spatiotemporal patterns in the data, akin to a dimensionality reduction, where a small number of states will lead to a very general, coarse description and a large number of states will lead to a very detailed, fine-grained description. Here, we chose a small number of states, K=6, to ensure that the group-level HMM states are general enough to be found in all subjects, since a larger number of states increases the chances of certain states being present only in a subset of subjects. The exact number of states is less relevant in this context, since the same HMM estimation is used for all kernels.”

      - (minor) Abstract: "structural aspects" - do you mean structural connectivity?

      With “structural aspects”, we refer to the various measures of brain structure that are used in predictive modelling. We have now specified: Ll. 14-15: “structural aspects, such as structural connectivity or cortical thickness”.

    1. eLife Assessment

      This important modeling study alters a previous model of the intact cat spinal locomotor network to simulate a lateral hemi-section of the spinal cord. The modeling and experimental work described provide convincing evidence that this model is capable of qualitatively predicting alterations to the swing and stance phase durations during locomotion at different speeds on intact or split-belt treadmills. This paper will interest neuroscientists studying vertebrate motor systems, including researchers working on motor dysfunction after spinal cord injury.

    2. Reviewer #1 (Public review):

      Summary:

      This study adapts a previously published model of the cat spinal locomotor network to make predictions of how phase durations of swing and stance at different treadmill speeds in tied-belt and split-belt conditions would be altered following a lateral hemisection. The simulations make several predictions that are replicated in experimental settings. This updated manuscript addressed well many of the reviewer comments made to the first version.

      Strengths:

      -Despite only altering the connections in the model, the model is able to replicate very well several experimental findings. This provides strong validation for the model and highlights its utility as a tool to investigate the operations of mammalian spinal locomotor networks.

      -The study provides insights about interactions between the left and right side of the spinal locomotor networks, and how these interactions depend on the mode of operation, as determined by speed and state of the nervous system.

      -The writing is logical, clear and easy to follow.

      Comments on revisions:

      My concerns were well addressed by the authors. I have no additional concerns

    3. Reviewer #2 (Public review):

      This is a nice article that presents interesting findings. The model's predictions match the data, which is good. The discussion points to modeling plasticity after SCI, which will be important.

      The manuscript is well-written and interesting, and the putative neural circuit mechanisms that the model uncovers are super cool if they can be tested in an animal.

    4. Author response:

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

      eLife Assessment

      The modeling and experimental work described provide solid evidence that this model is capable of qualitatively predicting alterations to the swing and stance phase durations during locomotion at different speeds on intact or split-belt treadmills, but a revision of the figures to overlay the model predictions with the experimental data would facilitate the assessment of this qualitative agreement. This paper will interest neuroscientists studying vertebrate motor systems, including researchers investigating motor dysfunction after spinal cord injury.

      Figures showing the overlay of the experimental data with the modeling predictions have been included as figure supplements for Figures 5-7. This highlights how accurate the model predictions were.

      Public Reviews:

      Reviewer #1 (Public review):

      We thank the reviewer for the positive evaluation of our paper and emphasizing its strengths in the Summary.

      Weaknesses:

      (1) Could the authors provide a statement in the methods or results to clarify whether there were any changes in synaptic weight or other model parameters of the intact model to ensure locomotor activity in the hemisected model?

      Such a statement has been inserted in Materials and Methods, section “Modeling”. Also, in the 1st paragraph of section “Spinal sensorimotor network architecture and operation after a lateral spinal hemisection”, we stated that no “additional changes or adjustments” were made.

      (2) The authors should remind the reader what the main differences are between state-machine, flexor-driven, and classical half-center regimes (lines 77-79).

      Short explanations/reminders have been inserted (see lines 80-83 of tracked changes document).

      (3) There may be changes in the wiring of spinal locomotor networks after the hemisection. Yet, without applying any sort of plasticity, the model is able to replicate many of the experimental data. Based on what was experimentally replicated or not, what does the model tell us about possible sites of plasticity after hemisection?

      Quantitative correspondence of changes in locomotor characteristics predicted by the model and those obtained experimentally provide additional validation of the model proposed in the preceding paper and used in this paper. This was our ultimate goal. None of the plastic changes during recovery were modeled because of a lack of precise information on these changes. The absence of possible plastic changes may explain the small discrepancies between our simulations and experimental data (see Supplemental Figures that have been added). However, the model only has a simplified description of spinal circuits without motoneurons and without real simulation of leg biomechanics. This limits our analysis or predictions of possible plastic changes within a reasonable degree of speculation. This issue is discussed in section: “Limitations and future directions” in the Discussion. We have also inserted a sentence: “The lack of possible plastic changes in spinal sensorimotor circuits of our model may explain the absence of exact/quantitative correspondences between simulated and experimental data.

      (4) Why are the durations on the right hemisected (fast) side similar to results in the full spinal transected model (Rybak et al. 2024)? Is it because the left is in slow mode and so there is not much drive from the left side to the right side even though the latter is still receiving supraspinal drive, as opposed to in the full transection model? (lines 202-203).

      This is correct. We have included this explanation in the text (lines 210-211 of tracked changes document).

      (5) There is an error with probability (line 280).

      This typo was corrected.

      Reviewer #2 (Public review):

      This is a nice article that presents interesting findings. One main concern is that I don't think the predictions from the simulation are overlaid on the animal data at any point - I understand the match is qualitative, which is fine, but even that is hard to judge without at least one figure overlaying some of the data.

      We thank the Reviewer for the constructive comments. Figures showing the overlay of the experimental data with the modeling predictions have been included as figure supplements for Figures 5-7. This highlights how accurate the model predictions were.

      Second is that it's not clear how the lateral coupling strengths of the model were trained/set, so it's hard to judge how important this hemi-split-belt paradigm is. The model's predictions match the data qualitatively, which is good; but does the comparison using the hemi-split-belt paradigm not offer any corrections to the model? The discussion points to modeling plasticity after SCI, which could be good, but does that mean the fit here is so good there's no point using the data to refine?

      The model has not been trained or retrained, but was used as it was described in the preceding paper. Response: Quantitative correspondence of changes in locomotor characteristics predicted by the model and those obtained experimentally provide additional validation of the model proposed in the preceding paper and used in this paper. This was our ultimate goal. None of the plastic changes during recovery were modeled because of a lack of precise information on these changes. The absence of possible plastic changes may explain the small discrepancies between our simulations and experimental data (see figure supplements that have been added). However, the model only has a simplified description of spinal circuits without motoneurons and without real simulation of leg biomechanics. This limits our analysis or predictions of possible plastic changes within a reasonable degree of speculation. This issue is discussed in section: “Limitations and future directions” in the Discussion.

      The manuscript is well-written and interesting. The putative neural circuit mechanisms that the model uncovers are great, if they can be tested in an animal somehow.

      We agree and we are considering how we can do this in an animal model.

      Page 2, lines 75-6: Perhaps it belongs in the other paper on the model, but it's surprising that in the section on how the model has been revised to have different regimes of operation as speed increases, there is no reference to a lot of past literature on this idea. Just one example would be Koditschek and Full, 1999 JEB Figure 3, where they talk about exactly this idea, or similarly Holmes et al., 2006 SIAM review Figure 7, but obviously many more have put this forward over the years (Daley and Beiwener, etc). It's neat in this model to have it tied down to a detailed neural model that can be compared with the vast cat literature, but the concept of this has been talked about for at least 25+ years. Maybe a review that discusses it should be cited?

      We have revised the Introduction to include the suggested references.

      Page 2, line 88: While it makes sense to think of the sides as supraspinal vs afferent driven, respectively, what is the added insight from having them coupled laterally in this hemisection model? What does that buy you beyond complete transection (both sides no supra) compared with intact?

      We are trying to make one model that could reproduce multiple experimental data in quadrupedal locomotion, including genetic manipulations with (silencing/removal) particular neuron types (and commissural interneurons), as pointed out in the section “Model Description” in the Results. These lateral connections are critical for reproducing and explaining other locomotor behaviors demonstrated experimentally. However, even in this study, these lateral interactions are necessary to maintain left-right coordination and equal left-right frequency (step period) during split-belt locomotion and after hemisection.

      I can see how being able to vary cycle frequencies separately of the two limbs is a good "knob" to vary when perturbing the system in order to refine the model. But there isn't a ton of context explaining how the hemi-section with split belt paradigm is important for refining the model, and therefore the science. Is it somehow importantly related to the new "regimes" of operation versus speed idea for the model?  

      We did not refine the model in this paper. We just used it for new simulations. The predictions strengthen the organization and operation of the model we recently proposed.

      Page 5, line 212: For the predictions from the model, a lot depends on how strong the lateral coupling of the model is, which, in turn, depends on the data the model was trained on. Were the model parameters (especially for lateral coupling of the limbs) trained on data in a context where limbs were pushed out of phase and neuronal connectivity was likely required to bring the limbs back into the same phase relationship? Because if the model had no need for lateral coupling, then it's not so surprising that the hemisected limbs behave like separate limbs, one with surpaspinal intact and one without.

      Please see our response above concerning the need for lateral interactions incorporated to the model.

      Page 8, line 360: The discussion of the mechanisms (increased influence of afferents, etc) that the model reveals could be causing the changes is exciting, though I'm not sure if there is an animal model where it can be tested in vivo in a moving animal.

      We agree it may be difficult to test right now but we are considering experimental approaches.

      Page 9, line 395: There are some interesting conclusions that rely on the hemi-split-belt paradigm here.

      We agree with this comment. Thanks.

      Reviewer #2 (Recommendations for the authors):

      Figures: Why aren't there any figures with the simulation results overlaid on the animal data?

      We followed this suggestion. Figures showing the overlay of the experimental data with the modeling predictions have been included as figure supplements.

    1. eLife Assessment

      This valuable study revealed numerous distinct lineages that evolved within a local human population in Alberta, Canada, leading to persistent cases of E. coli O157:H7 infections for over a decade and highlighting the ongoing involvement of local cattle in disease transmission, as well as the possibility of intermediate hosts and environmental reservoirs. This study also showed a shift towards more virulent stx2a-only strains becoming predominant in the local lineages. The evidence supporting the role played by cattle in the transmission system of human cases of E. coli O157:H7 in Alberta is solid.

    2. Reviewer #1 (Public review):

      Summary:

      This is a high-quality, well-thought through analysis of STEC transmission in Alberta, Canada.

      Strengths:

      * The combined human and animal sampling is a great foundation for this kind of study.<br /> * Phylogenetic analyses seem to have been carried out in a high quality fashion.

      Comments on the revised version:

      I'd like to thank the authors for the diligence with which they addressed my comments. I agree with their points and am happy for the manuscript to proceed.

    3. Author response:

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

      Reviewer #1 (Public Review):

      Summary:

      A nice study trying to identify the relationship between E. coli O157 from cattle and humans in Alberta, Canada.

      Strengths:

      (1) The combined human and animal sampling is a great foundation for this kind of study.

      (2) Phylogenetic analyses seem to have been carried out in a high-quality fashion.

      Weaknesses:

      I think there may be a problem with the selection of the isolates for the primary analysis. This is what I'm thinking:

      (1) Transmission analyses are strongly influenced by the sampling frame.

      (2) While the authors have randomly selected from their isolate collections, which is fine, the collections themselves are not random.

      (3) The animal isolates are likely to represent a broad swathe of diversity, because of the structured sampling of animal reservoirs undertaken (as I understand it).

      (4) The human isolates are all from clinical cases. Clinical cases of the disease are likely to be closely related to other clinical cases, because of outbreaks (either detected, or undetected), and the high ascertainment rate for serious infections.

      (5) Therefore, taking an equivalent number of animal and clinical isolates, will underestimate the total diversity in the clinical isolates because the sampling of the clinical isolates is less "independent" (in the statistical sense) than sampling from the animal isolates.

      (6) This could lead to over-estimating of transmission from cattle to humans.

      We appreciate the reviewer’s careful thoughts about our sampling strategy. We agree with points (1) and (2), and we have provided additional details on the animal collections as requested (lines 95-101).

      We agree with point (3) in theory but not in fact. As shown in Figure 3, the cattle isolates were very closely related, despite the temporal and geographic breadth of sampling within Alberta. The median SNP distance between cattle sequences was 45 (IQR 36-56), compared to 54 (IQR 43-229) SNPs between human sequences from cases in Alberta during the same years. Additionally, as shown in Figure 2, only clade A and B isolates – clades that diverge substantially from the rest of the tree – were dominated by human cases in Alberta. We have better highlight this evidence in the revision (lines 234-236 and 247-249).

      We agree with the reviewer in point (4) that outbreaks can be an important confounder of phylogenetic inference. This is why we down-sampled outbreaks (based on genetic relatedness, not external designation) in our extended analyses. We did not do this in the primary analysis, because there were no large clusters of identical isolates. Figure 3b shows a limited number of small clusters; however, clustered cattle isolates outnumbered clustered human isolates, suggesting that any bias would be in the opposite direction the reviewer suggests. In the revision, we down-sampled all analyses and, indeed, the proportion of human lineages descending from cattle lineages increased (lines 259-261). Regarding severe cases being oversampled among the clinical isolates, this is absolutely true and a limitation of all studies utilizing public health reporting data. We made this limitation to generalizability clearer in the discussion. However, as noted above, clinical isolates were more variable than cattle isolates, so it does not appear to have heavily biased the analysis (lines 490-495).

      We disagree with the reviewer on point (5). While the bias toward severe cases could make the human isolates less independent, the relative sampling proportions are likely to induce greater distance between clinical isolates than cattle isolates, which is exactly what we observe (see response to point (3) above). Cattle are E. coli O157:H7’s primary reservoir, and humans are incidental hosts not able to sustain infection chains long-term. Not only is the bacteria prevalent among cattle, cattle are also highly prevalent in Alberta. Thus, even with 89 sampling points, we are still capturing a small proportion of the E. coli O157:H7 in the province. Being able to sample only a small proportion of cattle’s E. coli O157:H7 increases the likelihood of only sampling from the center of the distribution, making extreme cases such as that shown at the very bottom of the tree in Figure 4, rare and important. In comparison, sampling from human cases constitutes a higher proportion of human infections relative to cattle, and is therefore more representative of the underlying distribution, including extremes. We added this point to the limitations (lines 495-504). As with the clustering above, if anything, this outcome would have biased the study away from identifying cattle as the primary reservoir. Additionally, the relatively small proportion of cattle sampled makes our finding that 15.7% of clinical isolates were within 5 SNPs of a cattle isolate, the distance most commonly used to indicate transmission for E. coli O157:H7, all the more remarkable.

      Because of the aforementioned points, we disagree with the reviewer’s conclusion in point (6). If a bias exists, we believe transmission from cattle-to-humans is likely underestimated for the reasons given above. Not only do all prior studies indicate ruminants as the primary reservoirs of E. coli O157:H7, and humans as only incidental hosts, our specific data do not support the reviewer’s individual contentions. The results of the sensitivity analysis the reviewer recommended is consistent with the points we outlined above, estimating that 94.3% of human lineages arose from cattle lineages (vs. 88.5% in the primary analysis). We have opted to retain the more conservative estimate of the primary analysis, which includes a more representative number of clinical cases.

      (7) We hypothesize that the large proportion of disease associated with local transmission systems is a principal cause of Alberta's high E. coli O157:H7 incidence" - this seems a bit tautological. There is a lot of O157 because there's a lot of transmission. What part of the fact it is local means that it is a principal cause of high incidence? It seems that they've observed a high rate of local transmission, but the reasons for this are not apparent, and hence the cause of Alberta's incidence is not apparent. Would a better conclusion not be that "X% of STEC in Alberta is the result of transmission of local variants"? And then, this poses a question for future epi studies of what the transmission pathway is.

      The reviewer is correct, and the suggestion for the direction of future studies was our intent with this statement. We have removed this sentence.

      Reviewer #1 (Recommendations For The Authors):

      (1) To address my concerns about the different sampling frames in humans and animals, I would suggest a sensitivity analysis, using something like the following strategy. Make a phylogeny of all the available genome sequences from humans and cattle from Alberta. Phylogenetically sub-sample the tree, using something like Treemer (https://github.com/fmenardo/Treemmer), to remove phylogenetically redundant isolates from the same host type. Randomly select 100 human and 100 animal isolates from this non-redundant tree, and re-do your analysis.

      Although we originally down-sampled outbreaks for our analysis of the extended Alberta tree (2007-2019), we had not done this systematically for all analyses. We were not able to use the recommended Treemer tool, because we did not see a way to incorporate the timing of sequences. Because the objective of our study was to evaluate persistence, we did not want to exclude identical sequences that were separated in time and thus could be indicating persistence. To accomplish this, we developed a utility that allowed us to incorporate the temporality of sequences. Using this utility, we systematically down-sampled all sequences that met the following conditions: 1) within 0-2 SNPs of another sequence and 2) no gaps in sequence set >2 months. The second condition means that for any set of sequences within 0-2 SNPs of one another, there can be no more than 2 months without a sequence from the set. Similar sequences that occur beyond this 2-month-cutoff would be considered a separate set for down-sampling. This cutoff was chosen based on the epidemiology of E. coli O157 outbreaks, which are generally either point-source or continuous-source outbreaks. Intermittent outbreaks of a single strain are believed to arise from distinct contamination events and are exactly the type of phenomena we are seeking to identify. We have added details on down-sampling to the Methods (lines 178-180).

      After down-sampling, our primary analysis included 115 human and 84 cattle isolates. T conduct the recommended sensitivity analysis, we further randomly subsampled the human isolates, selecting 84 to match the number of cattle isolates. As we suggested in our initial response, and contrary to the reviewer’s concern, subsampling in this way accentuated the results, with 94.3% of human lineages inferred as arising from cattle lineages, compared to 88.5% in the primary analysis. This sensitivity analysis also identified 10 of the 11 LPLs identified in the primary analysis. The LPL not identified had 5 isolates in the primary analysis, the minimum for definition as an LPL, and was reduced to 4 isolates through subsampling. This sensitivity analysis is shown in Suppl. Figure S3.

      (2) This is the first time I've seen target diagrams used for SNP distances, I'm not sure of their value compared with histograms. They seem to emphasise the maximum distance, rather than the largest number of isolates. I.e. most isolates are closely related, but the diagram emphasises the small number of divergent ones.

      In using the target diagrams, we sought to emphasize the bimodal distribution of human-to-closest-cattle SNP differences. However, this is still mostly visible in a histogram, so we have replaced the target diagrams with a histogram as suggested (Figure 3).

      (3) L130 - fastqc doesn't trim adapters and read ends, there will be something else like trimmomatic which does.

      The reviewer is correct, and we appreciate them catching this error. Trimmomatic is incorporated into the Shovill pipeline, which was the assembler we used through the Bactopia pipeline. We have updated the Methods to indicate this (lines 142-144).

      (4) I find the flow of the article a bit confusing. You have your primary analysis, but Figure 2, which is a secondary analysis, comes before Figure 3. Which is the primary analysis? For me, primary analysis results should come first, or at least signpost a bit better.

      Figure 2 is not a secondary analysis. It is intended to provide an overview of the isolates used from the phylogenetic perspective, just as the diagram in Figure 1 provides an overview of the isolates by analysis. The secondary analyses are shown in Figures 5-7. We have added a sub-header, “Description of Isolates”, to the section referring to Figure 2, to clarify (line 232).

      (5) Locally persistent lineage definition. What is the rationale for the different criteria signifying locally persistent lineages? There is nothing in some of your criteria e.g. all isolates <30 SNPs from each other, which indicates that it is locally persistent - could have been transmitted to Japan (just to pick a place at random), causing a bunch of cases there, and then come back for all we know. Would that be a locally persistent lineage? Did you use the MCC tree here? That is a sub-sample of your full dataset, I am not sure what exactly you're trying to say with the LPLs, but maybe using a larger dataset would be better? Also, there are lots of STEC genomes available from e.g. UK and USA, by only including a fraction of these, you limit the strength of the inferences you can make about locally persistent lineages unless you know that they don't see the G sub-lineage that you observe.

      The reviewer raises multiple points here. First, regarding our definition of LPLs, it is intended to identify those lineages that pose a threat to populations in the specific geographic area (“local”) for at least 1 year (“persistent”) that are likely to be harbored in local reservoirs. Each of the criteria contributes to this definition.

      (1) A single lineage of the MCC tree with a most recent common ancestor (MRCA) with ≥95% posterior probability: This criterion provides confidence in the given isolates being part of a single, defined lineage. The posterior probability gives the probability that the topology of the tree is accurate, based on the data provided and the chosen model of evolution. In other words, we required at least 95% probability that the lineage was correct, and in practice the posterior probability of the lineages we defined as LPLs was 99.7-100% (we have added this detail to the text, lines 269-270). We also added a sensitivity analysis, shown in Suppl. Figure S4, which shows all sampled trees. We find that the essential structure of the tree around the LPLs we defined is well-supported.

      (2) All isolates ≤30 core SNPs from one another: This criterion limited LPLs to those lineages where the isolates were closely related. We did not want to limit LPLs to those that might define an outbreak, for example using a 5-10 SNP threshold, because the point of the study is to identify lineages that persistently cause disease over longer periods than a normal outbreak. Pathogens evolve over time in their reservoirs, leading to greater SNP distances, and we wanted to allow for this. The U.S. CDC has acknowledged a similar concern for such persistent lineages in its definition of REP strains, which it has defined based on ranges of 13-104 allele differences by cgMLST. Thus, our choice of 30 core SNPs as the threshold is in line with current practice in the emerging science on persistence of enteric pathogens. We have also added a sensitivity analysis examining alternate SNP thresholds, shown in Suppl. Figure S5, which results in clusters of LPLs identified in the primary analysis being grouped into larger lineages. Additionally, in the tree showing our primary analysis (Figure 4), we now note the minimum number of SNPs all isolates within the lineage differ by.

      (3) Contained at least 1 cattle isolate: This criterion increases confidence that the lineage is indeed “local”. Unlike humans, cattle are not known to be routinely infected by imported food products, and they do not make roundtrip journeys to other locations, as humans infected during travel do. Cattle themselves may be imported into Alberta while infected, and cattle in Alberta can be infected by other imported animals. In these cases, if the STEC strains the cattle harbor persist for ≥1 year, they become the type of lineages we are interested in as LPLs, regardless where they previously came from, because they are now potential persistent sources of infection in Alberta. By including at least one cattle isolate in each LPL, the only way an identified LPL is not actually local is if cattle are imported from the lineage’s reservoir community elsewhere (e.g., in Japan, as the reviewer suggested), the lineage is persisting in that non-Alberta reservoir, and newly infected cattle are imported repeatedly over 1 or more years. This could feasibly explain G(vi)-AB LPL 5 (Figure 4), which is entirely composed of cattle. Indeed, such an explanation would be consistent with the lack of new cases from this LPL after 2015 in the extended analysis (Figure 5). However, for all other LPLs, which contain both cattle and human isolates, for the LPL to not be local, both cattle and human cases would have to be imported from the same non-Alberta reservoir. While this is possible, the probability of such a scenario is low, and it decreases the more isolates are in an LPL. For the average LPL, this means 4 human and 6 cattle cases would need to be imported from a non-Alberta reservoir over several years. Given that our study is only a random sample of the total STEC cases and cattle in Alberta from 2007-2015, these numbers are underestimates of the true absolute number of cases and cattle associated with LPLs that would have to be explained by importation if the LPL were not local. We have added some explanation of the possibility of importation in the Discussion where we discuss the LPL criteria (lines 376-380).

      (4) Contained ≥5 isolates: In concert with criterion 3, this criterion guards against anomalies being counted as LPLs. By requiring at least 5 isolates in an LPL after down-sampling, at least 5 infection events must have occurred from the LPL, reducing the likelihood of importation explaining the LPL and emphasizing more significant LPLs.

      (5) The isolates were collected at sampling events (for cattle) or reported (for humans) over a period of at least 1 year: This criterion defines the persistence aspect of the LPL. In the primary analysis, the LPLs we identified persisted for an average of 8 years, with the shortest persisting for 5 years (these details have been added to the text, lines 268-269). Incorporating the extended analysis, several LPLs persisted for the full 13 years of the study.

      Regarding using additional non-Alberta isolates to help rule out importation, we have expanded the number of U.S. and global isolates included in the importation analysis, over-sampling clade G isolates from the U.S. (Figure 7). As cattle trade is substantially more common with the U.S. than other countries, we felt it most important to focus on the U.S. as a potential source of both imported cattle and human cases. Our results from this analysis show that only 9 of 494 (1.8%) U.S. isolates occurred in the LPLs we defined in the primary analysis, and all occurred after Alberta isolates (lines 313-317). Although we also added more global isolates, we still found that none were associated with the Alberta LPLs.

      (6) Given the importance of sampling for a study like this, some more information on animal sampling studies should be included here.

      We have added details on the cattle sampling to the Methods (lines 95-101).

      (7) L172 - do you mean an MRCA with >- 95% probability of location in Alberta?

      Location in Alberta was not determined from the primary analysis, which defined the LPLs, as only Alberta isolates were included in that analysis. As described above, this criterion meant that we required at least 95% probability that the tree topology at the lineage’s MRCA was correct, and in practice the posterior probability of the lineages we defined as LPLs was 99.7-100%.

      (8) Need a supplementary figure of just clade G from Figure 2.

      We have added a sub-tree diagram of clade G(vi) as Figure 2b.

      Reviewer #2 (Public Review):

      This study identified multiple locally evolving lineages transmitted between cattle and humans persistently associated with E. coli O157:H7 illnesses for up to 13 years. Furthermore, this study mentions a dramatic shift in the local persistent lineages toward strains with the more virulent stx2a-only profile. The authors hypothesized that this phenomenon is the large proportion of disease associated with local transmission systems is a principal cause of Alberta's high E. coli O157:H7 incidence. These opinions more effectively explain the role of the cattle reservoir in the dynamics of E. coli O157:H7 human infections.

      (1) The authors acknowledge the possibility of intermediate hosts or environmental reservoirs playing a role in transmission. Further discussion on the potential roles of other animal species commonly found in Alberta (e.g., sheep, goats, swine) could enhance the understanding of the transmission dynamics. Were isolates from these species available for analysis? If not, the authors should clearly state this limitation.”

      We have expanded the discussion of other species in Alberta, as suggested, including other livestock, wildlife, and the potential role of birds and flies (lines 353-360). Unfortunately, we did not have sequences available from other species, which we have added to the limitations (lines 487-490).

      (2) The focus on E. coli O157:H7 is understandable given its prominence in Alberta and the availability of historical data. However, a brief discussion on the potential applicability of the findings to non-O157 STEC serogroups, and the limitations therein, would be beneficial. Are there reasons to believe the transmission dynamics would be similar or different for other serogroups?

      We appreciate this comment and have expanded our discussion of relevance to non-O157 STEC (lines 452-460). Other authors have proposed that transmission dynamics differ, and studies of STEC risk factors, including our own, support this. However, there has been very little direct study of non-O157 transmission dynamics and there is even less cross-species genomic and metadata available for non-O157 isolates of concern.

      (3) The authors briefly mention the need for elucidating local transmission systems to inform management strategies. A more detailed discussion on specific public health interventions that could be targeted at the identified LPLs and their potential reservoirs would strengthen the paper's impact.

      We agree with the reviewer that this would be a good addition to the manuscript. The public health implications for control are several and extend to non-STEC reportable zoonotic enteric infections, such as Campylobacter and Salmonella. We have added a discussion of these (lines 460-465, 467-485).

      (4) Understanding the relationship between specific risk factors and E. coli O157:H7 infections is essential for developing effective prevention strategies. Have case-control or cohort studies been conducted to assess the correlation between identified risk factors and the incidence of E. coli O157:H7 infections? What methodologies were employed to control for potential confounders in these studies?

      Yes, there have been several case-control studies of reported cases. Many of these are referenced in the discussion in terms of the contribution of different sources to infection. As risk factors were not the focus of the current study, we believe a thorough discussion of the literature on the aspects of these various studies is beyond our scope. However, we have added some details on the risk factors themselves (lines 72-79).

      (5) The study's findings are noteworthy, particularly in the context of E. coli O157:H7 epidemiology. However, the extent to which these results can be replicated across different temporal and geographical settings remains an open question. It would be constructive for the authors to provide additional data that demonstrate the replication of their sampling and sequencing experiments under varied conditions. This would address concerns regarding the specificity of the observed patterns to the initial study's parameters.

      We appreciate the reviewer’s comment, as we are currently building on this analysis with an American dataset with different types of data available than were used in this study. Aligned with this work, we have added a comment on the adaptation of our method to other settings with different types of data (lines 448-450). We also added a sensitivity analysis to the manuscript simulating a different sampling approach (Suppl. Fig. S3), which should be informative to this question.

      Reviewer #2 (Recommendations For The Authors):

      Minor comments.

      (1) Figure 1: The figure is a critical visual representation of the study's findings and should be given prominent emphasis. It is essential that the key discoveries of the research are clearly depicted and explained in this visual format. The authors should ensure that Figure 1 is detailed and informative enough to stand out as a central piece of the study.

      Figure 1 is the diagram of sample numbers, locations, and corresponding analyses. We assume that the reviewer means to refer to Figure 2. Although the inclusion of >1,200 isolates makes the tree difficult to see in detail, we have made some modifications to make the findings clearer. First, we changed the clade coloration such that the only subclade differentiated is G(vi). We have removed the stx metadata ring to focus attention on the location and species of the isolates, as stx data are described in Table 1. Finally, we have added a sub-tree diagram of clade G(vi), colored by location. This makes clear the large sections of the subclade dominated by isolates from one location or another, and the limited areas where they overlap.

      (2) Figures 2 and 4: While these figures contribute to the presentation of the data, they appear to be somewhat rudimentary in their current form. The lack of detailed annotations regarding the clustering of different strains is a notable omission. I recommend that the authors refine these figures to include comprehensive labeling that clearly delineates the various bacterial clusters. Enhanced graphical representation with clear annotations will aid readers in better understanding the study's findings.

      We appreciate this suggestion. We have remade all trees generated by the BEAST 2 analyses in R, rather than FigTree. This has allowed us to annotate the trees with additional information on the LPLs and we believe provides a clearer picture of each LPL.

      (3) Supplemental Table S1: The supplemental tables are an excellent opportunity to showcase additional data and findings that support the study's conclusions. For Supplemental Table S1, it is recommended that the authors highlight the innovative aspects or novel discoveries presented in this table.

      Suppl. Table S1 shows the modeling specifications and priors used in the analyses. These decisions were not in and of themselves novel. The innovation in our methods is due to the development of the LPLs based on the trees resulting from the analyses detailed in Suppl. Table S1, as well as from the application of these models to E. coli O157:H7 for the first time. However, we understand the reviewers point and have emphasized the importance of the results shown in Suppl. Table S2 (lines 391-395).

      (4) Line 35: "We assessed the role of persistent cross-species transmission systems in Alberta's E. coli O157:H7 epidemiology." change to "We assessed the impact of persistent cross-species transmission systems on the epidemiology of E. coli O157:H7 in Alberta."

      We have made this change.

      (5) To facilitate a deeper understanding of the core findings of the manuscript and to enable the development of effective response strategies, I suggest that the authors provide more information regarding the sequencing data used in the study. This information should at least include aspects such as data accessibility and quality control measures.

      We have included a Supplemental Data File that lists all isolates used in the analysis, and the QC measures are detailed in the Methods.

    1. eLife Assessment

      This work models reinforcement-learning experiments using a recurrent neural network. It examines if the detailed credit assignment necessary for back-propagation through time can be replaced with random feedback. In this useful study the authors show that it yields a satisfactory approximation but the evidence to support that it holds in general is incomplete. As only short temporal delays are used and the examples simulated are overly simple, the approximation would need to be tested on more complex task and with larger networks.

    2. Reviewer #1 (Public review):

      Summary:

      Can a plastic RNN serve as a basis function for learning to estimate value. In previous work this was shown to be the case, with a similar architecture to that proposed here. The learning rule in previous work was back-prop with an objective function that was the TD error function (delta) squared. Such a learning rule is non-local as the changes in weights within the RNN, and from inputs to the RNN depends on the weights from the RNN to the output, which estimates value. This is non-local, and in addition, these weights themselves change over learning. The main idea in this paper is to examine if replacing the values of these non-local changing weights, used for credit assignment, with random fixed weights can still produce similar results to those obtained with complete bp. This random feedback approach is motivated by a similar approach used for deep feed-forward neural networks.

      This work shows that this random feedback in credit assignment performs well but is not as well as the precise gradient-based approach. When more constraints due to biological plausibility are imposed performance degrades. These results are not surprising given previous results on random feedback. This work is incomplete because the delay times used were only a few time steps, and it is not clear how well random feedback would operate with longer delays. Additionally, the examples simulated with a single cue and a single reward are overly simplistic and the field should move beyond these exceptionally simple examples.

      Strengths:

      • The authors show that random feedback can approximate well a model trained with detailed credit assignment.<br /> • The authors simulate several experiments including some with probabilistic reward schedules and show results similar to those obtained with detailed credit assignments as well as in experiments.<br /> • The paper examines the impact of more biologically realistic learning rules and the results are still quite similar to the detailed back-prop model.

      Weaknesses:

      • The authors also show that an untrained RNN does not perform as well as the trained RNN. However, they never explain what they mean by an untrained RNN. It should be clearly explained. These results are actually surprising. An untrained RNN with enough units and sufficiently large variance of recurrent weights can have a high-dimensionality and generate a complete or nearly complete basis, though not orthonormal (e.g: Rajan&Abbott 2006). It should be possible to use such a basis to learn this simple classical conditioning paradigm. It would be useful to measure the dimensionality of network dynamics, in both trained and untrained RNN's.

      • The impact of the article is limited by using a network with discrete time-steps, and only a small number of time steps from stimulus to reward. What is the length of each time step? If it's on the order of the membrane time constant, then a few time steps are only tens of ms. In the classical conditioning experiments typical delays are of the order to hundreds of milliseconds to seconds. Authors should test if random feedback weights work as well for larger time spans. This can be done by simply using a much larger number of time steps.

      • In the section with more biologically constrained learning rules, while the output weights are restricted to only be positive (as well as the random feedback weights), the recurrent weights and weights from input to RNN are still bi-polar and can change signs during learning. Why is the constraint imposed only on the output weights? It seems reasonable that the whole setup will fail if the recurrent weights were only positive as in such a case most neurons will have very similar dynamics, and the network dimensionality would be very low. However, it is possible that only negative weights might work. It is unclear to me how to justify that bipolar weights that change sign are appropriate for the recurrent connections and inappropriate for the output connections. On the other hand, an RNN with excitatory and inhibitory neurons in which weight signs do not change could possibly work.

      • Like most papers in the field this work assumes a world composed of a single cue. In the real world there many more cues than rewards, some cues are not associated with any rewards, and some are associated with other rewards or even punishments. In the simplest case, it would be useful to show that this network could actually work if there are additional distractor cues that appear at random either before the CS, or between the CS and US. There are good reasons to believe such distractor cues will be fatal for an untrained RNN, but might work with a trained RNN, either using BPPT or random feedback. Although this assumption is a common flaw in most work in the field, we should no longer ignore these slightly more realistic scenarios.

    3. Reviewer #2 (Public review):

      Summary:

      Tsurumi et al. show that recurrent neural networks can learn state and value representations in simple reinforcement learning tasks when trained with random feedback weights. The traditional method of learning for recurrent network in such tasks (backpropagation through time) requires feedback weights which are a transposed copy of the feed-forward weights, a biologically implausible assumption. This manuscript builds on previous work regarding "random feedback alignment" and "value-RNNs", and extends them to a reinforcement learning context. The authors also demonstrate that certain non-negative constraints can enforce a "loose alignment" of feedback weights. The author's results suggest that random feedback may be a powerful tool of learning in biological networks, even in reinforcement learning tasks.

      Strengths:

      The authors describe well the issues regarding biologically plausible learning in recurrent networks and in reinforcement learning tasks. They take care to propose networks which might be implemented in biological systems and compare their proposed learning rules to those already existing in literature. Further, they use small networks on relatively simple tasks, which allows for easier intuition into the learning dynamics.

      Weaknesses:

      The principles discovered by the authors in these smaller networks are not applied to deeper networks or more complicated tasks, so it remains unclear to what degree these methods can scale up, or can be used more generally.

    4. Reviewer #3 (Public review):

      Summary:

      The paper studies learning rules in a simple sigmoidal recurrent neural network setting. The recurrent network has a single layer of 10 to 40 units. It is first confirmed that feedback alignment (FA) can learn a value function in this setting. Then so-called bio-plausible constraints are added: (1) when value weights (readout) is non-negative, (2) when the activity is non-negative (normal sigmoid rather than downscaled between -0.5 and 0.5), (3) when the feedback weights are non-negative, (4) when the learning rule is revised to be monotic: the weights are not downregulated. In the simple task considered all four biological features do not appear to impair totally the learning.

      Strengths:

      (1) The learning rules are implemented in a low-level fashion of the form: (pre-synaptic-activity) x (post-synaptic-activity) x feedback x RPE. Which is therefore interpretable in terms of measurable quantities in the wet-lab.

      (2) I find that non-negative FA (FA with non negative c and w) is the most valuable theoretical insight of this paper: I understand why the alignment between w and c is automatically better at initialization.

      (3) The task choice is relevant since it connects with experimental settings of reward conditioning with possible plasticity measurements.

      Weaknesses:

      (4) The task is rather easy, so it's not clear that it really captures the computational gap that exists with FA (gradient-like learning) and simpler learning rule like a delta rule: RPE x (pre-synpatic) x (post-synaptic). To control if the task is not too trivial, I suggest adding a control where the vector c is constant c_i=1.

      (5) Related to point 3), the main strength of this paper is to draw potential connection with experimental data. It would be good to highlight more concretely the prediction of the theory for experimental findings. (Ideally, what should be observed with non-negative FA that is not expected with FA or a delta rule (constant global feedback) ?).

      (6a) Random feedback with RNN in RL have been studied in the past, so it is maybe worth giving some insights how the results and the analyzes compare to this previous line of work (for instance in this paper [1]). For instance, I am not very surprised that FA also works for value prediction with TD error. It is also expected from the literature that the RL + RNN + FA setting would scale to tasks that are more complex than the conditioning problem proposed here, so is there a more specific take-home message about non-negative FA? or benefits from this simpler toy task?<br /> (6b) Related to task complexity, it is not clear to me if non-negative value and feedback weights would generally scale to harder tasks. If the task in so simple that a global RPE signal is sufficient to learn (see 4 and 5), then it could be good to extend the task to find a substantial gap between: global RPE, non-negative FA, FA, BP. For a well chosen task, I expect to see a performance gap between any pair of these four learning rules. In the context of the present paper, this would be particularly interesting to study the failure mode of non-negative FA and the cases where it does perform as well as FA.

      (7) I find that the writing could be improved, it mostly feels more technical and difficult than it should. Here are some recommendations:<br /> (7a) for instance the technical description of the task (CSC) is not fully described and requires background knowledge from other paper which is not desirable.<br /> (7b) Also the rationale for the added difficulty with the stochastic reward and new state is not well explained.<br /> (7c) In the technical description of the results I find that the text dives into descriptive comments of the figures but high-level take home messages would be helpful to guide the reader. I got a bit lost, although I feel that there is probably a lot of depth in these paragraphs.

      (8) Related to the writing issue and 5), I wished that "bio-plausibility" was not the only reason to study positive feedback and value weights. Is it possible to develop a bit more specifically what and why this positivity is interesting? Is there an expected finding with non-negative FA both in the model capability? or maybe there is a simpler and crisp take-home message to communicate the experimental predictions to the community would be useful?

      (1) https://www.nature.com/articles/s41467-020-17236-y

    5. Author response:

      Reviewer #1 (Public review):

      Summary:

      Can a plastic RNN serve as a basis function for learning to estimate value. In previous work this was shown to be the case, with a similar architecture to that proposed here. The learning rule in previous work was back-prop with an objective function that was the TD error function (delta) squared. Such a learning rule is non-local as the changes in weights within the RNN, and from inputs to the RNN depends on the weights from the RNN to the output, which estimates value. This is non-local, and in addition, these weights themselves change over learning. The main idea in this paper is to examine if replacing the values of these non-local changing weights, used for credit assignment, with random fixed weights can still produce similar results to those obtained with complete bp. This random feedback approach is motivated by a similar approach used for deep feed-forward neural networks.

      This work shows that this random feedback in credit assignment performs well but is not as well as the precise gradient-based approach. When more constraints due to biological plausibility are imposed performance degrades. These results are not surprising given previous results on random feedback. This work is incomplete because the delay times used were only a few time steps, and it is not clear how well random feedback would operate with longer delays. Additionally, the examples simulated with a single cue and a single reward are overly simplistic and the field should move beyond these exceptionally simple examples.

      Strengths:

      • The authors show that random feedback can approximate well a model trained with detailed credit assignment.

      • The authors simulate several experiments including some with probabilistic reward schedules and show results similar to those obtained with detailed credit assignments as well as in experiments.

      • The paper examines the impact of more biologically realistic learning rules and the results are still quite similar to the detailed back-prop model.

      Weaknesses:

      • The authors also show that an untrained RNN does not perform as well as the trained RNN. However, they never explain what they mean by an untrained RNN. It should be clearly explained. These results are actually surprising. An untrained RNN with enough units and sufficiently large variance of recurrent weights can have a high-dimensionality and generate a complete or nearly complete basis, though not orthonormal (e.g: Rajan&Abbott 2006). It should be possible to use such a basis to learn this simple classical conditioning paradigm. It would be useful to measure the dimensionality of network dynamics, in both trained and untrained RNN's.

      Thank you for pointing out the lack of explanation about untrained RNN. Untrained RNN in our simulations (except Fig. 6D/6E-gray-dotted) was randomly initialized RNN (i.e., connection weights were drawn from a pseudo normal distribution) that was used as initial RNN for training of value-RNNs. As you suggested, the performance of untrained RNN indeed improved as the number of units increased (Fig. 2J), and its highest part was almost comparable to the highest performance of trained value-RNNs (Fig. 2I). In the revision we will show the dimensionality of network dynamics (as you have suggested), and eigenvalue spectrum of the network.

      • The impact of the article is limited by using a network with discrete time-steps, and only a small number of time steps from stimulus to reward. What is the length of each time step? If it's on the order of the membrane time constant, then a few time steps are only tens of ms. In the classical conditioning experiments typical delays are of the order to hundreds of milliseconds to seconds. Authors should test if random feedback weights work as well for larger time spans. This can be done by simply using a much larger number of time steps.

      Thank you for pointing out this important issue, for which our explanation was lacking and our examination was insufficient. We do not consider that single time step in our models corresponds to the neuronal membrane time constant. Rather, for the following reasons, we assume that the time step corresponds to several hundreds of milliseconds:

      - We assume that single RNN unit corresponds to a small neuron population that intrinsically (for genetic/developmental reasons) share inputs/outputs and are mutually connected via excitatory collaterals.

      - Cortical activity is suggested to be sustained not only by fast synaptic transmission and spiking but also, even predominantly, by slower synaptic neurochemical dynamics (Mongillo et al., 2008, Science "Synaptic Theory of Working Memory" https://www.science.org/doi/10.1126/science.1150769).

      - In line with such theoretical suggestion, previous research examining excitatory interactions between pyramidal cells, to which one of us (the corresponding author Morita) contributed by conducting model fitting (Morishima, Morita, Kubota, Kawaguchi, 2011, J Neurosci, https://www.jneurosci.org/content/31/28/10380), showed that mean recovery time constant from facilitation for recurrent excitation among one of the two types of cortico-striatal pyramidal cells was around 500 milliseconds.

      If single time step corresponds to 500 milliseconds, three time steps from cue to reward in our simulations correspond to 1.5 sec, which matches the delay in the conditioning task used in Schultz et al. 1997 Science. Nevertheless, as you pointed out, it is necessary to examine whether our random feedback models can work for longer delays, and we will examine it in our revision.

      • In the section with more biologically constrained learning rules, while the output weights are restricted to only be positive (as well as the random feedback weights), the recurrent weights and weights from input to RNN are still bi-polar and can change signs during learning. Why is the constraint imposed only on the output weights? It seems reasonable that the whole setup will fail if the recurrent weights were only positive as in such a case most neurons will have very similar dynamics, and the network dimensionality would be very low. However, it is possible that only negative weights might work. It is unclear to me how to justify that bipolar weights that change sign are appropriate for the recurrent connections and inappropriate for the output connections. On the other hand, an RNN with excitatory and inhibitory neurons in which weight signs do not change could possibly work.

      Our explanation and examination about this issue were insufficient, and thank you for pointing it out and giving us helpful suggestion. In the Discussion (Line 507-510) of the original manuscript, we described "Regarding the connectivity, in our models, recurrent/feed-forward connections could take both positive and negative values. This could be justified because there are both excitatory and inhibitory connections in the cortex and the net connection sign between two units can be positive or negative depending on whether excitation or inhibition exceeds the other." However, we admit that the meaning of this description was not clear, and more explicit modeling will be necessary as you suggested.

      Therefore in our revision, we will examine models, in which inhibitory units (modeling fast-spiking (FS) GABAergic cells) will be incorporated, and neuron will follow Dale’s law.

      • Like most papers in the field this work assumes a world composed of a single cue. In the real world there many more cues than rewards, some cues are not associated with any rewards, and some are associated with other rewards or even punishments. In the simplest case, it would be useful to show that this network could actually work if there are additional distractor cues that appear at random either before the CS, or between the CS and US. There are good reasons to believe such distractor cues will be fatal for an untrained RNN, but might work with a trained RNN, either using BPPT or random feedback. Although this assumption is a common flaw in most work in the field, we should no longer ignore these slightly more realistic scenarios.

      Thank you very much for this insightful comment. In our revision, we will examine situations where there exist not only reward-associated cue but also randomly appeared distractor cues.

      Reviewer #2 (Public review):

      Summary:

      Tsurumi et al. show that recurrent neural networks can learn state and value representations in simple reinforcement learning tasks when trained with random feedback weights. The traditional method of learning for recurrent network in such tasks (backpropagation through time) requires feedback weights which are a transposed copy of the feed-forward weights, a biologically implausible assumption. This manuscript builds on previous work regarding "random feedback alignment" and "value-RNNs", and extends them to a reinforcement learning context. The authors also demonstrate that certain non-negative constraints can enforce a "loose alignment" of feedback weights. The author's results suggest that random feedback may be a powerful tool of learning in biological networks, even in reinforcement learning tasks.

      Strengths:

      The authors describe well the issues regarding biologically plausible learning in recurrent networks and in reinforcement learning tasks. They take care to propose networks which might be implemented in biological systems and compare their proposed learning rules to those already existing in literature. Further, they use small networks on relatively simple tasks, which allows for easier intuition into the learning dynamics.

      Weaknesses:

      The principles discovered by the authors in these smaller networks are not applied to deeper networks or more complicated tasks, so it remains unclear to what degree these methods can scale up, or can be used more generally.

      In our revision, we will examine more biologically realistic models with excitatory and inhibitory units, as well as more complicated tasks with distractor cues. We will also consider whether/how the depth of networks can be increased, though we do not currently have concrete idea on this last point. Thank you also for giving us the detailed insightful 'recommendations for authors'. We will address also them in our revision.

      Reviewer #3 (Public review):

      Summary:

      The paper studies learning rules in a simple sigmoidal recurrent neural network setting. The recurrent network has a single layer of 10 to 40 units. It is first confirmed that feedback alignment (FA) can learn a value function in this setting. Then so-called bio-plausible constraints are added: (1) when value weights (readout) is non-negative, (2) when the activity is non-negative (normal sigmoid rather than downscaled between -0.5 and 0.5), (3) when the feedback weights are non-negative, (4) when the learning rule is revised to be monotic: the weights are not downregulated. In the simple task considered all four biological features do not appear to impair totally the learning.

      Strengths:

      (1) The learning rules are implemented in a low-level fashion of the form: (pre-synaptic-activity) x (post-synaptic-activity) x feedback x RPE. Which is therefore interpretable in terms of measurable quantities in the wet-lab.

      (2) I find that non-negative FA (FA with non negative c and w) is the most valuable theoretical insight of this paper: I understand why the alignment between w and c is automatically better at initialization.

      (3) The task choice is relevant since it connects with experimental settings of reward conditioning with possible plasticity measurements.

      Weaknesses:

      (4) The task is rather easy, so it's not clear that it really captures the computational gap that exists with FA (gradient-like learning) and simpler learning rule like a delta rule: RPE x (pre-synpatic) x (post-synaptic). To control if the task is not too trivial, I suggest adding a control where the vector c is constant c_i=1.

      Thank you for this insightful comment. We have realized that this is actually an issue that would need multilateral considerations. A previous study of one of us (Wärnberg & Kumar, 2023 PNAS) assumed that DA represents a vector error rather than a scalar RPE, and thus homogeneous DA was considered as negative control because it cannot represent vector error other than the direction of (1, 1, .., 1). In contrast, the present work assumed that DA represents a scalar RPE, and then homogeneous DA (i.e., constant feedback) would not be said as a failure mode because it can actually represent a scalar RPE and FA to the direction of (1, 1, .., 1) should in fact occur. And this FA to (1, 1, ..., 1) may actually be interesting because it means that if heterogeneity of DA inputs is not large and the feedback is not far from (1, 1, ..., 1), states are learned to be represented in such a way that simple summation of cortical neuronal activity approximates value, thereby potentially explaining why value is often correlated with regional activation (fMRI BOLD signal) of not only striatal but also cortical regions (which I have been considering as an unresolved mystery). But on the other hand, the case with constant feedback is the same as the simple delta rule, as you pointed out, and then what could be obtained from the present analyses would be that FA is actually occurring behind the successful operation of such a simple rule. Anyway we will make further examinations and considerations on this issue.

      (5) Related to point 3), the main strength of this paper is to draw potential connection with experimental data. It would be good to highlight more concretely the prediction of the theory for experimental findings. (Ideally, what should be observed with non-negative FA that is not expected with FA or a delta rule (constant global feedback) ?).

      In response to this insightful comment, we considered concrete predictions of our models. In the FA model, the feedback vector c and the value-weight vector w are initially at random (on average orthogonal) relationships and become gradually aligned, whereas in the non-negative model, the vectors c and w are loosely aligned from the beginning. We considered how the vectors c and w can be experimentally measured. Each element of the feedback vector c is multiplied with TD-RPE, modulating the degree of update in each pyramidal cell (more accurately, pyramidal cell population that corresponds to single RNN unit). Thus each element of c could be measured as the magnitude of response of each pyramidal cell to DA stimulation. The element of the value-weight vector w corresponding to a given pyramidal cell could be measured, if striatal neuron that receives input from that pyramidal cell can be identified (although technically demanding), as the magnitude of response of the striatal neuron to activation of the pyramidal cell.

      Then, the abovementioned predictions can be tested by (i) identify cortical, striatal, and VTA regions that are connected by meso-cortico-limbic pathway and cortico-striatal-VTA pathway, (ii) identify pairs of cortical pyramidal cells and striatal neurons that are connected, (iii) measure the responses of identified pyramidal cells to DA stimulation, as well as the responses of identified striatal neurons to activation of the connected pyramidal cells, and (iv) test whether the DA->pyramidal responses and the pyramidal->striatal responses are associated across pyramidal cells, and whether such associations develop through learning. We will elaborate this tentative idea, and also other ideas, in our revision.

      (6a) Random feedback with RNN in RL have been studied in the past, so it is maybe worth giving some insights how the results and the analyzes compare to this previous line of work (for instance in this paper [https://www.nature.com/articles/s41467-020-17236-y]). For instance, I am not very surprised that FA also works for value prediction with TD error. It is also expected from the literature that the RL + RNN + FA setting would scale to tasks that are more complex than the conditioning problem proposed here, so is there a more specific take-home message about non-negative FA? or benefits from this simpler toy task?

      In reply to this suggestion, we will explore how our results compare to the previous studies including the paper [https://www.nature.com/articles/s41467-020-17236-y], and explore benefits of our models. At preset, we think of one possible direction. According to our results (Fig. 6E), under the non-negativity constraint, the model with random feedback and monotonic plasticity rule (bioVRNNrf) performed better, on average, than the model with backprop and non-monotonic plasticity rule (revVRNNbp) when the number of units was large, though the difference in the performance was not drastic. We will explore reasons for this, and examine if this also applies to cases with more realistic models, e.g., having separate excitatory and inhibitory units (as suggested by other reviewer).

      (6b) Related to task complexity, it is not clear to me if non-negative value and feedback weights would generally scale to harder tasks. If the task in so simple that a global RPE signal is sufficient to learn (see 4 and 5), then it could be good to extend the task to find a substantial gap between: global RPE, non-negative FA, FA, BP. For a well chosen task, I expect to see a performance gap between any pair of these four learning rules. In the context of the present paper, this would be particularly interesting to study the failure mode of non-negative FA and the cases where it does perform as well as FA.

      In reply to this comment and also other reviewer's comment, we will examine the performance of the different models in more complex tasks, e.g., having distractor cues or longer delays. We will also see whether or not the better performance of bioVRNNrf than revVRNNbp mentioned in the previous point applies to the different tasks.

      (7) I find that the writing could be improved, it mostly feels more technical and difficult than it should. Here are some recommendations:

      (7a) for instance the technical description of the task (CSC) is not fully described and requires background knowledge from other paper which is not desirable.

      (7b) Also the rationale for the added difficulty with the stochastic reward and new state is not well explained.

      (7c) In the technical description of the results I find that the text dives into descriptive comments of the figures but high-level take home messages would be helpful to guide the reader. I got a bit lost, although I feel that there is probably a lot of depth in these paragraphs.

      Thank you for your helpful suggestions. We will thoroughly revise our writings.

      (8) Related to the writing issue and 5), I wished that "bio-plausibility" was not the only reason to study positive feedback and value weights. Is it possible to develop a bit more specifically what and why this positivity is interesting? Is there an expected finding with non-negative FA both in the model capability? or maybe there is a simpler and crisp take-home message to communicate the experimental predictions to the community would be useful?

      We will make considerations on whether/how the non-negative constraints could have any benefits other than biological plausibility, in particular, in theoretical aspects or applications using neuro-morphic hardware, while we will also elaborate the links to biology and concretize the model's predictions.

    1. eLife Assessment

      The current human tissue-based study provides convincing evidence correlating hippocampal expressions of RNA guanine-rich G-quadruplexes with aging and with Alzheimer's Disease presence and severity. The results are important and hold promise for deeper understanding of AD's pathogenesis and potential new therapeutic strategies.

      [Editors' note: this paper was reviewed by Review Commons.]

    2. Reviewer #1 (Public review):

      This is an interesting manuscript where the authors systematically measure rG4 levels in brain samples at different ages of patients affected by AD. To the best of my knowledge this is the first time that BG4 staining is used in this context and the authors provide compelling evidence to show an association with BG4 staining and age or AD progression, which interestingly indicates that such RNA structure might play a role in regulating protein homeostasis as previously speculated. The methods used and the results reported seems robust and reproducible. There were two main things that needed addressing:

      (1) Usually in BG4 staining experiments to ensure that the signal detected is genuinely due to rG4 an RNase treatment experiment is performed. This does not have to be extended to all the samples presented but having a couple of controls where the authors observe loss of staining upon RNase treatment will be key to ensure with confidence that rG4s are detected under the experimental conditions. This is particularly relevant for this brain tissue samples where BG4 staining has never been performed before.

      (2) The authors have an association between rG4-formation and age/disease progression. They also observe distribution dependency of this, which is great. However, this is still an association which does not allow the model to be supported. This is not something that can be fixed with an easy experiment and it is what it is, but my point is that the narrative of the manuscript should be more fair and reflect the fact that, although interesting, what the authors are observing is a simple correlation. They should still go ahead and propose a model for it, but they should be more balanced in the conclusion and do not imply that this evidence is sufficient to demonstrate the proposed model. It is absolutely fine to refer to the literature and comment on the fact that similar observations have been reported and this is in line with those, but still this is not an ultimate demonstration.

      Comments on current version:

      The authors have now addressed my concerns.

    3. Reviewer #2 (Public review):

      RNA guanine-rich G-quadruplexes (rG4s) are non-canonical higher order nucleic acid structures that can form under physiological conditions. Interestingly, cellular stress is positively correlated with rG4 induction.

      In this study, the authors examined human hippocampal postmortem tissue for the formation ofrG4s in aging and Alzheimer Disease (AD). rG4 immunostaining strongly increased in the hippocampus with both age and with AD severity. 21 cases were used in this study (age range 30-92).

      This immunostaining co-localized with hyper-phosphorylated tau immunostaining in neurons. The BG4 staining levels were also impacted by APOE status. rG4 structure was previously found to drive tau aggregation. Based on these observations, the authors propose a model of neurodegeneration in which chronic rG4 formation drives proteostasis collapse.

      This model is interesting, and would explain different observations (e.g., RNA is present in AD aggregates and rG4s can enhance protein oligomerization and tau aggregation).

      Main issue from the previous round of review:

      There is indeed a positive correlation between Braak stage severity and BG4 staining, but this correlation is relatively weak and borderline significant ((R = 0.52, p value = 0.028). This is probably the main limitation of this study, which should be clearly acknowledged (together with a reminder that "correlation is not causality"). Related to this, here is no clear justification to exclude the four individuals in Fig 1d (without them R increases to 0.78). Please remove this statement. On the other hand, the difference based on APOE status is more striking.

      Comments on current version:

      The authors have made laudable efforts to address the criticisms I made in my evaluation of the original manuscript.

    4. Author response:

      Reviewer #1 (Evidence, reproducibility and clarity):

      This is an interesting manuscript where the authors systematically measure rG4 levels in brain samples at different ages of patients affected by AD. To the best of my knowledge this is the first time that BG4 staining is used in this context and the authors provide compelling evidence to show an association with BG4 staining and age or AD progression, which interestingly indicates that such RNA structure might play a role in regulating protein homeostasis as previously speculated. The methods used and the results reported seem robust and reproducible.

      In terms of the conclusions, however, I think that there are 2 main things that need addressing prior to publication:

      (1) Usually in BG4 staining experiments to ensure that the signal detected is genuinely due to rG4 an RNase treatment experiment is performed. This does not have to be extended to all the samples presented but having a couple of controls where the authors observe loss of staining upon RNase treatment will be key to ensure with confidence that rG4s are detected under the experimental conditions. This is particularly relevant for this brain tissue samples where BG4 staining has never been performed before.

      With what is now known about RNA rG4s and the recent reconciliation of the controversy on rG4 formation (Kharel, Nature Communications 2023), this experiment is no longer strictly required for demonstration of rG4 formation. Despite this change, we did attempt this experiment at the reviewer’s suggestion, but the controls were not successful, suggesting it may not be feasible with our fixing and staining conditions. That said, we agree that despite the G4 staining appearing primarily outside the nucleus, it would be helpful to have some direct indication of whether we were observing primarily RNA or DNA G4s, and so we performed an alternate experiment to determine this.

      In our previous submission, we had performed ribosomal RNA staining  (Figure S7), and the staining patterns were similar to that of BG4, especially the punctate pattern near the nuclei. Therefore, we directly asked whether the BG4 was largely binding to rRNA and have now shown the resulting co-stain in Figure 3b. These results show that at least a large amount of the BG4 staining does arise from rG4s in ribosomes. At high magnification, we observe that the BG4 stains a subset of the ribosomes, consistent with previous observations of high rG4 levels in ribosomes both in vitro and in cells (Mestre-Fos, 2019 J Mol Biol, Mestre-Fos 2019 PLoS One, Mestre-Fos 2020 J Biol Chem), but this had never been demonstrated in tissue. This experiment has therefore both answered the primary question of whether we are primarily observing rG4s, as well as provided more detailed information on the cellular sublocalization of rG4 formation, and provided the first evidence of rG4 formation on ribosomes in tissue.

      (2) The authors have an association between rG4-formation and age/disease progression. They also observe distribution dependency of this, which is great. However, this is still an association which does not allow the model to be supported. This is not something that can be fixed with an easy experiment and it is what it is, but my point is that the narrative of the manuscript should be more fair and reflect the fact that, although interesting, what the authors are observing is a simple correlation. They should still go ahead and propose a model for it, but they should be more balanced in the conclusion and do not imply that this evidence is sufficient to demonstrate the proposed model. It is absolutely fine to refer to the literature and comment on the fact that similar observations have been reported and this is in line with those, but still this is not an ultimate demonstration.

      We agree that these are correlative studies (of necessity when studying human tissue), but recent experiments have shown that rG4s affect the aggregation of Tau in vitro – and we have now better clarified this in the text itself. We have now also been more careful in drawing causative conclusions as shown in the revised text.

      Minor point:

      (3) rG4s themselves have been shown to generate aggregates in ALS models in the absence of any protein (Ragueso et al. Nat Commun 2023). I think this is also important in the light of my comment on the model, could well be that these rG4s are causing aggregates themselves that act as nucleation point for the proteins as reported in the paper I mentioned. Providing a broader and more unbiased view of the current literature on the topic would be fair, rather than focusing on reports more in line with the model proposed.

      We agree and have modified the discussion and added a broader context, including the Ragueso report described above.

      Reviewer #1 (Significance):

      This is a significant novel study, as per my comments above. I believe that such a study will be of impact in the G4 and neurodegenerative fields. Providing that the authors can address the criticisms above, I strongly believe that this manuscript would be of value to the scientific community. The main strength is the novelty of the study (never done before) the main weakness is the lack of the RNase control at the moment and the slightly over interpretation of the findings (see comments above).

      Reviewer #2 (Evidence, reproducibility and clarity):

      RNA guanine-rich G-quadruplexes (rG4s) are non-canonical higher order nucleic acid structures that can form under physiological conditions. Interestingly, cellular stress is positively correlated with rG4 induction.  In this study, the authors examined human hippocampal postmortem tissue for the formation ofrG4s in aging and Alzheimer Disease (AD). rG4 immunostaining strongly increased in the hippocampus with both age and with AD severity. 21 cases were used in this study (age range 30-92).  This immunostaining co-localized with hyper-phosphorylated tau immunostaining in neurons. The BG4 staining levels were also impacted by APOE status. rG4 structure was previously found to drive tau aggregation. Based on these observations, the authors propose a model of neurodegeneration in which chronic rG4 formation drives proteostasis collapse.

      This model is interesting, and would explain different observations (e.g., RNA is present in AD aggregates and rG4s can enhance protein oligomerization and tau aggregation).

      Main issue:

      There is indeed a positive correlation between Braak stage severity and BG4 staining, but this correlation is relatively weak and borderline significant ((R = 0.52, p value = 0.028). This is probably the main limitation of this study, which should be clearly acknowledged (together with a reminder that "correlation is not causality”.

      We believe that we had not explained this clearly enough in the text (based on the reviewer’s comment), as the correlation mentioned by the Reviewer was for the CA4 region only, and not the OML, which was substantially more correlated and statistically significant (Spearman R= 0.72, p = 0.00086). As a result, we believe this was a miscommunication that is rectified by the revised text:

      “In the OML, plotting BG4 percent area versus Braak stage demonstrated a strong correlation (Spearman R= 0.72) with highly significantly increased BG4 staining with higher Braak stages (p = 0.00086) (Fig. 2b).”

      Related to this, here is no clear justification to exclude the four individuals in Fig 1d (without them R increases to 0.78). Please remove this statement. On the other hand, the difference based on APOE status is more striking.

      We did not mean to imply that deleting these outliers was correct, but merely were demonstrating that they were in fact outliers. To avoid this misinterpretation, we have now deleted the sentence in the Figure 1d caption mentioning the outliers.

      Minor suggestions

      - "BG4 immunostaining was in many cases localized in the cytoplasm near the nucleus in a punctate pattern". Define "many"

      This is seen in nearly every cells and this is now altered in the text and is now identified as ribosomes containing rG4s using the rRNA antibody (Fig. 3b).

      - Specify that MABE917 corresponds to the specific single-chain version of the BG4 antibody

      Yes, this is correct, and this clarification has been added to the manuscript

      - Define PMI, Braak, CERAD (add a list of acronyms or insert these definitions in Fig 1b legend)

      These definitions have all been added when they first appear.

      - Fig 3: scale bar legend missing (50 micrometers?)

      This has been added, and the reviewer was correct that it was 50 micrometers.

      - Supplementary data Table 1: indicate target for all antibodies

      The target for each antibody has been added to supplementary Table 1.

      - Supplementary data Table 2: why give ages with different levels of precision? (e.g. 90.15 vs 63)

      We apologize for this oversight and have altered the ages to the same (whole years) in the figure.

      - Supplementary data Fig 1 X-axis legend: add "(nm)" after wavelength. Sequence can also be added in the legend. Why this one? Max/Min Wavelengths in the figure do not match indications in the experimental part. Not sure if that part is actually relevant for this study.

      The CD spectrum in Sup Fig 1 is the sequence that had previously been shown to aid in tau aggregation seeding, but had not been suspected by those authors to be a quadruplex. So we tested that here and showed it is a quadruplex, as described at the end of the introduction. We have added wording to the figure legend to clarify where its corresponding description in the main text can be found. We have also checked and corrected the wavelength and units.

      - Supplementary data Fig 7: Which ribosomal antibody was used?

      The details of this antibody have now been added to Supplementary Table 2 which lists all the antibodies used.

      Reviewer #2 (Significance):

      Provide a link between Alzheimer disease and RNA G-quadruplexes.

      Reviewer #3 (Evidence, reproducibility and clarity):

      This study investigated the formation of RNA G quadruplexes (rG4) in aging and AD in human hippocampal postmortem tissue. The rG4 immunostaining in the hippocampus increases strongly with age and with the severity of AD. Furthermore, rG4 is present in neurons with an accumulation of phosphorylated tau immunostaining.

      Major comments

      (1) The method used in this study is primarily immunostaining of BG4, and the results cannot be considered correct without additional data from more multifaceted analyses (biochemical analysis, RNA expression analysis, etc.).

      We respectfully disagree with the Reviewer’s assessment of the value of these experiments. The most relevant biochemical experiments at the cellular and molecular level showing the role of G4s in aggregation in general and Tau in particular have been done and are referenced in the text. The results here stand on their own and are highly novel and significant, as evaluated by both of the other reviewers. There has been no previous work demonstrating the presence of rG4s in human brain – either in controls or in patients with AD. AD is a complex condition that only occurs spontaneously in the human brain and no other species; because of this complexity, novel aspects are best first studied in human brain tissue using the methods employed here.

      (2) Overall, the quality of the stained images is poor, and detailed quantitative analysis using further high quality data is essential to conclude the authors' conclusions.

      We have again looked at our images and they are not poor quality -they are confocal images taken at recommended resolution of the confocal microscope. It is possible the poor quality came from pdf compression by the manuscript submission portal, which is beyond our control as they were uploaded at high resolution. These data were quantified by scientists who were blinded to the diagnosis of each case. The level of description on the detailed quantification is higher than we have observed in similar studies. We therefore disagree with the reviewer’s conclusion.

      Reviewer #3 (Significance):

      Overall, this study is not a deeply analyzed study. In addition, the authors of this study need further understanding regarding G4.

      It is also unclear why the reviewer believes that we do not have sufficient understanding of G4s, and would request that the reviewer instead provides specific comments regarding what is lacking in terms of knowledge on G4s, as we respectfully disagree with this judgement of our knowledge-base (see other G4 papers from the Horowitz lab, Begeman, 2020, Litberg 2023, Son, 2023 referenced below).

      Litberg TJ, Sannapureddi RKR, Huang Z, Son A, Sathyamoorthy B, Horowitz S. Why are G-quadruplexes good at preventing protein aggregation? Jan;20(1):495-509. doi: 10.1080/15476286.2023.2228572. RNA Biol. (2023)

      Son A, Huizar Cabral V, Huang Z, Litberg TJ, Horowitz S. G-quadruplexes rescuing protein folding. May 16;120(20):e2216308120. doi: 10.1073/pnas.2216308120. Proc Natl Acad Sci U S A (2023)

      Guzman BB, Son A, Litberg TJ, Huang Z, Dominguez , Horowitz S. Emerging Roles for G-Quadruplexes in Proteostasis FEBS J.doi: 10.1111/febs.16608. (2022)

      Begeman A, Son A, Litberg TJ, Wroblewski TH, Gehring T, Huizar Cabral V, Bourne J, Xuan Z, Horowitz S. G-Quadruplexes Act as Sequence Dependent Protein Chaperones. EMBO Reports Sep 18;e49735. doi: 10.15252/embr.201949735. (2020)

    1. eLife Assessment

      The revised report provides valuable findings for the field, suggesting a relationship between CRF1 receptors, sociability deficits in morphine-treated male mice yet not females, and a potential mechanism involving oxytocin neurons in the paraventricular nucleus of the hypothalamus. Generally, the strength of evidence is solid in terms of the methods, data, and analyses. This work will be of interest to those interested in social behavior and addiction.