Then the primaries became like a less diluted version, and with the more extreme people voting. Why did the less extreme stop going to the primaries? The vicious cycle mentioned above?

The most conservative and most liberal within a given party are the most likely to turn out, but mainly because the middle has stopped doing so.

More conservatives are likely to turn out

When compared to average general election voter?

But from 2012 to now would be damn interesting

Holy lots of work

Catering in part to new constituency

TLDR: they stayed republican afterwards

Ok the party shifting is explained now, still worried about racial superiority in the 60s

Which gives us polarized candidates whether we like it or not

Why did southern polarization spread

Polarization today can be traced back to southern reallignment

Not mixing of party ideology on the primary level

the more polarized voters

We can see the party shift but want to analyze voters

The more extreme people turn out at primaries even though the general public is not as pollarized

They use the stereotypes to exagerte them and further instill fear in the ignorant (b/c that's what they are , ignorant) to make belive, that all the deportations they are making is okay. B/c they would most likely not belive it otherwise.

Where would they go!!!???? It's as if white supremacy and racism made them forget thier own countrys history

I'd never noticed/heard of this idea of a law in some ways possibly taking away the "selection." In this case the law presumably spells out the elements required to print, but it feels uncomfortable. If the law hadn't been there, is there still anything creative about reprinting a selection of "all"? Are there ways to comply that could have still been creative?

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Reply to the reviewers

Manuscript number: RC-2025-03131

Corresponding author(s): Ginto George and Adriana Ordoñez

1. General Statements

We thank the reviewers for their careful evaluation of our work and for their constructive and insightful comments. We are pleased that both reviewers found the study to be well executed, clearly presented, and of interest to the ER stress and UPR community. We have carefully considered all comments and revised the manuscript accordingly. We believe these revisions have substantially strengthened the clarity, robustness, and conceptual impact of the study.

2. Point-by-point description of the revisions

Below we provide a detailed, point-by-point response to the reviewers' comments and describe the revisions and new data included in the revised manuscript.

Reviewer 1 & 2 (common points)

1. __ Description of the BiP::GFP reporter as a readout of ATF6α activity.__
2. Comment: Both reviewers are concerned about whether BiP::GFP is a reliable and specific reporter for ATF6α
3. Response: In response, we have clarified in the revised manuscript the details of the BiP promoter fragment used in this reporter, explicitly detailing the presence of an ERSE-I element motif (CCAAT-N9-CCACG), the most specifically and robustly activated by ATF6α (new Supplemental Fig. S1). This reporter was first characterised in our recently published study (Tung et al., 2024 eLife), where we demonstrated that BiP::GFP expression is ATF6α dependent, as CRISPR/Cas9-mediated disruption of endogenous ATF6α resulted in a marked reduction in BiP::GFP fluorescence compared with parental cells. Furthermore, treatment with ER stress in the presence of Ceapin-A7 (a small molecule that blocks ATF6⍺ activation by tethering it to the lysosome) effectively blocked activation of the ATF6⍺ fluorescent reporter, whereas the S1P inhibitor partially attenuated the BiP::sfGFP signal in stressed cells (Tung et al., 2024 eLife; Supplemental S1D). We have now reproduced these findings in the present study, further confirming that the BiP::GFP reporter is highly dependent on ATF6α activation, and we present these data in a new Supplemental Fig. S1B.

__ Correlation between BiP::GFP reporter activity and BiP expression levels.__

• Comment: Both reviewers requested correlation of the BiP::GFP reporter activity and endogenous BiP levels.
• __Response: __To address this point, we have measured BiP mRNA levels in parental and Slc33a1-depleted cells under both basal conditions and ER stress conditions. These measurements correlated well with the BiP::GFP reporter activity assessed by flow cytometry and are shown in Supplemental Fig. S3E.

__ Does ATF6α respond to other ER stressors in Slc33a1-deleted cells?__

• Comment: Both reviewers accepted our claim that ATF6α activation is partially attenuated in Slc33a1-deleted cells exposed to ER stressors tunicamycin (Tm) and 2-Deoxy-D-glucose (2DG) but raised the possibility that ATF6α signalling might respond differently to other ER stressors.
• Response: To address this point, we have performed new experiments assessing ATF6α activation (BiP::GFP activity) in both Slc33a1-deleted and parental cells in response to additional ER stressors, including dithiothreitol (DTT) and thapsigargin (Tg). These new data, presented in a new Supplemental Fig. S3B and S3C, show that Slc33a1-deletion also attenuates ATF6α signalling in cells treated with dithiothreitol (DTT) and thapsigargin (Tg).

__ Deletion of all NAT8 family members.__

• Comment: Both reviewers suggested that deletion of all NAT8 family members was required to conclusively distinguish their role from that of SLC33A1.
• __Response: __We agree with this assessment and have now generated cells in which both Nat8 and Nat8b are simultaneously deleted. These new data, included in a new Supplemental Fig. S9, strengthen the comparison with SLC33A1 deficiency and rule out potential redundancy among NAT8 family members. Notably, simultaneous inactivation of Nat8 and Nat8b resulted in the same phenotype observed upon single Nat8 deletion, namely activation of both the IRE1 and ATF6α branches of the UPR. These findings (discussed in detail) are consistent with previous studies implicating protein acetylation in ER proteostasis but suggest that a defect in protein acetylation is unlikely to contribute to the consequences of SLC33A1 deficiency in terms of ATF6α

__ Generalisability beyond CHO-K1 cells.__

• Comment: Reviewer 1 raised concerns regarding validation of our findings beyond CHO-K1 cells.
• Response: While we acknowledge that validation in additional cell types would further strengthen the study, we now explicitly discuss the technical challenges encountered when attempting to generate clonal Slc33a1 knockouts in aneuploid human cell lines, such as HeLa. This limitation is now clearly acknowledged in the revised version, and our conclusions are framed accordingly.

__ Relationship between basal ATF6 and IRE1 signalling.__

• Comment: Both reviewers argued that BiP::GFP does not appear to be active under basal conditions in parental cells, and therefore a failure to activate ATF6 would not be expected to affect the conditions of the cells basally. Thereby questioning how attenuated basal ATF6 activity in the SLC33a1 deleted cells could account for the derepression observed in the IRE1 pathway.

• Response: The logic of the reviewer's critique is impeccable, and we thank them for the opportunity to clarify this important issue. Whilst the basal fluorescent signal arising from BiP::GFP (the ATF6α reporter) is indeed weak, it is not null. This is evident by comparing the BiP::GFP signal in wildtype and ATF6α -deleted cells (new Supplemental Fig. S1B) These experiments revealed a significant reduction in basal BiP::GFP fluorescence in ATF6αΔ cells compared with parental dual-reporter cells, indicating that the BiP::GFP reporter has basal activity that is dependent on ATF6α. These new data are consistent with previous published observations demonstrated that treatment with Ceapin, an ATF6α-specific inhibitor, lowered BiP::GFP fluorescence in tunicamycin-treated cells to levels below those observed in untreated controls (Tung et al., eLife 2024). Together these observations indicate that ATF6α is active basally in CHO-K1 cells. Given the established cross-pathway repression of IRE1 by ATF6α signalling, it renders plausible our suggestion that the basal activation of the XBP1::mCherry (IRE1-reporter) observed basally in the SLC33a1 deleted cells arises from the partial interruption of ATF6α Reviewer 1 (additional points)

• __ Effect of deleting sialic acid-modifying acetyltransferases.__

• Comment: Reviewer 1 suggested that comparing the consequences of deleting SLC33a1 and the sialic acid- modifying acetyltransferases that operate downstream of the putative acetyl-CoA transporter could be informative.
• Response: In response to this valuable suggestion, we have now examined the impact of deleting Casd1, the gene encoding the Golgi acetyltransferase responsible for modifying sialic acids on ATF6α activity, comparing the consequences to Slc33a1. New Supplemental Fig. S8 reveals partial phenotypic overlap between the two deletions, suggesting that the loss of SLC33A1 exerts some of its effects on CHO cells by compromising sialic acid modification.

__ Potential effects on ATF6-like proteins (SREBP1/2, CREB3L).__

• Comment: Reviewer 1 suggested that we evaluate the effect of SLC33A1 loss on other ATF6-like transcription factors.

• Response: We took this advice to heart, but our attempts to compare SREBP2 processing in wildtype and SLC33A1 knockout cells were frustrated by the low quality of the antibodies available to us. Reviewer 2 (additional points)

• __ Physiological state and clonal adaptation of Slc33a1-deleted cells.__

• __Comment: __Reviewer 2 raised concerns regarding the physiological state of the Slc33a1-deleted cells and the potential impact of clonal adaptation or selection pressure on the consequences of genetic manipulation.
• Response: This is a valid concern. Deconvoluting direct from indirect effects are a challenge in any genetics-based experiment. To try and address this point, we compared the proliferation capacity of three pairs of parental CHO-K1 clones with their derivative Slc33a1-deletion variants using the IncuCyte assay. As shown in new Supplemental Fig. S2D, the Slc33a1 deletion variants had no consistent fitness disadvantage revealed by this assay. Whilst cell mass accretion is only one measure of comparability between cell lines, we deem these observations to indicate that a comparison between SLC33A1 wildtype and mutant CHO-K1 cells is unlikely to be compromised by gross underlying differences in cell fitness.

__ Responsiveness of PERK signalling to ER stress.__

• Comment: Reviewer 2 asked whether PERK signalling, which appears basally activated due to higher basal IRE1 signalling in the Slc33a1-deleted cells, remains responsive to ER stress.
• Response: To address this point, we treated cells with ER stressors and assessed PERK pathway activation. As shown in new Supplemental Fig. S4C, PERK signalling remains functional and responsive to ER stress in Slc33a1-depleted cells.

In addition to the points above, we have addressed several presentation and clarity issues raised by the reviewers, including figure labelling, image presentation, and schematic models. The Discussion has also been revised to more explicitly acknowledge the current limitations of the study while emphasising its central conceptual advance: namely, that loss of SLC33A1 results in a discordant UPR state in which IRE1 and PERK are activated, whereas ATF6α trafficking and transcriptional output are selectively compromised.

The following table summarises the major changes made to the figures in the revised manuscript to facilitate tracking the modifications introduced

Figure

Figure Panels

Amendment (if any)

Fig 4

4B (modified)

Scale bar added.

Fig 5

5B (modified)

Labelling correction according to the reviewer.

Fig S1 (new)

S1A-S1B

New data detailing the BiP promoter fragment and the reliability of the BiP::GFP reporter as a readout for ATF6α activity in cells.

Fig S2 (modified)

S2D (new)

New IncuCyte data added.

Fig S3 (modified)

S3B, S3C and S3E (new)

Panels B and C: New data from DTT and thapsigargin treatments, respectively. __Panel E: __New data from BiP mRNA levels under 2DG treatment in parental and Slc33a1-deleted cells.

Fig S4 (new)

S4C (new)

__Panels A and B: __Previously shown as panels in Fig. S2C and S2D.

__Panel C: __New data on the PERK response to ER stress in Slc33a1-deleted cells.

Fig S7 (new)

S7A-S7C (new)

New sanger sequencing chromatograms displaying the targeted exonic regions of the Casd1, Nat8 and Nat8b. * *

Fig S8 (new)

S8A-S8B (new)

Casd1-deleted data added.

Fig S9 (new)

Unique panel

New data comparing Nat8/Nat8b-deleted cells with single Nat8-deleted cells.

We thank the reviewers again for their insightful comments, which have significantly strengthened the manuscript. We believe the revised study clarifies key mechanistic points and provides a stronger conceptual advance regarding the role of SLC33A1 in UPR regulation.

Sincerely,

Adriana Ordóñez

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Referee #2

Evidence, reproducibility and clarity

Summary

The authors employed a genome-wide CRISPR-Cas9 screen to search for the genes selectively involved in the activation of ER stress sensor ATF6. Deletion of Slc33a1, which encodes a transporter of acetyl-CoA into the ER lumen, compromised the ATF6 pathway (as assessed by BiP::GFP reporter), while IRE1 and PERK were activated in basal conditions, in the absence of ER stress (as assessed by XBP1s::mCherry reporter and endogenous XBP1s and CHOP::GFP reporter). Moreover, IRE1, but not ATF6, replied to ER stress. Consistently, in Slc33a1Δ cells upon ER stress the levels of the processed N-ATF6α were significantly lowered compared to the parental cells, and microscopy study showed that in Slc33a1-deficient cells ATF6 is translocated to Golgi even in the absence of ER stress, but fails to reach the nucleus even after ER stress is imposed. Golgi-type sugar modification of ATF6α is decreased in Slc33a1Δ cells. These data show the importance of SLC33A1 for ATF6 processing and functioning through the mechanism which remains to be revealed.

Major comments.

Taken together, the reported data do support the conclusion about the role of SLC33A1 functioning in post-ER maturation of ATF6. Data and methods are presented in a reproducible way. Still, there are several issues worth attention.

1. While BiP::GFP reporter is very useful, it would be more convincing to show the level of BiP in Slc33a1Δ cells by WB.
2. Another concern is the state of Slc33a1Δ cells. While adaptation is a general problem of clonal cells, the cells used in this study (with XBP1 highly spliced, CHOP upregulated, and ATF6 pro-survival pathway inhibited) are probably very sick, and the selection pressure/adaptation is very strong in this cell line. I would suggest the authors to clarify this issue.
3. Authors showed that, based on CHOP::GFP reporter data, PERK was activated in the absence of ER stress and the activation was due to IRE1 signalling. But did PERK reply to the ER stress?
4. An important question is a subcellular location of SLC33A1. Huppke et al. (cited in the manuscript) showed that FLAG- and GFP-tagged SLC33A1 was colocalized with Golgi markers. While that may be due to overexpression of the protein, it deserves consideration, given that ATF6 is stuck in Golgi upon depletion of SLC33A1.
5. OPTIONAL. Regarding the role of acetylation in compromising ATF6 function: since both SLC33A1 deficiency and depletion of Nat8 have broad effects, glycosylation of ATF6 upon depletion of Nat8 should be assessed (similarly to Fig 5), to demonstrate the difference in glycosylation pattern upon the absence of SLC33A1 and Nat8 and strengthen the conclusions.

Minor comments.

1. Apart from the table of the cell lines, it would be useful to add to the supplementary a simple-minded scheme of the reporters used in this study (BiP::GFP, CHOP::GFP, XBP1s::mCherry) specifying the mechanism of the readout and the harbored protein and other important details (e.g., whether mRNA of XBP1s::mCherry reporter could be processed by IRE1).
2. Fig 2B and Fig 3A - the percentage of spliced XBP1 in parental cells is about 30% according to the graphs, but it looks more like 5%.
3. Fig 3B - It would probably be better to demonstrate the processing of endogenous ATF6. It could help to avoid the problems with alternative translation (even though anti-ATF6 antibodies are known to be tricky).
4. In Fig 4B - could be better to show Golgi marker separately. In Fig 4B and E the bars are missing (and cells in Fig 4B look bigger than in Fig 4E). Magnification of the insets should be further increased.
5. As the authors mention, 2-deoxy-D-glucose (2DG) is known to be the ER stress inducer, acting via prevention of N-glycosylation of proteins. Also, N-glycosylation state of ATF6 has been suggested to influence its trafficking. Thus, even if the control cells were treated in the same way, 2DG may not be the best ER-stress inducer to study ATF6 trafficking. Indeed, altered sugar modification of ATF6α in Slc33a1Δ cells (Fig 5) was tracked using Thapsigargin.
6. Minor comment on Fig 7 - recent data (Belyy et al., 2022) suggest IRE1 is a dimer even in the absence of ER stress.

Referee cross-commenting

I agree with Reviewer 1 that the authors need to clarify that authors need to clarify better how exactly BiP::GFP reporter works and whether it reflects ATF6 activation (rev 1 pointed to unclear responsiveness of the reporter to ATF6 and I asked to show the level of BiP by WB and the scheme of the mechanisms of readouts of the reporters)

I also agree with the comment on 2-DG which for some experiments may not be the best choice to activate UPR (or as Reviewer 1 pointed out shouldn't be the only one used to induce UPR). I still think that there's no contradiction in partial cleavage of ATF6 and suppression of BiP::GFP in Slc33a1Δ cells if then (as authors show) it doesn't reach nucleus.

Significance

General assessment. The article shows the necessity of SLC33A1, a transporter of acetyl-CoA in ER lumen, for ATF6 processing and functioning. It is well-written. However, the molecular mechanism which underlies the link is yet to be discovered (and this is clearly mentioned by the authors).

The study is of interest for the basic research and of potential interest for clinical research.

My main field of expertise is UPR. While I have broad knowledge and interest in protein science in general, my experience with protein glycosylation is rather limited.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this manuscript, the authors follow up on the results from a previous CRISPR screen in CHO-K1 cells demonstrating that knockout of the ER acetyl-CoA transporter Slc33a1 suppresses ATF6 activation. The authors show in these cells that, in response to 2-DG, the Slc33a1 deletion results in constitutive activation of the UPR except for the ATF6 pathway, which appears to traffic constitutively to the Golgi but to not be cleaved there. They show using an uncleavable ATF6 that loss of Slc33a1 delays formation of an O-glycosylated form of at least this version of the protein, and they also find that single deletion of the ER acetyltransferases NAT8 and NAT8B also constitutively activates the UPR, but that activation in this case includes activation of ATF6. The mechanism by which Acetyl-CoA might impact ATF6 activation is not elucidated.

Major Comments:

The following conclusions are well-supported:

• That loss of Slc33a1 results in IRE1 and PERK activation but not ATF6 activation
• That ATF6 traffics at least to some degree constitutively to the Golgi when Slc33a1 is deleted, which is a counterintuitive finding given the apparent lack of ATF6 activation
• That loss of Slc33a1 can alter the level O-glycosylation and the preponderance of sialylated N-glycans on at least ATF6
• Generally speaking, I find the wording to be careful and precise

The following claims are less convincing:

• That loss of Slc33a1 results in universal suppression of ATF6 activation. The effect in response to 2-DG is unquestionably strong at least at the level of Bip-GFP reporter (although it's not clear from this paper nor the previous one from this group how much of the Bip promoter this reporter encodes-which is important because only a minimal Bip promoter is exclusively responsive to ATF6). However, the impairment of ATF6 activation in response to tunicamycin (Fig. 1C) is very modest, and no other stressors were tested (DTT and TG were used for other purposes, not to test ATF6 activation). One might actually expect this pathway, if it affects glycosylation pathways, to be particularly sensitive to a stressor like 2-DG that would have knock-on effects on glycosylation. Admittedly, it does seem to be true in the basal condition (i.e., absent an exogenous ER stress) that IRE1 and PERK are activated where ATF6 is not. At some level, it's hard to reconcile the almost complete suppression of Bip-GFP induction in Slc33a1 cells in response to 2DG with the fact that in Fig. 3, cleavage clearly seems to be occurring, albeit to a lesser extent
• That regulation of ATF6 is a broadly applicable consequence of Slc33a1 action. Unless I've missed it, all experiments are performed in CHO-K1 cells, so how broadly applicable this pathway is not clear.
• That loss of Slc33a1 "deregulated activation of the IRE1 branch of the UPR." It is clear that IRE1 is activated when Slc33a1 is deleted (that the authors show this repeatedly in different parental cell lines provides a high degree of rigor). However, at least through the CHOP-GFP reporter, PERK is activated as well. Although 4u8C suppresses this activation, the suppression is not complete, there are no orthogonal ways of showing this (e.g., loss of KD of IRE1), and the converse experiment (examining IRE1 activation when PERK is lost or inhibited) was not done. Thus, while I agree that the data shown are consistent with PERK activation being downstream of IRE1, they are not definitive enough to, in my opinion, rule out the more parsimonious explanation for their own data and what is already published in the field that loss of Slc33a1 causes ER stress (thus in principle activating all 3 pathways of the UPR-including ATF6 transit to the Golgi) but that it also, separately, inhibits activation of ATF6 (and possibly other things? See below)-a possibility acknowledged towards the end of the Discussion.
• That "Nat8 and Slc33a1 influence ER homeostasis and ATF6 signaling through distinct mechanisms". This conclusion would require simultaneous deletion of both Nat8 and NAT8B because of possible redundancy/compensatory effects.
• If I'm understanding the authors' argument correctly, they seem to be invoking that the ATF6 activation defect underlies/is upstream of the activation of IRE1 in Slc33a1 KO cells. But if that understanding is correct, it seems fairly unlikely, as the authors' data show no evidence that ATF6 is activated in parental cells under basal conditions (Fig. 3B) and thus no reason to expect that failure to activate ATF6 by itself would result in appreciable phenotype in cells-an idea also consistent with the general lack of phenotype in ATF6-null MEF and other cells.

Minor Comments:

• The alteration in O-glycosylation levels of ATF6 is interesting, but it might or might not be relevant to ATF6 activation, and if it isn't, then the paper provides no mechanism for why loss of Slc33a1 has the effects on ATF6 that it does. What about other similar molecules, like ATF6B (surprising that this was not examined), SREBP1/2, a non-glycoyslatable ATF6, and/or one of the other CREB3L proteins?
• Does Slc33a1 deletion cause other ER resident proteins to constitutively mislocalize to the Golgi?
• As mentioned above, does loss/knockdown of Slc33a1 activate IRE1 and PERK but not ATF6 in other cell types?
• Also as mentioned above, how do the UPR (all 3 branches) in cells lacking Slc33a1 respond to TG or DTT? This and the preceding comments are important toward making the claim that Slc33a1 is actually a regulator of ATF6. The time required to do these experiments will depend on whether creation of more stable lines is required, and whether they are worth doing depends on how broad the authors wish the scope of the paper to be.
• It's surprising that the authors didn't do comparable experiments to what is shown in Fig. 6 but deleting the acetyltransferases that modify sialic acids, which I believe are known.
• The authors mis-describe the data from Fig. 5B. EndoH and PNGaseF should collapse ATF6 to a 0N form, not a 1N form (what is labeled as 2N should be 1N, and it looks like the true 2N band is partially obscured by the strong 3N band.

Referee cross-commenting

While reviewer #2 and I have somewhat different opinions on the strength of the evidence, we seem fairly well-aligned on the overall significance of the work.

Significance

The conceptual advance in this paper is that, while loss of Slc33a1 seems widely disruptive to ER function-an idea that has been advanced in the literature before-it seems to have unique and discordant effects on ATF6 relative to the other UPR pathways. The paper does not offer a conclusive mechanism by which these effects are realized, and the sole focus on ATF6 makes it difficult to fully contextualize the findings, but the data are of high quality and, while the scope is somewhat narrow, the phenotype is likely to be of interest to those concerned with ER stress and UPR signaling, which also describes my own expertise.

For me personally, I am really bad with confrontation, so the "opening the door" microintervention would be something I need to work on. This is an issue that even need to be solved within my home, and its where I see microaggressions within the family and towards other groups the most. This could be a "comfortably uncomfortable" place where I can practice opening the door.

i’m surprised that microinterventions has that definition, but i’m glad that this is listed as a solution. I feel like while it’s crucial to address the ones creating tension, the initially more efficient solution is to invest in microinterventions. It's so important to open up those bridges and build one, and it might be easier before trying to revive another's burned bridge.

I wonder how certain immoralities tie into breaking down the barriers of injustice and micro aggressions. whenever i would see this growing up, before knowing what micro aggression was, i would always criticize instances as the offender having too much pride. so, breaking down barriers and key issues, should we focus on inner values or visable issues that would deny those instances of corruption in the heart?

needs to be range and associted to the assumptions made for the economical values.

Zivver is originally Dutch, but got bought by the US-ian Kiteworks https://www.zivver.com/nl/blog/persbericht-zivver-sluit-zich-aan-bij-kiteworks in #2025/06 Kiteworks is run by ex-intelligence cadre of the IDF. Zivver specialises in secure comms for various gov entities and hospitals etc.

juxtaposition

I think I have read a larger discussion on the importance of oral traditions in African cultures, both past and current.

classifications of sermons discussed by the author mentioned in text. all listed below.

Is indeed practical magic

Shame that the word programming is not an adequate way of thinking about what's actually hapenning

eLife Assessment

The study presents a valuable resource of proline hydroxylation proteins for molecular biology studies in oxygen-sensing and cell signaling with the characterization of Repo-man proline hydroxylation site. The evidence supporting the claim of the authors is solid, although further clarification of the overall efficiency of the HILIC analysis, the specificity/sensitivity of immonium ion analysis, as well as quantification of proline hydroxylation identifications will be helpful. The work will be of interest to researchers studying post-translational modification, oxygen sensing, and cell signaling.

Reviewer #1 (Public review):

Summary:

The manuscript by Hao Jiang et al described a systematic approach to identify proline hydroxylation proteins. The authors implemented a proteomic strategy with HILIC-chromatographic separation and reported an identification with high confidence of 4993 sites from HEK293 cells (4 replicates) and 3247 sites from RCC4 cells with 1412 sites overlapping between the two cell lines. A small fraction of about 200 sites from each cell line were identified with HyPro immonium ion. The authors investigated the conditions and challenges of using HyPro immonium ions as a diagnostic tool. The study focused the validation analysis of Repo-man (CDCA2) proline hydroxylation comparing MS/MS spectra, retention time and diagnostic ions of purified proteins with corresponding synthetic peptides. Using SILAC analysis and recombinant enzyme assay, the study evaluated Repo-man HyPro604 as a target of PHD1 enzyme.

Strengths:

The study involved extensive LCMS runs for in-depth characterization of proline hydroxylation proteins including four replicated analysis of 293 cells and three replicated analysis of RCC4 cells with 32 HILIC fractions in each analysis. The identification of over 4000 confident proline hydroxylation sites from the two cell lines would be a valuable resource for the community. The characterization of Repo-man proline hydroxylation is a novel finding.

Weaknesses:

As a study mainly focused on methodology, there are some potential technical weaknesses discussed below.

(1) The study applied HILIC-based chromatographic separation with a goal to enrich and separate hydroxyproline containing peptides. The separation effects for peptides from 293 cells and RCC4 cells seems somewhat different (Figure 2A and Figure S1A), which may indicate that the application efficiency of the strategy may be cell line dependent.

(2) The study evaluated the HyPro immonium ion as a diagnostic ion for HyPro identification showcasing multiple influential factors and potential challenges. It is important to note that with only around 5% of the identifications had HyPro immonium ion, it would be very challenging to implement this strategy in a global LCMS analysis to either validate or invalidate HyPro identifications. In comparison, acetyllysine immonium ion was previously reported to be a useful marker for acetyllysine peptides (PMID: 18338905) and the strategy offered a sensitivity of 70% with a specificity of 98%.

(3) The authors aimed to identify potential PHD targets by comparing the HyPro proteins identified with or without PHD inhibitor FG-4592 treatment. The workflow followed a classification strategy, rather than a typical quantitative proteomics approach for comprehensive analysis.

(4) The authors performed inhibitor treatment and in vitro PHD1 enzyme assay to validate that Repo-man can be hydroxylated by PHD1. It remains unknown if PHD1 expression in cells is sufficient to stimulate Repo-man hydroxylation.

Reviewer #2 (Public review):

Summary:

In this manuscript, Jiang et al. developed a robust workflow for identifying proline hydroxylation sites in proteins. They identified proline hydroxylation sites in HEK293 and RCC4 cells, respectively. The authors found that the more hydrophilic HILIC fractions were enriched in peptides containing hydroxylated proline residues. These peptides showed differences in charge and mass distribution compared to unmodified or oxidized peptides. The intensity of the diagnostic hydroxyproline iminium ion depended on parameters including MS collision energy, parent peptide concentration, and the sequence of amino acids adjacent to the modified proline residue. Additionally, they demonstrate that a combination of retention time in LC and optimized MS parameter settings reliably identifies proline hydroxylation sites in peptides, even when multiple proline residues are present

Strengths:

Overall, the manuscript presents an advanced, standardized protocol for identifying proline hydroxylation. The experiments were well designed, and the developed protocol is straightforward, which may help resolve confusion in the field.

Comments on revisions:

All of my concerns have been resolved by the authors. It is ready for publication.

Reviewer #3 (Public review):

Summary:

The authors present a new method for detecting and identifying proline hydroxylation sites within the proteome. This tool utilizes traditional LC-MS technology with optimized parameters, combined with HILIC-based separation techniques. The authors show that they pick up known hydroxy-proline sites and also validate a new site discovered through their pipeline.

Strengths:

The manuscript utilizes state-of-the-art mass spectrometric techniques with optimized collision parameters to ensure proper detection of the immonium ions, which is an advance compared to other similar approaches before. The use of synthetic control peptides on the HILIC separation step clearly demonstrates the ability of the method to reliably distinguish hydroxy-proline from oxidized methionine - containing peptides. Using this method, they identify a site on CDCA2, which they go on to validate in vitro and also study its role in regulation of mitotic progression in an associated manuscript.

Weaknesses:

Despite the authors claim about the specificity of this method in picking up the intended peptides, there is a good amount of potential false positives that also happen to get picked (owing to the limitations of MS-based readout), and the authors' criteria for downstream filtering of such peptides requires further clarification. In the same vein, greater and more diverse cell-based validation approach will be helpful to substantiate the claims regarding enrichment of peptides in the described pathway analyses. Experiments must show reproducibility and contain appropriate controls wherever necessary.

Comments on revisions:

I thank the authors for their clarifications and opinions on my questions and suggestions. Based on the response, the following points are important while considering the significance of this manuscript:

- The manuscript provides a novel method to detect and identify proline hydroxylation residues in the proteome. While this provides several advances over previous methods, the probability of false positives, loss of true positives and incomplete removal of the interference of methionine oxidation in this strategy need to be addressed clearly in the discussion section of the manuscript, so that the strengths and limitations of this method are made aware to the reader.

- Going by the standards of publication in eLife, reproducibility is very important in the experiments done. Hence, I strongly recommend that the authors perform the experiments in triplicate with error bars to confirm reproducibility. Graphs with single data points do not convey that, and this is very important for eLife.

- As for Figure 9C, the authors have rejected the request for a control lane in the figure. It may sound trivial to the authors, but for completeness of the experiment, all applicable controls must be performed and shown alongside the main data. It is essential to show the PHD1 only control to rule out the possibility of the contribution of any non-specific signal in the dot blot by PHD1.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript by Hao Jiang et al described a systematic approach to identify proline hydroxylation proteins. The authors implemented a proteomic strategy with HILIC-chromatographic separation and reported an identification of 4993 sites from HEK293 cells (4 replicates) and 3247 sites from RCC4 sites (3 replicates) with 1412 sites overlapping between the two cell lines. From the analysis, the authors identified 225 sites and 184 sites respectively from 293 and RCC4 cells with HyPro diagnostic ion. The identifications were validated by analyzing a few synthetic peptides, with a specific focus on Repo-man (CDCA2) through comparing MS/MS spectra, retention time, and diagnostic ions. With SILAC analysis and recombinant enzyme assay, the study showed that Repo-man HyPro604 is a target of the PHD1 enzyme.

Strengths:

The study involved extensive LC-MS analysis and was carefully implemented. The identification of over 4000 confident proline hydroxylation sites would be a valuable resource for the community. The characterization of Repo-man proline hydroxylation is a novel finding.

Weaknesses:

However, as a study mainly focused on methodology, the findings from the experimental data did not convincingly demonstrate the sensitivity and specificity of the workflow for site-specific identification of proline hydroxylation in global studies.

Proline hydroxylation is an enzymatic post translational protein modification, catalysed by prolyl Hydroxylases (PHDs), which can have profound biological significance, e.g. altering protein half-life and/or the stability of protein-protein interactions. Furthermore, there has been controversy in the field as to the true number of protein targets for PHDs in cells. Thus, there is a clear need for methods to enable the robust identification of genuine PHD targets and to reliably map sites of PHD-catalysed proline hydroxylation in proteins. We believe, therefore, that our methodology, as reported here in Jiang et al., is an important contribution towards this goal. We note that our methodology has already been used successfully by others

(https://doi.org/10.1016/j.mcpro.2025.100969). While further improvements in this methodology may of course be developed in future, we are not currently aware of any superior methods that have been reported previously in the literature. The criticism made by the reviewer notably does not include reference to any such alternative published methodology that interested researchers can use which would offer superior results to the approach we document in this study.

Major concerns:

(1) The study applied HILIC-based chromatographic separation with a goal of enriching and separating hydroxyproline-containing peptides. However, as the authors mentioned, such an approach is not specific to proline hydroxylation. In addition, many other chromatography techniques can achieve deep proteome fractionation such as high pH reverse phase fractionation, strong-cation exchange etc. There was no data in this study to demonstrate that the strategy offered improved coverage of proline hydroxylation proteins, as the identifications of the HyPro sites could be achieved through deep fractionation and a highly sensitive LCMS setup. The data of Figure 2A and S1A were somewhat confusing without a clear explanation of the heat map representations. 

The data we present in this study demonstrate clearly that peptides with hydroxylated prolines are enriched in specific HILIC fractions (F10-F18), in comparison with total unfractionated peptides derived from cell extracts. We also refer the reviewer to our previously published study by Bensaddek et al (International Journal of Mass Spectrometry: doi:10.1016/j.ijms.2015.07.029), which was reference 41 in this study, in which we compared directly the performance of both HILIC and strong anionic exchange chromatography, (hSAX). This showed that HILIC provided superior enrichment to hSAX for enrichment of peptides containing hydroxylated proline residues. To clarify this point for readers, we have now included a specific reference to our previous study at the start of the Results section in our current revision. Currently, we use HILIC to provide a degree of enrichment for proline hydroxylated peptides because we are not aware of alternative chromatographic methods that in our hands provide better results.

We have included descriptions of the information shown in the heatmaps in the associated figure legends and captions.

(2) The study reported that the HyPro immonium ion is a diagnostic ion for HyPro identification. However, the data showed that only around 5% of the identifications had such a diagnostic ion. In comparison, acetyl-lysine immonium ion was previously reported to be a useful marker for acetyllysine peptides (PMID: 18338905), and the strategy offered a sensitivity of 70% with a specificity of 98%. In this study, the sensitivity of HyPro immonium ion was quite low. The authors also clearly demonstrated that the presence of immonium ion varied significantly due to MS settings, peptide sequence, and abundance. With further complications from L/I immonium ions, it became very challenging to implement this strategy in a global LC-MS analysis to either validate or invalidate HyPro identifications.

The reviewer appears to have misunderstood the point we make with regard to the identification of the immonium ion and its use as a diagnostic marker for proline hydroxylation in MS analyses. We do not claim that this immonium ion is an essential diagnostic marker for proline hydroxylation. As the reviewer notes, with respect to the acetyl-lysine modification, the corresponding immonium ion is often used in MS studies as a diagnostic for identification of specific post translational modifications. Previous studies have reported that the immonium ion for hydroxylated proline is detected when the transcription factor HIF is analysed, but is often absent with other putative PHD targets, which has been used as an argument that these targets are not genuine proline hydroxylation sites. We are not, therefore, introducing the idea in this study that the hydroxy-proline immonium ion is a required diagnostic marker for proline hydroxylation, but instead demonstrating that detection of this ion, at least in some peptide sequences, may require the use of higher MS collision energies than are typically required for routine peptide identification. We believe that this is an interesting observation that can help to clear up discussions in the literature regarding the true prevalence of PHD-catalysed proline hydroxylation in different target proteins. Our data suggest that, in future MS studies analysing suspected PHD target proteins, two different collision energy might need to be used, i.e., normal collision energy for the routine identification of a peptide, combined with use of a higher collision energy if the hydroxy-proline immonium ion was not already detected.

(3) The study aimed to apply the HILIC-based proteomics workflow to identify HyPro proteins regulated by the PHD enzyme. However, the quantification strategy was not rigorous. The study just considered the HyPro proteins not identified by FG-4592 treatment as potential PHD targeted proteins. There are a few issues. First, such an analysis was not quantitative without reproducibility or statistical analysis. Second, it did not take into consideration that data-dependent LC-MS analysis was not comprehensive and some peptide ions may not be identified due to background interferences. Lastly, FG-4592 treatment for 24 hrs could lead to wide changes in gene expressions and protein abundances. Therefore, it is not informative to draw conclusions based on the data for bioinformatic analysis.

We refer the reviewer to the data we present in this study using SILAC analysis, combined with our MS workflow. to achieve a more accurate quantitative picture of proline hydroxylation levels. While we agree that the point the reviewer makes is valid, regarding our data dependent LC-MS/MS analysis potentially not being comprehensive, this means, however, that we are potentially underestimating the true prevalence of proline hydroxylated peptides, not overestimating the level of these modified peptides. We also refer the reviewer to the accompanying study by Druker et al., (eLife 2025; doi.org/10.7554/eLife.108131.1)  in which we present a detailed follow-on study demonstrating the functional significance of the novel proline hydroxylation site we detected in the protein RepoMan (CDCA2). Therefore, even if we have not achieved a fully comprehensive analysis of all proline hydroxylated peptides catalysed by PHD enzymes, we believe that we have advanced the field by documenting a workflow that is able to identify and validate novel PHD targets.

(4) The authors performed an in vitro PHD1 enzyme assay to validate that Repo-man can be hydroxylated by PHD1. However, Figure 9 did not show quantitatively PHD1-induced increase in Repo-man HyPro abundance and it is difficult to assess its reaction efficiency to compare with HIF1a HyPro.

The analysis shown in Figure 9 was not intended to quantify the efficiency of in vitro hydroxylation of RepoMan by PHD1, but rather to answer the question, ‘Can recombinant PHD1 alone hydroxylate P604 on RepoMan in vitro, yes or no?’. The data show that the answer here is ‘yes’. Clearly, the HIF peptide is a more efficient substrate in vitro for recombinant PHD1 than the RepoMan peptide and we have now included a statement in the Discussion that addresses the significance of this observation more directly.

Reviewer #2 (Public review):

Summary:

In this manuscript, Jiang et al. developed a robust workflow for identifying proline hydroxylation sites in proteins. They identified proline hydroxylation sites in HEK293 and RCC4 cells, respectively. The authors found that the more hydrophilic HILIC fractions were enriched in peptides containing hydroxylated proline residues. These peptides showed differences in charge and mass distribution compared to unmodified or oxidized peptides. The intensity of the diagnostic hydroxyproline iminium ion depended on parameters including MS collision energy, parent peptide concentration, and the sequence of amino acids adjacent to the modified proline residue. Additionally, they demonstrate that a combination of retention time in LC and optimized MS parameter settings reliably identifies proline hydroxylation sites in peptides, even when multiple proline residues are present.

Strengths:

Overall, the manuscript presents an advanced, standardized protocol for identifying proline hydroxylation. The experiments were well designed, and the developed protocol is straightforward, which may help resolve confusion in the field.

Weaknesses:

(1) The authors should provide a summary of the standard protocol for identifying proline hydroxylation sites in proteins that can easily be followed by others.

This is a good suggestion and we have now included a figure (Figure 10) with a summary of our workflow in the current revision.

(2) Cockman et al. proposed that HIF-α is the only physiologically relevant target for PHDs. Their approach is considered the gold standard for identifying PHD targets. Therefore, the authors should discuss the major progress they made in this manuscript that challenges Cockman's conclusion.

While we had mentioned the Cockman et al., paper in the Introduction, we had not focussed on this somewhat controversial issue. However, in response to the Reviewer’s request, we have now added a comment in the Discussion section in the current revision of how our new data address the proposal discussed previously by Cockman et al. In brief, we believe that our findings are not consistent with a model in which PHDs have no protein targets other than HIFs.

Reviewer #3 (Public review): 

Summary:

The authors present a new method for detecting and identifying proline hydroxylation sites within the proteome. This tool utilizes traditional LC-MS technology with optimized parameters, combined with HILIC-based separation techniques. The authors show that they pick up known hydroxy-proline sites and also validate a new site discovered through their pipeline.

Strengths:

The manuscript utilizes state-of-the-art mass spectrometric techniques with optimized collision parameters to ensure proper detection of the immonium ions, which is an advance compared to other similar approaches before. The use of synthetic control peptides on the HILIC separation step clearly demonstrates the ability of the method to reliably distinguish hydroxy-proline from oxidized methionine - containing peptides. Using this method, they identify a site on CDCA2, which they go on to validate in vitro and also study its role in regulation of mitotic progression in an associated manuscript.

Weaknesses:

Despite the authors' claim about the specificity of this method in picking up the intended peptides, there is a good amount of potential false positives that also happen to get picked (owing to the limitations of MS-based readout), and the authors' criteria for downstream filtering of such peptides require further clarification. In the same vein, greater and more diverse cell-based validation approach will be helpful to substantiate the claims regarding enrichment of peptides in the described pathway analyses.

We of course agree that false positives may arise, as is true for essentially all PTM studies. There are two issues here; first, are identified sites technically correct? (i.e. not misidentifications from the MS data) and second, are the identified modifications of biological significance? We have addressed this using the popular MaxQuant software suite to evaluate the modifications identified and to control the false discovery rate (FDR) at both the precursor and protein level, as described in the manuscript. We are aware that false positives could arise from confusing oxidation of methionine with hydroxylation of proline. Therefore, to address the issue as to whether we could identify bona fide PHD protein targets outside of the HIF family, we adopted a conservative approach by simply filtering out peptides where there was a methionine residue within three amino acids of the predicted proline hydroxylation site. This was a pragmatic decision made to reduce the likelihood of false positives in our dataset and we recognise that this likely results in us overlooking some genuine proline hydroxylation sites that occur nearby methionine residues. To address the potential biological relevance of the proline hydroxylation sites identified, we analysed extracts from cells treated with FG inhibitors. Of course a detailed understanding of biological significance relies upon follow-on experimental analyses for each site, which we have performed for P604 on RepoMan in accompanying study by Druker et al., (eLife 2025; doi.org/10.7554/eLife.108131.1).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The finding that the immonium ion intensities of L/I did not increase with increasing collision energy was surprising. Was this specific to this synthetic peptide?

We agree this is an interesting and unexpected finding. We have no reason to believe that it is specific to synthetic peptides per se, but rather think this reflects an effect of amino acid composition in the peptides analysed. It will be interesting to explore this phenomenon in more detail in future.

(2) The sequence logos in Figure 4 seemed to lack any amino acid enrichment in most positions except for collagen peptides. Have these findings been tested with statistical analysis?

The results we show for sequence logo analysis were generated using WebLogo (10.1101/gr.849004) and correspond to an analysis of all proline hydroxylated peptides we detected across all cell lines and replicates analysed. The fact that collagens are highly abundant proteins with very high levels of proline hydroxylation likely explains why collagen peptides dominated the outcome of the sequence logo analysis. There is clearly scope for more detailed follow up analysis in future of the sequence specificity of proline hydroxylation sites in no- collagen proteins that are validated PHD targets.

(3) Overall figure quality was not ideal. The resolution and font sizes of figures should be carefully evaluated and adjusted. The figure legend should contain a title for the figure. Annotations of the figures were somewhat confusing. 

We agree with the criticism of the figure resolution in the review copies - the lower resolution appears to have been generated after we had uploaded higher resolution original images. We are providing again higher resolution versions of all figures for the current revision.

Reviewer #3 (Recommendations for the authors):

Certain concerns regarding portions of the manuscript that need addressing:

(1) " These data show that two different cell lines show unique profiles of proteins with hydroxylated peptides." - It is difficult to conclusively say this statement after profiling the prolyl hydroxy proteome from just two cell lines, especially since the amino acids with the highest frequency in the most enriched peptides are similar in both cell lines.

We agree with this point and have changed the current revision to state instead, “This shows that each of the two cell lines analysed have distinct profiles.”

(2) "We noted that there was a high frequency of a methionine residues appearing either at the first, second, or even third positions after the HyPro site.." - according to the authors, claim, the advantage of their method was that they were able to overcome the limitation of older methods that couldn't separate methionine oxidation from proline hydroxylation. However, in this statement, they say that the high frequency of methionine residues may be because of the similar mass shift. These statements are contradictory. The authors should either tone down the claim or prove that those are indeed hydroxyproline sites. Is it possible that in the filtering step of excluding these high-frequency of methionine - containing peptides, we are losing potential positive hits for hydroxy-proline sites? What is the authors' take on this?

We respectfully do not agree that our, “statements are contradictory”, with respect to the potential confusion between identification of methionine oxidation and proline hydroxylation, but acknowledge that we have not explained this issue clearly enough. It is a fact that the similar mass shift resulting from proline hydroxylation and methionine oxidation is a technical challenge that can potentially lead to misidentifications in MS studies and that is what we state clearly in the manuscript. We have addressed this issue head on experimentally in this study and show using synthetic peptides how detailed analysis of specific proline hydroxylation sites in target proteins can be distinguished from methionine oxidation, based upon differential chromatographic behaviour of peptides with either hydroxylated proline or oxidised methionine, as well as by detailed analysis of fragmentation spectra. However, in the case of our global analysis, as we were not able to perform synthetic peptide comparisons for every putative site identified, we took the pragmatic approach of filtering out examples of peptides where a methionine residue was present within three residues of a potential proline hydroxylation site. This was done simply to reduce the possibility of misidentification in the set of novel proline hydroxylated peptides identified and we accept that as a consequence we are likely filtering out peptides that include bona fide proline hydroxylation sites. We have clarified this point in the current revision and hope to be able to address this issue more comprehensively in future studies.

(3) "Accordingly, a score cut-off of 40 for hydroxylated peptides and a localisation probability cut-off of more than 0.5 for hydroxylated peptides was performed." Could the authors shed more light and clarify what was the basis for this value of cut-off to be used in this filtering step? Is this sample dependent? What should be the criteria to determine this value?

We used MaxQuant software (10.1016/j.cell.2006.09.026), for PTM analysis, in which a localization probability score of 0.75 and score cut-off of 40 is a commonly used threshold to define high confidence. The reason that we used 0.5 at the first step was to investigate how likely it might be that the misassignment of delta m/z +16 Da (oxidation) on Methionine would affect the identification of hydroxylation on Proline. However, we note that in the final results set used for analysis, all putative proline hydroxylated peptides that had a Methionine residue near to the hydroxylated proline were disregarded as a pragmatic step to reduce the probability of false identifications.

(4) The authors are requested to kindly make the HPLC and MS traces more legible and use highresolution images, with clearly labeled values on the peaks. Kindly extract coordinates from the underlying data files to plot the curves if needed to make it clearer.

We have reviewed the clarity of all images and figures in the current revision.

(5) There seems to be no error bars in Figure 3, Figure 7E, and panels of Figure 8 with bar graphs. Are those single replicate data?

These specific figures are from single replicate data.

(6) For Figure 9C, the control with only PHD1 (no peptide) is missing. 

The ‘no peptide control’ was not included in the figure because it is simply a blank lane and there is nothing to see.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary:

Damaris et al. perform what is effectively an eQTL analysis on microbial pangenomes of E. coli and P. aeruginosa. Specifically, they leverage a large dataset of paired DNA/RNA-seq information for hundreds of strains of these microbes to establish correlations between genetic variants and changes in gene expression. Ultimately, their claim is that this approach identifies non-coding variants that affect expression of genes in a predictable manner and explain differences in phenotypes. They attempt to reinforce these claims through use of a widely regarded promoter calculator to quantify promoter effects, as well as some validation studies in living cells. Lastly, they show that these non-coding variations can explain some cases of antibiotic resistance in these microbes.

Major comments

Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

The authors convincingly demonstrate that they can identify non-coding variation in pangenomes of bacteria and associate these with phenotypes of interest. What is unclear is the extent by which they account for covariation of genetic variation? Are the SNPs they implicate truly responsible for the changes in expression they observe? Or are they merely genetically linked to the true causal variants. This has been solved by other GWAS studies but isn't discussed as far as I can tell here.

We thank the reviewer for their effective summary of our study. Regarding our ability to identify variants that are causal for gene expression changes versus those that only “tag” the causal ones, here we have to again offer our apologies for not spelling out the limitation of GWAS approaches, namely the difficulty in separating associated with causal variants. This inherent difficulty is the main reason why we added the in-silico and in-vitro validation experiments; while they each have their own limitations, we argue that they all point towards providing a causal link between some of our associations and measured gene expression changes. We have amended the discussion (e.g. at L548) section to spell our intention out better and provide better context for readers that are not familiar with the pitfalls of (bacterial) GWAS.

They need to justify why they consider the 30bp downstream of the start codon as non-coding. While this region certainly has regulatory impact, it is also definitely coding. To what extent could this confound results and how many significant associations to expression are in this region vs upstream?

We agree with the reviewer that defining this region as “non-coding” is formally not correct, as it includes the first 10 codons of the focal gene. We have amended the text to change the definition to “cis regulatory region” and avoided using the term “non-coding” throughout the manuscript. Regarding the relevance of this including the early coding region, we have looked at the distribution of associated hits in the cis regulatory regions we have defined; the results are shown in Supplementary Figure 3.

We quantified the distribution of cis associated variants and compared them to a 2,000 permutations restricted to the -200bp and +30bp window in both E. coli * (panel A) and P. aeruginosa* (panel B). As it can be seen, the associated variants that we have identified are mostly present in the 200bp region and the +30bp region shows a mild depletion relative to the random expectation, which we derived through a variant position shuffling approach (2,000 replicates). Therefore, we believe that the inclusion of the early coding region results in an appreciable number of associations, and in our opinion justify its inclusion as a putative “cis regulatory region”.

The claim that promoter variation correlates with changes in measured gene expression is not convincingly demonstrated (although, yes, very intuitive). Figure 3 is a convoluted way of demonstrating that predicted transcription rates correlate with measured gene expression. For each variant, can you do the basic analysis of just comparing differences in promoter calculator predictions and actual gene expression? I.e. correlation between (promoter activity variant X)-(promoter activity variant Y) vs (measured gene expression variant X)-(measured gene expression variant Y). You'll probably have to

We realize that we may not have failed to properly explain how we carried out this analysis, which we did exactly in the way the reviewer suggests here. We had in fact provided four example scatterplots of the kind the reviewer was requesting as part of Figure 4. We have added a mention of their presence in the caption of Figure 3.

Figure 7 it is unclear what this experiment was. How were they tested? Did you generate the data themselves? Did you do RNA-seq (which is what is described in the methods) or just test and compare known genomic data?

We apologize for the lack of clarity here; we have amended the figure’s caption and the corresponding section of the results (i.e. L411 and L418) to better highlight how the underlying drug susceptibility data and genomes came from previously published studies.

Are the data and the methods presented in such a way that they can be reproduced?

No, this is the biggest flaw of the work. The RNA-Seq experiment to start this project is not described at all as well as other key experiments. Descriptions of methods in the text are far too vague to understand the approach or rationale at many points in the text. The scripts are available on github but there is no description of what they correspond to outside of the file names and none of the data files are found to replicate the plots.

We have taken this critique to heart, and have given more details about the experimental setup for the generation of the RNA-seq data in the methods as well as the results sections. We have also thoroughly reviewed any description of the methods we have employed to make sure they are more clearly presented to the readers. We have also updated our code repository in order to provide more information about the meaning of each script provided, although we would like to point out that we have not made the code to be general purpose, but rather as an open documentation on how the data was analyzed.

Figure 8B is intended to show that the WaaQ operon is connected to known Abx resistance genes but uses the STRING method. This requires a list of genes but how did they build this list? Why look at these known ABx genes in particular? STRING does not really show evidence, these need to be substantiated or at least need to justify why this analysis was performed.

We have amended the Methods section (“Gene interaction analysis”, L799) to better clarify how the network shown in this panel was obtained. In short, we have filtered the STRING database to identify genes connected to members of the waa operon with an interaction score of at least 0.4 (“moderate confidence”), excluding the “text mining” field. Antimicrobial resistance genes were identified according to the CARD database. We believe these changes will help the readers to better understand how we derived this interaction.

Are the experiments adequately replicated and statistical analysis adequate?

An important claim on MIC of variants for supplementary table 8 has no raw data and no clear replicates available. Only figure 6, the in vitro testing of variant expression, mentions any replicates.

We have expanded the relevant section in the Methods (“Antibiotic exposure and RNA extraction”, L778) to provide more information on the way these assays were carried out. In short, we carried out three biological replicates, the average MIC of two replicates in closest agreement was the representative MIC for the strain. We believe that we have followed standard practice in the field of microbiology, but we agree that more details were needed to be provided in order for readers to appreciate this.

Minor comments

Specific experimental issues that are easily addressable..

Are prior studies referenced appropriately?

There should be a discussion of eQTLs in this. Although these have mostly been in eukaryotes a. https://doi.org/10.1038/s41588-024-01769-9 ; https://doi.org/10.1038/nrg3891.

We have added these two references, which provide a broader context to our study and methodology, in the introduction.

Line 67. Missing important citation for Ireland et al. 2020 https://doi.org/10.7554/eLife.55308

Line 69. Should mention Johns et al. 2018 (https://doi.org/10.1038/nmeth.4633) where they study promoter sequences outside of E. coli

Line 90 - replace 'hypothesis-free' with unbiased

We have implemented these changes.

Line 102 - state % of DEGs relative to the entire pan-genome

Given that the study is focused on identifying variants that were associated with changes in expression for reference genes (i.e. those present in the reference genome), we think that providing this percentage would give the false impression that our analysis include accessory genes that are not encoded by the reference isolate, which is not what we have done.

Figure 1A is not discussed in the text

We have added an explicit mention of the panels in the relevant section of the results.

Line 111: it is unclear what enrichment was being compared between, FIgures 1C/D have 'Gene counts' but is of the total DEGs? How is the p-value derived? Comparing and what statistical test was performed? Comparing DEG enrichment vs the pangenome? K12 genome?

We have amended the results and methods section, as well as Figure 1’s caption to provide more details on how this analysis was carried out.

Line 122-123: State what letters correspond to these COG categories here

We have implemented the clarifications and edits suggested above

Line 155: Need to clarify how you use k-mers in this and how they are different than SNPs. are you looking at k-mer content of these regions? K-mers up to hexamers or what? How are these compared. You can't just say we used k-mers.

We have amended that line in the results section to more explicitly refer to the actual encoding of the k-mer variants, which were presence/absence patterns for k-mers extracted from each target gene’s promoter region separately, using our own developed method, called panfeed. We note that more details were already given in the methods section, but we do recognize that it’s better to clarify things in the results section, so that more distracted readers get the proper information about this class of genetic variants.

Line 172: It would be VERY helpful to have a supplementary figure describing these types of variants, perhaps a multiple-sequence alignment containing each example

We thank the reviewer for this suggestion. We have now added Supplementary Figure 3, which shows the sequence alignments of the cis-regulatory regions underlying each class of the genetic marker for both E. coli and P. aeruginosa.

Figure 4: THis figure is too small. Why are WaaQ and UlaE being used as examples here when you are supposed to be explicitly showing variants with strong positive correlations?

We rearranged the figure’s layout to improve its readability. We agree that the correlation for waaQ and ulaE is weaker than for yfgJ and kgtP, but our intention was to not simply cherry-pick strong examples, but also those for which the link between predicted promoter strength and recorded gene expression was less obvious.

Figure 4: Why is there variation between variants present and variant absent? Is this due to other changes in the variant? Should mention this in the text somewhere

Variability in the predicted transcription rate for isolates encoding for the same variant is due to the presence of other (different) variants in the region surrounding the target variant. PromoterCalculator uses nucleotide regions of variable length (78 to 83bp) to make its predictions, while the variants we are focusing on are typically shorter (as shown in Figure 4). This results in other variants being included in the calculation and therefore slightly different predicted transcription rates for each strain. We have amended the caption of Figure 4 to provide a succinct explanation of these differences.

Line 359: Need to talk about each supplementary figure 4 to 9 and how they demonstrate your point.

We have expanded this section to more explicitly mention the contents of these supplementary figures and why they are relevant for the findings of this section (L425).

Are the text and figures clear and accurate?

Figure 4 too small

We have fixed the figure, as described above

Acronyms are defined multiple times in the manuscript, sometimes not the first time they are used (e.g. SNP, InDel)

Figure 8A - Remove red box, increase label size

Figure 8B - Low resolution, grey text is unreadable and should be darker and higher resolution

Line 35 - be more specific about types of carbon metabolism and catabolite repression

Line 67 - include citation for ireland et al. 2020 https://doi.org/10.7554/eLife.55308

Line 74 - You talk about looking in cis but don't specify how mar away cis is

Line 75 - we encoded genetic variants..... It is unclear what you mean here

Line 104 - 'were apart of operons' should clarify you mean polycistronic or multi-gene operons. Single genes may be considered operonic units as well.

We have addressed all the issues indicated above.

Figure 2: THere is no axis for the percents and the percents don't make sense relative to the bars they represent??

We realize that this visualization might not have been the most clear for readers, and have made the following improvement: we have added the number of genes with at least one association before the percentage. We note that the x-axis is in log scale, which may make it seem like the light-colored bars are off. With the addition of the actual number of associated genes we think that this confusion has been removed.

Figure 2: Figure 2B legend should clarify that these are individual examples of Differential expression between variants

Line 198-199: This sentence doesn't make sense, 'encoded using kmers' is not descriptive enough

Line 205: Should be upfront about that you're using the Promoter Calculator that models biophysical properties of promoter sequences to predict activity.

Line 251: 'Scanned the non-coding sequences of the DEGs'. This is far too vague of a description of an approach. Need to clarify how you did this and I didn't see in the method. Is this an HMM? Perfect sequence match to consensus sequence? Some type of alignment?

Line 257-259: This sentence lacks clarity

We have implemented all the suggested changes and clarified the points that the reviewer has highlighted above.

Line346: How were the E. coli isolates tested? Was this an experiment you did? This is a massive undertaking (1600 isolates * 12 conditions) if so so should be clearly defined

While we have indicated in the previous paragraph that the genomes and antimicrobial susceptibility data were obtained from previously published studies, we have now modified this paragraph (e.g. L411 and L418) slightly to make this point even clearer.

Figure 6A: The tile plot on the right side is not clearly labeled and it is unclear what it is showing and how that relates to the bar plots.

In the revised figure, we have clarified the labeling of the heatmap to now read “Log2(Fold Change) (measured expression)” to indicate that it represents each gene’s fold changes obtained from our initial transcriptomic analysis. We have also included this information in the caption of the figure, making the relationship between the measured gene expression (heatmap) and the reporter assay data (bar plots) clear to the reader.

FIgure 6B: typo in legend 'Downreglation'

We thank the review for pointing this out. The typo has been corrected to “Down regulation” in the revised figure.

Line 398: Need to state rationale for why Waaq operon is being investigated here. WHy did you look into individual example?

We thank the reviewer for asking for a clarification here. Our decision to investigate the waaQ gene was one of both biological relevance and empirical evidence. In our analysis associating non-coding variants with antimicrobial resistance using the Moradigaravand et al. dataset, we identified a T>C variant at position 3808241 that was associated with resistance to Tobramycin. We also observed this variant in our strain collection, where it was associated with expression changes of the gene, suggesting a possible functional impact. The waa operon is involved in LPS synthesis, a central determinant of the bacteria’s outer membrane integrity and a well established virulence factor. This provided a plausible biological mechanism through which variation could influence antimicrobial susceptibility. As its role in resistance has not been extensively characterized, this represents a good candidate for our experimental validation. We have now included this rationale in our revised manuscript (i.e. L476).

Figure 8: Can get rid of red box

We have now removed the red box from Figure 8 in the revised version.

Line 463 - 'account for all kinds' is too informal

Mix of font styles throughout document

We have implemented all the suggestions and formatting changes indicated above.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In their manuscript "Cis non-coding genetic variation drives gene expression changes in the E. coli and P. aeruginosa pangenomes", Damaris and co-authors present an extensive meta-analysis, plus some useful follow up experiments, attempting to apply GWAS principles to identify the extent to which differences in gene expression between different strains within a given species can be directly assigned to cis-regulatory mutations. The overall principle, and the question raised by the study, is one of substantial interest, and the manuscript here represents a careful and fascinating effort at unravelling these important questions. I want to preface my review below (which may otherwise sound more harsh than I intend) with the acknowledgment that this is an EXTREMELY difficult and challenging problem that the authors are approaching, and they have clearly put in a substantial amount of high quality work in their efforts to address it. I applaud the work done here, I think it presents some very interesting findings, and I acknowledge fully that there is no one perfect approach to addressing these challenges, and while I will object to some of the decisions made by the authors below, I readily admit that others might challenge my own suggestions and approaches here. With that said, however, there is one fundamental decision that the authors made which I simply cannot agree with, and which in my view undermines much of the analysis and utility of the study: that decision is to treat both gene expression and the identification of cis-regulatory regions at the level of individual genes, rather than transcriptional units. Below I will expand on why I find this problematic, how it might be addressed, and what other areas for improvement I see in the manuscript:

We thank the reviewer for their praise of our work. A careful set of replies to the major and minor critiques are reported below each point.

In the entire discussion from lines roughly 100-130, the authors frequently dissect out apparently differentially expressed genes from non differentially expressed genes within the same operons... I honestly wonder whether this is a useful distinction. I understand that by the criteria set forth by the authors it is technically correct, and yet, I wonder if this is more due to thresholding artifacts (i.e., some genes passing the authors' reasonable-yet-arbitrary thresholds whereas others in the same operon do not), and in the process causing a distraction from an operon that is in fact largely moving in the same direction. The authors might wish to either aggregate data in some way across known transcriptional units for the purposes of their analysis, and/or consider a more lenient 'rescue' set of significance thresholds for genes that are in the same operons as differentially expressed genes. I would favor the former approach, performing virtually all of their analysis at the level of transcriptional units rather than individual genes, as much of their analysis in any case relies upon proper assignment of genes to promoters, and this way they could focus on the most important signals rather than get lots sometimes in the weeds of looking at every single gene when really what they seem to be looking at in this paper is a property OF THE PROMOTERS, not the genes. (of course there are phenomena, such as rho dependent termination specifically titrating expression of late genes in operons, but I think on the balance the operon-level analysis might provide more insights and a cleaner analysis and discussion).

We agree with the reviewer that the peculiar nature of transcription in bacteria has to be taken into account in order to properly quantify the influence of cis variants in gene expression changes. We therefore added the exact analysis the reviewer suggested; that is, we ran associations between the variants in cis to the first gene of each operon and a phenotype that considered the fold-change of all genes in the operon, via a weighted average (see Methods for more details). As reported in the results section (L223), we found a similar trend as with the original analysis: we found the highest proportion of associations when encoding cis variants using k-mers (42% for E. coli and 45% for P. aeruginosa). More importantly, we found a high degree of overlap between this new “operon-level” association analysis and the original one (only including the first gene in each operon). We found a range of 90%-94% of associations overlapping for E. coli and between 75% and 91% for P. aeruginosa, depending on the variant type. We note that operon definitions are less precise for P. aeruginosa, which might explain the higher variability in the level of overlap. We have added the results of this analysis in the results section.

This also leads to a more general point, however, which I think is potentially more deeply problematic. At the end of the day, all of the analysis being done here centers on the cis regulatory logic upstream of each individual open reading frame, even though in many cases (i.e., genes after the first one in multi-gene operons), this is not where the relevant promoter is. This problem, in turn, raises potentially misattributions of causality running in both directions, where the causal impact on a bona fide promoter mutation on many genes in an operon may only be associated with the first gene, or on the other side, where a mutation that co-occurs with, but is causally independent from, an actual promoter mutation may be flagged as the one driving an expression change. This becomes an especially serious issue in cases like ulaE, for genes that are not the first gene in an operon (at least according to standard annotations, the UlaE transcript should be part of a polycistronic mRNA beginning from the ulaA promoter, and the role played by cis-regulatory logic immediately upstream of ulaE is uncertain and certainly merits deeper consideration. I suspect that many other similar cases likewise lurk in the dataset used here (perhaps even moreso for the Pseudomonas data, where the operon definitions are likely less robust). Of course there are many possible explanations, such as a separate ulaE promoter only in some strains, but this should perhaps be carefully stated and explored, and seems likely to be the exception rather than the rule.

While we again agree with the reviewer that some of our associations might not result in a direct causal link because the focal variant may not belong to an actual promoter element, we also want to point out how the ability to identify the composition of transcriptional units in bacteria is far from a solved problem (see references at the bottom of this comment, two in general terms, and one characterizing a specific example), even for a well-studied species such as E. coli. Therefore, even if carrying out associations at the operon level (e.g. by focusing exclusively on variants in cis for the first gene in the operon) might be theoretically correct, a number of the associations we find further down the putative operons might be the result of a true biological signal.

1. Conway, T., Creecy, J. P., Maddox, S. M., Grissom, J. E., Conkle, T. L., Shadid, T. M., Teramoto, J., San Miguel, P., Shimada, T., Ishihama, A., Mori, H., & Wanner, B. L. (2014). Unprecedented High-Resolution View of Bacterial Operon Architecture Revealed by RNA Sequencing. mBio, 5(4), 10.1128/mbio.01442-14. https://doi.org/10.1128/mbio.01442-14

2. Sáenz-Lahoya, S., Bitarte, N., García, B., Burgui, S., Vergara-Irigaray, M., Valle, J., Solano, C., Toledo-Arana, A., & Lasa, I. (2019). Noncontiguous operon is a genetic organization for coordinating bacterial gene expression. Proceedings of the National Academy of Sciences, 116(5), 1733–1738. https://doi.org/10.1073/pnas.1812746116

3. Zehentner, B., Scherer, S., & Neuhaus, K. (2023). Non-canonical transcriptional start sites in E. coli O157:H7 EDL933 are regulated and appear in surprisingly high numbers. BMC Microbiology, 23(1), 243. https://doi.org/10.1186/s12866-023-02988-6

Another issue with the current definition of regulatory regions, which should perhaps also be accounted for, is that it is likely that for many operons, the 'regulatory regions' of one gene might overlap the ORF of the previous gene, and in some cases actual coding mutations in an upstream gene may contaminate the set of potential regulatory mutations identified in this dataset.

We agree that defining regulatory regions might be challenging, and that those regions might overlap with coding regions, either for the focal gene or the one immediately upstream. For these reasons we have defined a wide region to identify putative regulatory variants (-200 to +30 bp around the start codon of the focal gene). We believe this relatively wide region allows us to capture the most cis genetic variation.

Taken together, I feel that all of the above concerns need to be addressed in some way. At the absolute barest minimum, the authors need to acknowledge the weaknesses that I have pointed out in the definition of cis-regulatory logic at a gene level. I think it would be far BETTER if they performed a re-analysis at the level of transcriptional units, which I think might substantially strengthen the work as a whole, but I recognize that this would also constitute a substantial amount of additional effort.

As indicated above, we have added a section in the results section to report on the analysis carried out at the level of operons as individual units, with more details provided in the methods section. We believe these results, which largely overlap with the original analysis, are a good way to recognize the limitation of our approach and to acknowledge the importance of gaining a better knowledge on the number and composition of transcriptional units in bacteria, for which, as the reference above indicates, we still have an incomplete understanding.

Having reached the end of the paper, and considering the evidence and arguments of the authors in their totality, I find myself wondering how much local x background interactions - that is, the effects of cis regulatory mutations (like those being considered here, with or without the modified definitions that I proposed above) IN THE CONTEXT OF A PARTICULAR STRAIN BACKGROUND, might matter more than the effects of the cis regulatory mutations per se. This is a particularly tricky problem to address because it would require a moderate number of targeted experiments with a moderate number of promoters in a moderate number of strains (which of course makes it maximally annoying since one can't simply scale up hugely on either axis individually and really expect to tease things out). I think that trying to address this question experimentally is FAR beyond the scope of the current paper, but I think perhaps the authors could at least begin to address it by acknowledging it as a challenge in their discussion section, and possibly even identify candidate promoters that might show the largest divergence of activities across strains when there IS no detectable cis regulatory mutation (which might be indicative of local x background interactions), or those with the largest divergences of effect for a given mutation across strains. A differential expression model incorporating shrinkage is essential in such analysis to avoid putting too much weight on low expression genes with a lot of Poisson noise.

We again thank the reviewer for their thoughtful comments on the limitations of correlative studies in general, and microbial GWAS in particular. In regards to microbial GWAS we feel we may have failed to properly explain how the implementation we have used allows to, at least partially, correct for population structure effects. That is, the linear mixed model we have used relies on population structure to remove the part of the association signal that is due to the genetic background and thus focus the analysis on the specific loci. Obviously examples in which strong epistatic interactions are present would not be accounted for, but those would be extremely challenging to measure or predict at scale, as the reviewer rightfully suggests. We have added a brief recap of the ability of microbial GWAS to account for population structure in the results section (“A large fraction of gene expression changes can be attributed to genetic variations in cis regulatory regions”, e.g. L195).

I also have some more minor concerns and suggestions, which I outline below:

It seems that the differential expression analysis treats the lab reference strains as the 'centerpoint' against which everything else is compared, and yet I wonder if this is the best approach... it might be interesting to see how the results differ if the authors instead take a more 'average' strain (either chosen based on genetics or transcriptomics) as a reference and compared everything else to that.

While we don’t necessarily disagree with the reviewer that a “wild” strain would be better to compare against, we think that our choice to go for the reference isolates is still justified on two grounds. First, while it is true that comparing against a reference introduces biases in the analysis, this concern would not be removed had we chosen another strain as reference; which strain would then be best as a reference to compare against? We think that the second point provides an answer to this question; the “traditional” reference isolates have a rich ecosystem of annotations, experimental data, and computational predictions. These can in turn be used for validation and hypothesis generation, which we have done extensively in the manuscript. Had we chosen a different reference isolate we would have had to still map associations to the traditional reference, resulting in a probable reduction in precision. An example that will likely resonate with this reviewer is that we have used experimentally-validated and high quality computational operon predictions to look into likely associations between cis-variants and “operon DEGs”. This analysis would have likely been of worse quality had we used another strain as reference, for which operon definitions would have had to come from lower-quality predictions or be “lifted” from the traditional reference.

Line 104 - the statement about the differentially expressed genes being "part of operons with diverse biological functions" seems unclear - it is not apparent whether the authors are referring to diversity of function within each operon, or between the different operons, and in any case one should consider whether the observation reflects any useful information or is just an apparently random collection of operons.

We agree that this formulation could create confusion and we have elected to remove the expression “with diverse biological functions”, given that we discuss those functions immediately after that sentence.

Line 292 - I find the argument here somewhat unconvincing, for two reasons. First, the fact that only half of the observed changes went in the same direction as the GWAS results would indicate, which is trivially a result that would be expected by random chance, does not lend much confidence to the overall premise of the study that there are meaningful cis regulatory changes being detected (in fact, it seems to argue that the background in which a variant occurs may matter a great deal, at least as much as the cis regulatory logic itself). Second, in order to even assess whether the GWAS is useful to "find the genetic determinants of gene expression changes" as the authors indicate, it would be necessary to compare to a reasonable, non-straw-man, null approach simply identifying common sequence variants that are predicted to cause major changes in sigma 70 binding at known promoters; such a test would be especially important given the lack of directional accuracy observed here. Along these same lines, it is perhaps worth noting, in the discussion beginning on line 329, that the comparison is perhaps biased in favor of the GWAS study, since the validation targets here were prioritized based on (presumably strong) GWAS data.

We thank the reviewer for prompting us into reasoning about the results of the in-vitro validation experiments. We agree that the agreement between the measured gene expression changes agree only partly with those measured with the reporter system, and that this discrepancy could likely be attributed to regulatory elements that are not in cis, and thus that were not present in the in-vitro reporter system. We have noted this possibility in the discussion. Additionally, we have amended the results section to note that even though the prediction in the direction of gene expression change was not as accurate as it could be expected, the prediction of whether a change would be present (thus ignoring directionality) was much higher.

I don't find the Venn diagrams in Fig 7C-D useful or clear given the large number of zero-overlap regions, and would strongly advocate that the authors find another way to show these data.

While we are aware that alternative ways to show overlap between sets, such as upset plots, we don’t actually find them that much easier to parse. We actually think that the simple and direct Venn diagrams we have drawn convey the clear message that overlaps only exist between certain drug classes in E. coli, and virtually none for P. aeruginosa. We have added a comment on the lack of overlap between all drug classes and the differences between the two species in the results section (i.e. L436 and L465).

In the analysis of waa operon gene expression beginning on line 400, it is perhaps important to note that most of the waa operon doesn't do anything in laboratory K12 strains due to the lack of complete O-antigen... the same is not true, however, for many wild/clinical isolates. It would be interesting to see how those results compare, and also how the absolute TPMs (rather than just LFCs) of genes in this operon vary across the strains being investigated during TOB treatment.

We thank the reviewer for this helpful suggestion. We examined the absolute expression (TPMs) of waa operon genes under the baseline (A) and following exposure to Tobramycin (B). The representative TPMs per strain were obtained by averaging across biological replicates. We observed a constitutive expression of the genes in the reference strain (MG1655) and the other isolates containing the variant of interest (MC4100, BW25113). In contrast, strains lacking the variants of interest (IAI76 and IAI78), showed lower expression of these operon genes under both conditions. Strain IAI77, on the other hand, displayed increased expression of a subset of waa genes post Tobramycin exposure, indicating strain-specific variation in transcriptional response. While the reference isolate might not have the O-antigen, it certainly expresses the waa operon, both constitutively and under TOB exposure.

I don't think that the second conclusion on lines 479-480 is fully justified by the data, given both the disparity in available annotation information between the two species, AND the fact that only two species were considered.

While we feel that the “Discussion” section of a research paper allows for speculative statements, we have to concede that we have perhaps overreached here. We have amended this sentence to be more cautious and not mislead readers.

Line 118: "Double of DEGs"

Line 288 - presumably these are LOG fold changes

Fig 6b - legend contains typos

Line 661 - please report the read count (more relevant for RNA-seq analysis) rather than Gb

We thank the reviewer for pointing out the need to make these edits. We have implemented them all.

Source code - I appreciate that the authors provide their source code on github, but it is very poorly documented - both a license and some top-level documentation about which code goes with each major operation/conclusion/figure should be provided. Also, ipython notebooks are in general a poor way in my view to distribute code, due to their encouragement of nonlinear development practices; while they are fine for software development, actual complete python programs along with accompanying source data would be preferrable.

We agree with the reviewer that a software license and some documentation about what each notebook is about is warranted, and we have added them both. While we agree that for “consumer-grade” software jupyter notebooks are not the most ergonomic format, we believe that as a documentation of how one-time analyses were carried out they are actually one of the best formats we could think of. They in fact allow for code and outputs to be presented alongside each other, which greatly helped us to iterate on our research and to ensure that what was presented in the manuscript matched the analyses we reported in the code. This is of course up for debate and ultimately specific to someone’s taste, and so we will keep the reviewer’s critique in mind for our next manuscript. And, if we ever decide to package the analyses presented in the manuscript as a “consumer-grade” application for others to use, we would follow higher standards of documentation and design.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

In this manuscript, Damaris et al. collected genome sequences and transcriptomes from isolates from two bacterial species. Data for E. coli were produced for this paper, while data for P. aeruginosa had been measured earlier. The authors integrated these data to detect genes with differential expression (DE) among isolates as well as cis-expression quantitative trait loci (cis-eQTLs). The authors used sample sizes that were adequate for an initial exploration of gene regulatory variation (n=117 for E. coli and n=413 for P. aeruginosa) and were able to discover cis eQTLs at about 39% of genes. In a creative addition, the authors compared their results to transcription rates predicted from a biophysical promoter model as well as to annotated transcription factor binding sites. They also attempted to validate some of their associations experimentally using GFP-reporter assays. Finally, the paper presents a mapping of antibiotic resistance traits. Many of the detected associations for this important trait group were in non-coding genome regions, suggesting a role of regulatory variation in antibiotic resistance.

A major strength of the paper is that it covers an impressive range of distinct analyses, some of which in two different species. Weaknesses include the fact that this breadth comes at the expense of depth and detail. Some sections are underdeveloped, not fully explained and/or thought-through enough. Important methodological details are missing, as detailed below.

We thank the reviewer for highlighting the strengths of our study. We hope that our replies to their comments and the other two reviewers will address some of the limitations.

Major comments:

1. An interesting aspect of the paper is that genetic variation is represented in different ways (SNPs & indels, IRG presence/absence, and k-mers). However, it is not entirely clear how these three different encodings relate to each other. Specifically, more information should be given on these two points:

2. it is not clear how "presence/absence of intergenic regions" are different from larger indels.

In order to better guide readers through the different kinds of genetic variants we considered, we have added a brief explanation about what “promoter switches” are in the introduction (“meaning that the entire promoter region may differ between isolates due to recombination events”, L56). We believe this clarifies how they are very different in character from a large deletion. We have kept the reference to the original study (10.1073/pnas.1413272111) describing how widespread these switches are in E. coli as a way for readers to discover more about them.

• I recommend providing more narration on how the k-mers compare to the more traditional genetic variants (SNPs and indels). It seems like the k-mers include the SNPs and indels somehow? More explanation would be good here, as k-mer based mapping is not usually done in other species and is not standard practice in the field. Likewise, how is multiple testing handled for association mapping with k-mers, since presumably each gene region harbors a large number of k-mers, potentially hugely increasing the multiple testing burden?

We indeed agree with the reviewer in thinking that representing genetic variants as k-mers would encompass short variants (SNP/InDels) as well as larger variants and promoters presence/absence patterns. We believe that this assumption is validated by the fact that we identify the highest proportion of DEGs with a significant association when using this representation of variants (Figure 2A, 39% for both species). We have added a reference to a recent review on the advantages of k-mer methods for population genetics (10.1093/molbev/msaf047) in the introduction. Regarding the issue of multiple testing correction, we have employed a commonly recognized approach that, unlike a crude Bonferroni correction using the number of tested variants, allows for a realistic correction of association p-values. We used the number of unique presence/absence patterns, which can be shared between multiple genetic variants, and applied a Bonferroni correction using this number rather than the number of variants tested. We have expanded the corresponding section in the methods (e.g. L697) to better explain this point for readers not familiar with this approach.

1. What was the distribution of association effect sizes for the three types of variants? Did IRGs have larger effects than SNPs as may be expected if they are indeed larger events that involve more DNA differences? What were their relative allele frequencies?

We appreciate the suggestion made by the reviewer to look into the distribution of effect sizes divided by variant type. We have now evaluated the distribution of the effect sizes and allele frequencies for the genetic markers (SNPs/InDels, IGRs, and k-mers) for both species (Supplementary Figure 2). In E. coli, IGR variants showed somewhat larger median effect sizes (|β| = 4.5) than SNPs (|β| = 3.8), whereas k-mers displayed the widest distribution (median |β| = 5.2). In P. aeruginosa, the trend differed with IGRs exhibiting smaller effects (median |β| = 3.2), compared to SNPs/InDels (median |β| =5.1) and k-mers (median |β| = 6.2). With respect to allele frequencies, SNPs/InDels generally occured at lower frequencies (median AF = 0.34 for E.coli, median AF = 0.33 for P. aeruginosa), whereas IGRs (median AF = 0.65 for E. coli and 0.75 for P. aeruginosa) and k-mers (median AF = 0.71 for E. coli and 0.65 for P. aeruginosa) were more often at the intermediate to higher frequencies respectively. We have added a visualization for the distribution of effect sizes (Supplementary Figure 2).

1. The GFP-based experiments attempting to validate the promoter effects for 18 genes are laudable, and the fact that 16 of them showed differences is nice. However, the fact that half of the validation attempts yielded effects in the opposite direction of what was expected is quite alarming. I am not sure this really "further validates" the GWAS in the way the authors state in line 292 - in fact, quite the opposite in that the validations appear random with regards to what was predicted from the computational analyses. How do the authors interpret this result? Given the higher concordance between GWAS, promoter prediction, and DE, are the GFP assays just not relevant for what is going on in the genome? If not, what are these assays missing? Overall, more interpretation of this result would be helpful.

We thanks the reviewer for their comment, which is similar in nature to that raised by reviewer #2 above. As noted in our reply above we have amended the results and discussion to indicate that although the direction of gene expression change was not highly accurate, focusing on the magnitude (or rather whether there would be a change in gene expression, regardless of the direction), resulted in a higher accuracy. We postulate that the cases in which the direction of the change was not correctly identified could be due to the influence of other genetic elements in trans with the gene of interest.

1. On the same note, it would be really interesting to expand the GFP experiments to promoters that did not show association in the GWAS. Based on Figure 6, effects of promoter differences on GFP reporters seem to be very common (all but three were significant). Is this a higher rate than for the average promoter with sequence variation but without detected association? A handful of extra reporter experiments might address this. My larger question here is: what is the null expectation for how much functional promoter variation there is?

We thank the reviewer for this comment. We agree that estimating the null expectation for the functional promoter would require testing promoter alleles with sequence variation that are not associated in the GWAS. Such experiments, which would directly address if the observed effects in our study exceeds background, would have required us to prepare multiple constructs, which was unfortunately not possible for us due to staff constraints. We therefore elected to clarify the scope of our GFP reporter assays instead. These experiments were designed as a paired comparison of the wild-type and the GWAS-associated variant alleles of the same promoter in an identical reporter background, with the aim of testing allele-specific functional effects for GWAS hits (Supplementary Figure 6). We also included a comparison in GFP fluorescence between the promoterless vector (pOT2) and promoter-containing constructs; we observed higher GFP signals in all but four (yfgJ, fimI, agaI, and yfdQ) variant-containing promoter constructs, which indicates that for most of the construct we cloned active promoter elements. We have revised the manuscript text accordingly to reflect this clarification and included the control in the supplementary information as Supplementary Figure 6.

1. Were the fold-changes in the GFP experiments statistically significant? Based on Figure 6 it certainly looks like they are, but this should be spelled out, along with the test used.

We thank the reviewer for pointing this out. We have reviewed Figure 6 to indicate significant differences between the test and control reporter constructs. We used the paired student’s t-test to match the matched plate/time point measurements. We also corrected for multiple testing using the Benhamini-Hochberg correction. As seen in the updated Figure 6A, 16 out of the 18 reporter constructs displayed significant differences (adjusted p-value

1. What was the overall correlation between GWAS-based fold changes and those from the GFP-based validation? What does Figure 6A look like as a scatter plot comparing these two sets of values?

We thank the reviewer for this helpful suggestion, which allows us to more closely look into the results of our in-vitro validation. We performed a direct comparison of RNAseq fold changes from the GWAS (x-axis) with the GFP reporter measurements (y-axis) as depicted in the figure above. The overall correlation between the two was weak (Pearson r = 0.17), reflecting the lack of thorough agreement between the associations and the reporter construct. We however note that the two metrics are not directly comparable in our opinion, since on the x-axis we are measuring changes in gene expression and on the y-axis changes in fluorescence expression, which is downstream from it. As mentioned above and in reply to a comment from reviewer 2, the agreement between measured gene expression and all other in-silico and in-vitro techniques increases when ignoring the direction of the change. Overall, we believe that these results partly validate our associations and predictions, while indicating that other factors in trans with the regulatory region contribute to changes in gene expression, which is to be expected. The scatter plot has been included as a new supplementary figure (Supplementary Figure 7).

1. Was the SNP analyzed in the last Results section significant in the gene expression GWAS? Did the DE results reported in this final section correspond to that GWAS in some way?

The T>C SNP upstream of waaQ did not show significant association with gene expression in our cis GWAS analysis. Instead, this variant was associated with resistance to tobramycin when referencing data from Danesh et al, and we observed the variant in our strain collection. We subsequently investigated whether this variant also influenced expression of the waa operon under sub-inhibitory tobramycin exposure. The differential expression results shown in the final section therefore represent a functional follow-up experiment, and not a direct replication of the GWAS presented in the first part of the manuscript.

1. Line 470: "Consistent with the differences in the genetic structure of the two species" It is not clear what differences in genetic structure this refers to. Population structure? Genome architecture? Differences in the biology of regulatory regions?

The awkwardness of that sentence is perhaps the consequence of our assumption that readers would be aware of the differences in population genetics differences between the two species. We however have realized that not much literature is available (if at all!) about these differences, which we have observed during the course of this and other studies we have carried out. As a result, we agree that we cannot assume that the reader is similarly familiar with these differences, and have changed that sentence (i.e. L548) to more directly address the differences between the two species, which will presumably result in a diverse population structure. We thank the reviewer for letting us be aware of a gap in the literature concerning the comparison of pangenome structures across relevant species.

1. Line 480: the reference to "adaption" is not warranted, as the paper contains no analyses of evolutionary patterns or processes. Genetic variation is not the same as adaptation.

We have amended this sentence to be more adherent to what we can conclude from our analyses.

1. There is insufficient information on how the E. coli RNA-seq data was generated. How was RNA extracted? Which QC was done on the RNA; what was its quality? Which library kits were used? Which sequencing technology? How many reads? What QC was done on the RNA-seq data? For this section, the Methods are seriously deficient in their current form and need to be greatly expanded.

We thank the reviewer for highlighting the need for clearer methodological detail. We have expanded this section (i.e. L608) to fully describe the generation and quality control of the E. coli RNA-seq data including RNA extraction and sequencing platform.

1. How were the DEG p-values adjusted for multiple testing?

As indicated in the methods section (“Differential gene expression and functional enrichment analysis”), we have used DEseq2 for E. coli, and LPEseq for P. aeruginosa. Both methods use the statistical framework of the False Discovery Rate (FDR) to compute an adjusted p-value for each gene. We have added a brief mention of us following the standard practice indicated by both software packages in the methods.

1. Were there replicates for the E. coli strains? The methods do not say, but there is a hint there might have been replicates given their absence was noted for the other species.

In the context of providing more information about the transcriptomics experiments for E. coli, we have also more clearly indicated that we have two biological replicates for the E. coli dataset.

1. There needs to be more information on the "pattern-based method" that was used to correct the GWAS for multiple tests. How does this method work? What genome-wide threshold did it end up producing? Was there adjustment for the number of genes tested in addition to the number of variants? Was the correction done per variant class or across all variant classes?

In line with an earlier comment from this reviewer, we have expanded the section in the Methods (e.g. L689) that explains how this correction worked to include as many details as possible, in order to provide the readers with the full context under which our analyses were carried out.

1. For a paper that, at its core, performs a cis-eQTL mapping, it is an oversight that there seems not to be a single reference to the rich literature in this space, comprising hundreds of papers, in other species ranging from humans, many other animals, to yeast and plants.

We thank both reviewer #1 and #3 for pointing out this lack of references to the extensive literature on the subject. We have added a number of references about the applications of eQTL studies, and specifically its application in microbial pangenomes, which we believe is more relevant to our study, in the introduction.

Minor comments:

1. I wasn't able to understand the top panels in Figure 4. For ulaE, most strains have the solid colors, and the corresponding bottom panel shows mostly red points. But for waaQ, most strains have solid color in the top panel, but only a few strains in the bottom panel are red. So solid color in the top does not indicate a variant allele? And why are there so many solid alleles; are these all indels? Even if so, for kgtP, the same colors (i.e., nucleotides) seem to seamlessly continue into the bottom, pale part of the top panel. How are these strains different genotypically? Are these blocks of solid color counted as one indel or several SNPs, or somehow as k-mer differences? As the authors can see, these figures are really hard to understand and should be reworked. The same comment applies to Figure 5, where it seems that all (!) strains have the "variant"?

We thank the reviewer for pointing out some limitations with our visualizations, most importantly with the way we explained how to read those two figures. We have amended the captions to more explicitly explain what is shown. The solid colors in the “sequence pseudo-alignment” panels indicate the focal cis variant, which is indicated in red in the corresponding “predicted transcription rate” panels below. In the case of Figure 5, the solid color indicates instead the position of the TFBS in the reference.

1. Figure 1A & B: It would be helpful to add the total number of analyzed genes somewhere so that the numbers denoted in the colored outer rings can be interpreted in comparison to the total.

We have added the total number of genes being considered for either species in the legend.

1. Figure 1C & D: It would be better to spell out the COG names in the figure, as it is cumbersome for the reader to have to look up what the letters stand for in a supplementary table in a separate file.

While we do not disagree with the awkwardness of having to move to a supplementary table to identify the full name of a COG category, we also would like to point out that the very long names of each category would clutter the figure to a degree that would make it difficult to read. We had indeed attempted something similar to what the reviewer suggests in early drafts of this manuscript, leading to small and hard to read labels. We have therefore left the full names of each COG category in Supplementary Table 3.

1. Line 107: "Similarly," does not fit here as the following example (with one differentially expressed gene in an operon) is conceptually different from the one before, where all genes in the operon were differentially expressed.

We agree and have amended the sentence accordingly.

1. Figure 5 bottom panel: it is odd that on the left the swarm plots (i.e., the dots) are on the inside of the boxplots while on the right they are on the outside.

We have fixed the position of the dots so that they are centered with respect to the underlying boxplots.

1. It is not clear to me how only one or a few genes in an operon can show differential mRNA abundance. Aren't all genes in an operon encoded by the same mRNA? If so, shouldn't this mRNA be up- or downregulated in the same manner for all genes it encodes? As I am not closely familiar with bacterial systems, it is well possible that I am missing some critical fact about bacterial gene expression here. If this is not an analysis artifact, the authors could briefly explain how this observation is possible.

We thanks the reviewer for their comment, which again echoes one of the main concerns from reviewer #2. As noted in our reply above, it has been established in multiple studies (see the three we have indicated above in our reply to reviewer #2) how bacteria encode for multiple “non-canonical” transcriptional units (i.e. operons), due to the presence of accessory terminators and promoters. This, together with other biological effects such as the presence of mRNA molecules of different lengths due to active transcription and degradation and technical noise induced by RNA isolation and sequencing can result in variability in the estimation of abundance for each gene.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, Damaris et al. collected genome sequences and transcriptomes from isolates from two bacterial species. Data for E. coli were produced for this paper, while data for P. aeruginosa had been measured earlier. The authors integrated these data to detect genes with differential expression (DE) among isolates as well as cis-expression quantitative trait loci (cis-eQTLs). The authors used sample sizes that were adequate for an initial exploration of gene regulatory variation (n=117 for E. coli and n=413 for P. aeruginosa) and were able to discover cis eQTLs at about 39% of genes. In a creative addition, the authors compared their results to transcription rates predicted from a biophysical promoter model as well as to annotated transcription factor binding sites. They also attempted to validate some of their associations experimentally using GFP-reporter assays. Finally, the paper presents a mapping of antibiotic resistance traits. Many of the detected associations for this important trait group were in non-coding genome regions, suggesting a role of regulatory variation in antibiotic resistance. A major strength of the paper is that it covers an impressive range of distinct analyses, some of which in two different species. Weaknesses include the fact that this breadth comes at the expense of depth and detail. Some sections are underdeveloped, not fully explained and/or thought-through enough. Important methodological details are missing, as detailed below.

Major comments:

1. An interesting aspect of the paper is that genetic variation is represented in different ways (SNPs & indels, IRG presence/absence, and k-mers). However, it is not entirely clear how these three different encodings relate to each other. Specifically, more information should be given on these two points:

2. it is not clear how "presence/absence of intergenic regions" are different from larger indels.

3. I recommend providing more narration on how the k-mers compare to the more traditional genetic variants (SNPs and indels). It seems like the k-mers include the SNPs and indels somehow? More explanation would be good here, as k-mer based mapping is not usually done in other species and is not standard practice in the field. Likewise, how is multiple testing handled for association mapping with k-mers, since presumably each gene region harbors a large number of k-mers, potentially hugely increasing the multiple testing burden?

4. What was the distribution of association effect sizes for the three types of variants? Did IRGs have larger effects than SNPs as may be expected if they are indeed larger events that involve more DNA differences? What were their relative allele frequencies?
5. The GFP-based experiments attempting to validate the promoter effects for 18 genes are laudable, and the fact that 16 of them showed differences is nice. However, the fact that half of the validation attempts yielded effects in the opposite direction of what was expected is quite alarming. I am not sure this really "further validates" the GWAS in the way the authors state in line 292 - in fact, quite the opposite in that the validations appear random with regards to what was predicted from the computational analyses. How do the authors interpret this result? Given the higher concordance between GWAS, promoter prediction, and DE, are the GFP assays just not relevant for what is going on in the genome? If not, what are these assays missing? Overall, more interpretation of this result would be helpful.
6. On the same note, it would be really interesting to expand the GFP experiments to promoters that did not show association in the GWAS. Based on Figure 6, effects of promoter differences on GFP reporters seem to be very common (all but three were significant). Is this a higher rate than for the average promoter with sequence variation but without detected association? A handful of extra reporter experiments might address this. My larger question here is: what is the null expectation for how much functional promoter variation there is?
7. Were the fold-changes in the GFP experiments statistically significant? Based on Figure 6 it certainly looks like they are, but this should be spelled out, along with the test used.
8. What was the overall correlation between GWAS-based fold changes and those from the GFP-based validation? What does Figure 6A look like as a scatter plot comparing these two sets of values?
9. Was the SNP analyzed in the last Results section significant in the gene expression GWAS? Did the DE results reported in this final section correspond to that GWAS in some way?
10. Line 470: "Consistent with the differences in the genetic structure of the two species" It is not clear what differences in genetic structure this refers to. Population structure? Genome architecture? Differences in the biology of regulatory regions?
11. Line 480: the reference to "adaption" is not warranted, as the paper contains no analyses of evolutionary patterns or processes. Genetic variation is not the same as adaptation.
12. There is insufficient information on how the E. coli RNA-seq data was generated. How was RNA extracted? Which QC was done on the RNA; what was its quality? Which library kits were used? Which sequencing technology? How many reads? What QC was done on the RNA-seq data? For this section, the Methods are seriously deficient in their current form and need to be greatly expanded.
13. How were the DEG p-values adjusted for multiple testing?
14. Were there replicates for the E. coli strains? The methods do not say, but there is a hint there might have been replicates given their absence was noted for the other species.
15. There needs to be more information on the "pattern-based method" that was used to correct the GWAS for multiple tests. How does this method work? What genome-wide threshold did it end up producing? Was there adjustment for the number of genes tested in addition to the number of variants? Was the correction done per variant class or across all variant classes?
16. For a paper that, at its core, performs a cis-eQTL mapping, it is an oversight that there seems not to be a single reference to the rich literature in this space, comprising hundreds of papers, in other species ranging from humans, many other animals, to yeast and plants.

Minor comments:

1. I wasn't able to understand the top panels in Figure 4. For ulaE, most strains have the solid colors, and the corresponding bottom panel shows mostly red points. But for waaQ, most strains have solid color in the top panel, but only a few strains in the bottom panel are red. So solid color in the top does not indicate a variant allele? And why are there so many solid alleles; are these all indels? Even if so, for kgtP, the same colors (i.e., nucleotides) seem to seamlessly continue into the bottom, pale part of the top panel. How are these strains different genotypically? Are these blocks of solid color counted as one indel or several SNPs, or somehow as k-mer differences? As the authors can see, these figures are really hard to understand and should be reworked. The same comment applies to Figure 5, where it seems that all (!) strains have the "variant"?
2. Figure 1A & B: It would be helpful to add the total number of analyzed genes somewhere so that the numbers denoted in the colored outer rings can be interpreted in comparison to the total.
3. Figure 1C & D: It would be better to spell out the COG names in the figure, as it is cumbersome for the reader to have to look up what the letters stand for in a supplementary table in a separate file.
4. Line 107: "Similarly," does not fit here as the following example (with one differentially expressed gene in an operon) is conceptually different from the one before, where all genes in the operon were differentially expressed.
5. Figure 5 bottom panel: it is odd that on the left the swarm plots (i.e., the dots) are on the inside of the boxplots while on the right they are on the outside.
6. It is not clear to me how only one or a few genes in an operon can show differential mRNA abundance. Aren't all genes in an operon encoded by the same mRNA? If so, shouldn't this mRNA be up- or downregulated in the same manner for all genes it encodes? As I am not closely familiar with bacterial systems, it is well possible that I am missing some critical fact about bacterial gene expression here. If this is not an analysis artifact, the authors could briefly explain how this observation is possible.

Significance

To my knowledge, this work represents the first cis-eQTL mapping in bacteria. As such, it is a useful and interesting exploration of this space that complements the large body of literature on this question in eukaryotic systems. This expansion to bacterial systems is especially interesting given the unique features of bacterial compared to eukaryotic genomes, including a small (10-15%) noncoding fraction of the genome and gene organization in operons. The work will be of interest to readers in the fields of complex trait genetics, gene expression, and regulatory variation. For context of this assessment, I am an expert in genomics and the study of genetic variation in gene expression in eukaryotic systems. I have limited knowledge about bacterial genetics and biology, as well as of antibiotic resistance.

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Referee #2

Evidence, reproducibility and clarity

In their manuscript "Cis non-coding genetic variation drives gene expression changes in the E. coli and P. aeruginosa pangenomes", Damaris and co-authors present an extensive meta-analysis, plus some useful follow up experiments, attempting to apply GWAS principles to identify the extent to which differences in gene expression between different strains within a given species can be directly assigned to cis-regulatory mutations. The overall principle, and the question raised by the study, is one of substantial interest, and the manuscript here represents a careful and fascinating effort at unravelling these important questions. I want to preface my review below (which may otherwise sound more harsh than I intend) with the acknowledgment that this is an EXTREMELY difficult and challenging problem that the authors are approaching, and they have clearly put in a substantial amount of high quality work in their efforts to address it. I applaud the work done here, I think it presents some very interesting findings, and I acknowledge fully that there is no one perfect approach to addressing these challenges, and while I will object to some of the decisions made by the authors below, I readily admit that others might challenge my own suggestions and approaches here. With that said, however, there is one fundamental decision that the authors made which I simply cannot agree with, and which in my view undermines much of the analysis and utility of the study: that decision is to treat both gene expression and the identification of cis-regulatory regions at the level of individual genes, rather than transcriptional units. Below I will expand on why I find this problematic, how it might be addressed, and what other areas for improvement I see in the manuscript:

In the entire discussion from lines roughly 100-130, the authors frequently dissect out apparently differentially expressed genes from non differentially expressed genes within the same operons... I honestly wonder whether this is a useful distinction. I understand that by the criteria set forth by the authors it is technically correct, and yet, I wonder if this is more due to thresholding artifacts (i.e., some genes passing the authors' reasonable-yet-arbitrary thresholds whereas others in the same operon do not), and in the process causing a distraction from an operon that is in fact largely moving in the same direction. The authors might wish to either aggregate data in some way across known transcriptional units for the purposes of their analysis, and/or consider a more lenient 'rescue' set of significance thresholds for genes that are in the same operons as differentially expressed genes. I would favor the former approach, performing virtually all of their analysis at the level of transcriptional units rather than individual genes, as much of their analysis in any case relies upon proper assignment of genes to promoters, and this way they could focus on the most important signals rather than get lots sometimes in the weeds of looking at every single gene when really what they seem to be looking at in this paper is a property OF THE PROMOTERS, not the genes. (of course there are phenomena, such as rho dependent termination specifically titrating expression of late genes in operons, but I think on the balance the operon-level analysis might provide more insights and a cleaner analysis and discussion).

This also leads to a more general point, however, which I think is potentially more deeply problematic. At the end of the day, all of the analysis being done here centers on the cis regulatory logic upstream of each individual open reading frame, even though in many cases (i.e., genes after the first one in multi-gene operons), this is not where the relevant promoter is. This problem, in turn, raises potentially misattributions of causality running in both directions, where the causal impact on a bona fide promoter mutation on many genes in an operon may only be associated with the first gene, or on the other side, where a mutation that co-occurs with, but is causally independent from, an actual promoter mutation may be flagged as the one driving an expression change. This becomes an especially serious issue in cases like ulaE, for genes that are not the first gene in an operon (at least according to standard annotations, the UlaE transcript should be part of a polycistronic mRNA beginning from the ulaA promoter, and the role played by cis-regulatory logic immediately upstream of ulaE is uncertain and certainly merits deeper consideration. I suspect that many other similar cases likewise lurk in the dataset used here (perhaps even moreso for the Pseudomonas data, where the operon definitions are likely less robust). Of course there are many possible explanations, such as a separate ulaE promoter only in some strains, but this should perhaps be carefully stated and explored, and seems likely to be the exception rather than the rule. Another issue with the current definition of regulatory regions, which should perhaps also be accounted for, is that it is likely that for many operons, the 'regulatory regions' of one gene might overlap the ORF of the previous gene, and in some cases actual coding mutations in an upstream gene may contaminate the set of potential regulatory mutations identified in this dataset. Taken together, I feel that all of the above concerns need to be addressed in some way. At the absolute barest minimum, the authors need to acknowledge the weaknesses that I have pointed out in the definition of cis-regulatory logic at a gene level. I think it would be far BETTER if they performed a re-analysis at the level of transcriptional units, which I think might substantially strengthen the work as a whole, but I recognize that this would also constitute a substantial amount of additional effort. Having reached the end of the paper, and considering the evidence and arguments of the authors in their totality, I find myself wondering how much local x background interactions - that is, the effects of cis regulatory mutations (like those being considered here, with or without the modified definitions that I proposed above) IN THE CONTEXT OF A PARTICULAR STRAIN BACKGROUND, might matter more than the effects of the cis regulatory mutations per se. This is a particularly tricky problem to address because it would require a moderate number of targeted experiments with a moderate number of promoters in a moderate number of strains (which of course makes it maximally annoying since one can't simply scale up hugely on either axis individually and really expect to tease things out). I think that trying to address this question experimentally is FAR beyond the scope of the current paper, but I think perhaps the authors could at least begin to address it by acknowledging it as a challenge in their discussion section, and possibly even identify candidate promoters that might show the largest divergence of activities across strains when there IS no detectable cis regulatory mutation (which might be indicative of local x background interactions), or those with the largest divergences of effect for a given mutation across strains. A differential expression model incorporating shrinkage is essential in such analysis to avoid putting too much weight on low expression genes with a lot of Poisson noise.

I also have some more minor concerns and suggestions, which I outline below: It seems that the differential expression analysis treats the lab reference strains as the 'centerpoint' against which everything else is compared, and yet I wonder if this is the best approach... it might be interesting to see how the results differ if the authors instead take a more 'average' strain (either chosen based on genetics or transcriptomics) as a reference and compared everything else to that.

Line 104 - the statement about the differentially expressed genes being "part of operons with diverse biological functions" seems unclear - it is not apparent whether the authors are referring to diversity of function within each operon, or between the different operons, and in any case one should consider whether the observation reflects any useful information or is just an apparently random collection of operons. Line 292 - I find the argument here somewhat unconvincing, for two reasons. First, the fact that only half of the observed changes went in the same direction as the GWAS results would indicate, which is trivially a result that would be expected by random chance, does not lend much confidence to the overall premise of the study that there are meaningful cis regulatory changes being detected (in fact, it seems to argue that the background in which a variant occurs may matter a great deal, at least as much as the cis regulatory logic itself). Second, in order to even assess whether the GWAS is useful to "find the genetic determinants of gene expression changes" as the authors indicate, it would be necessary to compare to a reasonable, non-straw-man, null approach simply identifying common sequence variants that are predicted to cause major changes in sigma 70 binding at known promoters; such a test would be especially important given the lack of directional accuracy observed here. Along these same lines, it is perhaps worth noting, in the discussion beginning on line 329, that the comparison is perhaps biased in favor of the GWAS study, since the validation targets here were prioritized based on (presumably strong) GWAS data.

I don't find the Venn diagrams in Fig 7C-D useful or clear given the large number of zero-overlap regions, and would strongly advocate that the authors find another way to show these data.

In the analysis of waa operon gene expression beginning on line 400, it is perhaps important to note that most of the waa operon doesn't do anything in laboratory K12 strains due to the lack of complete O-antigen... the same is not true, however, for many wild/clinical isolates. It would be interesting to see how those results compare, and also how the absolute TPMs (rather than just LFCs) of genes in this operon vary across the strains being investigated during TOB treatment.

I don't think that the second conclusion on lines 479-480 is fully justified by the data, given both the disparity in available annotation information between the two species, AND the fact that only two species were considered.

Line 118: "Double of DEGs"

Line 288 - presumably these are LOG fold changes

Fig 6b - legend contains typos

Line 661 - please report the read count (more relevant for RNA-seq analysis) rather than Gb

Source code - I appreciate that the authors provide their source code on github, but it is very poorly documented - both a license and some top-level documentation about which code goes with each major operation/conclusion/figure should be provided. Also, ipython notebooks are in general a poor way in my view to distribute code, due to their encouragement of nonlinear development practices; while they are fine for software development, actual complete python programs along with accompanying source data would be preferrable.

Significance

Overall the key strength of the study is the heroic merging of large genetic and transcriptomic datasets to address the question of how much variation in gene expression can be assigned to cis regulatory mutations in E. coli and in P. aeruginosa. The authors find that only a minority of genes can have such an assignment explaining expression variation, which highlights both the many factors (local and global) impacting gene expression, and the difficulty in trying to predict and understand expression patterns in different strains. I believe that with suitable modification, the manuscript will be of great interest to a broad audience interested in bacterial genomics, gene regulation, and systems/synthetic biology.

Reviewer Expertise: I consider myself a bacterial systems biologist and routinely use high throughput experiments to understand bacterial gene regulation.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Damaris et al. perform what is effectively an eQTL analysis on microbial pangenomes of E. coli and P. aeruginosa. Specifically, they leverage a large dataset of paired DNA/RNA-seq information for hundreds of strains of these microbes to establish correlations between genetic variants and changes in gene expression. Ultimately, their claim is that this approach identifies non-coding variants that affect expression of genes in a predictable manner and explain differences in phenotypes. They attempt to reinforce these claims through use of a widely regarded promoter calculator to quantify promoter effects, as well as some validation studies in living cells. Lastly, they show that these non-coding variations can explain some cases of antibiotic resistance in these microbes.

Major comments

Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

The authors convincingly demonstrate that they can identify non-coding variation in pangenomes of bacteria and associate these with phenotypes of interest. What is unclear is the extent by which they account for covariation of genetic variation? Are the SNPs they implicate truly responsible for the changes in expression they observe? Or are they merely genetically linked to the true causal variants. This has been solved by other GWAS studies but isn't discussed as far as I can tell here.

They need to justify why they consider the 30bp downstream of the start codon as non-coding. While this region certainly has regulatory impact, it is also definitely coding. To what extent could this confound results and how many significant associations to expression are in this region vs upstream?

The claim that promoter variation correlates with changes in measured gene expression is not convincingly demonstrated (although, yes, very intuitive). Figure 3 is a convoluted way of demonstrating that predicted transcription rates correlate with measured gene expression. For each variant, can you do the basic analysis of just comparing differences in promoter calculator predictions and actual gene expression? I.e. correlation between (promoter activity variant X)-(promoter activity variant Y) vs (measured gene expression variant X)-(measured gene expression variant Y). You'll probably have to

Figure 7 it is unclear what this experiment was. How were they tested? Did you generate the data themselves? Did you do RNA-seq (which is what is described in the methods) or just test and compare known genomic data?

Are the data and the methods presented in such a way that they can be reproduced?

No, this is the biggest flaw of the work. The RNA-Seq experiment to start this project is not described at all as well as other key experiments. Descriptions of methods in the text are far too vague to understand the approach or rationale at many points in the text. The scripts are available on github but there is no description of what they correspond to outside of the file names and none of the data files are found to replicate the plots.

Figure 8B is intended to show that the WaaQ operon is connected to known Abx resistance genes but uses the STRING method. This requires a list of genes but how did they build this list? Why look at these known ABx genes in particular? STRING does not really show evidence, these need to be substantiated or at least need to justify why this analysis was performed.

Are the experiments adequately replicated and statistical analysis adequate?

An important claim on MIC of variants for supplementary table 8 has no raw data and no clear replicates available. Only figure 6, the in vitro testing of variant expression, mentions any replicates.

Minor comments

Specific experimental issues that are easily addressable.. Are prior studies referenced appropriately?

There should be a discussion of eQTLs in this. Although these have mostly been in eukaryotes a. https://doi.org/10.1038/s41588-024-01769-9 ; https://doi.org/10.1038/nrg3891

Line 67. Missing important citation for Ireland et al. 2020 https://doi.org/10.7554/eLife.55308 Line 69. Should mention Johns et al. 2018 (https://doi.org/10.1038/nmeth.4633) where they study promoter sequences outside of E. coli Line 90 - replace 'hypothesis-free' with unbiased Line 102 - state % of DEGs relative to the entire pan-genome Figure 1A is not discussed in the text Line 111: it is unclear what enrichment was being compared between, FIgures 1C/D have 'Gene counts' but is of the total DEGs? How is the p-value derived? Comparing and what statistical test was performed? Comparing DEG enrichment vs the pangenome? K12 genome? Line 122-123: State what letters correspond to these COG categories here Line 155: Need to clarify how you use k-mers in this and how they are different than SNPs. are you looking at k-mer content of these regions? K-mers up to hexamers or what? How are these compared. You can't just say we used k-mers. Line 172: It would be VERY helpful to have a supplementary figure describing these types of variants, perhaps a multiple-sequence alignment containing each example Figure 4: THis figure is too small. Why are WaaQ and UlaE being used as examples here when you are supposed to be explicitly showing variants with strong positive correlations? Figure 4: Why is there variation between variants present and variant absent? Is this due to other changes in the variant? Should mention this in the text somewhere Line 359: Need to talk about each supplementary figure 4 to 9 and how they demonstrate your point.

Are the text and figures clear and accurate? Figure 4 too small Acronyms are defined multiple times in the manuscript, sometimes not the first time they are used (e.g. SNP, InDel) Figure 8A - Remove red box, increase label size Figure 8B - Low resolution, grey text is unreadable and should be darker and higher resolution Line 35 - be more specific about types of carbon metabolism and catabolite repression Line 67 - include citation for ireland et al. 2020 https://doi.org/10.7554/eLife.55308 Line 74 - You talk about looking in cis but don't specify how mar away cis is Line 75 - we encoded genetic variants..... It is unclear what you mean here Line 104 - 'were apart of operons' should clarify you mean polycistronic or multi-gene operons. Single genes may be considered operonic units as well. Figure 2: THere is no axis for the percents and the percents don't make sense relative to the bars they represent?? Figure 2: Figure 2B legend should clarify that these are individual examples of Differential expression between variants Line 198-199: This sentence doesn't make sense, 'encoded using kmers' is not descriptive enough Line 205: Should be upfront about that you're using the Promoter Calculator that models biophysical properties of promoter sequences to predict activity. Line 251: 'Scanned the non-coding sequences of the DEGs'. This is far too vague of a description of an approach. Need to clarify how you did this and I didn't see in the method. Is this an HMM? Perfect sequence match to consensus sequence? Some type of alignment? Line 257-259: This sentence lacks clarity Line346: How were the E. coli isolates tested? Was this an experiment you did? This is a massive undertaking (1600 isolates * 12 conditions) if so so should be clearly defined Figure 6A: The tile plot on the right side is not clearly labeled and it is unclear what it is showing and how that relates to the bar plots. FIgure 6B: typo in legend 'Downreglation' Line 398: Need to state rationale for why Waaq operon is being investigated here. WHy did you look into individual example? Figure 8: Can get rid of red box Line 463 - 'account for all kinds' is too informal Mix of font styles throughout document

Significance

Provide contextual information to readers (editors and researchers) about the novelty of the study, its value for the field and the communities that might be interested. The following aspects are important:General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

This study applies eQTL concepts to bacterial pangenomes to understand how genetic variation in non-coding regions contributes to microbial phenotypes, which is clever and has not been done in bacterial communities (although has been done in yeast isolates, see citation above). They characterize these same variants using in silico promoter predictions, in vitro experiments, layer biological mechanism via transcription factor binding site mapping, and study associated antibiotic resistance phenotypes. These are all good ideas, but none of these points are very developed. The antibiotic work in particular was a missed opportunity as this is the most impactful demonstration of their approach. For instance, to what extent do these eQTLs explain resistance across isolates vs coding changes? Are non-coding variants more responsible for antibiotic resistance than coding variants? Given how easy it is to adapt gene expression vs establishing other mechanisms, this is plausible. How could knowing this change how we treat infections? While a general overview of their strategy is fine, the approaches are under-described and unclear so difficult to truly assess.

Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

To my knowledge and from a cursory search, this is the first pan-genome mapping of non-coding variants to transcriptional changes in bacteria. This is a good idea that could be applied to any microbe for which large transcriptomic datasets of strains are available or could be generated and is helpful for understanding genetic variation and the architecture of bacterial regulatory systems.

Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

This would be of interest to individuals interested in population genetics, gene regulation, and microbial evolution. It could inspire similar studies of other microbes to understand the contribution of non-coding changes to phenotypes across whole genomes.

Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I am an expert on bacterial gene regulation, especially concerning how promoter activity is encoded within sequences. I have less experience on using GWAS.

Germany fines Amazon 59 million Euro and prohibits their price control mechanisms. (both the US Amazon and Irish entity) Meaning they are not allowed to force pricing by traders on their platforms (this is imo the way they ensure that Amazon is always the lowest price online) This is a German effort, but coord'd with EC wrt DMA, though the fine seems based on German laws.

eLife Assessment

This valuable study presents findings on how prokaryotic antibiotics affect translation in mitochondrial ribosomes. Using mitoribosome profiling, the authors provide solid evidence that most tested antibiotics act similarly on bacterial and mitochondrial translation. Additionally, this work shows that alternative translation initiation events might exist in two specific mt-mRNAs (MT-ND1 and MT-ND5). However, additional biochemical and structural experiments are needed to support these findings.

Reviewer #1 (Public review):

Summary:

This study aimed to determine whether bacterial translation inhibitors affect mitochondria through the same mechanisms. Using mitoribosome profiling, the authors found that most antibiotics, except telithromycin, act similarly in both systems. These insights could help in the development of antibiotics with reduced mitochondrial toxicity.

They also identified potential novel mitochondrial translation events, proposing new initiation sites for MT-ND1 and MT-ND5. These insights not only challenge existing annotations but also open new avenues for research on mitochondrial function.

Strengths:

Ribosome profiling is a state-of-the-art method for monitoring the translatome at very high resolution. Using mitoribosome profiling, the authors convincingly demonstrate that most of the analyzed antibiotics act in the same way on both bacterial and mitochondrial ribosomes, except for telithromycin. Additionally, the authors report possible alternative translation events, raising new questions about the mechanisms behind mitochondrial initiation and start codon recognition in mammals.

Weaknesses:

All the weaknesses I previously highlighted were adequately addressed.

Reviewer #3 (Public review):

Summary:

Recently, the off-target activity of antibiotics on human mitoribosome has been paid more attention in the mitochondrial field. Hafner et al applied mitoribosome profilling to study the effect of antibiotics on protein translation in mitochondria as there are similarities between bacterial ribosome and mitoribosome. The authors conclude that some antibiotics act on mitochondrial translation initiation by the same mechanism as in bacteria. On the other hand, the authors showed that chloramphenicol, linezolid and telithromycin trap mitochondrial translation in a context-dependent manner. More interesting, during deep analysis of 5' end of ORF, the authors reported the alternative start codon for ND1 and ND5 proteins instead of previously known one. This is a novel finding in the field and it also provide another application of the technique to further study on mitochondrial translation.

Strengths:

This is the first study which applied mitoribosome profiling method to analyze mutiple antibiotics treatment cells. The mitoribosome profiling method had been optimized carefully and has been suggested to be a novel method to study translation events in mitochondria. The manuscript is constructive and well-written.

Weaknesses:

This is a novel and interesting study, however, most of conclusion comes from mitoribosome profiling analysis, as the result, the manuscript lacks the cellular biochemical data to provide more evidence and support the findings.

Comments on revisions:

The authors addressed most of my concerns and comments, although there is still no biochemical assay which should be performed to support mitoribsome profiling data.

The author also carefully investigated the structure of complex I, however, I am surprised that the author chose to analyse a low resolution structure (3.7 A). Recently, there are more high resolution structures of mammalian complex I published (7R41, 7V2C, 7QSM, 9I4I). Furthermore, the authors should not only respond to the reviewers but also (somehow) discuss these points in the manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study aimed to determine whether bacterial translation inhibitors affect mitochondria through the same mechanisms. Using mitoribosome profiling, the authors found that most antibiotics, except telithromycin, act similarly in both systems. These insights could help in the development of antibiotics with reduced mitochondrial toxicity.

They also identified potential novel mitochondrial translation events, proposing new initiation sites for MT-ND1 and MT-ND5. These insights not only challenge existing annotations but also open new avenues for research on mitochondrial function.

Strengths:

Ribosome profiling is a state-of-the-art method for monitoring the translatome at very high resolution. Using mitoribosome profiling, the authors convincingly demonstrate that most of the analyzed antibiotics act in the same way on both bacterial and mitochondrial ribosomes, except for telithromycin. Additionally, the authors report possible alternative translation events, raising new questions about the mechanisms behind mitochondrial initiation and start codon recognition in mammals.

Weaknesses:

The main weaknesses of this study are:

While the authors highlight an interesting difference in the inhibitory mechanism of telithromycin on bacterial and mitochondrial ribosomes, mechanistic explanations or hypotheses are lacking.

We acknowledge that we were not able to present a clear explanation for potential mechanistic differences of telithromycin inhibition between mitochondrial and bacterial ribosomes. In future work, structural analyses in collaboration with experts will provide these insights.

The assignment of alternative start codons in MT-ND1 and MT-ND5 is very interesting but does not seem to fully align with structural data.

We appreciate the reviewer’s comment and consulted a cryo-EM expert to review our findings in the context of the available structural data. We downloaded the density map and reviewed the N-termini of MT-ND1 and MT-ND5. We only observed the density of the N-terminus of MT-ND1 at low confidence. At an RMSD of 2, we could not observe density for the side chains of Met and Pro, and there are gaps in the density for what is modeled as the main chain. The assignment of these residues may have been overlooked due to the expectation that they should be present in the peptide.

For MT-ND5, we did observe some density that could be part of the main chain; however, it did not fill out until we reduced the stringency, and we did not observe density mapping to side chain residues. To summarize, we do not confidently see density for either the side chain or the main chain for either peptide.

The newly proposed translation events in the ncRNAs are preliminary and should be further substantiated with additional evidence or interpreted with more caution.

We agree with the reviewer that we did not provide conclusive evidence that our phased ribosome footprinting data on mitochondrial non-coding RNAs are proof of novel translation events. We do acknowledge this in the main text:” Due to both the short ORFs, minimal read coverage, and lack of a detectable peptide we could not determine if translation elongation occurred on the mitochondrial tRNAs. These sites may be unproductive mitoribosome binding events or simply from tRNAs partially digesting during MNase treatment.”

Reviewer #2 (Public review):

In this study, the authors set out to explore how antibiotics known to inhibit bacterial protein synthesis also affect mitoribosomes in HEK cells. They achieved this through mitoribosome profiling, where RNase I and Mnase were used to generate mitoribosome-protected fragments, followed by sequencing to map the regions where translation arrest occurs. This profiling identified the codon-specific impact of antibiotics on mitochondrial translation.

The study finds that most antibiotics tested inhibit mitochondrial translation similarly to their bacterial counterparts, except telithromycin, which exhibited distinct stalling patterns. Specifically, chloramphenicol and linezolid selectively inhibited translation when certain amino acids were in the penultimate position of the nascent peptide, which aligns with their known bacterial mechanism. Telithromycin stalls translation at an R/K-X-R/K motif in bacteria, and the study demonstrated a preference for arresting at an R/K/A-X-K motif in mitochondria. Additionally, alternative translation initiation sites were identified in MT-ND1 and MT-ND5, with non-canonical start codons. Overall, the paper presents a comprehensive analysis of antibiotics in the context of mitochondrial translation toxicity, and the identification of alternative translation initiation sites will provide valuable insights for researchers in the mitochondrial translation field.

From my perspective as a structural biologist working on the human mitoribosome, I appreciate the use of mitoribosome profiling to explore off-target antibiotic effects and the discovery of alternative mitochondrial translation initiation sites. However, the description is somewhat limited by a focus on this single methodology. The authors could strengthen their discussion by incorporating structural approaches, which have contributed significantly to the field. For example, antibiotics such as paromomycin and linezolid have been modeled in the human mitoribosome (PMID: 25838379), while streptomycin has been resolved (10.7554/eLife.77460), and erythromycin was previously discussed (PMID: 24675956). The reason we can now describe off-target effects more meaningfully is due to the availability of fully modified human mitoribosome structures, including mitochondria-specific modifications and their roles in stabilizing the decoding center and binding ligands, mRNA, and tRNAs (10.1038/s41467-024-48163-x).

These and other relevant studies should be acknowledged throughout the paper to provide additional context.

We appreciate the work that has previously revealed how different antibiotics bind the mitochondrial ribosome. We have included these references in the manuscript to provide background and context for this work in relationship to the field.

Reviewer #3 (Public review):

Summary:

Recently, the off-target activity of antibiotics on human mitoribosome has been paid more attention in the mitochondrial field. Hafner et al applied mitoribosome profilling to study the effect of antibiotics on protein translation in mitochondria as there are similarities between bacterial ribosome and mitoribosome. The authors conclude that some antibiotics act on mitochondrial translation initiation by the same mechanism as in bacteria. On the other hand, the authors showed that chloramphenicol, linezolid and telithromycin trap mitochondrial translation in a context-dependent manner. More interesting, during deep analysis of 5' end of ORF, the authors reported the alternative start codon for ND1 and ND5 proteins instead of previously known one. This is a novel finding in the field and it also provides another application of the technique to further study on mitochondrial translation.

Strengths:

This is the first study which applied mitoribosome profiling method to analyze mutiple antibiotics treatment cells.

The mitoribosome profiling method had been optimized carefully and has been suggested to be a novel method to study translation events in mitochondria. The manuscript is constructive and written well.

Weaknesses:

This is a novel and interesting study, however, most of the conclusion comes from mitoribosome profiling analysis, as a result, the manuscript lacks the cellular biochemical data to provide more evidence and support the findings.

We thank the reviewer for the positive assessment of our work. We agree that future biochemical and structural experiments will strengthen the conclusions we derive from the ribosome profiling.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

In Fig. 1A, the quality of the Western blot for the sucrose gradient is suboptimal. I recommend enhancing the quality of the Western blot image and providing the sucrose gradient sedimentation patterns for both the mtSSU and mtLSU to confirm the accurate selection of the monosome fraction. Additionally, to correctly assign the A260 peaks to mitochondrial and cytosolic ribosomes, it would be helpful to include markers for both the cytoribosomal LSU and SSU, too. Furthermore, do the authors observe mitochondrial polysomes in their sucrose gradient? If so, were those fractions fully excluded from the analysis?

We repeated our sucrose gradient and Western blotting with antibodies for the large and small subunits of the mitoribosome. We did not repeat western blotting for the cytoribosomes as the 40S, 60S, and 80S peaks are present in their canonical heights and locations on a sucrose gradient. Western blotting indicates that the large and small subunits of the mitoribosome are located in the fraction taken for mitoribo-seq. We do see trace amounts of mitoribosome in fractions past the 55S site. Those fractions were excluded from library preparation.

The MNase footprints exhibited a bimodal distribution, which the authors suggest may indicate that "MNase-treatment may have captured two distinct conformations of the ribosome." It would be relevant to clarify whether an enzyme titration was performed, as excessive MNase could lead to ribosomal RNA degradation, potentially influencing the footprints.

We did not perform a titration and instead based our concentration on the protocol from Rooijers et al, 2013. We included a statement of this and a reference to the concentration in the methods.

Is there an explanation for why RNase I footprinting reveals a very high peak at the 5'-end of the MT-CYB transcript, whereas this is not observed with MNase footprinting?

It is not clear. The intensity of peaks at the 5’ end of the transcripts varies. We do observe that the relative intensity of the 5’ peak is greater for RNase I footprinted samples than MNase-treated samples.

I understand that throughout the manuscript, the authors use MT-CYB as an example to illustrate the effects of the antibiotics on mitochondrial translation. However, to strengthen the generality of the conclusions, it would be beneficial to provide the read distribution across the entire mitochondrial transcriptome, possibly in the supplementary material. Additionally, I suggest including the read distribution for MT-CYB in untreated cells to improve data interpretation and enhance the clarity of figures (e.g., Figs. 1B, 2B, 3B).

As these experiments were generated across multiple mitoribo-seq experiments, each was done with its own control experiment. It would be inaccurate to show a single trace as representative of all experiments. Instead, we include Supplementary Figure 1, which shows the untreated MT-CYB trace for all control samples and indicates which treatment they pair with.

It would be very valuable to label each individual data point in the read phasing shown in Fig. 1D with the corresponding transcripts. For improved data visualization, I suggest assigning distinct colors to each transcript.

We are concerned that including the name of each gene in the main figure would be too difficult for the reader to accurately interpret. Instead, we have added a Supplementary Table with those values.

How do the authors explain the significant peak (approx. 10,000 reads) at the 5' end of the transcript in the presence of tiamulin (Fig. 2B)? Does this peak correspond to the start codon, and how does it relate to the quantification reported in Fig. 2C?

Yes, this represents the start codon. These reads are likely derived from the start codon as they are mapping to the 5’ end of the transcript. There are differences in sequencing depth depending on the experiment, so what is critical is the relative distribution of reads on the transcript rather than comparing absolute reads between experiments. MT-CYB has 54% of the reads at the start site, which is representative of what we see across all genes.

Throughout the manuscript, I found the usage of the terms "5' end" and "start codon" somewhat confusing, as they appear to be used synonymously in some instances. For example, in Fig. 2C, the y-axis label states "ribosomes at start codon," while the figure caption mentions "...percentage of reads that map to the 5' end of mitochondrial transcripts." Given the size of the graphs, it is also challenging for the reader to determine whether the peaks correspond specifically to the start codon or if multiple peaks accumulate at the initial codons.

We were selected for this language because two different types of analysis are being carried out. Ribosome profiling carried out in Figures 2 and 3 is carried out with RNase I, which poorly maps the ribosomes at the start codon when we do the read length analysis in Figure 4. Ribosome footprint at the 5’ end may include ribosomes that are on the 2-4 codons following the start codon, so it would not be accurate to label those as “ribosomes at a start codon.” We have renamed the axis to “Ribosomes at 5’ end”. Wig files are available online for all mitoribosome profiling experiments. In this case, the assigned “P-site” is several codons after the start codon due to the offset applied and the minimal 5’ UTR. Thus, it is less important to see which codon density is assigned to, but rather the general distribution of the reads.

The authors state, "Cells treated with telithromycin did show a slight increase in MRPF abundance at the 5' end of MT-CYB" and "the cumulative distribution of MRPFs suggested that ribosome density was biased towards the 5' end of the gene for chloramphenicol and telithromycin, but not significantly for linezolid." Could this observation be linked to the presence of specific stalling motifs in that region? If so, it would be beneficial to display such motifs on the graphs of the read distribution across the transcriptome to substantiate the context-dependent inhibition.

Thank you for this suggestion. For chloramphenicol and linezolid, alanine, serine, and threonine make up nearly 25% of the mitochondrial proteome. As such, there are numerous stall sites across the transcript. Given their identical stalling motifs, the difference between chloramphenicol and linezolid is due to sequence-specific differences. Potentially, this could be due to conditions such as the final concentration of antibiotic inside the mitochondria and the on/off rate of an inhibitor with the translating mitoribosome. Both may affect the kinetics of stalling and allow mitoribosomes to evade early stall sites.

We have also included the sites of all A/K/R-X-K motifs located in the genome and the calculated fold change for each position. As a note, this includes sites that do not pass the minimum filter set by our analysis and we note this in the text.

The comment raises an additional question: Does the increased density at the 5’ end derive from stalled mitoribosomes or queued mitoribosomes behind a stalling event? Recent work by Iwasaki’s group shows that mitoribosomes can form disomes and queue behind each other. However, we could not observe 30 aa periodicities behind stalling events that would be indicative of collided mitoribosomes.

In Fig. 3E, the authors report an additional and very interesting observation that is not discussed. Linezolid treatment causes reduced ribosome occupancy when proline or glycine codons occupy the P-site, or when the amino acids have been incorporated into the polypeptide chain and occupy the -1 position. It is known that the translation of proline and glycine frequently leads to ribosome stalling due to the physicochemical properties of these amino acids. Has this effect of linezolid been reported in the bacterial translation system? Additionally, can the authors propose hypotheses for the mechanism behind this observation? A similar observation is noted for telithromycin when glycine occupies the same positions, as well as when aspartate occupies the P- and A-sites.

In bacteria, Linezolid does have an “anti-stalling” motif when glycine is present in the A-site. However, this is due to the size of the residue being compatible with antibiotic binding.

The most likely cause of this effect is a redistribution of ribosome footprints. As the antibiotics introduce new arrest sites, ribosome density at other sites relatively decreases. This is likely an artifact from mitoribosomes redistributing from endogenously slow codons to new arrest sites. When looking at carrying out our disome profiling in the presence of anisomycin, we see a similar effect. Cytoribosomes are redistributed from endogenous stalling sites, such as proline, and are redistributed throughout the gene. As a result, translation at proline appears “more efficient” upon treatment with an inhibitor but is instead an artifact of analysis.

Figure 3F could benefit from indicating which mtDNA-encoded protein corresponds to each of the strongest stalling motifs.

We have included a supplementary figure to highlight which mitochondrially-encoded genes containing the R/K/A-X-K motif and noted in the text that mitochondrial translation may be unevenly inhibited.

The legend "increasing mRPF abundance" in Fig. 4C may be missing the corresponding colors.

The legend applies to all sections of the figure. We double-checked the range of the colors in the tables, and they do match the legend.

The observation that the start codons in MT-ND1 and MT-ND5 might differ from the annotated canonical ones is intriguing. While the ribosome profiling data appear clear, mass spectrometry (MS) analysis may be misleading. The absence of evidence does not necessarily imply evidence of absence. How does this proposed conclusion correlate with the structural data obtained from HEK cells? For instance, the cryo-EM structural model of a complex I-containing human supercomplex (PDB: 5XTD, PMID: 28844695) shows the presence of Pro2 in MT-ND1 and the full-length MT-ND5 protein. The authors should carefully examine structural data to ascertain whether alternative forms of MT-ND1 and MT-ND5 are actually observed in the assembled complex I.

We really appreciate this comment. We sat down with an expert in cryo-EM and reviewed the figure. We downloaded the density map and reviewed the N-termini of MT-ND1 and MT-ND5. We only observed the density of the N-terminus of MT-ND1 at low confidence. At an RMSD of 2, we could not observe density for the side chains of Met and Pro, and there are gaps in the density for what is modeled as the main chain. The assignment of these residues may have been overlooked due to the expectation that they should be present in the peptide.

For MT-ND5, we did observe some density that could be part of the main chain; however, it did not fill out until we reduced the stringency, and we did not observe density mapping to side chain residues. To summarize, we do not confidently see density for either the side chain or the main chain for either peptide.

Given that ribosome profiling is based on the assumption that ribosomes protect mRNA fragments from RNase digestion, interpreting the data related to Fig. 5 and the proposed existence of translation events involving ncRNAs is challenging. Most importantly, tRNAs and rRNAs are highly folded RNA molecules and, by definition, are protected by ribosomal proteins. Simultaneously, as the authors point out, "These reads could either be products of random digestion of the abundant background of ncRNAs or be genuine MRPFs." RNase I preferentially digests single-stranded RNA (ssRNA), but excess enzyme can still lead to degradation. Consequently, many random tRNA/rRNA fragments may be generated by RNase digestion, potentially resulting in artifacts. I suggest that the authors examine what happens to these reads when mitochondrial translation is inhibited.

We have low-quality mitoribo-seq with initiation inhibitors and Mnase showing footprints of the same size. We do not have a small-molecule inhibitor that is able to completely ablate translation, as they instead stabilize mitoribosomes at different steps in translation. We have considered alternative ways of capturing a background rRNA and tRNA digestion pattern; however, these have their own drawbacks. Dissociation with EDTA prior to digestion or carrying out library prep on the small and large subunits may capture mitoribosomes no longer in the process of translation; however, dissociated subunits would have different surfaces now available for digestion and may not capture tRNAs.

Regarding the statement, "While the ORF on MT-TS1 is longer, MRPF density was low and we did not observe read phasing and thus it is likely not translated (not shown)," the data should not be excluded unless a clear explanation is provided for why translation would not occur from this specific RNA.

We have included this value in the graph as well as in Supplementary Figure 1.

The graph in Fig. 5B shows the periodicity of only the putative RNR1 ORF, but not that of the other proposed ORFs. What is the reason for this?

We have included the MT-TS1 putative ORF in Figure 5 and Figure S1. Other ORFs did not have density in the ORF. If these are real mitoribosome footprints at these start codons, it may be due to them being transient binding events that never result in elongation. Alternatively, they may be due to tRNA degradation during library preparation.

The assumption that the UUG codon can serve as a start site for mitochondrial translation has not been substantiated. Recent data have identified translation initiation events from non-ATG/ATA codons (near-cognate and sub-cognate) using retapamulin, but UUG was not among them. Can the authors detect such events in their ribosome profiling data collected in the presence of retapamulin, tiamulin, or josamycin?

The report of translation initiation at non-ATG/ATA codons strongly disagrees with our findings. We report that sites of translation initiation observed within annotated coding regions in mitochondria occur at the annotated start sites, while the other report finds frequent alternative initiation events. We have looked for those arrest sites and did not observe them.

In the section "Mitoribosome profiling reveals novel translation events," the title may be misleading given the preliminary nature of the results. To support such a claim, the authors should provide experimental evidence demonstrating that the proposed translation events genuinely exist and result in the synthesis of previously unidentified polypeptides. Alternatively, the interpretation should be approached with greater caution and more clearly indicated as preliminary.

We agree with the reviewers that a distinction should be made between reporting truly novel translation events, like the recently reported MT-ND5-dORF, and sites we suspect mitoribosomes may be binding and that require detailed follow-up. We altered the section title to suggest that this may be showing novel translation events. Additionally, we included a statement in the discussion that these MRPFs may be simply tRNA digestion by RNase I.

Although located at the 5' end of RNR1, the newly identified ORF is situated 79 nt downstream. According to current knowledge, this appears to be a lengthened 5' UTR that may hinder mitoribosome loading. The authors should speculate on potential initiation mechanisms.

The start of the putative ORF is not located 79 nts down, but at the 8^th nucleotide. The reviewer may be including the tRNA-Phe in their calculation, which is cleaved from MT-RNR1. This start site is closer to the 5’ end than our findings with MT-ND5.

To enhance the interpretation of the mitoribosome profiling data, the authors could complement their findings with classical metabolic labeling using (35)S-methionine. This approach would allow for a different assessment of the stringency of the inhibition under the tested experimental conditions.

We are currently working on these experiments using mito-funcats. A future direction we are taking this work is to understand how the cell responds to different mechanisms of translation inhibition. For example, we are trying to understand if telithromycin, which appears highly selective, only partially inhibits translation of the mtDNA-encoded proteome.

Reviewer #2 (Recommendations for the authors):

Other small editorial comments:

Line 24: "translate proteins"?

Revised for clarity

Line 24: The sentence describing mitochondrial translation as "closely resembling the one in prokaryotes" could be reformulated. While the core of the mitoribosome is conserved, the entire apparatus has many mitochondria-specific features.

Since this is the abstract, we simplified the point by saying that mitoribosomes are more similar to prokaryotic than cytosolic ribosomes.

Clarified to highlight that the mitochondrial system is more similar to the bacterial system than the eukaryotic system.

Line 33: "novel" or "previously unrecognized" ?

Rewritten for clarity.

Lines 33-35: The claim made here is not shown in the paper.

We removed the more aspirational goal of this paper and focused on the main findings of the paper.

Lines 44, 47, 89 (and elsewhere): "cytoplasmic" or "cytosolic" ?

Both terms are used in the literature. We opted for cytoplasmic as it can also include ribosomes not free in the cytosol, such as those bound to the ER.

Reviewer #3 (Recommendations for the authors):

(1) The authors should state why they chose these antibiotics for mitoribosome profiling analysis over other antibiotics from same group. Did they screen multiple antibiotics to determine the candidates for next step?

We selected antibiotics that had a known stalling motif in bacteria (initiation or context-dependent elongation inhibitors). In addition, we carried out mitoribosome profiling with erythromycin, azithromycin, thiostrepton, and kanamycin in this work. However, we did not see any effect from these drugs in mitoribosome profiling. We are currently testing other inhibitors, such as doxycycline and tigecycline, and looking at optimizing treatment conditions to identify stalling motifs in samples that previously showed no difference.

(2) What is the reason for choosing the concentration of antibiotics retapamulin, tiamulin and josamycin, this is IC50 value or above this value? On the other hand, none of this information has been provided for the antibiotics in the next part. The authors should provide biochemical study for the effect of these antibiotics on cell survival and/or protein translation such as S35 assay or steady state level of mtDNA-encoded proteins upon cell treatment with these antibiotics.

Prior to mitoribo-seq, we carried out time and concentration assays with all antibiotics. 100 µg/ml and a 30-minute treatment was tolerable for all antibiotics except retapamulin. We aimed to treat cells with a relatively high concentration of inhibitor in order to capture actively translating mitoribosomes. We were concerned that longer treatments may lead to decreased translation initiation, leading to the capture of fewer mitoribosomes. These concentrations were nearly identical to contemporary conditions carried out in Bibel et al, RNA 2025.

(3) Why did the authors choose MT-CYB as the representative for further analysis in the second and third parts of the manuscript?

We chose MT-CYB because its length allowed for easy visualization. Some mitochondrial genes, such as MT-ND6, had a propensity for stronger stalling at initiation. While coverage was throughout the genes, it was difficult to visualize the changes within the ORF. Also, MT-CYB was less visually complex than polycistronic transcripts. All wigs were uploaded to GEO.

(4) Page 11, line 233-234: the authors state that telithromycin induces stalling at R/K/A-X-K motif. The authors should do further analysis on mitochondrial genome which proteins contain this motif. Furthermore, same as comment 2: the authors should confirm by 35S assay or WB to know which mtDNA-encoded proteins are affected.

We have included a supplementary figure showing which mitochondrial genes contain these motifs.

(5) The results and conclusion from the fourth paragraph are very interesting. The authors suggest alternative start codon for two mtDNA encoded proteins: ND1 and ND5 based on ribosome profiling analysis. Again, I have several comments on this part: <br /> (a) For the accumulation of the alternative start codon of ND1 and ND5 as suggested in the manuscript, do the authors observe this trend with the initiation inhibitors used in the second paragraphs of the manuscript?

We did not observe similar read lengths with retapamulin, tiamulin, or josamycin, which produced read lengths that were consistent with other RNase I footprinted samples.

(b) This observation was further confirmed by MS with a peptide form ND1 protein, the authors should show MS peak indicating MW of the peptide and MS/MS data for the peptide which supports this hypothesis.

We are including the MS/MS report for this peptide.

(c) Interestingly, several high-resolution structures of mammalian complex I have been reported so far (PMID: 7614227, 10396290, 38870289), ND1 and ND5 protein express full sequences with fMet at the distal N-terminal. This is different to the suggestion from the manuscript. Could the author discuss or comment on that?

This point was brought up by another reviewer. We have carefully analyzed the density map of PMID: 28844695. We sat down with an expert in cryo-EM and reviewed the figure. We downloaded the density map and reviewed the N-termini of MT-ND1 and MT-ND5. We only observed the density of the N-terminus of MT-ND1 at low confidence. At an RMSD of 2, we could not observe density for the sidechains of Met and Pro, and there is a gap in density for what is modeled as the main chain. The assignment of these residues may have been overlooked due to the expectation that they should be present in the peptide.

For MT-ND5, we did observe some density that could be part of the main chain; however, it did not fill out until we reduced the stringency, and we did not observe density mapping to side chain residues. To summarize, we do not confidently see density for either the side chain or the main chain for either peptide.

Minor comments:

The method should be written more accurately for easily repeating experiments by other groups. For example:

(1) The authors should indicate what was exact HEK293 cell line used in this study.

We have indicated the exact cell line.

(2) Page 22, line 471: which (number) fractions had been collected. The Western Blot analysis shown in Figure 1A should be repeated with both proteins from small and large subunits.

We have repeated the Western blot with antibodies for large and small subunits. We took fractions 8 and 9, which are now indicated in the text and figure.

(3) Page 23, line 502: is this number of cells used for MS experiment is correct? Or is this number of cells per mL?

This is correct and is based on the kit protocol. It is not cells per mL. We have clarified the kit being used in the methods.

eLife Assessment

This work provides an important resource identifying 72 proteins as novel candidates for plasma membrane and/or cell wall damage repair in budding yeast, and describes the temporal coordination of exocytosis and endocytosis during the repair process. The data are convincing; however, additional experimental validation will better support the claim that repair proteins shuttle between the bud tip and the damage site.

Reviewer #1 (Public review):

Summary:

In this manuscript, Yamazaki et al. conducted multiple microscopy-based GFP localization screens, from which they identified proteins that are associated with PM/cell wall damage stress response. Specifically, the authors identified that bud-localized TMD-containing proteins and endocytotic proteins are associated with PM damage stress. The authors further demonstrated that polarized exocytosis and CME are temporally coupled in response to PM damage, and CME is required for polarized exocytosis and the targeting of TMD-containing proteins to the damage site. From these results, the authors proposed a model that CME delivers TMD-containing repair proteins between the bud tip and the damage site.

Strengths:

Overall, this is a well-written manuscript, and the experiments are overall well-conducted. The authors identified many repair proteins and revealed the temporal coordination of different categories of repair proteins. Furthermore, the authors demonstrated that CME is required for targeting of repair proteins to the damage site, as well as cellular survival in response to stress related to PM/cell wall damage. Although the roles of CME and bud-localized proteins in damage repair are not completely new to the field, this work does have conceptual advances by identifying novel repair proteins and proposing the intriguing model that the repairing cargoes are shuttled between the bud tip and the damaged site through coupled exocytosis and endocytosis.

Weaknesses:

While the results presented in this manuscript are convincing, they might not be sufficient to support some of the authors' claims. Especially in the last two result sessions, the authors claimed CME delivers TMD-containing repair proteins from the bud tip to the damage site. The model is no doubt highly possible based on the date, but caveats still exist. For example, the repair proteins might not be transported from one localization to another localization, but are degraded and re-synthesized. Although the Gal-induced expression system can further support the model to some extent, I think more direct verification (such as FLIP or photo-convertible fluorescence tags to distinguish between pre-existing and newly synthesized proteins) would significantly improve the strength of evidence.

Review on revised version:

The authors addressed most of concerns that were originally raised, primarily by revising the text and figures and expanding the discussion, which improves the clarity of the manuscript. Although the authors did not address my major concern on the shuttling/trafficking model experimentally, I do understand the limitation of resources and time. The authors noted that they planned to do these experiments for their future work, and such studies would be more definitive evaluations for the proposed model. Overall I think this is a very interesting and well-conducted study and I enjoyed reading this manuscript. I look forward to their following research of this study.

Reviewer #2 (Public review):

This paper remarkably reveals the identification of plasma membrane repair proteins, revealing spatiotemporal cellular responses to plasma membrane damage. The study highlights a combination of sodium dodecyl sulfate (SDS) and lase for identifying and characterizing proteins involved in plasma membrane (PM) repair in Saccharomyces cerevisiae. From 80 PM, repair proteins that were identified, 72 of them were novel proteins. The use of both proteomic and microscopy approaches provided a spatiotemporal coordination of exocytosis and clathrin-mediated endocytosis (CME) during repair. Interestingly, the authors were able to demonstrate that exocytosis dominates early and CME later, with CME also playing an essential role in trafficking transmembrane-domain (TMD) containing repair proteins between the bud tip and the damage site.

Weaknesses/limitations:

- Still, there is a lack of clarity about mentioning Pkc1 as the best characterized repair protein, or why is Pkc1 mentioned only as it is changing the localization?!

- The use of a C-terminal GFP-tagged library for the laser damage assay may have limited the identification of proteins whose localization or function depends on an intact N-terminus. N-terminal regions might contain targeting or regulatory elements; therefore, some relevant repair factors may have been missed. Analysis of endogenously N-terminally tagged strains, at least for selected candidates, could help address this limitation.

- The authors appropriately discuss the limitations of SDS- and laser-induced plasma membrane damage, including the possibility that these approaches may not capture proteins involved in other forms of membrane injury, such as mechanical or osmotic stress.

Reviewer #3 (Public review):

Summary:

This work aims to understand how cells repair damage to the plasma membrane (PM). This is important as failure to do so will result in cell lysis and death. Therefore, this is an important fundamental question with broad implications for all eukaryotic cells. Despite this importance, there are relatively few proteins known to contribute to this repair process. This study expands the number of experimentally validated PM from 8 to 80. Further, they use precise laser-induced damage of the PM/cell wall and use live-cell imaging to track the recruitment of repair proteins to these damage sites. They focus on repair proteins that are involved in either exocytosis or clathrin-mediated endocytosis (CME) to understand how these membrane remodeling processes contribute to PM repair. Through these experiments, they find that while exocytosis and CME both occur at the sites of PM damage, exocytosis predominates the early stages of repairs, while CME predominates in the later stages of repairs. Lastly, they propose that CME is responsible for diverting repair proteins localized to the growing bud cell to the site of PM damage.

Strengths:

The manuscript is very well written and the experiments presented flow logically. The use of laser-induced damage and live-cell imaging to validate the proteome-wide screen using SDS induced damage strengthen the role of the identified candidates in PM/cell wall repair.

Comments on revisions:

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

Author response:

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

eLife Assessment

This work provides an important resource identifying 72 proteins as novel candidates for plasma membrane and/or cell wall damage repair in budding yeast, and describes the temporal coordination of exocytosis and endocytosis during the repair process. The data are convincing; however, additional experimental validation will better support the claim that repair proteins shuttle between the bud tip and the damage site.

We thank the editors and reviewers for their positive assessment of our work and the constructive feedback to improve our manuscript. We agree with the assessment that additional validation of repair protein shuttling between the bud tip and the damage site is required to further support the model.

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Yamazaki et al. conducted multiple microscopy-based GFP localization screens, from which they identified proteins that are associated with PM/cell wall damage stress response. Specifically, the authors identified that budlocalized TMD-containing proteins and endocytotic proteins are associated with PM damage stress. The authors further demonstrated that polarized exocytosis and CME are temporally coupled in response to PM damage, and CME is required for polarized exocytosis and the targeting of TMD-containing proteins to the damage site. From these results, the authors proposed a model that CME delivers TMD-containing repair proteins between the bud tip and the damage site.

Strengths:

Overall, this is a well-written manuscript, and the experiments are well-conducted. The authors identified many repair proteins and revealed the temporal coordination of different categories of repair proteins. Furthermore, the authors demonstrated that CME is required for targeting of repair proteins to the damage site, as well as cellular survival in response to stress related to PM/cell wall damage. Although the roles of CME and bud-localized proteins in damage repair are not completely new to the field, this work does have conceptual advances by identifying novel repair proteins and proposing the intriguing model that the repairing cargoes are shuttled between the bud tip and the damaged site through coupled exocytosis and endocytosis.

Weaknesses:

While the results presented in this manuscript are convincing, they might not be sufficient to support some of the authors' claims. Especially in the last two result sessions, the authors claimed CME delivers TMD-containing repair proteins from the bud tip to the damage site. The model is no doubt highly possible based on the data, but caveats still exist. For example, the repair proteins might not be transported from one localization to another localization, but are degraded and resynthesized. Although the Gal-induced expression system can further support the model to some extent, I think more direct verification (such as FLIP or photo-convertible fluorescence tags to distinguish between pre-existing and newly synthesized proteins) would significantly improve the strength of evidence.

Major experiment suggestions:

(1) The authors may want to provide more direct evidence for "protein shuttling" and for excluding the possibility that proteins at the bud are degraded and synthesized de novo near the damage site. For example, if the authors could use FLIP to bleach budlocalized fluorescent proteins, and the damaged site does not show fluorescent proteins upon laser damage, this will strongly support the authors' model. Alternatively, the authors could use photo-convertible tags (e.g., Dendra) to differentiate between preexisting repair proteins and newly synthesized proteins.

We thank the reviewer for evaluating our work and giving us important feedback. We agree that the FLIP and photo-convertible experiments will further confirm our model. Here, due to time and resource constraints, we decided not to perform this experiment. Instead, we have discussed this limitation in 363-366. Our proposed model of repair protein shuttling should be further tested in our future work.

(2) In line with point 1, the authors used Gal-inducible expression, which supported their model. However, the author may need to show protein abundance in galactose, glucose, and upon PM damage. Western blot would be ideal to show the level of fulllength proteins, or whole-cell fluorescence quantification can also roughly indicate the protein abundance. Otherwise, we cannot assume that the tagged proteins are only expressed when they are growing in galactose-containing media.

Thank you very much for raising the concern and suggesting the important experiments.We agree that the Western blot experiment to confirm the mNG-Snc1 expression in each medium will further strengthen our conclusion. Along with point (1), further investigation of repair protein shuttling between the bud tip and the damage site and the mechanisms underlying it will be an important future direction. As described above, we have discussed this limitation in 363-366.

(3) Similarly, for Myo2 and Exo70 localization in CME mutants (Figure 4), it might be worth doing a western or whole-cell fluorescence quantification to exclude the caveat that CME deficiency might affect protein abundance or synthesis.

We thank the reviewer for suggesting the point. Following the reviewer’s suggestion, we quantified the whole-cell fluorescence of WT and CME mutants and verified that the effect of the CME deletion on the expression levels of Myo2-sfGFP and Exo70-mNG is minimal ( Figure S6). We added the description in lines 211-212.

(4) From the authors' model in Figure 7, it looks like the repair proteins contribute to bud growth. Does laser damage to the mother cell prevent bud growth due to the reduction of TMD-containing repair proteins at the bud? If the authors could provide evidence for that, it would further support the model.

Thank you very much for raising the important point. We speculate that the reduction of TMD-containing proteins at the bud by CME is one of the causes of cell growth arrest after PM damage (1). This is because TMD-containing repair proteins at the bud tip, including phospholipid flippases (Dnf1/Dnf2), Snc1, and Dfg5, are involved in polarized cell growth (2-4). This will be an important future direction as well.

(5) Is the PM repair cell-cycle-dependent? For example, would the recruitment of repair proteins to the damage site be impaired when the cells are under alpha-factor arrest?

Thank you for raising this interesting point. Indeed, the senior author Kono previously performed this experiment when she was in David Pellman’s lab. The preliminary results suggest that Pkc1 can be targeted to the damage site, without any impairment, under alpha-factor arrest. A more comprehensive analysis in the future will contribute to concluding the relation between PM repair and the cell cycle.

Reviewer #2 (Public review):

This paper remarkably reveals the identification of plasma membrane repair proteins, revealing spatiotemporal cellular responses to plasma membrane damage. The study highlights a combination of sodium dodecyl sulfate (SDS) and lase for identifying and characterizing proteins involved in plasma membrane (PM) repair in Saccharomyces cerevisiae. From 80 PM, repair proteins that were identified, 72 of them were novel proteins. The use of both proteomic and microscopy approaches provided a spatiotemporal coordination of exocytosis and clathrin-mediated endocytosis (CME) during repair. Interestingly, the authors were able to demonstrate that exocytosis dominates early and CME later, with CME also playing an essential role in trafficking transmembrane-domain (TMD)containing repair proteins between the bud tip and the damage site.

Weaknesses/limitations:

(1) Why are the authors saying that Pkc1 is the best characterized repair protein? What is the evidence?

We would like to thank the reviewer for taking his/her time to evaluate our work and for valuable suggestions. We described Pkc1 as “best characterized” because it was the first protein reported to accumulate at the laser damage site in budding yeast (5). However, as the reviewer suggested, we do not have enough evidence to describe Pkc1 as “best characterized”. We therefore used “one of the known repair proteins” to mention Pkc1 in the manuscript (lines 90-91).

(2) It is unclear why the authors decided on the C-terminal GFP-tagged library to continue with the laser damage assay, exclusively the C-terminal GFP-tagged library. Potentially, this could have missed N-terminal tag-dependent localizations and functions and may have excluded functionally important repair proteins

Thank you very much for the comments. We decided to use the C-terminal GFP-tagged library for the laser damage assay because we intended to evaluate the proteins of endogenous expression levels. The N-terminal sfGFP-tagged library is expressed by the NOP1 promoter, while the C-terminal GFP-tagged library is expressed by the endogenous promoters. We clarified these points in lines 114-118. We agree with the reviewer on that we may have missed some portion of repair proteins in the N-terminaldependent localization and functions by this approach. Therefore, in our manuscript, we discussed these limitations in lines 281-289.

(3) The use of SDS and laser damage may bias toward proteins responsive to these specific stresses, potentially missing proteins involved in other forms of plasma membrane injuries, such as mechanical, osmotic, etc.). SDS stress is known to indirectly induce oxidative stress and heat-shock responses.

Thank you very much for raising this point. We agree that the combination of SDS and laser may be biased to identify PM repair proteins. Therefore, in the manuscript, we discussed this point as a limitation of this work in lines 292-298.

(4) It is unclear what the scale bars of Figures 3, 5, and 6 are. These should be included in the figure legend.

We apologize for the missing scale bars. We added them to the legends of the figures in the manuscript.

(5) Figure 4 should be organized to compare WT vs. mutant, which would emphasize the magnitude of impairment.

Thank you for raising this point. Following the suggestion, we updated Figure 4. In the Figure 4, we compared WT vs mutant in the manuscript. We clarified it in the legends in the manuscript. 

(6) It would be interesting to expand on possible mechanisms for CME-mediated sorting and retargeting of TMD proteins, including a speculative model.

Thank you very much for this important suggestion. We think it will be very important to characterize the mechanism of CME-mediated TMD protein trafficking between the bud tip and the damage site. In the manuscript, we discussed the possible mechanism for CME activation at the damage site in lines 328-333. We speculate that the activation of the CME may facilitate the retargeting of the TMD proteins from the damage site to the bud tip.

We do not have a model of how CMEs activate at the bud tip to sort and target the TMD proteins to the damage site. One possibility is that the cell cycle arrest after PM damage (1) may affect the localization of CME proteins because the cell cycle affects the localization of some of the CME proteins (6). We will work on the mechanism of repair protein sorting from the bud tip to the damage site in our future work.

Reviewer #3 (Public review):

Summary:

This work aims to understand how cells repair damage to the plasma membrane (PM). This is important, as failure to do so will result in cell lysis and death. Therefore, this is an important fundamental question with broad implications for all eukaryotic cells. Despite this importance, there are relatively few proteins known to contribute to this repair process. This study expands the number of experimentally validated PM from 8 to 80. Further, they use precise laser-induced damage of the PM/cell wall and use livecell imaging to track the recruitment of repair proteins to these damage sites. They focus on repair proteins that are involved in either exocytosis or clathrin-mediated endocytosis (CME) to understand how these membrane remodeling processes contribute to PM repair. Through these experiments, they find that while exocytosis and CME both occur at the sites of PM damage, exocytosis predominates in the early stages of repairs, while CME predominates in the later stages of repairs. Lastly, they propose that CME is responsible for diverting repair proteins localized to the growing bud cell to the site of PM damage.

Strengths:

The manuscript is very well written, and the experiments presented flow logically. The use of laser-induced damage and live-cell imaging to validate the proteome-wide screen using SDS-induced damage strengthens the role of the identified candidates in PM/cell wall repair.

Weaknesses:

(1) Could the authors estimate the fraction of their candidates that are associated with cell wall repair versus plasma membrane repair? Understanding how many of these proteins may be associated with the repair of the cell wall or PM may be useful for thinking about how these results are relevant to systems that do or do not have a cell wall. Perhaps this is already in their GO analysis, but I don't see it mentioned in the manuscript.

We would like to thank the reviewer for taking his/her time to evaluate our work and valuable suggestions. We agree that this is important information to include. Although it may be difficult to completely distinguish the PM repair and cell wall repair proteins, we have identified at least six proteins involved in cell wall synthesis (Flc1, Dfg5, Smi1, Skg1, Tos7, and Chs3). We included this information in lines 142-146 in the manuscript.

(2) Do the authors identify actin cable-associated proteins or formin regulators associated with sites of PM damage? Prior work from the senior author (reference 26) shows that the formin Bnr1 relocalizes to sites of PM damage, so it would be interesting if Bnr1 and its regulators (e.g., Bud14, Smy1, etc) are recruited to these sites as well. These may play a role in directing PM repair proteins (see more below).

Thank you for the suggestion. We identified several Bnr1-interacting proteins, including Bud6, Bil1, and Smy1 (Table S2), although Bnr1 itself was not identified in our screening. This could be attributed to the fact that (1) C-terminal GFP fusion impaired the function of Bnr1, and (2) a single GFP fusion is not sufficient to visualize the weak signal at the damage site. Indeed, in reference 26, 3GFP-Bnr1 (N-terminal 3xGFP fusion) was used.

(3) Do the authors suspect that actin cables play a role in the relocalization of material from the bud tip to PM damage sites? They mention that TMD proteins are secretory vesicle cargo (lines 134-143) and that Myo2 localizes to damage sites. Together, this suggests a possible role for cable-based transport of repair proteins. While this may be the focus of future work, some additional discussion of the role of cables would strengthen their proposed mechanism (steps 3 and 4 in Figure 7).

Thank you very much for the suggestion. We agree that actin cables may play a role in the targeting of vesicles and repair proteins to the damage site. Following the reviewer’s suggestion, we discussed the roles of Bnr1 and actin cables for repair protein trafficking in lines 309-313 in the manuscript.

(4) Lines 248-249: I find the rationale for using an inducible Gal promoter here unclear. Some clarification is needed.

Thank you for raising this point. We clarified this as possible as we could in lines 249255 in the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The N-terminal GFP collection screen is interesting but seems irrelevant to the rest of the results. The authors discussed that in the discussion part, but it might be worth showing how many hits from the laser damage screen (in Figure 2) overlap with the Nterminal GFP screen hits.

Thank you for the suggestion. We found that 48 out of 80 repair proteins are hits in the N-terminal GFP library (Table S1 and S2). This result suggested that the N-terminal library is also a useful resource for identifying repair proteins. In the manuscript, we discussed it in lines 288-289.

(2) SDS treatment seems a harsh stressor. As the authors mentioned, the overlapped hits from the N- and C-terminal GFP screen might be more general stress factors. Thus, I think Line 84 (the subtitle) might be overclaiming, and the authors might need to tone down the sentence.

Thank you for the suggestion. Following the reviewer’s suggestion, we changed the sentence to “Proteome-scale identification of SDS-responsive proteins” in the manuscript. We believe that the new sentence describes our findings more precisely.

(3) Line 103-106, it does not seem obvious to me that the protein puncta in the cytoplasm are due to endocytosis. The authors might need to provide more experimental evidence for the conclusion, or at least provide more reasoning/references on that aspect (e.g.,several specific protein hits belonging to that group have been shown to be endocytosed).

Thank you very much for raising this point. We agree with the reviewer and deleted the description that these puncta are due to endocytosis in the manuscript.

(4) For Figure 1D and S1C, the authors annotated some of the localization changes clearly, but some are confusing to me. For example," from bud tip/neck" to where? And from where to "Puncta/foci"? A clearer annotation might help the readers to understand the categorization.

Thank you very much for the suggestion. These annotations were defined because it is difficult to conclusively describe the protein localization after SDS treatment. To convincingly identify the destination of the GFP fusion proteins, the dual color imaging of proteins with organelle markers or deep learning-based localization estimation is required. We feel that this might be out of the scope of this work. Therefore, as criteria, we used the localization of protein localization in normal/non-stressed conditions reported in (7) and the Saccharomyces Genome Database (SGD). We clarified this annotation definition in the manuscript (lines 413-436).

(5) For localization in Figure 2C, as I understand, does it refer to6 the "before damage/normal" localization? If so, I think it would be helpful to state that these localizations are based on the untreated/normal conditions in the text.

Yes, it refers to the “before damage/normal localization”. Following the reviewer’s suggestion, we stated that these localizations are based on these conditions in the manuscript (line 130).

(6) The authors mentioned "four classes" in Line 120, but did not mention the "PM to cytoplasm" class in the text. It would be helpful to discuss/speculate why these transporters might contribute to PM damage repair.

Thank you very much for this suggestion. We speculated that these transporters are endocytosed after PM damage because endocytosis of PM proteins contributes to cell adaptation to environmental stress (8). We mentioned it in the manuscript (lines 120-122).

(7) Line 175-180 My understanding of the text is that the signals of Exo70-mNG/Dnf1mNG peak before the Ede1-mSc-I peaks. They occur simultaneously, but their dominating phase are different. It is clearer when looking at the data, but I think the conclusion sentences themselves are confusing to me. The authors might consider rewriting the sentences to make them more straightforward.

Thank you very much for pointing this out. Following the reviewer’s suggestion, we revised the sentence (lines 177-182 in the manuscript).

Reviewer #2 (Recommendations for the authors):

It would be interesting to expand on the functional characterization of the 72 novel candidates and explore possible mechanisms for CME-mediated sorting and retargeting of TMD proteins by including a speculative model.

Thank you very much for the comment. We agree that the further characterization of novel repair proteins and exploration of the possible mechanisms for CME-mediated TMD protein sorting and retargeting are truly important. This should be our important future direction.

Reviewer #3 (Recommendations for the authors):

The x-axis in Figure 1C is labeled 'Ratio' - what is this a ratio of?

Thank you for raising this point. It is the ratio of the number of proteins associated with a GO term to the total number of proteins in the background. We clarified it in the legend of Figure 1C in the manuscript.

References

(1) K. Kono, A. Al-Zain, L. Schroeder, M. Nakanishi, A. E. Ikui, Plasma membrane/cell wall perturbation activates a novel cell cycle checkpoint during G1 in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A 113, 6910-6915 (2016).

(2) A. Das et al., Flippase-mediated phospholipid asymmetry promotes fast Cdc42 recycling in dynamic maintenance of cell polarity. Nat Cell Biol 14, 304-310 (2012).

(3) M. Adnan et al., SNARE Protein Snc1 Is Essential for Vesicle Trafficking, Membrane Fusion and Protein Secretion in Fungi. Cells 12 (2023).

(4) H.-U. Mösch, G. R. Fink, Dissection of Filamentous Growth by Transposon Mutagenesis in Saccharomyces cerevisiae. Genetics 145, 671-684 (1997).

(5) K. Kono, Y. Saeki, S. Yoshida, K. Tanaka, D. Pellman, Proteasomal degradation resolves competition between cell polarization and cellular wound healing. Cell 150, 151-164 (2012).

(6) A. Litsios et al., Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle. Cell 187, 1490-1507.e1421 (2024).

(7) W.-K. Huh et al., Global analysis of protein localization in budding yeast.Nature 425, 686-691 (2003).

(8) T. López-Hernández, V. Haucke, T. Maritzen, Endocytosis in the adaptation to cellular stress. Cell Stress 4, 230-247 (2020).

ToC of [[Sovereign Minds by Nadia Alter]] interesting terms, like 'opportunity literacy'

[[Sovereign Minds by Nadia Alter]] looks very worthwhile to get. mentions agency in the subtitle.

(via [[Alberto Cottica p]] on masto)

Reviewer #2 (Public review):

Summary:

Feng, Jing-Xin et al. studied the hemogenic capacity of the endothelial cells in the adult mouse bone marrow. Using Cdh5-CreERT2 in vivo inducible system, though rare, they characterized a subset of endothelial cells expressing hematopoietic markers that were transplantable. They suggested that the endothelial cells need the support of stromal cells to acquire blood-forming capacity ex vivo. These endothelial cells were transplantable and contributed to hematopoiesis with ca. 1% chimerism in a stress hematopoiesis condition (5-FU) and recruited to the peritoneal cavity upon Thioglycolate treatment. Ultimately, the authors detailed the blood lineage generation of the adult endothelial cells in a single cell fashion, suggesting a predominant HSPCs-independent blood formation by adult bone marrow endothelial cells, in addition to the discovery of Col1a2+ endothelial cells with blood-forming potential, corresponding to their high Runx1 expressing property.

The conclusion regarding the characterization of hematopoietic-related endothelial cells in adult bone marrow is well supported by data. However, the paper would be more convincing, if the function of the endothelial cells were characterized more rigorously.

(1) Ex vivo culture of CD45-VE-Cadherin+ZsGreen EC cells generated CD45+ZsGreen+ hematopoietic cells. However, given that FACS sorting can never achieve 100% purity, there is a concern that hematopoietic cells might arise from the ones that got contaminated into the culture at the time of sorting. The sorting purity and time course analysis of ex vivo culture should be shown to exclude the possibility.

(2) Although it was mentioned in the text that the experimental mice survived up to 12 weeks after lethal irradiation and transplantation, the time-course kinetics of donor cell repopulation (>12 weeks) would add a precise and convincing evaluation. This would be absolutely needed as the chimerism kinetics can allow us to guess what repopulation they were (HSC versus progenitors). Moreover, data on either bone marrow chimerism assessing phenotypic LT-HSC and/or secondary transplantation would dramatically strengthen the manuscript.

(3) The conclusion by the authors, which says "Adult EHT is independent of pre-existing hematopoietic cell progenitors", is not fully supported by the experimental evidence provided (Figure 4 and Figure S3). More recipients with ZsGreen+ LSK must be tested.

Strengths:

The authors used multiple methods to characterize the blood-forming capacity of the genetically - and phenotypically - defined endothelial cells from several reporter mouse systems. The polylox barcoding method to trace the adult bone marrow endothelial cell contribution to hematopoiesis is a strong insight to estimate the lineage contribution.

Weaknesses:

It is unclear what the biological significance of the blood cells de novo generated from the adult bone marrow endothelial cells is. Moreover, since the frequency is very rare (<1% bone marrow and peripheral blood CD45+), more data regarding its identity (function, morphology, and markers) are needed to clearly exclude the possibility of contamination/mosaicism of the reporter mice system used.

Reviewer #1 (Public review):

Summary:

This manuscript by Feng et al. uses mouse models to study the embryonic origins of HSPCs. Using multiple types of genetic lineage tracing, the authors aimed to identify whether BM-resident endothelial cells retain hematopoietic capacity in adult organisms. Through an important mix of various labeling methodologies (and various controls), they reach the conclusion that BM endothelial cells contribute up to 3% of hematopoietic cells in young mice.

Strengths:

The major strength of the paper lies in the combination of various labeling strategies, including multiple Cdh5-CreER transgenic lines, different CreER lines (col1a2), and different reporters (ZsGreen, mTmG), including a barcoding-type reporter (PolyLox). This makes it highly unlikely that the results are driven by a rare artifact due to one random Cre line or one leaky reporter. The transplantation control (where the authors show no labeling of transplanted LSKs from the Cdh5 model) is also very supportive of their conclusions.

Weaknesses:

We believe that the work of ruling out alternative hypotheses, though initiated, was left incomplete. We specifically think that the authors need to properly consider whether there is specific, sparse labeling of HSPCs (in their native, non-transplant, model, in young animals). Polylox experiments, though an exciting addition, are also incomplete without additional controls. Some additional killer experiments are suggested.

eLife Assessment

The study proposed hemogenic endothelium in adult BM using lineage tracing. Though the study is potentially valuable, the data is incomplete due to the lack of control and insufficient analysis. There is potential for the study to be improved by further revision.

Corporate Europe Observatory: shedding light on corporate lobbying in the EU.

Keep watch on a number of specific domains: - agri - energy / climate - lobbying regulations - trade / investment - economy / finance - other topics Digi is missing in this list, perhaps under other?

into the fire

Hook includes a personal account/story instantly establishing a connection with the author, perhaps through a shared experience. Is the reader on the side of the author, or the opposer.

Multimodal media used here to strengthen the argument featuring a woman happy to be eating alone; main point of this article.

Another touch to bring us back into reality in a an emotional and relatable way.

Her largest appeal working in her favor is directly placing the reader into her situation to make them feel as if they're having the experience and really selling how amazing that would be for them. She does this with vivid imagery and realistic diction.

I noticed the author constantly uses location markers, and primary accounts of quotes and experiences.

Another display of Pathos to convince the reader that this experience is unacceptable, and shouldn't happen just because someone wants to eat alone. The reader has sympathy for the author which in tern, strengthens their connection and her argument.

DOI: 10.1016/j.neuron.2025.12.031

Resource: (IMSR Cat# JAX_030323,RRID:IMSR_JAX:030323)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:030323

What is this?

DOI: 10.1016/j.cell.2025.12.045

Resource: RRID:IMSR_JAX:007909

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:007909

What is this?

DOI: 10.1016/j.cell.2025.12.045

Resource: RRID:IMSR_JAX:003529

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:003529

What is this?

DOI: 10.1126/scisignal.ads6584

Resource: None

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.1038/s43018-024-00742-z

Resource: None

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:001303

What is this?

This is a TEST annotation by Gregoire

eLife Assessment

This important study demonstrates that paternal diet influences not only testicular morphology but also placental and fetal development, supporting a role for paternal contributions to offspring health. The authors combine transcriptomic and histological analyses across multiple tissues, and the evidence supporting the central conclusions is convincing. While aspects of the paternal gut phenotype remain largely descriptive, and the paternal and fetoplacental findings are discussed separately, clearer integration of these elements and additional methodological clarification would strengthen interpretation.

Reviewer #1 (Public review):

Summary:

Morgan et al. studied how paternal dietary alteration influenced testicular phenotype, placental and fetal growth using a mouse model of paternal low protein diet (LPD) or Western Diet (WD) feeding, with or without supplementation of methyl-donors and carriers (MD). They found diet- and sex-specific effects of paternal diet alteration. All experimental diets decreased paternal body weight and the number of spermatogonial stem cells, while fertility was unaffected. WD males (irrespective of MD) showed signs of adiposity and metabolic dysfunction, abnormal seminiferous tubules, and dysregulation of testicular genes related to chromatin homeostasis. Conversely, LPD induced abnormalities in the early placental cone, fetal growth restriction, and placental insufficiency, which were partly ameliorated by MD. The paternal diets changed the placental transcriptome in a sex-specific manner and led to a loss of sexual dimorphism in the placental transcriptome. These data provide a novel insight into how paternal health can affect the outcome of pregnancies, which is often overlooked in prenatal care.

Strengths:

The authors have performed a well-designed study using commonly used mouse models of paternal underfeeding (low protein) and overfeeding (Western diet). They performed comprehensive phenotyping at multiple timepoints, including the fathers, the early placenta, and the late gestation feto-placental unit. The inclusion of both testicular and placental morphological and transcriptomic analysis is a powerful, non-biased tool for such exploratory observational studies. The authors describe changes in testicular gene expression revolving around histone (methylation) pathways that are linked to altered offspring development (H3.3 and H3K4), which is in line with hypothesised paternal contributions to offspring health. The authors report sex differences in control placentas that mimic those in humans, providing potential for translatability of the findings. The exploration of sexual dimorphism (often overlooked) and its absence in response to dietary modification is novel and contributes to the evidence-base for the inclusion of both sexes in developmental studies.

Weaknesses:

The data are overall consistent with the conclusions of the authors. The paternal and pregnancy data are discussed separately, instead of linking the paternal phenotype to offspring outcomes. Some clarifications regarding the methods and the model would improve the interpretation of the findings.

(1) The authors insufficiently discuss their rationale for studying methyl-donors and carriers as micronutrient supplementation in their mouse model. The impact of the findings would be better disseminated if their role were explained in more detail.

(2) It is unclear from the methods exactly how long the male mice were kept on their respective diets at the time of mating and culling. Male mice were kept on the diet between 8 and 24 weeks before mating, which is a large window in which the males undergo a considerable change in body weight (Figure 1A). If males were mated at 8 weeks but phenotyped at 24 weeks, or if there were differences between groups, this complicates the interpretation of the findings and the extrapolation of the paternal phenotype to changes seen in the fetoplacental unit. The same applies to paternal age, which is an important known factor affecting male fertility and offspring outcomes.

(3) The male mice exhibited lower body weights when fed experimental diets compared to the control diet, even when placed on the hypercaloric Western Diet. As paternal body weight is an important contributor to offspring health, this is an important confounder that needs to be addressed. This may also have translational implications; in humans, consumption of a Western-style diet is often associated with weight gain. The cause of the weight discrepancy is also unaddressed. It is mentioned that the isocaloric LPD was fed ad libitum, while it is unclear whether the WD was also fed ad libitum, or whether males under- or over-ate on each experimental diet.

(4) The description and presentation of certain statistical analyses could be improved.

(i) It is unclear what statistical analysis has been performed on the time-course data in Figure 1A (if any). If one-way ANOVA was performed at each timepoint (as the methods and legend suggest), this is an inaccurate method to analyse time-course data.

(ii) It is unclear what methods were used to test the relative abundance of microbiome species at the family level (Figure 2L), whether correction was applied for multiple testing, and what the stars represent in the figure. 3) Mentioning whether siblings were used in any analyses would improve transparency, and if so, whether statistical correction needed to be applied to control for confounding by the father.

Reviewer #2 (Public review):

Summary:

The authors investigated the effects of a low-protein diet (LPD) and a high sugar- and fat-rich diet (Western diet, WD) on paternal metabolic and reproductive parameters and feto-placental development and gene expression. They did not observe significant effects on fertility; however, they reported gut microbiota dysbiosis, alterations in testicular morphology, and severe detrimental effects on spermatogenesis. In addition, they examined whether the adverse effects of these diets could be prevented by supplementation with methyl donors. Although LPD and WD showed limited negative effects on paternal reproductive health (with no impairment of reproductive success), the consequences on fetal and placental development were evident and, as reported in many previous studies, were sex-dependent.

Strengths:

This study is of high quality and addresses a research question of great global relevance, particularly in light of the growing concern regarding the exponential increase in metabolic disorders, such as obesity and diabetes, worldwide. The work highlights the importance of a balanced paternal diet in regulating the expression of metabolic genes in the offspring at both fetal and placental levels. The identification of genes involved in metabolic pathways that may influence offspring health after birth is highly valuable, strengthening the manuscript and emphasizing the need to further investigate long-term outcomes in adult offspring.

The histological analyses performed on paternal testes clearly demonstrate diet-induced damage. Moreover, although placental morphometric analyses and detailed histological assessments of the different placental zones did not reveal significant differences between groups, their inclusion is important. These results indicate that even in the absence of overt placental phenotypic changes, placental function may still be altered, with potential consequences for fetal programming.

Weaknesses:

Overall, this manuscript presents a rich and comprehensive dataset; however, this has resulted in the analysis of paternal gut dysbiosis remaining largely descriptive. While still valuable, this raises questions regarding why supplementation with methyl donors was unable to restore gut microbial balance in animals receiving the modified diets.

Author response:

Public Reviews: 

Reviewer #1 (Public review):

Summary:

Morgan et al. studied how paternal dietary alteration influenced testicular phenotype, placental and fetal growth using a mouse model of paternal low protein diet (LPD) or Western Diet (WD) feeding, with or without supplementation of methyl-donors and carriers (MD). They found diet- and sex-specific effects of paternal diet alteration. All experimental diets decreased paternal body weight and the number of spermatogonial stem cells, while fertility was unaffected. WD males (irrespective of MD) showed signs of adiposity and metabolic dysfunction, abnormal seminiferous tubules, and dysregulation of testicular genes related to chromatin homeostasis. Conversely, LPD induced abnormalities in the early placental cone, fetal growth restriction, and placental insufficiency, which were partly ameliorated by MD. The paternal diets changed the placental transcriptome in a sex-specific manner and led to a loss of sexual dimorphism in the placental transcriptome. These data provide a novel insight into how paternal health can affect the outcome of pregnancies, which is often overlooked in prenatal care.

Strengths:

The authors have performed a well-designed study using commonly used mouse models of paternal underfeeding (low protein) and overfeeding (Western diet). They performed comprehensive phenotyping at multiple timepoints, including the fathers, the early placenta, and the late gestation feto-placental unit. The inclusion of both testicular and placental morphological and transcriptomic analysis is a powerful, non-biased tool for such exploratory observational studies. The authors describe changes in testicular gene expression revolving around histone (methylation) pathways that are linked to altered offspring development (H3.3 and H3K4), which is in line with hypothesised paternal contributions to offspring health. The authors report sex differences in control placentas that mimic those in humans, providing potential for translatability of the findings. The exploration of sexual dimorphism (often overlooked) and its absence in response to dietary modification is novel and contributes to the evidence-base for the inclusion of both sexes in developmental studies.

Weaknesses:

The data are overall consistent with the conclusions of the authors. The paternal and pregnancy data are discussed separately, instead of linking the paternal phenotype to offspring outcomes. Some clarifications regarding the methods and the model would improve the interpretation of the findings.

(1) The authors insufficiently discuss their rationale for studying methyl-donors and carriers as micronutrient supplementation in their mouse model. The impact of the findings would be better disseminated if their role were explained in more detail.

We acknowledge the Reviewer’s comments regarding the amount of detail in support of the inclusion of methyl carriers and donors within our diet. Therefore, we will revise the manuscript to include more justification, especially within the Introduction section, for their inclusion.

(2) It is unclear from the methods exactly how long the male mice were kept on their respective diets at the time of mating and culling. Male mice were kept on the diet between 8 and 24 weeks before mating, which is a large window in which the males undergo a considerable change in body weight (Figure 1A). If males were mated at 8 weeks but phenotyped at 24 weeks, or if there were differences between groups, this complicates the interpretation of the findings and the extrapolation of the paternal phenotype to changes seen in the fetoplacental unit. The same applies to paternal age, which is an important known factor affecting male fertility and offspring outcomes.

We thank the Reviewer for their comments regarding the ages of the males analysed. We will provide more detailed descriptions of the males in our manuscript. However, all male ages were balanced across all groups.

(3) The male mice exhibited lower body weights when fed experimental diets compared to the control diet, even when placed on the hypercaloric Western Diet. As paternal body weight is an important contributor to offspring health, this is an important confounder that needs to be addressed. This may also have translational implications; in humans, consumption of a Western-style diet is often associated with weight gain. The cause of the weight discrepancy is also unaddressed. It is mentioned that the isocaloric LPD was fed ad libitum, while it is unclear whether the WD was also fed ad libitum, or whether males under- or over-ate on each experimental diet.

We agree with the Reviewer that the general trend towards a lighter body weight for our experimental animals is unexpected. We can confirm that all diets were fed ad libitum. However, as males were group housed, we were unable to measure food consumption for individual males. We also observed that for males fed the high fat diets, they often shredded significant quantities of their diet, rather than eating it, so preventing accurate measurement of food intake.

We also agree with the Reviewer that body weight can be a significant confounder for many paternal and offspring parameters. However, while the experimental males did become lighter, there were no statistical differences between groups in mean body weight. As such, body weight was not included as a variable within our statistical analysis.

(4) The description and presentation of certain statistical analyses could be improved.

(i) It is unclear what statistical analysis has been performed on the time-course data in Figure 1A (if any). If one-way ANOVA was performed at each timepoint (as the methods and legend suggest), this is an inaccurate method to analyse time-course data.

(ii) It is unclear what methods were used to test the relative abundance of microbiome species at the family level (Figure 2L), whether correction was applied for multiple testing, and what the stars represent in the figure. 3) Mentioning whether siblings were used in any analyses would improve transparency, and if so, whether statistical correction needed to be applied to control for confounding by the father.

We apologize for the lack of clarity regarding the statistical analyses. Going forward, we will revise the manuscript and include a more detailed description of the different analyses, the inclusion of siblings, and the correction for multiple testing.

Reviewer #2 (Public review):

Summary:

The authors investigated the effects of a low-protein diet (LPD) and a high sugar- and fat-rich diet (Western diet, WD) on paternal metabolic and reproductive parameters and fetoplacental development and gene expression. They did not observe significant effects on fertility; however, they reported gut microbiota dysbiosis, alterations in testicular morphology, and severe detrimental effects on spermatogenesis. In addition, they examined whether the adverse effects of these diets could be prevented by supplementation with methyl donors. Although LPD and WD showed limited negative effects on paternal reproductive health (with no impairment of reproductive success), the consequences on fetal and placental development were evident and, as reported in many previous studies, were sex-dependent.

Strengths:

This study is of high quality and addresses a research question of great global relevance, particularly in light of the growing concern regarding the exponential increase in metabolic disorders, such as obesity and diabetes, worldwide. The work highlights the importance of a balanced paternal diet in regulating the expression of metabolic genes in the offspring at both fetal and placental levels. The identification of genes involved in metabolic pathways that may influence offspring health after birth is highly valuable, strengthening the manuscript and emphasizing the need to further investigate long-term outcomes in adult offspring.

The histological analyses performed on paternal testes clearly demonstrate diet-induced damage. Moreover, although placental morphometric analyses and detailed histological assessments of the different placental zones did not reveal significant differences between groups, their inclusion is important. These results indicate that even in the absence of overt placental phenotypic changes, placental function may still be altered, with potential consequences for fetal programming.

Weaknesses:

Overall, this manuscript presents a rich and comprehensive dataset; however, this has resulted in the analysis of paternal gut dysbiosis remaining largely descriptive. While still valuable, this raises questions regarding why supplementation with methyl donors was unable to restore gut microbial balance in animals receiving the modified diets.

We thank the Reviewer for their considered thoughts on the gut dysbiosis induced in our models the minimal impact of the methyl donors and carriers. We will include additional text within the Discussion to acknowledge this. However, at this point in time, we are unsure as to why the methyl donors had minimal impact. It could be that the macronutrients (i.e. protein, fat, carbohydrates) have more of an influence on gut bacterial profiles than micronutrients. Alternatively, due to the prolonged nature of our feeding regimens, any initial influences of the methyl donors may become diluted out over time. We will amend the text to reflect these potential factors.

Successful literature reviews enable your readers to create their own opinions or new thought processes, based on the research findings you included in your paper.

I better understand the purpose of a literature review thanks to this simple and straight- to- the- point definition. I will definitely repeat this statement to myself while looking for research or if I face writer's block during my literature review writing process.

This part stood out to me bcause it frames the systematic reviews as the most rigorous approach, especially in fields like medical science.

Before the reading, I was not really equipped with the idea of how differences between, systematic, semi-systematic, and integrative reviews. This helped me clarify how each approach serves as a different purpose.

This overall secrtion stood out to me because of the amount of clarity it provided on why literature reviews are essential across all research disciplines ,not just as background but aspart of the research process.

An interesting paradox mentioned by Marx. I'm interested to know the context behind his proposed "utopia" and if they were truly similar to those hypothesized by Plato and Aristotle.

Reiterating that values are fluid and simply a product of the society that deemed these "values" to exist in the first place.

It seems like he is basically summarizing that every revolution that has ever occurred was violent.

There is a delicate balance between theory and practice, in which theory is influenced by practice but also the fact that theory only has purpose in the presence of practice (and vice versa).

In some views, labor was empowering due to the opportunity for economic prosperity, but it also gave way to an increase in the wealth gap between the wealthy and working class. I wonder if what Marx thought about the concept of labor as economically empowering and what that would look like in his ideal society.

It is interesting that the author suggests it is impossible to revert back to a political thought. It seems that challenging "traditional" thought only pushes us forward, and not backward.

What does "things" refer to here?

I think the transformations brought about by nineteenth century philosophers were considered radical because they were defined in terms of traditional thought. I wonder at what point revolutions in political thought become the new "tradition".

Could this connect to Marx's empowerment of the working class in which everyone would have the leisure to engage in philosophical thought?

It seems like the distinction between the free man and the slave lies in the use of violence. Does this mean that, in this definition, violence is an irrational action devoid of any deliberate thought?

I wonder if Aristotle's definition of leisure time reflects the stark class divide present during that time period, where there was no consideration of the servant class. Marx challenges this by focusing on the working class

I wonder if the shift towards secularism in later periods also influenced political philosophy, or what the relationship is between religious thought and political thought since they are historically often intertwined.

This could also reflect that antique political thought did not fully apply to the societal problems faced during the Marxian era, which is why Marx sought to revolutionize political thought and "flip it upside-down".

I found the phrase "precisely in them" an interesting contrast from the previous understandings of political thought from Plato and wonder if this also connects to his emphasis on the working class.

Saying that violence is the midwife of history does not equal violence is the most dignified of all forms of human action.

The answer is hobbies. If there is no need to labor for survival, people would pick up hobbies.

I think this reading of Marx as glorifying violence is not sound. If associating an analysis on how violence is historically used and will very likely be used by future revolutions is a glorification, I must have a very different definition on glorifying violence.

The idea that the city-state of Athens was not held together by a monopoly of state violence is silly in my opinion.

This Marx guy is speaking facts. But yes, if there is no overseeing force to enforce laws through the use of violence, then there is no government. If your town is violently controlled by ISIS, ISIS is now the government. If your town is violently controlled by a cartel, that cartel is now the government.

Ehh, it depends on how we define labor. Are/Were hunter gathers laborers? If so, considering that this is humans in their "most natural" state, why wouldn't any other animal doing the same be considered laboring?

These advances only came about after the threat of socialization by a very militant labor movement.

I mean Marx's idea of labor abolishing itself stems from the rapid automation and accumulation he was seeing. He thus presumed that a socialist society with continued automation will lead to very little work.

Throughout the history of nations and societies, they have all come about though the exercise of violence.

Seems incredibly ironic

When things begin to change what traditions can still apply? Is it slightly out of tune or needs a lot of fixing to bring harmony

Juxtaposition of the words glorify and violence shows radical nature of Marx's vision for change

Excited to see how this plays out when reading the different thinkers

Leisure is seeming to have a different definition here than what I originally thought it meant. It wasn't just a break from labor and work but a time to find yourself and for development

Leads me to think who would even want this? I feel like it almost takes away purpose - at least for me it would

Is this not just money? I feel like I am missing something.

I viewed this as saying that as ideas are fresh and new they are "pure". However similar to a song, when played out of melody (or in modern times), the song sounds wrong (the older ideas don't fit).

The abolition of the true world ends the idea that life has meaning. Since there is no true world, our current world of appearances is flawed and meaningless.

This quote jumped out at me because man created labor, not the inverse. Clearly what Marx was getting at was the productivity of goods man produces defines human development. This deviates from traditional philosophy, where humans are the centerpiece.

Both the inception and conclusion of tradition provide us with some sort of clarity relating to fundamental political problems. It forces us to reflect and think of what potential transformation would look like.

The metaphor of the "fundamental chord" is an interesting one. It explains the influence of foundational ideas in Western thought and how it has remained relevant throughout history.

I can't help but wonder what challenges might arise if we attempt to achieve this dual idea of a classless and stateless society. One that involved leisure from labor and leisure from politics.

Does this mean the possibility of positive new beginnings? I like how Arendt's also sees the potential that comes with the loss of tradition. In a way, this system change creates space for new meaning and understanding of our world.

Arendt's thoughts on the impact of modernity, specifically the way it challenges the traditional ways of thinking is eye opening. It forces us to reevaluate the concept of truth in relation to thought.

If labor is essential to human identity and overall societal function, how can it be abolished without undermining the idea of human life and freedom?

I enjoy how Arendt sees this an opportunity. The modern detachment from tradition is maybe not that bad!! In some ways, it allows for fresh engagement with the past.

I had never thought about the role of authority when talking about tradition before this. With the decline of tradition, the term authority is harder to define and sustain, consequently making us question the legitimacy of tradition.

Does this posit that there can only ever be modernity? If it is ever-changing, will this new time period of philosophy/politics always be modernity? I feel as though the definition of modernity is too vague and is exaggerated in order to make new theories stand out more than traditional theories.

I agree with this perspective. A lot of modernists see no place for religion in the future--Marx especially. It seems as though philosophers go to extremes with their views of modernity and change because I don't see how religion can't coexist with modernity. I think Theodor Adorno gives some valuable insight into this as well.

On the bottom of page 12 it says tradition still have power over the minds of men, but here it says through these philosophers tradition has lost its authority. This appears to be contradictory but I may be missing something.

I agree strongly with this sentence. Every time I read Marx's work, it seems so far of and based on such aggressive assumptions. This gives insightful background into the nature of Marx's work and provides basis for his predictions.

I think the gap between philosophy and results is not to be blamed on traditional philosophy, but rather on the failure to act of society. Change requires trial and error, not just theories and conversation.

I am somewhat confused with this statement. If tradition still holds power in our minds, will we potentially see a reversion from modernity to tradition. Will this be a struggle for ideological power like Hegel's master-slave dialectic?

This is an interesting point, but I think it fails to capture the scope of "being ruled." I think that in some sense every state's ruler[s] are actually ruled behind the scenes. Examples that come to mind are politicians chasing money, support from influential people, and foreign powers. While there is a hierarchy to ruling, I don't think there is any one true ruler[s].

Is this necessarily true? I feel as though humanity has evolved to some extent, and some changes happen without said violence. And if this is true, can this be changed?

I've heard this idea a lot, especially in reference to how communism played out in the U.S.S.R where people grew to be apathetic and lose their political individuality within a communist system.

I didn't know this. I realize that Marx found violence necessary to make change, but I've never thought of it as a hostility to speech.

So Marx is a sort of "activist philosopher" where he writes theory to convince people to practically change and form the system instead of stepping back and observing.

I feel like Arendt is constraining the role of labor in Marx's work. From my cursory understanding of Marx, labor itself isn't bad but the extracting of alienated labor that makes someone an "animal laborans"

Arendt basically is saying here: there's a reason why Athenian society excluded politicians from being laborers: because political work is legitimate. Marx's ideal stateless society where a cook could manage it is impractical

My best guess to what Arendt is saying is that Marx changed the tradition of political theory by focusing on practical aspects like human action and interaction—the practice of politics rather than broad theoretical frameworks that aim to achieve a higher truth on the human condition.

Basically modern's society focus on efficiency has deprived its sense of "wonder" and practice of philosophizing. I guess capitalist culture has made us focus on what is in front of us rather than broader senses of understanding, but idk...I often feel that the philosophizing of Plato's time only existed on the backs of the rampant slavery and exploitation in Ancient Greece. Not to detract from Arendt's broader point, but I feel like there is some implicit glazing of Ancient Greece here.

They all sought to reverse and twist the traditions that preceded them in a convergent manner—a tradition within itself.

If totalitarian movements are the true catalysts for breaks in tradition, is it fair to say that anti-traditionalist political thought may buttress these totalitarian movements, or do these movements stand on their own, only nominally tied to the movements that precede them?

It is interesting that through Kierkegaard's attempt to defend religion in a modern context, he goes against the tradition of relgion and opens the door for modern religious scrutiny.

I don't completely understand this. How can traditions become more oppressive as they lose relevance? Why didn't the growth of anti-traditionalist thinking from Kierkegaard, Marx, and Nietzche cause a break in tradition? I feel like these things would naturally follow.

I agree with this. If we policed work that break tradition, enforcing a singular traditionalist view, we support current and past oppressive structures for the future.

I have always been skeptical about whether privacy on social media is truly “private.” In many cases, so-called private messages are still accessible to platform developers or automated systems, which means users are trusting companies to protect their privacy rather than actually controlling it themselves. While this access can be helpful in situations like reporting threats or harassment, it also raises questions about who ultimately benefits from this arrangement. If only social media companies are able to see and manage my private data, I am not sure that this kind of “privacy” genuinely serves users’ interests rather than the platforms’ own priorities.

It is interesting that using symbols, uppercase letters, and numbers does not significantly increase the difficulty of brute-force attacks, while increasing the length of a password dramatically raises the cost of cracking it. However, many social media platforms still emphasize “complex” password rules rather than encouraging longer passwords. This can create a false sense of security for users, who may believe their passwords are strong when they are not. Ironically, these complexity requirements can even make passwords harder to remember, leading users to reuse them or choose predictable patterns, which ultimately gives attackers more opportunities.

This statement shifts the focus of "protection" from the company level to the individual level, emphasizing that users still have actionable self-protection strategies. The significance of 2FA lies in the fact that even if a password is leaked, it is difficult for attackers to log in directly using only the password, thus reducing the probability of credential stuffing and account takeover. It also reminds us that security is layered – even the best platform security can be compromised by individual vulnerabilities such as phishing or weak passwords. However, this also highlights a limitation: 2FA can only reduce the risk of "account theft," but it cannot solve structural problems such as the platform itself leaking your private data.

This statement highlights the fragility of the "trust relationship" between users and the platform: we share information assuming the platform will protect it properly. It also implies that the problem is not an isolated incident, but "happens frequently," suggesting that security failures may be related to systems, processes, or business priorities. More importantly, the consequences of a single security breach often extend beyond the platform itself, affecting users' accounts on other websites and posing risks to their real lives. Readers will naturally ask: why did the platform fail—was it due to insufficient technical capabilities, management negligence, or prioritizing growth and convenience over security?

This suggests that exercise is an exceptionally reliable "first-line" intervention for the two most common complaints among healthcare workers: which is high stress and poor recovery.

This indicates that "being on your feet all day" at a hospital is not the same as purposeful exercise, and reinforces the need for structured interventions that happen outside of clinical duties.

This quote provides a powerful "business case" for hospitals and clinics to invest in staff exercise programs, framing them as a cost-saving measure rather than an expense.

By focusing solely on aerobic and resistance training, the review provides a more "clinical" look at how physical movement, independent of mindfulness, impacts the brain.

This shows that physical activity programs are a safety intervention, a healthier, more active workforce is statistically less likely to commit medical errors.

ince the significant gap between groups disappeared one month after the sessions ended, the researchers imply that physical activity must be a continuous lifestyle habit rather than a one-time "treatment" to keep burnout at bay.

This frames exercise and yoga programs as a "public health" measure for the hospital unit—by treating one nurse's burnout, managers may prevent it from spreading to others.

This provides a biological rationale for the results: yoga focuses on "rest and digest" (parasympathetic), which may more directly counteract the "fight or flight" (sympathetic) stress common in clinical settings.

This underscores that burnout isn't just a general workplace issue but a specific crisis within the nursing profession that requires specialized self-care strategies.

This suggests that for healthcare workers specifically suffering from the "drained" feeling of emotional exhaustion, yoga may be a more targeted intervention than traditional cardio.

eLife Assessment

This valuable paper describes the regulation of the association of meiotic chromosome axis proteins on chromosome ends with sub-telomeric elements in budding yeast. The genome-wide analyses of binding of chromosome components as well as chromatin regulators, complemented with the mapping of meiotic DNA double-strand breaks on chromosome ends, provided incomplete evidence to support the authors' conclusion. The results in the paper are of interest to researchers in meiotic recombination and the structure of genomes and chromosomes.

Reviewer #1 (Public review):

Meiotic recombination at chromosome ends can be deleterious, and its initiation-the programmed formation of DSBs-has long been known to be suppressed. However, the underlying mechanisms of this suppression remained unclear. A bottleneck has been the repetitive sequences embedded within chromosome ends, which make them challenging to analyze using genomic approaches. The authors addressed this issue by developing a new computational pipeline that reliably maps ChIP-seq reads and other genomic data, enabling exploration of previously inaccessible yet biologically important regions of the genome.

In budding yeast, chromosome ends (~20 kb) show depletion of axis proteins (Red1 and Hop1) important for recruiting DSB-forming proteins. Using their newly developed pipeline, the authors reanalyzed previously published datasets and data generated in this study, revealing heretofore unseen details at chromosome ends. While axis proteins are depleted at chromosome ends, the meiotic cohesin component Rec8 is not. Y' elements play a crucial role in this suppression. The suppression does not depend on the physical chromosome ends but on cis-acting elements. Dot1 suppresses Red1 recruitment at chromosome ends but promotes it in interior regions. Sir complex renders subtelomeric chromatin inaccessible to the DSB-forming machinery.

The high-quality data and extensive analyses provide important insights into the mechanisms that suppress meiotic DSB formation at chromosome ends. To fully realise this value, several aspects of data presentation and interpretation should be clarified to ensure that the conclusions are stated with appropriate precision and that remaining future issues are clearly articulated.

(1) To assess the chromosome fusion effects on overall subtelomeric suppression, authors should guide how to look at the data presented in Figure 2b-c. Based on the authors' definition of the terminal 20 kb as the suppressed region, SK1 chrIV-R and S288c chrI-L would be affected by the chromosome fusion, if any. In addition, I find it somewhat challenging to draw clear conclusions from inspecting profiles to compare subtelomeric and internal regions. Perhaps, applying a quantitative approach - such as a bootstrap-based analysis similar to those presented earlier-would be easier to interpret.

(2) The relationship between coding density and Red1 signal needs clarification. An important conclusion from Figure 3 is that the subtelomeric depletion of Red1 primarily reflects suppression of the Rec8-dependent recruitment pathway, whereas Rec8-independent recruitment appears similar between ends and internal regions. Based on the authors' previous papers (referencess 13, 16), I thought coding (or nucleosome) density primarily influences the Rec8-independent pathway. However, the correlations presented in Figure 2d-e (also implied in Figure 3a) appear opposite to my expectation. Specifically, differences in axis protein binding between chromosome ends and internal regions (or within chromosome ends), where the Rec8-dependent pathway dominates, correlate with coding density. In contrast, no such correlation is evident in rec8Δ cells, where only the Rec8-independent pathway is active and end-specific depletion is absent. One possibility is that masking coding regions within Y' elements influences the correlation analysis. Additional analysis and a clearer explanation would be highly appreciated.

(3) The Dot1-Sir3 section staring from L266 should be clarified. I found this section particularly difficult to follow. It begins by stating that dot1∆ leads to Sir complex spreading, but then moves directly to an analysis of Red1 ChIP in sir3∆ without clearly articulating the underlying hypothesis. I wonder if this analysis is intended to explain the differences observed between dot1∆ and H3K79R mutants in the previous section. I also did not get the concluding statement - Dot1 counteracts Sir3 activity. As sir3Δ alone does not affect subtelomeric suppression, it is unclear what Dot1 counteracts. Perhaps, explicitly stating the authors' working model at the outset of this section would greatly clarify the rationale, results, and conclusions.

Reviewer #2 (Public review):

Summary:

In this manuscript, Raghavan and his colleagues sought to identify cis-acting elements and/or protein factors that limit meiotic crossover at chromosome ends. This is important for avoiding chromosome rearrangements and preventing chromosome missegregation.

By reanalyzing published ChIP datasets, the researchers identified a correlation between low levels of protein axis binding - which are known to modulate homologous recombination - and the presence of cis-acting elements such as the subtelomeric element Y' and low gene density. Genetic analyses coupled with ChIP experiments revealed that the differential binding of the Red1 protein in subtelomeric regions requires the methyltransferase Dot1. Interestingly, Red1 depletion in subtelomeric regions does not impact DSB formation. Another surprising finding is that deleting DOT1 has no effect on Red1 loading in the absence of the silencing factor Sir3. Unlike Dot1, Sir3 directly impacts DSB formation, probably by limiting promoter access to Spo11. However, this explains only a small part of the low levels of DSBs forming in subtelomeric regions.

Strengths:

(1) This work provides intriguing observations, such as the impact of Dot1 and Sir3 on Red1 loading and the uncoupling of Red1 loading and DSB induction in subtelomeric regions.

(2) The separation of axis protein deposition and DSB induction observed in the absence of Dot1 is interesting because it rules out the possibility that the binding pattern of these proteins is sufficient to explain the low level of DSB in subtelomeric regions.

(3) The demonstration that Sir3 suppresses the induction of DSBs by limiting the openness of promoters in subtelomeric regions is convincing.

Weaknesses:

(1) The impact of the cis-encoded signal is not demonstrated. Y' containing subtelomeres behave differently from X-only, but this is only correlative. No compelling manipulation has been performed to test the impact of these elements on protein axis recruitment or DSB formation.

(2) The mechanism by which Dot1 and Sir3 impact Red1 loading is missing.

(3) Sir3's impact on DSB induction is compelling, yet it only accounts for a small proportion of DSB depletion in subtelomeric regions. Thus, the main mechanisms suppressing crossover close to the ends of chromosomes remain to be deciphered.

Reviewer #3 (Public review):

Summary:

The paper by Raghavan et. al. describes pathways that suppress the formation of meiotic DNA double-strand breaks (DSBs) for interhomolog recombination at the end of chromosomes. Previously, the authors' group showed that meiotic DSB formation is suppressed in a ~20kb region of the telomeres in S. cerevisiae by suppressing the binding of meiosis-specific axis proteins such as Red1 and Hop1. In this study, by precise genome-wide analysis of binding sites of axis proteins, the authors showed that the binding of Red1 and Hop1 to sub-telomeric regions with X and Y' elements is dependent on Rec8 (cohesin) and/or Hop1's chromatin-binding region (CBR). Furthermore, Dot1 functions in a histone H3K79 trimethylation-independent manner, and the silencing proteins Sir2/3 also regulate the binding of Red1 and Hop1 and also the distribution of DSBs in sub-telomeres.

Strengths:

The experiments were conducted with high quality and included nice bioinformatic analyses, and the results were mostly convincing. The text is easy to read.

Weaknesses:

The paper did not provide any new mechanistic insights into how DSB formation is suppressed at sub-telomeres.

eLife Assessment

This important study reveals intriguing connections between chromosome breakage and DNA elimination during programmed genome rearrangement in the ciliate Tetrahymena thermophila. By developing a novel FISH approach that distinguishes germline and somatic telomeres, the authors provide compelling evidence that chromosome breakage removes germline telomeres along with hundreds of kilobases of germline-limited sequences. By disrupting a single chromosome breakage site, they further showed that DNA elimination was globally affected, which opens up a new direction for mechanistic studies. Thus, this work reveals additional similarity between the programmed DNA elimination in ciliates and nematodes that underlies the transition from germline to somatic telomeres.

Reviewer #1 (Public review):

Summary:

In this study, Nagao and Mochizuki examine the fate of germline chromosome ends during somatic genome differentiation in the ciliate Tetrahymena thermophila. During sexual reproduction, a new somatic genome is created from a zygotic, germline-derived genome by extensive programmed DNA elimination events. It has been known for some time that the termini of the germline chromosomes are eliminated, but the exact process and kinetics of the elimination events have not been thoroughly investigated. The authors first use germline-specific telomere probes to show that the loss of these chromosome ends occurs with similar timing as other DNA elimination events. By comparative analysis of the assembled germline and somatic genomes, the authors find that the ends of each of the germline chromosomes are composed of a few hundred kilobases of micronuclear limited sequences (MLS) that are removed starting around 14 hours after the start of conjugation, which initiates sexual development. They then develop an in situ hybridization assay to track the fate of one end of chromosome 4 while simultaneously following the adjacent macronuclear destined sequence (MDS) retained in the new somatic genome. This allows the authors to more clearly show that these adjacent chromosomal segments are initially amplified in the developing genome before the terminal MLS is eliminated. Finally, they mutate the chromosome breakage sequence (CBS) that normally separates the MLS terminus from the adjacent MDS region, to show that strains that develop with only one mutant chromosome can produce viable sexual progeny, but it appears that both the MLS and the MDS from the mutant chromosome are lost. If both chromosome copies have the CBS mutation, the cells arrest during development and do not eliminate many germline-limited sequences and fail to produce viable progeny. Overall, this study provides many new insights into the fate of germline chromosome ends during somatic genome remodeling and suggests extensive coordination of different DNA elimination events in Tetrahymena.

Strengths:

Overall, the experiments were well executed with appropriate controls. The findings are generally robust. Importantly, the study provides several novel findings. First, the authors provide a fairly comprehensive characterization of the size of the MLS at the end of each germline chromosome. I'm not sure whether this has been published elsewhere. Second, the authors develop a novel method to study the fate of chromosome termini during development and use it to conclusively track the elimination of these termini. Third, the authors show that the elimination of these termini appears to occur concurrently with most other DNA elimination events during somatic genome differentiation. And fourth, the authors show that failure to separate these eliminated sequences from the normally retained chromosome alters the fate of these adjacent MDS and the loss of the cells' ability to produce viable progeny.

Weaknesses:

It appears the authors did extensive analysis of the MLS chromosome ends, but did not provide too much information related to their composition. If this has not been published elsewhere, it would be useful to describe the proportion of unique and repetitive sequences and provide more information about the general composition of the chromosome ends. Such information would help the reader understand the nature of these MLS and how they may or may not differ from other eliminated sequences. Although the development of the novel FISH probes for large chromosome ends allowed for these novel discoveries, the signal in several images was visible, but often quite faint. I'm not sure there is anything the authors could do to improve the signal-to-noise ratio, but one needs to stare at the images carefully to understand the findings. One main weakness in the opinion of this reviewer is that the authors did very little to understand why, when a terminal MLS and the adjacent MDS fail to get separated because of failure in chromosome breakage, both segments are eliminated. The authors propose that possibly essential genes in the MDS get silenced, and the resulting lack of gene expression is the issue, but this and other possibilities were not tested. The study would provide more mechanistic insight if they had tried to assess whether the MDS on the CBS mutant chromosome becomes enriched in silencing modifications (e.g., H3K9me3). Alternatively, the authors could have examined changes in gene expression for some of the loci on the neighbouring MDS. The other main weakness is that since the authors only mutated the end of one germline chromosome, it is not clear whether the elimination of the MDS adjacent to the terminal MLS on chromosome 4 when the CBS is mutated is a general phenomenon, i.e., would happen at all chromosome ends, or is unique to the situation at Chromosome 4R. Knowing whether it is a general phenomenon or not would provide important insight into the authors' findings.

Reviewer #2 (Public review):

Summary:

Nagao and Mochizuki investigated how the germline (MIC) telomere was removed during programmed genome rearrangement in the developing somatic nucleus (MAC). Using an optimized oligo-FISH procedure, the authors demonstrated that MIC telomeres were co-eliminated with a large region of MIC-limited sequences (MLS) demarcated on the opposite side by a sub-telomeric chromosome breakage site (CBS). This conclusion was corroborated by the latest assembly of the Tetrahymena MIC genome. They further employed CRISPR-Cas9 mutagenesis to disrupt a specific sub-telomeric CBS (4R-CBS). In uniparental progeny (mutant X WT), DNA elimination of the sub-telomeric MLS was not affected, but the adjacent MAC-destined sequence (MDS) may be co-eliminated. However, in biparental progeny (mutant X mutant), global DNA elimination was arrested, revealing previously unrecognized connections between chromosome breakage and DNA elimination. It also paves the way for future studies into the underlying molecular mechanisms. The work is rigorous, well-controlled, and offers important insights into how eukaryotic genomes demarcate genic regions (retained DNA) and regions derived from transposable elements (TE; eliminated DNA) during differentiation. The identification of chromosome breakage sequences as barriers preventing the spread of silencing (and ultimately, DNA elimination) from TE-derived regions into functional somatic genes is a key conceptual contribution.

Strengths:

New method development: Oligo-FISH in Tetrahymena. This allows high-resolution visualization of critical genome rearrangement events during MIC-to-MAC differentiation. This method will be a very powerful tool in this area of study.

Integration of cytological and genomic data. The conclusion is strongly supported by both analyses.

Rigorous genetic analysis of the role played by 4R-CBS in separating the fate of sub-telomeric MLS (elimination) and MDS (retention). DNA elimination in ciliates has long been regarded as an extreme form of gene silencing. Now, chromosome breakage sequences can be viewed as an extreme form of gene insulators.

Weaknesses:

The finding of global disruption of DNA elimination in 4R-CBS mutant progeny is highly intriguing, but it's mostly presented as a hypothesis in the Discussion. The authors propose that the failure to separate MLS from MDS allows aberrant heterochromatin spreading from the former into the latter, potentially silencing genes required for DNA elimination itself. While supported by prior literature on heterochromatin feedback loops, the specific targets silenced are not identified. While results from ChIP-seq and small RNA-seq can greatly strengthen the paper, the reviewer understands that direct molecular characterization may be beyond the scope of the current work.

Reviewer #3 (Public review):

Programmed DNA elimination (PDE) is a process that removes a substantial amount of genomic DNA during development. While it contradicts the genome constancy rule, an increasing number of organisms have been found to undergo PDE, indicating its potential biological function. Single-cell ciliates have been used as a prominent model system for studying PDE, providing important mechanistic insights into this process. Many of those studies have focused on the excision of internally eliminated sequences (IES) and the subsequent repair using non-homologous end joining (NHEJ). These studies have led to the identification of small RNAs that mark retained or eliminated regions and the transposons that generate double-strand breaks.

In this manuscript, Nagao and Mochizuki examined the other type of breaks in ciliates that were healed with telomere addition. They specifically focused on the sequences at the ends of the germline (MIC) chromosomes, which have received relatively less attention due to the technical challenges associated with the highly repetitive nature of the sequences. The authors used the Tetrahymena model and developed a set of new tools. They used a novel FISH strategy that enables the distinction between germline and somatic telomeres, as well as the retained and eliminated DNA near the chromosome ends. This allows them to track these sequences at the cellular level throughout the development process, where PDE occurs. They also analyzed the more comprehensive germline and somatic genomes and determined at the sequence level the loss of subtelomeric and telomere sequences at all chromosome ends. Their result is reminiscent of the PDE observed in nematodes, where all germline chromosome ends are removed and remodeled. Thus, the finding connects two independent PDE systems, a protozoan and a metazoan, and suggests the convergent evolution of chromosome end removal and remodeling in PDE.

The majority of sites (8/10) at the junctions of retained and eliminated DNA at the chromosome ends contain a chromosome breakage sequence (CBS). The authors created a set of mutants that modify the CBS at the ends of chromosome 4R. CBS regions are challenging for CRISPR due to their AT-rich sequences, making the creation of the 4R-CBS mutants a significant breakthrough. They used the FISH assay to determine if PDE still occurs in these mutant strains with compromised CBS. Surprisingly, they found that instead of blocking PDE, its adjacent retained DNA is now eliminated, suggesting a co-elimination event when the breakage is impaired. Furthermore, in biparental mutant crosses, no PDE occurred, and no viable progeny were produced, indicating that the removal of chromosome ends is crucial for proper PDE and sexual progeny development. Overall, the work demonstrates a critical role for 4R-CBS in separating retained and eliminated DNA.

It is hard to ascertain whether Zhao cares about women's education genuinely (even though it is within a patriarchal context), or if this is a means to an end (which is to further cement men's power in a marriage).

I'm interested to know what Zhao would think of widows and remarriage, as well as adulterous husbands.

The author's original claim of being unable to do anything remarkable in life and struggling with her duties seems to show here. This comment on how women should always live in fear seems to be projection from her own time as a 'lowly subject'.

The gendering of strength and weakness is particularly interesting here because Zhao does not think that either one is inferior to the other; he just thinks that both must exist to complement the other. It is interesting to consider the possibility of this proverb hinting at an observation of men and women, particularly that there is a 'fear' that women may turn into tigers.

R0:

Reviewer #1:

Methodology: study design & datasource, heading overlap with data source and extraction. You may consider differentiating the study design+population from the data source+data extraction to improve clarity and avoid repetition.

The covariates are presented in a table. Consider adding a concise paragraph describing key variables and their categorization. Perhaps detailed coding and operational definitions should be presented in a supplementary table.

The analysis plan refers to Objective 2 and Objective 3 however, the study objectives are not stated anywhere earlier in the manuscript. They should be explicitly stated to improve coherence

Also, this contradicts “shared frailty at the individual level”. Please clarify the intended clustering structure. Did you model with the shared or Individual-level frailty?

Reviewer #2:

This manuscript addresses an important and under-researched area by examining long-term trends in attrition from HIV care and associated predictors among adolescent girls and young women (AGYW) with non-viral load suppression in Tanzania. The use of routine programmatic data is a major strength and enhances the relevance of the findings for the national program. The objectives are clear, and the results provide insights into patterns of disengagement from care in this vulnerable population. However, the manuscript would benefit from clearer operational definitions of key outcomes particularly inconsistent viral load suppression. Further discussion linking the findings to existing regional and global literature, as well as clearer description of programmatic and policy implications, would strengthen the paper. I recommend minor revisions to improve clarity and consistency.

R1:

Reviewer #1:

All comments have been addressed

Reviewer #2:

General review of the revised manuscript I have reviewed the revised manuscript and found that the authors have addressed all the comments; however, a few minor edits are still required before publication. Please see the table below.

Section: Introduction <br /> Comment: Correct spelling UANIDS <br /> Line number: 58

Section: Methods <br /> Comment: It is essential that the term inconsistent viral load suppression needs to be clearly defined in the manuscript. Note that two viral load results ≥1000 copies/ml at least six months after ART initiation is the standard definition for non-viral suppression or ART virological failure.<br /> Ideally, inconsistent viral load suppression would have been a situation whereby PLHIV achieves an undetectable viral load at least 6 months after ART initiation, but subsequently experience detectable viral load result/s ≥1000 copies/ml. To avoid confusion, refer to this sentence in conclusion: Notably, individuals with an initial unsuppressed viral load were more likely to disengage from care.

Section: Results <br /> Comment: Table 1: First line and second line not defined. Not every reader knows what Tanzanian ART regimens. Line number: 170-171

Section: Discussion Comment: A citation is not inserted <br /> Line number: 253

Section: Discussion Comment: Correct the word form to read from Line number: 254

Your ideas should be In order so that they make sense for yourself and the reader as well.

Great way to think about it!

If there is a pattern you should fix it to better your writing.

Take your time because at the end of the day your are trying to help improve a piece of writing

Read the whole thing first before giving feedback.

Be formal and respectful when giving feedback to others.

Always do the instructor questions first because that's what's most likely to be graded.

You will make mistakes so it's best to revise at least once.

We need to understand the evidence we use so that the reader understand as well.

eLife Assessment

This important study presents new insights into the post-transcriptional mechanisms that govern cortical development. Through state-of-the-art methodology to track neuronal birth order, the data provide compelling evidence that Imp1 (Igf2bp1/Zbp1) orchestrates radial glia fate transitions and cortical neurogenesis. The findings establish a new framework for understanding how post-transcriptional mechanisms integrate with transcriptional and epigenetic regulatory layers to control cortical temporal patterning.

Reviewer #1 (Public review):

Summary:

A hallmark of cortical development is the temporal progression of lineage programs in radial glia progenitors (RGs) that orderly generate a large set of glutamatergic projection neuron types, which are deployed to the cortex in a largely inside-out sequence. This process is thought to contribute to the formation of proper cortical circuitry, but the underlying cellular and molecular mechanisms remain poorly understood. To a large extent, this is due to technical limitations that can fate map RGs and their progeny with cell type resolution, and manipulate gene expression with proper cell and temporal resolution. Building on the TEMPO technique that Tsumin Lee group developed, here Azur et al show that the RNA binding protein Imp1 functions as a dosage- and stage-dependent post-transcriptional mechanism that orchestrates developmental stage transitions in radial glial progenitors, and controls neuronal fate decisions and spatial organization of neuronal and glial cell progeny. Their results suggest that while transcriptional regulators define available cellular states and gate major transitions, post-transcriptional mechanisms like Imp1 provide an additional layer of control by modulating stage-specific transcript stability. Imp1 thus acts as a temporal coordinator whose dosage and timing determine whether developmental transitions are temporarily delayed or blocked. These findings establish a new framework for understanding how post-transcriptional mechanisms integrate with transcriptional and epigenetic regulatory layers to control cortical temporal patterning.

Strengths:

The authors apply a novel genetic fate mapping and gene manipulation technology (TEMPO) with cellular resolution. This reveals a dosage- and stage-dependent post-transcriptional mechanism that orchestrates developmental stage transitions in radial glial progenitors, and controls neuronal fate decisions and spatial organization of neuronal and glial cell/astrocyte progeny.

Weaknesses:

The endogenous developmental expression pattern of Imp1 and TEMPO-mediated overexpression are not well described or characterized with cellular resolution (whether only in radial glial cells or also in post-mitotic neurons). Thus, the interpretations of the overexpression phenotypes are not always clear.

Reviewer #2 (Public review):

Summary:

In this manuscript, Azur et al seek to determine the role of Imp1/Igf2bp1 in regulating the temporal generation of cortical neuron types. The authors showed that overexpression of Imp1 changes the laminar distribution of cortical neurons and suggest that Imp1 plays a temporal role in specifying cell fates.

Strengths:

The study uniquely used TEMPO to investigate the temporal effects of Imp1/Igf2bp1 in cortical development. The disrupted laminar distribution and delayed fate transition are interesting. The results are presented with proper quantification, they are generally well interpreted, and suggest important roles for Imp1.

Weaknesses:

(1) While the results suggest Imp1 is important in regulating cortical neurogenesis, it remains unclear when and where it is expressed to execute such temporal functions. For instance, where is Imp1 expressed in the developing brain? Is it specific to the radial glial cells or ubiquitous in progenitors and neurons? Does it show temporal expression in RGCs?

(2) The advantage and interpretation of TEMPO need further clarification. TEMPO is an interesting method and appears useful in simultaneously labelling cells and controlling gene expression. Since the reporter, Cas9, and gRNA triggers are all driven by ubiquitous promoters and integrated into the genome using piggyBac, it appears logical that the color transition should happen in all cells over time. The color code appears to track the time when the plasmids got integrated instead of the birthday of neurons. Is this logically true? If the TEMPO system is introduced into postmitotic neurons and the CAG promoter is not silenced, would the tri-color transition happen?

(3) The accumulation of neurons at the subplate region would benefit from showing larger views of the affected hemisphere. IUE is invasive. The glass pipette may consistently introduce focal damages and truncate RGCs. It is important to examine slices covering the whole IUE region.

Reviewer #3 (Public review):

Summary:

The work by Azur and colleagues makes use of the TEMPO (Temporal Encoding and Manipulation in a Predefined Order) methodology to trace cortical neurogenesis in combination with overexpression of Imp1. Imp1 is a mammalian homologue of the Drosophila Imp, which has been shown to control temporal identity in a stem cell context. In their work, they show that overexpression of Imp1 in radial glia, which generate neurons and macroglia in a sequential manner during cortical development, leads to a disruption of faithful neuron/glia generation. They show that continuous overexpression leads to a distinct phenotypic outcome when compared to paradigms where Imp1 was specifically overexpressed in defined temporal windows, enabled by the unique TEMPO approach. Interestingly, the observed phenotype with 'ectopic' generation of mainly lower cortical layer neurons appears not to be due to migration deficits. Strikingly, the overexpression of Imp1 specifically at later stages also leads to ectopic glia-like foci throughout the developing cortical plate. Altogether, the new data provide new insights regarding the role of the post-transcriptional Imp1 regulator in controlling temporal fate in radial glia for the faithful generation of neurons and glia during cortical development.

Strengths:

The TEMPO approach provides excellent experimental access to probe Imp1 gene function at defined temporal windows. The data is very robust and convincing. The overexpression paradigm and its associated phenotypes match very well the expected outcome based on Imp1 loss-of-function. Overall, the study contributes significantly to our understanding of the molecular cues that are associated with the temporal progression of radial glia fate potential during cortical development.

Weaknesses:

The authors provide some experimental evidence, including live imaging, that deficits related to Imp1 overexpression and subsequent overabundance of lower-layer neurons, or accumulation at the subplate, appear to evolve independently of neuronal migration deficits. However, the analysis at the population level might not suffice to make the claim robust. To analyze neuronal migration in more depth, the authors could trace individual neurons and establish speed and directional parameters for comparison.

In their analysis, the authors mainly rely on temporal parameters/criteria to associate the generation of certain neuron fates. While two markers were used to identify the neuronal fate, the variance seems quite high. The authors could consider utilizing an antibody against Satb2, which would provide additional data points that could help to establish statistical significance in some of the analyses.

The analysis of glia was done at postnatal day 10, although gliogenesis and, in particular, astrocyte maturation last at least until postnatal day 28. The authors could consider extending their analysis to capture the full spectrum of their astrocyte phenotype.

R0:

Reviewer #1:

The article is relevant and timely. Please use COREQ guidelines/other community guidelines for qualitative research listed on the EQUATOR network to improve the rigour and flow of the article. Additional suggestions for your consideration are listed below. 1. The rationale for the article as well as research question needs to be made more explicit. Additionally, the novelty of the article is relatively unclear. 2. The use of a framework devised for infectious diseases needs to be examined- why was this framework chosen? 3. Line 52-54: It may be useful to examine the cause of death in context of age; additionally, the later use of terms like young-old-middle aged are ambiguous later in the article, please provide an operational definition. 4. Line 54-57: The reason for prioritising lived experience is unclear. The logical flow establishing the rationale for this inquiry needs strengthening. 5. The linkages of NCDs with urbanisation need more citation and can be explored better. The authors may consider reassessing the introduction section which seems to appear definitive without providing a research gap. 6. Why is this research needed? What makes it relevant for global health? This needs to be elucidated in the text. 7. The use of the word "drug peddler" should be in quotes at least at first use, to ensure that it is seen in its local context. 8. The narrative findings are rich and well presented- the global health impact of these findings/future paths based on these findings need to be be made explicit.

Reviewer #2:

This manuscript is well-written and engaging, and I would like to commend the authors for presenting such rich and dense information.

Here are my comments, which I believe will enhance the content of this manuscript.

1. Page 2, line 27 (abstract section): The term "oppression" appears quite strong, and I do not find any evidence supporting it in the results section. Could the authors clarify what they mean by "oppression"? Additionally, the sentence “as gender norms and experiences of masculinities and femininities reflected the division of roles and access to resources by men and women, which in turn shaped their ability to seek early and better healthcare interventions” seems to contradict the notion of oppression if participants are able to seek early and improved healthcare interventions.

2. Page 6, line 133: typo: a dot is in the middle of the sentence. Consider removing.

3. Page 7 on selection of study participants: How did researchers identify participants based on diabetes and hypertension criteria, given that these medical conditions require a confirmed diagnosis from health professionals? Additionally, were any participants identified using a snowball sampling technique?

4. It is mentioned that this study was conducted as part of the broad ARISE project. Can the authors clarify whether the study participants were enrolled into the main project and if so, to what extent this initial involvement of the main study had impacted the data collection?

5. An important section on data collection is missing. I would like authors to include this in order to explain what specific data were collected at each round, why was it necessary to split data collection over different timepoints, where and in which language were data collected, how long lasted interviews on average, and who collected the data, any bias that may result from the way the study participants perceived the data collection team during fieldwork?

6. The data analysis section requires further clarification. It would be beneficial if the authors could elaborate on their data coding strategy. It appears that a deductive approach was employed, yet they did not explain how the coding frame was initially developed before its application to the data, nor did they specify who conducted these procedures. Additionally, information regarding the software used to facilitate the data analysis is missing.

7. The use of relevant qualitative data reporting guideline is missing. This is important to include as it will help ensure that ensure that the methods and findings are clearly and transparently communicated in the manuscript.

8. In relation to adapting the framework for Infectious Diseases of Poverty Research, it would be beneficial if the authors could clarify the specific adaptations made to the framework and how it was applied during data analysis.

9. They mentioned that participants who faced distress during the interviews were allowed to grieve and were consoled. How many participants developed such condition during interviews at which round of data collection, and to what extent did this impact on data collected?

10. This study seeks to explore the lived experiences of individuals affected by NCDs in informal settlements. However, the findings presented in the results section, particularly the initial part titled "lived experience of NCD," fall short of adequately conveying the participants' current experiences with these health conditions. Firstly, the authors include excerpts from three informants but fail to demonstrate how these accounts are representative of all study participants. Secondly, the discussion of informants' lives prior to developing their current health conditions renders the information less pertinent to the study's objectives. It would be beneficial if the authors could focus on participants current lives to discuss the intersectionality of NCDs with social, economic, and gender factors within the participants' living environments, and how these elements contribute to shaping the management of their health conditions. This way of presenting the findings restricts our understanding, as the same observation is reiterated throughout the discussion section.

11. In the context of chronic disease, it can be assumed that participants with diabetes, hypertension, or comorbidities might encounter similar yet distinct challenges. It would be beneficial for the authors to discuss the findings across these cases, highlighting both similarities and differences, and to establish a hierarchy within the data.

12. The use of terms like "people," "women," or "men" in the results section can be misleading, as they might suggest that the findings apply to the general population. Instead, it is advisable to use terms such as "informants," "study participants," or "men" or "women in this study" to ensure that the information reported is limited to those individuals who participated in this data collection.

The "semi-hidden" file like .git in a git repository implies the existence of a higher order of hidden-ness.

I searched this up and eventually ended up on the Wikipedia article linked below. The explanation bifurcates the idea of a hidden file into stuff hidden for convenience and super important stuff that the OS uses, which "remain hidden unless another setting, called "Hide protected operating system files (Recommended)," is cleared." This fully hidden-ness for sys files likely is to prevent a PEBKAC.

https://en.wikipedia.org/wiki/Hidden_file_and_hidden_directory#:~:text=or%20clear%20attributes.-,File%20Explorer,of%20hidden%20items%20semi%2Dtransparently.

I agree with this, and this limitation was exemplified by when I was going through a command line maze, but the directories used spaces instead of dashes or underscores, which initially led to a bit of friction in me figuring out how to proceed to the next terminal (I used quotes).

eLife Assessment

This is a well-executed intrathecal MRI tracer study that provides valuable early in vivo evidence for CSF drainage into human skull bone marrow and explores clinically relevant associations using robust imaging methodology and regional analyses. However, the evidence supporting the interpretation of early (4 h) tracer signal as impaired clearance is incomplete, and appears difficult to reconcile with established CSF tracer kinetics. They also note that the reported links to sleep and cognitive performance are weakened by reliance on subjective, retrospective questionnaires rather than objective physiological measurements.

Reviewer #1 (Public review):

Summary:

This manuscript examines the passage of an intrathecal CSF tracer into skull bone marrow, cortex, and venous compartments using serial MRI at multiple time points. The study builds on recent anatomical and imaging work suggesting direct communication between CSF spaces and bone marrow in the skull. It extends these observations to a larger, clinically heterogeneous human cohort. The imaging methodology is carefully executed, and the dataset is rich. The findings are potentially important for understanding CSF drainage pathways and their associations with inflammation, sleep quality, and cognition. However, key aspects of the interpretation - particularly regarding tracer kinetics and the definition of "clearance" - require clarification and, in my view, reconsideration.

Strengths:

(1) The study employs a well-established intrathecal contrast-enhanced MRI approach with multiple post-injection time points, enabling the assessment of regional tracer dynamics.

(2) The analysis of skull bone marrow in distinct anatomical regions (near the superior sagittal sinus, lateral fissure, and cisterna magna) is novel and informative.

(3) The cohort size is relatively large for an intrathecal tracer study in humans, and the authors make commendable efforts to relate imaging findings to clinical variables such as inflammation, sleep quality, and cognitive performance.

(4) The manuscript is clearly written, the figures are informative, and the discussion is well grounded in recent anatomical and experimental literature on skull-meningeal connections.

Weaknesses:

The central interpretation that a higher percentage increase in skull bone marrow tracer signal at 4.5 hours reflects reduced clearance is not convincingly justified. Based on the existing CSF tracer literature, the 4-6 hour time window is generally considered an enrichment or inflow phase rather than a clearance phase. Later time points (15 and 39 hours) are more likely to reflect clearance or washout. An alternative interpretation - that a higher signal at 4.5 hours reflects more pronounced tracer entry - should be considered and discussed.

Relatedly, the manuscript lacks a clear conceptual separation between tracer enrichment and clearance phases across time points. If 4.5 hours is intended to represent clearance, this assumption requires more vigorous justification and alignment with prior work.

CSF passage via the nasal/olfactory pathway is insufficiently discussed. Previous human imaging studies have questioned the importance of peri-olfactory CSF clearance, yet the present findings suggest delayed enrichment in the nasal turbinates. This discrepancy should be explicitly addressed, including a discussion of potential methodological limitations (e.g., timing of acquisitions, ROI definition, or sensitivity to slow drainage pathways).

More generally, given the descriptive nature of the study and the limited temporal sampling, some conclusions regarding directionality and efficiency of "drainage" may be overstated and would benefit from more cautious framing.

Reviewer #2 (Public review):

Summary

Zhou et al. utilize longitudinal, intrathecal contrast-enhanced MRI to investigate a novel physiological pathway: the drainage of cerebrospinal fluid (CSF) into the human skull bone marrow. By mapping tracer enrichment across 87 patients at multiple time points, the authors identify regional variations in drainage speed and link these dynamics to systemic factors like aging, hypertension, and diabetes. Most notably, the study suggests that this drainage function serves as a significant mediator between sleep quality and cognitive performance.

Strengths

(1) The study provides a significant transition from murine models to human subjects, showing that CSF-to-marrow communication is a broader phenomenon in clinical cohorts.

(2) The use of four imaging time points (0h to 39h) allows for a precise characterization of tracer kinetics, revealing that the parietal region near the superior sagittal sinus (SSS) is a rapid exit route.

(3) The statistical finding that skull bone marrow drainage accounts for approximately 38% of the link between sleep and cognition provides a provocative new target for neurodegenerative research.

Weaknesses

(1) Figure 1: The figure relies on a single representative brain to illustrate a process that likely varies significantly across different skull anatomies and disease states. In the provided grayscale MRI scans, the tracer enrichment is essentially imperceptible to the naked eye. Without heatmaps or digital subtraction maps (Post-injection minus Baseline) for the entire cohort, it is difficult to substantiate the quantitative "percentage change" data visually.

Reliance on a single, manually placed circular Region of Interest (ROI) is susceptible to sampling bias. A more robust approach would involve averaging multiple ROIs per region (multi-sampling) to ensure the signal is representative of the whole marrow compartment.

(2) Methodological Rigor of Sleep Analysis: The study relies exclusively on the self-reported Pittsburgh Sleep Quality Index (PSQI), which is retrospective and highly prone to recall bias, particularly in a cohort with cognitive impairment. There is no objective verification of sleep (e.g., actigraphy or polysomnography). Since waste clearance is physiologically tied to specific stages, such as Slow-Wave Sleep, subjective scores cannot determine whether drainage is linked to sleep physiology or reflects a higher general disease burden. The MRI captures an acute state during hospitalization, whereas the sleep quality reported covers the month preceding admission. This mismatch complicates the claim that the current drainage function directly reflects historical sleep quality.

Appraisal and Impact

The authors demonstrate the feasibility of monitoring CSF-to-skull marrow drainage in humans. However, the strength of the associations with sleep and cognition is currently attenuated by a lack of visual "proof" in the raw data and a reliance on subjective behavioral metrics. If these technical gaps are explicitly addressed through the use of population heatmaps and more rigorous multi-ROI sampling, this work will significantly advance our understanding of the brain's waste-clearance systems and their role in systemic health.

Reviewer #3 (Public review):

Summary:

In this study, the authors injected a contrast agent into patients and followed the induced signal change with MRI. Doing so, they observed cerebrospinal fluid (CSF) drainage whose magnitude and dynamics varied by anatomical location and scaled with a range of cognitive and socio-demographic metrics, including sleep scores and sex.

Strengths:

I would first like to stress that I am not a specialist in the topic of that paper; so my comments should be taken with a grain of salt, and feedback from the other reviewers should also be carefully considered.

I found the text concise and the figures straightforward to understand. Although they are manually defined, the authors compared drainage across different anatomical locations, which is a positive feature. Albeit purely correlative, the attempt to connect these otherwise 'peripheral' measures to cognitive variables is quite interesting. I also particularly liked the last paragraph of the discussion, which listed the main limitations of the study.

Weaknesses:

In the paragraph starting at line 446, the authors interpret poor sleep quality as being a cause and a consequence of impaired CSF clearance, but their approach is purely correlational. In other words, a third variable could be driving both of these parameters (correct?), thereby explaining their correlation. Later, they also proposed that therapeutically altering CSF clearance could improve cognitive symptoms, but, again, if there's a hidden cause of the correlation, that does not seem like a valid possibility. I believe there were other instances of this sort of inferential problem in the Discussion. It seems essential, particularly in clinical research, to precisely identify what the available evidence supports (correlation) and what is speculation (causation).

Assuming I did not miss it, the approach for testing and reporting correlations is not specified. In particular, the authors report correlation with CSF drainage and a variety of other metrics. But how many tests did the authors perform? They solely mention that they used the Benjamini-Hochberg method to correct for multiple comparisons. How were the decisions to test for this or that effect determined? Or did they test all the metrics they had? Also, that particular correction method is limited when statistics are negatively correlated. It would be helpful to validate findings with another approach.

I assume many of the metrics the authors use are also correlated with one another. Is it possible that a single principal component is driving the different correlations they see? Performing dimensionality reduction across available metrics and relating the resulting principal components to CSF drainage would help clarify the forces at play here.

In their interpretations, the authors claim that the CSF drainage they observe occurs through the bone marrow of the skull. How confident can we be in that claim? Is it that there are no other likely possibilities? It might be an unnecessary question, but given there seems to be no causal intervention (which is fine), and no consideration of alternatives, I am wondering whether this is because other possibilities are improbable or whether they were not adequately considered.

R0:

Reviewer #1: Title: Probabilistic Forecasting of Monthly Dengue Cases Using Epidemiological and Climate Signals: A BiLSTM–Naive Bayes Model Versus Mechanistic and Count-Model Baselines. Manuscript Number: PGPH-D-25-03170

This manuscript presents a rigorous comparative study of probabilistic forecasting models for monthly dengue incidence in Freetown, Sierra Leone, covering the period 2015–2025. It evaluates four major model classes—NB-GLM, INGARCH-NB, Renewal-NB, and BiLSTM-NB—under a leakage-safe rolling-origin evaluation. The article demonstrates strong methodological maturity, careful control of data leakage, and thorough probabilistic evaluation using proper scoring rules, interval coverage, sharpness metrics, PIT diagnostics, and Diebold–Mariano tests. The manuscript is generally well-written, technically sound, and addresses an important operational public health problem. It positions itself as one of the few works offering aligned comparisons of mechanistic, statistical, and deep-learning models under realistic constraints for West African dengue surveillance. This article presents a methodologically rigorous comparison of four probabilistic forecasting approaches—NB-GLM, INGARCH-NB, Renewal-NB, and BiLSTM-NB—applied to monthly dengue case data from Freetown, Sierra Leone (2015–2025). The study addresses an important gap by evaluating mechanistic, statistical, and deep-learning models under aligned, leakage-safe conditions. While the work is comprehensive and technically strong, several critical issues affect its accessibility, interpretability, and broader applicability.

Strengths The study excels in methodological rigor. Its strict leakage safeguards, careful feature-timing rules, and use of expanding-window rolling-origin evaluation significantly strengthen reliability. The inclusion of proper scoring rules, interval coverage, sharpness metrics, PIT histograms, and Diebold–Mariano tests provides a complete probabilistic evaluation rarely seen in dengue forecasting studies. The horizon-specific findings—INGARCH-NB outperforming at 1–2 months and BiLSTM-NB excelling at 3 months—are well supported by aligned comparisons and statistical significance tests. The transparency of data, code, and alignment artefacts enhances reproducibility and credibility. Additionally, the manuscript offers practical guidance for operational forecasting, including a realistic “light climate” input strategy suitable for resource-limited settings.

Limitations Despite its strengths, the manuscript is heavily technical, with extensive mathematical exposition in the main text. This may limit accessibility for public-health practitioners who are likely part of the target audience. The mechanistic renewal model is presented as a baseline but is arguably underspecified; the use of a short, fixed 3-month kernel may not realistically capture dengue’s generation interval dynamics, likely contributing to its poor performance. This limits the interpretive value of the mechanistic comparison. This limitation should be addressed. The study’s climate treatment, while intentionally conservative, may underexploit important environmental drivers. Although justified operationally, this constraint restricts exploration of potentially meaningful lag structures or seasonal climate anomalies. The analysis is limited to a single city and monthly data frequency, raising questions about generalizability across geographies with different climate patterns and dengue transmission dynamics. Moreover, the monthly temporal resolution may obscure rapid outbreak shifts, possibly disadvantaging mechanistic and hybrid models that rely on finer-grained dynamics. This should be addressed. The manuscript makes a valuable and original contribution to dengue forecasting, offering robust methodological innovations and practical insights for real-time surveillance systems. However, improved clarity, stronger justification for mechanistic assumptions, and expanded discussion of generalizability would enhance its usefulness and scholarly impact. With revisions to improve accessibility and contextual depth, the study is well positioned for publication and for informing operational forecasting practice in similar settings.

Reviewer #2: 1. What is PIT in the abstract stand for? The authors should avoid using abbreviations in the abstract. 2. The authors should providing some additional analysis, such as experimenting with alternative or longer serial-interval kernels, or simple sensitivity checks (e.g., different window lengths, or, if possible, finer temporal resolution). 3. Please, justifies the small climate feature set, mentioning any exploratory work with larger sets. 4. The authors should add a clearly labelled missing-data handling subsection that specifies: The imputation method, the number of imputed months, and how they were used in training/evaluation, plus any sensitivity. 5. While the architecture, optimization, and calibration steps are described, the process for choosing hyperparameters is not fully audit-ready. 6. I recommend that the authors conduct an additional experiment to demonstrate the generalizability of the proposed model.

eLife Assessment

This valuable work describes a computational and experimental workflow that turns a moderately stable α-helical bundle into a very stable fold. The authors advance our understanding of α-helix stabilization and provide a convenient framework with implications for the protein design field. The main claims are supported by convincing evidence through sound and well-validated methods, yet further characterization would strengthen specific conclusions for the design of mechanically, thermally, and chemically stable α-helical bundles.

Reviewer #1 (Public review):

Summary:

In the work from Qiu et al., a workflow aimed at obtaining the stabilization of a simple small protein against mechanical and chemical stressors is presented.

Strengths:

The workflow makes use of state-of-the-art AI-driven structure generation and couples it with more classical computational and experimental characterizations in order to measure its efficacy. The work is well presented, and the results are thorough and convincing.

Weaknesses:

I will comment mostly on the MD results due to my expertise.

The Methods description is quite precise, but is missing some important details:

(1) Version of GROMACS used.

(2) The barostat used.

(3) pH at which the system is simulated.

(4) The pulling is quite fast (but maybe it is not a problem)

(5) What was the value for the harmonic restraint potential? 1000 is mentioned for the pulling potential, but it is not clear if the same value is used for the restraint, too, during pulling.

(6) The box dimensions.

From this last point, a possible criticism arises: Do the unfolded proteins really still stay far enough away from themselves to not influence the result? This might not be the major influence, but for correctness, I would indicate the dimensions of the box in all directions and plot the minimum distance of the protein from copies of itself across the boundary conditions over time.

Additionally, no time series are shown for the equilibration phases (e.g., RMSD evolution over time), which would empower the reader to judge the equilibration of the system before either steered MD or annealing MD is performed.

Reviewer #2 (Public review):

Summary:

Qiu, Jun et. al., developed and validated a computational pipeline aimed at stabilizing α-helical bundles into very stable folds. The computational pipeline is a hierarchical computational methodology tasked to generate and filter a pool of candidates, ultimately producing a manageable number of high-confidence candidates for experimental evaluation. The pipeline is split into two stages. In stage I, a large pool of candidate designs is generated by RFdiffusion and ProteinMPNN, filtered down by a series of filters (hydropathy score, foldability assessed by ESMFold and AlphaFold). The final set is chosen by running a series of steered MD simulations. This stage reached unfolding forces above 100pN. In stage II, targeted tweaks are introduced - such as salt bridges and metal ion coordination - to further enhance the stability of the α-helical bundle. The constructs undergo validation through a series of biophysical experiments. Thermal stability is assessed by CD, chemical stability by chemical denaturation, and mechanical stability by AFM.

Strengths:

A hierarchical computational approach that begins with high-throughput generation of candidates, followed by a series of filters based on specific goal-oriented constraints, is a powerful approach for a rapid exploration of the sequence space. This type of approach breaks down the multi-objective optimization into manageable chunks and has been successfully applied for protein design purposes (e.g., the design of protein binders). Here, the authors nicely demonstrate how this design strategy can be applied to successfully redesign a moderately stable α-helical bundle into an ultrastable fold. This approach is highly modular, allowing the filtering methods to be easily swapped based on the specific optimization goals or the desired level of filtering.

Weaknesses:

Assessing the change in stability relative to the WT α-helical bundle is challenging because an additional helix has been introduced, resulting in a comparison between a three-helix bundle and a four-helix bundle. Consequently, the appropriate reference point for comparison is unclear. A more direct and informative approach would have been to redesign the original α-helical bundle of the human spectrin repeat R15, allowing for a more straightforward stability comparison.

While the authors have shown experimentally that stage II constructs have increased the mechanical stability by AFM, they did not show that these same constructs have increased the thermal and chemical stabilities. Since the effects of salt bridges on stability are highly context dependent (orientation, local environment, exposed vs buried, etc.), it is difficult to assess the magnitude of the effect that this change had on other types of stabilities.

The three constructs chosen are 60-70% identical to each other, either suggesting overconstrained optimization of the sequence or a physical constraint inherent to designing ultrastable α-helical bundles. It would be interesting to explore these possible design principles further.

While the use of steered MD is an elegant approach to picking the top N most stable designs, its computational cost may become prohibitive as the number of designs increases or as the protein size grows, especially since it requires simulating a water box that can accommodate a fully denatured protein.

Reviewer #3 (Public review):

Summary:

Qiu et al. present a hierarchical framework that combines AI and molecular dynamics simulation to design an α-helical protein with enhanced thermal, chemical, and mechanical stability. Strategically, chemical modification by incorporating additional α-helix, site-specific salt bridges, and metal coordination further enhanced the stability. The experimental validation using single-molecule force spectroscopy and CD melting measurements provides fundamental physical chemical insights into the stabilization of α-helices. Together with the group's prior work on super-stable β strands (https://www.nature.com/articles/s41557-025-01998-3), this research provides a comprehensive toolkit for protein stabilization. This framework has broad implications for designing stable proteins capable of functioning under extreme conditions.

Strengths:

The study represents a complete framework for stabilizing the fundamental protein elements, α-helices. A key strength of this work is the integration of AI tools with chemical knowledge of protein stability.<br /> The experimental validation in this study is exceptional. The single-molecule AFM analysis provided a high-resolution look at the energy landscape of these designed scaffolds. This approach allows for the direct observation of mechanical unfolding forces (exceeding 200 pN) and the precise contribution of individual chemical modifications to global stability. These measurements offer new, fundamental insights into the physicochemical principles that govern α-helix stabilization.

Weaknesses:

(1) The authors report that appending an additional helix increases the overcall stability of the α-helical protein. Could the author provide a more detailed structural explanation for this? Why does the mechanical stability increase as the number of helixes increase? Is there a reported correlation between the number of helices (or the extent of the hydrophobic core) and the stability?

(2) The author analyzed both thermal stability and mechanical stability. It would be helpful for the author to discuss the relationship between these two parameters in the context of their design. Since thermal melting probes equilibrium stability (ΔG), while mechanical stability probes the unfolding energy barriers along the pulling coordinate.

(3) While the current study demonstrates a dramatic increase in global stability, the analysis focuses almost exclusively on the unfolding (melting) process. However, thermodynamic stability is a function of both folding (kf) and unfolding (ku) rates. It remains unclear whether the observed ultrastability is primarily driven by a drastic decrease in the unfolding rate (ku) or if the design also maintains or improves the folding rate (kf)?

(4) The authors chose the spectrin repeat R15 as the starting scaffold for their design. R15 is a well-established model known for its "ultra-fast" folding kinetics, with folding rates (kf ~105s), near three orders of magnitude faster than its homologues like R17 (Scott et.al., Journal of molecular biology 344.1 (2004): 195-205). Does the newly designed protein, with its additional fourth helix and site-specific chemical modifications, retain the exceptionally high folding rate of the parent R15?

Round off to 2 decimal places

I enjoy this part because it showcases that despite the implied negatives of tourism, the accessibility of Mt. Fuji creates an environment where there are several ways to successfully climb the mountain. Someone does not have to be in peak physical condition to make the trek.

The author's inclusion of the monetary price even in common rituals on the mountain shows how deeply embedded the tourism culture is within a hike that used to be about religion.

The use of the word melting ties the piece together well as is fits with the previous weather mentions. For example, the author's highlight of both the extreme heatwave and two typhoons connect to the connotation of the description melting.

The emphasis on 9th century building adds to the foreign feeling. A general example of an old American building are colonial style buildings from the 17th century, which is nearly 800 years later. Additionally, the dedication to the Chinese scholar who died in 852 AD, implies the existence of preserved records from that time period which feels alien to me.

I like this visual. Having wet clothes drying on a line outside is something everyone recognizes, whether it is something they grew up doing, or they simply saw in multiple pieces of media. Thus it is very easy to picture that scene in your head. At the same time, the author manages to make it hint at the location by specifying that kimonos are drying as opposed to just leaving it at a general statement of clothes are drying.

1. How can workplaces better adapt policies, expectations, and environments to support neurodivergent employees rather than expecting them to conform to traditional norms?
   1. What responsibility do employers have to recognize neurodiversity as a strength while still addressing productivity and performance standards?

Connection: Mental disorders causing distress or disability really shows up for me in how hard it can be to balance school and staying physically active at the same time. When my mental health is struggling, even things I want to do—like going to class prepared, studying, or working out—start to feel overwhelming. Some days my body feels heavy, my motivation drops, and it becomes harder to stay consistent, even though I know movement usually helps my mental health.

This connection helps me see that these challenges aren’t about laziness or lack of discipline, but about how mental health directly affects functioning. Trying to meet academic expectations while pushing myself to stay active can create a lot of internal pressure, especially when I feel like I should be able to “handle it all.” Recognizing this helps me give myself more grace and reminds me that needing balance, flexibility, and rest is part of taking care of both my mental and physical well-being.