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• We thank the reviewers for their useful suggestions regarding how to improve our manuscript.
• Reviewer 3 declared that s/he did not find and evaluate the provided Supplementary Materials. As a result, many of her/his criticisms seem invalid: the requested data, validations etc. were already there in the Supplementary Figures and Tables.
• To avoid confusion, we renamed the transgene that is commonly used as a readout for STAT-activated transcription from 10xStat92E-GFP to 10xStat92E DNA binding site-GFP (please see comments by Reviewer 2 that show how easily one can think that Stat92E protein levels go up because of the misleading name of this transgene).
• One co-author, Martin Csordós was among the authors by mistake. Although first considered, his contribution was not included in either the original or the current manuscript version, so we removed his name from the revised version with his permission.
• We prefer to use colour coding for Sections 2., 3. and 4. in our responses to Reviewer comments rather than splitting the responses to queries in separate sections, because many of our answers contain a mixture of planned experiments (labeled as bold), already available data (labeled as underlined), and *explanations why we think that no additional analyses are necessary* (between asterisks). Data already provided in the original submission but missed by Reviewers has white background in our responses. Reviewer comments

Reviewer 1

Major comments:

R1/1. ”Figure 6E seems to indicate that a subset of Su(var)2-10/PIAS isoforms may bind to ATG8 (directly or indirectly). This leads to the straightforward prediction that this subset should be differentially affected by the selective autophagy at the center of the manuscript. That could be tested to strengthen that point. “

Response:

The Atg8a-binding subset of Su(var)2-10/PIAS isoforms could indeed be differentially affected by selective autophagy__. To test this, we will analyze in vivo Su(var)2-10 isoform abundance on western blots with an anti- Su(var)2-10 antibody in __Atg8aΔ12and ____Atg8aK48A/Y49A (Atg8aLDS) mutants.

Minor comments:

R1/2. “ in Fig S1B,C the colocalization between GFP reporters for STAT92E and AP-1 activity and glia marker does not seem convincing, indicating other cell types may be expressing them as well.”

*Response: *

*The overlap between glia labelling and STAT92E and AP-1 transcriptional readout reporter expression is indeed not complete. First of all, epithelial cells in the wing display both STAT92E and AP-1 activity even in uninjured conditions when glial expression of these reporters is not yet observed. Transcriptional reporter activity outside of the wing nerve was previously indicated in figures with arrowheads, now the epithelium is labeled and the regions containing nerve glia are outlined everywhere. *

The fiber-like reporter expression after injury in the wing nerve could correspond to either glia or axons1–3. Glia in the wing nerve have a filament-like appearance resembling axons in confocal images, even glial nuclei are flat/elongated1. Importantly, STAT92E enhancer-driven GFP also labels the nucleus in expressing cells, as opposed to glially driven mtdTomato that is membrane-tethered (and thus excluded from the nucleus: see Fig. S1B, C). Of note, TRE-GFP and Stat-GFP are not expressed in neurons because the cell bodies and nuclei of wing vein neurons are never GFP-positive, see Fig. 2C, Figs. S1, S4 in Neukomm et al.1 and Figure 1 for Reviewers. We also explain this better now in the revised manuscript (please see the legend of Fig. S1).

Nonetheless, we plan to analyze colocalization of mtdTomato-labeled neurons and TRE-GFP and Stat-GFP around the neuronal cell bodies to unequivocally show their different identities. Additionally, we will include transverse confocal sections of the genotypes in Fig. S1B, C that may better illustrate the colocalization.

Fig. 1 for Reviewers. Neuronal (nSyb+) and Stat92E-GFP+ cell morphology in the L1 vein at the anterior wing margin around the neuronal cell bodies which occupy a stereotypical position at the sensilla1. The location and shape of neuronal nuclei (left panel) are different from Stat-GFP+ cell nuclei (right panel, please see also Fig. S1B, C) based on the circumferential GFP signal. Therefore, cells expressing TRE-GFP and Stat-GFP in injured wing nerves are glia and not neurons.

R1/3. “p.7 Instead of "Su(var)2-10 is mainly nuclear due to its transcriptional repressor and chromatin organizer functions" It may be better to say" .. .consistent with its transcriptional repressor and chromatin organizer functions"”

Response:

We have modified the manuscript accordingly.

R1/4. “It is not clear whether the differences in Su(var)2-10/PIAS accumulation between Atg16 and Atg101 RNAi indicate functional differences of blocking autophagy at different stages or simply differences in RNAi efficiency (Atg16) versus the Atg101 mutant.”

Response:

We have added glial Atg1 (the catalytic subunit of the autophagy initiation complex that also includes Atg101) knockdown experiments that show the same lack of Su(var)2-10 accumulation in uninjured conditions as seen in the Atg101 null mutant (please see Fig. S6C). Please note that Atg16-Atg5-Atg12 dependent conjugation of LC3/Atg8a is involved in various vesicle trafficking pathways in addition to autophagy4–6, alterations of which may perturb baseline Su(var)2-10 levels in uninjured animals.

Significance:

R1/5. “STAT92E-dependent glial upregulation of vir-1, but not Draper, is shown, but consequences for glial functions in nerve injury are not tested.”

Response:

We will test antimicrobial peptide (AMP) expression in glia after nerve injury and whether this is affected by STAT92E and vir-1. Certain AMPs such as Attacin C are known to be regulated by both the Stat and NF-____κΒpathways7, and AMPs can be generally upregulated in response to brain injury8,9. This could serve pathogen clearance functions after defence lines such as the epithelium and blood-brain barrier are compromised. In addition, we will test the recruitment of glial processes into the antennal lobe after olfactory nerve injury in animals with glial STAT92E or vir-1 deficiency. Glial invasion is an adaptive response to axon injury and a first step towards debris clearance10.

R1/6. “experiments indicate a role for Su(var)2-10/PIAS SUMOylation activity in tis autophagic degradation, but it is not clear whether the critical substrata Su(var)2-10/PIAS itself or another protein.”

“binding of Su(var)2-10/PIAS to ATG8 is indicated, but no in vitro experiment performed to test whether this is direct and perhaps SUMOylation dependent.”

Response:

*We aimed to answer this question by using a point mutant form of Su(var)2-10: CTD2, which is unable to properly autoSUMOylate itself11, see Fig. 6D. CTD2 mutant Su(var)2-10 levels increased in S2 cells transfected with the mutant construct relative to the wild-type, similar to lysosome inhibition affecting the wild-type protein level but not the mutant variant. Importantly, wild-type Su(var)2-10 is present in CTD2 mutant Su(var)2-10-transfected cells, which can still SUMOylate other Su(var)2-10 targets. It is thus the intrinsic SUMOylation defect of the CTD2 mutant that results in its impaired degradation. It is firmly established that increased Su(var)2-10/PIAS levels repress STAT92E activity12, mammalian example: Liu et al., 199813, pointing to Su(var)2-10 as the critical substrate for autophagy during STAT92E derepression.*

We will further address this point and investigate if Su(var)2-10 directly binds to Atg8a by in vitro SUMOylation of GST-Su(var)2-10 and subsequent GST pulldown assay with HA-Atg8a. In vitro SUMOylation reaction with purified GST-Su(var)2-10 and negative controls are available via in-house collaboration11. We will incubate the resulting proteins and non-SUMOylated counterparts with in vitro transcribed /translated HA-Atg8a, and interactions will be tested by anti-HA western blotting with quantitative fluorescent LICOR Odyssey CLX detection.

Reviewer 2

Major comments:

R2/1. “The working hypothesis is that upon injury, Su(var)2-10 is degraded by autophagy and, as a consequence, Stat92E induces vir-1 expression.

Could the authors clarify why do Stat92E levels increase upon injury? Does Stat92E stability increase upon ATG mediated Su(var)2-10 degradation? Or does it expression/nuclear translocation change?“

Response:

We did not state that Stat92E levels increase during injury - we only used the 10xStat92E DNA binding site-GFP reporter (we have renamed it as such in our revised manuscript to avoid confusion) that is commonly referred to as 10xStat92E-GFP in the literature14, as a readout for Stat92E-dependent transcription.

To address these questions, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene (https://flybase.org/reports/FBti0147707.htm) to test if STAT92E stability/expression or translocation is altered during injury or upon disruption of selective autophagy. We have already tested this reporter and it is detected in the wing nerve nuclei after injury (Figure 2 for Reviewers, panel A).

As the Atg8aLDS mutation specifically impairs selective autophagy, we will use this mutant and wild-type controls to assess STAT92E::GFP::FLAG abundance on western blots from fly lysates with anti-GFP antibody. To assess STAT92E::GFP::FLAG nuclear translocation as well as stability/expression, we will use independently Atg8aLDS and Su(var)2-10 RNAi in glia to perturb STAT92E -dependent transactivation and visualize glia cell membrane by membrane-tethered tdTomato, glial nuclei by DAPI/anti-Repo and STAT92E with the STAT92E::GFP::FLAG fusion transgene in dissected brains. We can also evaluate STAT92E nuclear translocation with the same genotypes in the injured wing nerve glia. Of note, studies in mammals failed to identify an obvious effect of PIAS1 on STAT1 abundance13, please see Figure 2B from this paper as Figure 2 for Reviewers, panel B. Rather, PIAS family proteins bind tyrosine-phosporylated STAT dimers and impair their DNA binding thereby their transcriptional activation function15.

A.

Proc. Natl. Acad. Sci. USA Vol. 95, pp. 10626–10631

https://doi.org/10.1073/pnas.95.18.10626.

Fig. 2 for Reviewers.

1. Stat92E::GFP::FLAG expression and nuclear appearance in the wing nerve before and after injury
2. Increasing PIAS1 (Su(var)2-10 ortholog) levels does not affect STAT1 abundance in mammalian cells R2/2. “Also, since Su(var) levels increase upon ATG RNAi, independently of injury, do ATG levels increase upon injury? It does not seem to be the case from Fig 6D, but then, if the ATG levels do not increase, how to explain the injury mediated effects of Su(var)2-10? “

Response:

*We have not seen an effect of injury on the rate of autophagic degradation (flux) using the common flux reporter GFP-mCherry -Atg8a in glia after injury (shown in Fig. S2D – not 6D). Also, levels of the typical autophagic cargo p62/Ref(2)P and core autophagy proteins such as Atg12, Atg5, Atg16 do not change after nervous system injury16suggesting no change in general autophagic turnover. *

*An increase in general autophagy would be one option to promote degradation of a given cargo. Just as for the ubiquitin-proteasome system, in selective autophagy the labelling of the cargo/substrate for degradation is a regulated process. Dynamic ubiquitylation of a cargo often promotes its autophagic degradation17. We hypothesize that SUMO may fulfil a similar role in labelling cargo for elimination and this may be promoted by injury in the case of Su(var)2-10, which warrants future studies. *

R2/3. “Su(var)2-10 levels in control and injured wings are different between ATG18RNAi and ATG101 mutant (Fig 5). Could the authors explain the rational for using two ATG mutants? and the meaning of this difference? Also, why comparing data using the RNAi approach and a mutation?”

Response:

This issue was also raised in R1/4 and we refer the Reviewer/Editor to that section for our new Atg1 knockdown data and explanations.

*There is a consensus in the autophagy community that mutants for multiple Atg genes should always be used to ensure that it is indeed canonical autophagy that is affected (because Atg proteins can have non-autophagic roles, as is the case for Atg16 in regulation of phagosome maturation - LAP). *

R2/4. “Fig 6 What is the relevance of the Atg8, Sumo and Su(var)2-10 colocalization at puncta, since there is a lot of colocalization outside the puncta and also lots of Su(var)2-10 or Atg8 labeling that does not colocalize? “

Response:

*Su(var)2-10 orthologs PIAS1-4 localize to the nuclear matrix and certain foci in the chromatin and may play roles in heterochromatin formation, DNA repair, and repression of transposable elements in addition to transcriptional repression18–20. SUMO-modified proteins accumulate in response to PIAS activity in phase-separated foci also referred to as SUMO glue21. We show colocalization of Atg8a with similar Su(var)2-10 and SUMO double positive structures in foci. *

*We do not expect a full overlap between Su(var)2-10 and Atg8a labeling for a number of reasons. First, Su(var)2-10 has many different roles that may not be regulated by autophagy. Second, Atg8a+ autophagosomes in the cytoplasm deliver not only indidivual proteins such as Su(var)2-10 for degradation but also many other cellular components. Third, nuclear Atg8a is implicated in the removal of the Sequoia transcriptional repressor from autophagy genes that is unlikely to involve Su(var)2-1022. Now we include these points in the Discussion section.*

R2/5. “The statement made in the first sentence of the discussion is very strong: 'we have uncovered an activation mechanism for Stat92E', without sufficient supporting evidence.”

Response:

We have rephrased this section as follows:

Here we have uncovered the autophagy-dependent clearance of a direct repressor of the Stat92E transcription factor. This, synergistically with injury-induced Stat92E phosphorylation, may ensure proper Stat92E-dependent responses in glia after nerve injury to promote glial reactivity.

R2/6. “Could the authors validate (some) expression data by in situ hybridization experiments?”

Response:

*Our gene expression data were derived from wing nerve imaging or wing tissue. Unfortunately, in situ hybridization is not feasible in this organ because probes do not penetrate the thick chitin-based cuticule and wax cover of the wing (and the same is true for wing immunostaining).* We do provide independent evidence for vir-1 upregulation in the wing after injury via quantitative PCR (qPCR) in Fig. S5C. To corroborate reporter-based data, we will also analyze drpr in qPCR using wing material after injury at the same time points.

R2/7. “Could the authors validate the RNAi lines molecularly (or refer to published data on these lines?”

Response:

*Almost all RNAi lines have already been validated by qPCR, western blot, or immunostaining in Szabo et al., 202316 and other publications23–25. The only exception is Su(var)2-10JF03384 and we show that it is indistinguishable from the validated Su(var)2-10HMS00750 RNAi line (which causes 95% transcript reduction): it also strongly derepresses STAT activity. These reagents have also been widely used in the community (e.g. https://flybase.org/reports/FBal0242556.htm, https://flybase.org/reports/FBal0233496.htm).*

R2/8. „Clarifying the role of Su(var)2-10 on Stat92E would benefit to the presented work. Does Atg8-Su(var)2-10 binding affect Stat92E accumulation, expression, translocation to the nucleus? Some of these experiments could be obtained in S2 cell transfection assays, if too complex in vivo.”

Response:

As explained in R2/1, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene to test if STAT92E stability/expression or translocation is altered upon disruption of selectiveautophagy (in Atg8aLDS mutant flies).

R2/9. „Also, what happens to the axons in the mutant conditions described in the manuscript? This would higher the impact of the work, but would require in vivo work with fly stocks containing several transgenes.”

Response:

We have already published in our previous paper, Szabo et al., 202316 that the mutants used in the current study display normal axon morphology__. There are only two mutants that we did not test in that paper: Atg8aLDS and our new Atg8anull and we will examine these remaining two during the revision, __but we already published in the above paper that axons appear normal in Atg8aΔ4, a widely used Atg8a mutant allele.

R2/10. „It has been published that Draper is involved in the response to injury in the adult wing nerve. See for example Neukomm et al (2014). The authors should discuss how this fits with their hypothesis and data. In this respect, Fig S4B, which should support the hypothesis, should be improved. It is rather hard to interpret it.”

Response:

Fig. S3 (draper protein trap-Gal4 driven GFP-RFP reporter expression) and S4B (intronic STAT92E binding site of the draper gene driven GFP-RFP reporter expression) show similar results: drpr is already expressed in wing nerve glia before injury, which is in line with Draper’s crucial role in the injury response because Draper-mediated glial signaling triggers glial reactivity. This has been added to the Discussion.

Minor comments:

R2/11. „Rubicon is also a negative regulator of autophagy (doi:10.1038/s41598-023-44203-6). in (Fig2 B, D) we have a higher GFP intensity in both uninjured and injured, and the difference between Injured/uninjured is less significant compared to control. It is possible that Rubicon KD causes more autophagy leading to a higher activation of Stat92E even in control. I wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”

Response:

Loss of Rubicon could indeed potentially remove more Su(var)2-10 via increased autophagy, leading to higher Stat92E activity. However, there is no statistically significant difference between injured and uninjured controls and injured and uninjured Rubicon knockdown, respectively, in Fig2 B, D (p=0.6975 and >0.9999 for each comparison). We are puzzled by the statement that the reviewer „wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”. We analyzed Rubicon as a factor critical for LAP and its deficiency does not prevent Stat transcriptional activity following injury unlike the loss of Atg8a, Atg16, Atg13 and Atg5. We will further support this result with a mutant of Atg16 with part of the WD40 domain deleted, because this region is critical for LAP but not for autophagy.16,26,27

R2/12. „The rationale for using both repoGal4 and repoGS is unclear. If, as mentioned, the goal is to avoid developmental defects, repoGS should be consistently used. Especially I don't understand how both were utilized to knock down the same genes, such as Atg16”

Response:

*We had to use repoGS (a drug-inducible Gal4 active in glia) because knocking down Su(var)2-10 with repoGal4 resulted in no viable adult progeny. Su(var)2-10 is an essential gene as opposed to most autophagy genes and its absence results in embryonic lethality24. Thus all Su(var)2-10 silencing experiments were done with repoGS. Similarly, Stat92E is involved in various developmental processes and its loss is embryonic lethal. repoGal4 was used for genes generally not having an adverse effect when absent during development16 in the first two figures. In Fig. 4D, we silenced Atg16 by repoGS because it is one of the controls for testing a genetic epistasis between Su(var)2-10 and Atg16. Please note that we see exactly the same phenotype in case of Atg16 knockdown when using either Gal4 version.* This has been explained in the revised methods section.

R2/13. „In the third paragraph of the introduction, I am confused whether Stat92E regulates drpr of the reverse”

Response:

Upon antennal injury, Drpr receptor binding to phagocytic cargo initiates a positive feedback loop in glial cells to promote its own transcription28. Drpr receptor in the plasma membrane regulates Stat92E and AP-1 activity via signal transduction. Stat92E and AP-1, in turn, increases drpr transcription10,28–30 that will result in more plasma membrane Drpr protein expression. We have explained this more clearly in the revised Introduction.

R2/14. „I cannot find the evidence for vir-1 being expressed in glia and target of Gcm in the refences that have been cited.”

Response:

We apologize for not explaining this better: vir-1 is called CG5453 in Freeman et al., 200331. It is listed in Table 1 as a Gcm target since there is no detectable CG5453 expression in a Gcm null mutant, please see below. We have updated the manuscript with this gene name.

.....

.....

Part of Table 1 from Freeman et al., 200331.

R2/15. „The presence of a Stat92E binding site on the vir-1 promoter has already bene described in the paper from Imler and collaborators, Nature immunology 2005. Actually, if this site is present in their transgenic line, it would help the authors strengthen the argument that Stat92E has a direct role on vir1 (for which they make a very strong statement in the discussion, with no direct evidence).”

Response:

*The evidence that Stat92E may have a direct role in vir-1 transcription in glia comes exactly from the same reporter transgene described by Imler and collaborators in the mentioned paper32. We received this transgenic line from the Imler group and monitored its expression after injury upon depletion of Stat92E (Fig. 3B). It thus contains the studied Stat binding site. This was referenced in the Methods and in all relevant sections of the main text, and we now explicitly state this in the revised text.*

R2/16. „In the Fig S2D, I do not see a lot of GFP+ (Glia) cells. I see more Atg8a in injured 3 dpi regardless of colocalization with glia”

Response:

Fig S2D uses one of the standard assays for autophagic turnover, which we now explain in more detail in the Results section. Basically, the dual tagged GFP::mCherry::Atg8a transgene is expressed in glia, and GFP is quenched in lysosomes after delivery by autophagy while mCherry remains fluorescent. So, in addition to double positive dots (autophagosomes), there are mCherry dots lacking GFP (autolysosomes) if autophagy is functional. All of these dots are in glia but the cell boudaries are not visible.

The images shown are single optical slices. The number of mCherry+ puncta are around 7-8 per field in both uninjured and injured (3 dpi) conditions, but puncta brightness is always variable. Since most mCherry+ puncta were rather bright in the original 3 dpi image, we changed it to a more representative image.

R2/17. „The quantification of the signals is made in a specific region of the wing, I guess throughout the nerve thickness. This could be represented more carefully in a schematic and It would also help defining colocalization in the first figure, by using a transverse section.”

Response:

The quantification method is described in Materials and Methods and we have added that quantification was done on single optical slices. The imaged region is depicted in Fig. S1A, where we indicated the rectangular region used in Fiji for image quantification. We will add transverse sections of wings as suggested.

R2/18. „A number of ATG genes are considered in the manuscript, but the rational for using them is not always clear. Showing a schematic would help clarify this. „

Response:

We have added a table showing the different steps of autophagy where the studied Atg genes/proteins function (now Supplementary Table 1). We also added whether the gene is considered specific for autophagy or can play a role in another process, e.g. LAP. We studied different autophagy genes in line with the assumption that disabling distinct autophagic complexes should produce the same phenotype if this process is indeed autophagy (and not LC3-associated phagocytosis for example).

R2/19. „Fig 7 is not cited and its legend is very short.”

Response:

We have now cited Fig 7 and expanded its legend.

R2/20. „Clarify the color coding in Fig S1E”

Response:

We added that red is injured, black is uninjured.

R2/21. „What is the tandem tagged autophagic fly reporter in fig S2D?”

Response:

This is one of the most common tools to study autophagy, please see the updated explanation above at your first question regarding Fig. S2D.

R2/22. „Add a schematic on the vir-1 isoforms.”

Response:

We have added a a schematic showing the vir-1 isoforms in Fig. S5B.

R2/23. „Fig S6B and Fig 5 relate on the levels of Su(var)2-10 upon Atg16 RNAi, but the scale is not the same, why?”

Response:

*The scales are different because these two images measure different things. Fig. 5 indeed displays quantification of Su(var)2-10 levels in brain glia. However, Fig S6B shows quantification of Stat92E-induced GFP reporter levels (as a proxy of Stat92E transcriptional activity) in the wing nerve upon Atg16 knockdown. *

Reviewer 3

R3/1. „The claim that the negative regulator of Stat92E signaling is removed by selective autophagy, involving selective autophagy receptors different from/in addition to Ref(2)P/p62 is not convincingly shown. This claim probably needs to be softened.”

Response:

*We have rephrased this sentence as follows: *

„These data suggest that selective autophagy is involved in Stat92E-dependent transcriptional activation in glia.”

R3/2. „The reporter that was used (10xSTAT92E-eGFP) is not a dynamic reporter of STAT92E activity. It accumulates in glia and is highly stable. The appropriate reporter to look at dynamic changes would be 10XSTAT92E-dGFP, which has a degradable (unstable) GFP that is required to see dynamic changes even in the CNS. All of the claims about STAT92E regulation use this reporter, so they are questionable.”

Response:

10XSTAT92E-dGFP featuring destabilized GFP could be a more appropriate tool for monitoring dynamic changes in transcription when short term- e.g. few hours - changes are investigated. However, we did not see any expression of 10XSTAT92E-dGFP (we tried 2 different transgenic insertions) in the wing nerve, please see Figure 3 for Reviewers. In the brain, dGFP expression with this reporter is also several times lower than stable GFP, please compare Fig. 4A and B in Doherty et al28.

The use of 10xSTAT92E-eGFP to follow dynamic expression changes is justified by many lines of evidence. First, there is no 10xSTAT92E-EGFP expression in uninjured wing nerves (Fig. S1D,E). Injury induces EGFP expression in the wing nerve with a sustained activation from 1 to 3 dpi (days post injury), and the EGFP expression returns to the baseline by 5 dpi (Fig. S1D, E). Second, the initial Stat-dependent upregulation of drpr and the 10XSTAT92E-dGFP signal in the brain both occur in the first 24 hours after injury and are sustained for 72 hours28 similar to our results with 10xSTAT92E-EGFP ((Fig. S1D,E). These results indicate that the dynamics of 10xSTAT92E-EGFP expression allows monitoring changes in Stat-dependent transcription occurring over days.

Figure 3 for Reviewers. Lack of 10XSTAT92E-dGFP signal in the wing nerve from two independent insertions of the same transgene at the indicated time points after wing injury.

R3/3. „The claim that glial drpr is not upregulated by wing injury and drpr accumulation is not apparently a prerequisite for efficient debris processing within the wing is weak. First, they did not stain for Draper using antibodies, rather they used expression constructs. Dee7 is a promoter that was found to be injury activated in the CNS (were they able to replicate that result? I did not receive the supplemental data), but it might not be the crucial regulator in the periphery. The MIMIC line that was converted is better, but might not represent the full spectrum of regulatory events at the draper locus. Finally, they never actually test for endogenous RNA changes, or use the antibody on westerns. Their lack of evidence is not as compelling as it could be.”

Response:

The__ original Supplemental Material already provides answers for this and subsequent questions of Reviewer 3__. We deposited the Supplemental Material to bioRxiv at the time of the first Review Commons submission and it was/is available at https://www.biorxiv.org/content/10.1101/2024.08.28.610109v2.supplementary-material.

Figs. S3 and S4 show in the wing and the brain (using two different drpr reporters for its transcriptional regulation) that drpr expression does not change much in the wing after nerve injury, as opposed to the brain.

*We did indeed replicate that dee7-Gal4 expression is induced in the brain after antennal injury using UAS- TransTimer (Fig. S4A). In contrast, wing cell nuclei already show expression of both fluorescent proteins in uninjured conditions, and RFP+ nucleus numbers do no change after wing injury (Fig. S4B, C). drpr-Gal4 was generated by conversion of a MiMIC gene trap element into a Gal4 that traps all transcripts. drprMI07659 is in an intron that is common in all drpr isoforms so it should capture the regulation of all transcript isoforms. *

We will further analyze drpr expression via independent methods during the revision: qPCR amplification of a common region of drpr transcripts, and western blot with anti-Drpr antibody to compare injured and uninjured wing material. Of note, we see no upregulation of drpr 2 days after wing injury in our (unpublished) RNAseq results either.

*Unfortunately, immunostaining of the adult wing is not feasible because antibodies do not penetrate the thick chitin-based cuticle and wax cover of the wing.*

R3/4. „The authors claim autophagy contributes to glial reactive states in part by acting on JAK-STAT pathway via regulation of Stat92E. They did not investigate other potential STAT92E targets. Does Atg16 knockdown alter STAT92E expression? Apparently Vir1 is still upregulated in the absence of Atg16 following injury, but they don’t show STAT92E changes.”

Response:

We did investigate other potential STAT92E targets besides vir-1. This is referred to in the text as „*immunity-related gene reporters” and it again can be found in the Supplemental Material (____Supplementary Table 2). None of these genes showed glia-specific upregulation following injury. *

We will investigate STAT92E expression with the STAT92E::GFP::FLAG protein-protein fusion transgene after disrupting autophagy as also suggested by Reviewer 2. Please see our detailed answer to the first comment of Reviewer 2.

*We do not agree with the comment that „Vir1 is still upregulated in the absence of Atg16 following injury” because Fig. 3F,G show that lack of Atg16 abolishes the upregulation of the vir-1 reporter: the change from uninjured to injured becomes statistically not significant and the mean GFP intensities are practically identical. *

R3/5. „The authors claim Su(var)2-10 is an autophagic cargo. They should better characterize Su(var)2-10 degradation and its regulation, and image quality needs to be improved (better images, merged examples, and clearer indication of what they are highlighting. There are many arrows in figures that I don't know what they are pointing to. Much of the labeling in Fig 1 (and others) looks like axons. Could TRE-GFP be turned on in neurons? How did they discriminate?”

Response:

As also explained to Reviewer 1’s last comment, we will carry out experiments to address whether SUMOylated Su(var)2-10 binds Atg8a, which can provide evidence for a direct SUMO-dependent autophagic elimination of Su(var)2-10. Please see our detailed response there.

We will further improve image quality for brain images and we already incorporated new images in Fig. S6. *Merged images were missing only in Fig 5, which we have included in the current version. Arrows and arrowheads were used as described in Figure legends, but instead of those, we now clearly label the epithelium and we outlined the region of wing nerve glia in all images. *

Please see our response to the first minor comment of Reviewer 1 regarding the expression of reporters in wing tissues.

R3/6. „The authors claim interaction of Su(var)2-10 with Atg8a in the nucleus and cytoplasm can trigger autophagic breakdown, involving Su(var)2-10 SUMOylation. The paper would benefit from showing direct SUMOylation of Su(var)2-10 after injury. Is there any way to examine this in vivo?”

Response:

We will test direct SUMOylation of Su(var)2-10 using a recently described method by Andreev et al., 202233. FLAG-GFP-Smt3 (SUMO)____ is expressed under SUMO transcriptional regulation and we will immunoprecipitate FLAG-GFP-SUMO and GFP alone as negative control with GFPTrap beads from lysates of heads subjected to traumatic brain injury that results in glial reactivity16____, and also from uninjured head lysates. We will use anti-____Su(var)2-10 ____western blotting to visualize SUMOylated Su(var)2-10 and whether its levels are modulated by brain injury.

R3/7. „The authors state in discussion "we find that draper is highly expressed in wing nerve glia already in uninjured conditions and it is not further induced by wing transection - indicating high phagocytic capacity in wing glia ... axon debris clearance takes substantially longer in the wing nerve than in antennal lobe glomeruli, thus draper levels may not readily predict actual phagocytic activity in glia". However, they never actually assess this in their experiments. All the conclusions about Draper are made from promoter fusions of integrated reporters, which are imperfect. This conclusion cannot be made.”

Response:

As described in our response to R3/3, we will further test drpr expression changes after wing injury using two independent methods: qPCR and western blot .

We deleted this part from the Discussion that were criticized by the reviewer because these are not important for the main message of our manuscript.

R3/8. „Both STAT92E and Jun are activated by a stress response. Could this be a stress response to disrupting autophagy that is somehow enhance by injury?”

Response:

*Stress responses are indeed relayed by AP-1 and Stat signaling, and impaired autophagy could be a source of stress. We would like to emphasize, though, that the main finding of our manuscript is that disrupting autophagy suppresses Stat-dependent transcription. Autophagy inhibition does not increase Stat signaling in uninjured wing nerves and while control flies upregulate Stat activity upon injury, autophagy-deficient animals fail to do so (Fig. 1). Thus, Stat signaling is not activated by loss of autophagy – it is activated by injury (that is the stress) and Stat activation requires autophagy in this setting.*

R3/9. „Minor:

I don't think that "glially" is a word.”

Response:

Online dictionaries such as Wiktionary list glially as a word, and many scientific articles use it: https://doi.org/10.1016/j.conb.2022.102653, https://doi.org/10.1016/j.yexcr.2013.08.016,https://doi.org/10.1016/j.jpain.2006.04.001*, to give some examples. *

We nonetheless refrain from using it in the updated text.

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26. Fletcher, K., Ulferts, R., Jacquin, E., Veith, T., Gammoh, N., Arasteh, J.M., Mayer, U., Carding, S.R., Wileman, T., Beale, R., et al. (2018). The WD40 domain of ATG16L1 is required for its non‐canonical role in lipidation of LC3 at single membranes. EMBO J 37, e97840. https://doi.org/10.15252/embj.201797840.
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Referee #3

Evidence, reproducibility and clarity

In this study the authors explore a potential role for STAT92E and Su(var)2-10 in glial responses to injury in the adult Drosophila wing. The major claims are that canonical autophagy and not LAP sustains STAT92E signaling after in jury. The negative regulator STAT92E is removed by selective autophagy, but this is not ref(2)p/p62 (perhaps). Glial draper expression is not upregulated and Draper accumulation is not apparently a prerequisite for efficient debris clearance in the wing. Su(var)2-10 is an autophagic cargo, mediator of STAT92E-dependennt transcription; and interacts with Atg8a, perhaps sumoylating targets. In general, the model is reasonable, but the data do not support the conclusions, and the quality of the data needs improvement before firm conclusions can be reached. Concerns include:

1. The claim that the negative regulator of Stat92E signaling is removed by selective autophagy, involving selective autophagy receptors different from/in addition to Ref(2)P/p62 is not convincingly shown. This claim probably needs to be softened.
2. The reporter that was used (10xSTAT92E-eGFP) is not a dynamic reporter of STAT92E activity. It accumulates in glia and is highly stable. The appropriate reporter to look at dynamic changes would be 10XSTAT92E-dGFP, which has a degradable (unstable) GFP that is required to see dynamic changes even in the CNS. All of the claims about STAT92E regulation use this reporter, so they are questionable.
3. The claim that glial drpr is not upregulated by wing injury and drpr accumulation is not apparently a prerequisite for efficient debris processing within the wing is weak. First, they did not stain for Draper using antibodies, rather they used expression constructs. Dee7 is a promoter that was found to be injury activated in the CNS (were they able to replicate that result? I did not receive the supplemental data), but it might not be the crucial regulator in the periphery. The MIMIC line that was converted is better, but might not represent the full spectrum of regulatory events at the draper locus. Finally, they never actually test for endogenous RNA changes, or use the antibody on westerns. Their lack of evidence is not as compelling as it could be.
4. The authors claim autophagy contributes to glial reactive states in part by acting on JAK-STAT pathway via regulation of Stat92E. They did not investigate other potential STAT92E targets. Does Atg16 knockdown alter STAT92E expression? Apparently Vir1 is still upregulated in the absence of Atg16 following injury, but they don't show STAT92E changes.
5. The authors claim Su(var)2-10 is an autophagic cargo. They should better characterize Su(var)2-10 degradation and its regulation, and image quality needs to be improved (better images, merged examples, and clearer indication of what they are highlighting. There are many arrows in figures that I don't know what they are pointing to. Much of the labeling in Fig 1 (and others) looks like axons. Could TRE-GFP be turned on in neurons? How did they discriminate?
6. The authors claim interaction of Su(var)2-10 with Atg8a in the nucleus and cytoplasm can trigger autophagic breakdown, involving Su(var)2-10 SUMOylation. The paper would benefit from showing direct SUMOylation of Su(var)2-10 after injury. Is there any way to examine this in vivo? The authors state in discussion "we find that draper is highly expressed in wing nerve glia already in uninjured conditions and it is not further induced by wing transection - indicating high phagocytic capacity in wing glia ... axon debris clearance takes substantially longer in the wing nerve than in antennal lobe glomeruli, thus draper levels may not readily predict actual phagocytic activity in glia". However, they never actually assess this in their experiments. All the conclusions about Draper are made from promoter fusions of integrated reporters, which are imperfect. This conclusion cannot be made. Both STAT92E and Jun are activated by a stress response. Could this be a stress response to disrupting autophagy that is somehow enhance by injury?

Minor:

I don't think that "glially" is a word.

Significance

Based on the quality of the data, it is hard to consider this manuscript having made a major step forward. A significant amount of work needs to be done to firm up the conclusions. In its present form, the major contributions are the identification vir-1 as upregualted (maybe) and a potential role for autophagy.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The manuscript by Vincze et al. explores the regulatory mechanisms of Stat92E in glial reactivity following axonal injury. Utilizing a wing injury model in Drosophila, the study demonstrates the role of autophagy in regulating Stat92E expression in glia during injury. Through genetic and biochemical assays, the authors reveal that autophagy facilitates the degradation of Su(var)2-10, a negative regulator of Stat92E, thereby enabling the activation of this pathway. Overall, this study highlights a crucial role for autophagy in glial immunity during axonal injury.

Major comments:

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

The working hypothesis is that upon injury, Su(var)2-10 is degraded by autophagy and, as a consequence, Stat92E induces vir-1 expression.<br /> Could the authors clarify why do Stat92E levels increase upon injury? Does Stat92E stability increase upon ATG mediated Su(var)2-10 degradation? Or does it expression/nuclear translocation change? Also, since Su(var) levels increase upon ATG RNAi, independently of injury, do ATG levels increase upon injury? It does not seem to be the case from Fig 6D, but then, if the ATG levels do not increase, how to explain the injury mediated effects of Su(var)2-10? Su(var)2-10 levels in control and injured wings are different between ATG18RNAi and ATG101 mutant (Fig 5). Could the authors explain the rational for using two ATG mutants? and the meaning of this difference? Also, why comparing data using the RNAi approach and a mutation? Fig 6 What is the relevance of the Atg8, Sumo and Su(var)2-10 colocalization at puncta, since there is a lot of colocalization outside the puncta and also lots of Su(var)2-10 or Atg8 labeling that does not colocalize? The statement made in the first sentence of the discussion is very strong: 'we have uncovered an activation mechanism for Stat92E', without sufficient supporting evidence. - Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.

Could the authors validate (some) expression data by in situ hybridization experiments? Could the authors validate the RNAi lines molecularly (or refer to published data on these lines? - If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL". - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.

Clarifying the role of Su(var)2-10 on Stat92E would benefit to the presented work. Does Atg8-Su(var)2-10 binding affect Stat92E accumulation, expression, translocation to the nucleus? Some of these experiments could be obtained in S2 cell transfection assays, if too complex in vivo. Also, what happens to the axons in the mutant conditions described in the manuscript? This would higher the impact of the work, but would require in vivo work with fly stocks containing several transgenes. - Are the data and the methods presented in such a way that they can be reproduced?

It has been published that Draper is involved in the response to injury in the adult wing nerve. See for example Neukomm et al (2014). The authors should discuss how this fits with their hypothesis and data. In this respect, Fig S4B, which should support the hypothesis, should be improved. It is rather hard to interpret it. - Are the experiments adequately replicated and statistical analysis adequate?

Yes

Minor comments:

• Specific experimental issues that are easily addressable.

Rubicon is also a negative regulator of autophagy (doi:10.1038/s41598-023-44203-6). in (Fig2 B, D) we have a higher GFP intensity in both uninjured and injured, and the difference between Injured/uninjured is less significant compared to control. It is possible that Rubicon KD causes more autophagy leading to a higher activation of Stat92E even in control. I wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis The rationale for using both repoGal4 and repoGS is unclear. If, as mentioned, the goal is to avoid developmental defects, repoGS should be consistently used. Especially I don't understand how both were utilized to knock down the same genes, such as Atg16. In the third paragraph of the introduction, I am confused whether Stat92E regulates drpr of the reverse? - Are prior studies referenced appropriately?

Published work should be acknowledged properly. I cannot find the evidence for vir-1 being expressed in glia and target of Gcm in the refences that have been cited.

The presence of a Stat92E binding site on the vir-1 promoter has already bene described in the paper from Imler and collaborators, Nature immunology 2005. Actually, if this site is present in their transgenic line, it would help the authors strengthen the argument that Stat92E has a direct role on vir1 (for which they make a very strong statement in the discussion, with no direct evidence). - Are the text and figures clear and accurate? - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

In the Fig S2D, I do not see a lot of GFP+ (Glia) cells. I see more Atg8a in injured 3 dpi regardless of colocalization with glia. The quantification of the signals is made in a specific region of the wing, I guess throughout the nerve thickness. This could be represented more carefully in a schematic and It would also help defining colocalization in the first figure, by using a transverse section. A number of ATG genes are considered in the manuscript, but the rational for using them is not always clear. Showing a schematic would help clarify this. Fig 7 is not cited and its legend is very short.

Clarify the color coding in Fig S1E

What is the tandem tagged autophagic fly reporter in fig S2D?

Add a schematic on the vir-1 isoforms.

Fig S6B and Fig 5 relate on the levels of Su(var)2-10 upon Atg16 RNAi, but the scale is not the same, why?

Significance

• 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? The manuscript by Vincze et al. investigates the regulatory mechanisms of Stat92E in glial reactivity following axonal injury. This research addresses a significant topic relevant to neuroinflammatory conditions in humans, such as neurodegenerative diseases. Utilizing a wing injury model in Drosophila, the study identifies a novel upstream regulatory mechanism of Stat92E. Specifically, after axonal injury, autophagy facilitates the degradation of Su(var)2-10, a negative regulator of Stat92E in glia. This process enables a non-canonical activation of the JAK-STAT pathway, leading to the induction of downstream target genes, such as Vir-1, highlighted in this study. Altogether, the manuscript advances our understanding of the glial response to neuronal damage, building on previous work by this group and others. Notably, it highlights progress in the role of both autophagy machinery and JAK-STAT pathway in this context.
• Limitations and possible improvements: A more mechanistic analysis will higher the impact of the findings. Clarifying the role of Su(var)2-10 on STAT92E would benefit to the presented work. Does Atg8-Su(var)2-10 binding affect STAT92E accumulation, expression, translocation to the nucleus? Also, what happens to the axons in the mutant conditions described in the manuscript?
• 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,...). The work provides a conceptual advance in the field by assessing the role of ATG genes and a novel pathway linked to STAT and vir-1.
• 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? A broad audience working on neurodegeneration will be interested in the described work.
• 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. Neural development and the transcriptional mechanisms involved to the process.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Regulation of immune pathway responses in glia is critical after nervous system injuries. The authors use the nerve fibers in the Drosophila wing as a model system to further analyze molecular mechanisms of glial responses with a focus on the regulation of the STAT92E protein, the single STAT family protein in Drosophila, which in glial injury responses has previously been shown to be independent of the canonical Domeless receptor / JAK kinase pathway.

Here, the authors show convincingly that STAT92E activation depends on the selective autophagic degradation of the SUMOligase Su(var)2-10/PIAS in the absence of elevated bulk autophagy. IF and IP experiments indicated that direct or indirect interactions with Atg8 may drive this selective autophagy of Su(var)2-10/PIAS and that its SUMOylation activity appears to promote its degradation.

Further observations show that STAT92E in this context does not result in elevated expression of the glial phagocytic Draper receptor and instead yields elevated vir-1 expression with unknown consequences for neuronal health.

All key conclusions in the paper are well supported by experimental evidence and careful quantification.

Major comments:

Figure 6E seems to indicate that a subset of Su(var)2-10/PIAS isoforms may bind to ATG8 (directly or indirectly). This leads to the straightforward prediction that this subset should be differentially affected by the selective autophagy at the center of the manuscript. That could be tested to strengthen that point.

Minor comments:

• in Fig S1B,C the colocalization between GFP reporters for STAT92E and AP-1 activity and glia marker does not seem convincing, indicating other cell types may be expressing them as well.
• p.7 Instead of "Su(var)2-10 is mainly nuclear due to its transcriptional repressor and chromatin organizer functions" It may be better to say" .. .consistent with its transcriptional repressor and chromatin organizer functions"
• It is not clear whether the differences in Su(var)2-10/PIAS accumulation between Atg16 and Atg101 RNAi indicate functional differences of blocking autophagy at different stages or simply differences in RNAi efficiency (Atg16) versus the Atg101 mutant.

Significance

The manuscript convincingly shows that autophagic degradation is an important component of the regulation of STAT92E, an important transcriptions factor for glial responses to nerve injuries. That is a novel observation that will be of interest to experts in the field of autophagy and its roles in brain homeostasis.

In addition. some other interesting initial observations are reported, but without much follow up that could have significantly strengthened the paper:

• STAT92E-dependent glial upregulation of vir-1, but not Draper, is shown, but consequences for glial functions in nerve injury are not tested.
• experiments indicate a role for Su(var)2-10/PIAS SUMOylation activity in tis autophagic degradation, but it is not clear whether the critical substrata Su(var)2-10/PIAS itself or another protein.
• binding of Su(var)2-10/PIAS to ATG8 is indicated, but no in vitro experiment performed to test whether this is direct and perhaps SUMOylation dependent.

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Reply to the reviewers

Reviewer # 1: The study is well-executed, and the claims are supported by appropriate experiments. As introduced by the authors in their introduction, ubiquitin-dependent endocytosis of AA transporters has been previously shown in S. cerevisiae and TXNIP has previously been identified as a regulator of glucose uptake by promoting endocytosis of GLUT1 and GLUT4. Here, the authors identify the molecular mechanism by which TXNIP promotes the endocytosis, and degradation of amino acid transporters (SLC7A5-SLC7A3) through its interaction with HECT-type ubiquitin ligases. This is an advance in the field and will be of interest for researchers in the fields of quiescence, metabolism and cell biology. Experiments are well designed and important controls have been performed. Overall, the claims and the conclusions are supported by the data.* *

Response: We thank the reviewer for the thorough evaluation of our manuscript and for the insightful, constructive comments. Reviewer 1 had five minor comments, and we have addressed them all.

Minor comment 1: The authors should indicate how often western blot experiments were repeated with similar results. Ideally band quantification (as in Fig. 2b) for the most relevant proteins should be provided for all shown Western blots.* *

Response: Each Western Blot (WB) experiment has been performed at least 3 times and each WB result for SLC7A5 is complemented by immunofluorescence and/or additionally by FACS analysis, across the manuscript.

In the partially revised version of the manuscript, we already__ incorporated WB quantifications of SLC7A5 protein levels__ for Figures 1c, f, h, Figure 3b, Figure 4b, and Figure 5a, c in Supplementary Figure 1b, Supplementary Figure 2c, f, Supplementary Figure 4a, e, and in Supplementary Figure 5a, c, respectively.

Minor comment 2: For confocal images no n number of experiments/analyzed cells is stated. Often only 2-3 cells are shown in these images. In some figures, conclusions from these confocal images are additionally supported by cell surface FACS.

Response: Each immunofluorescence experiment has been performed at least 3 times.

Minor ____comment 3: For panels with missing cell surface FACS quantifications, the authors should consider using the existing imaging data to perform quantifications of the membrane signal. In this way the reader can get the right impression of the reproducibility of the phenotype described.* *

Response: Each immunofluorescence experiment has been performed at least 3 times. In the partially revised version of the manuscript, line-scan quantification of immunofluorescence (IF) of SLC3A2 at the plasma membrane (PM) is now provided for immunofluorescence experiments in Figure 1e, g, Figure 3c, e in Supplementary Figure 2b, e, Supplementary Figure 4b, c, and for SLC2A1 in Supplementary Figure 3i, were FACS data was missing. In addition, WB experiments complement the results of each IF experiment.

Minor comment 4: I appreciate that the authors have also investigated SLC2A1 endocytosis in their experimental setup. Interestingly, they found that TXNIP mediated downregulation of SLC7A5-SLC3A2 was not linked to TXNIP mediated SLC2A1 endocytosis. Since the role of TXNIP in glucose metabolism has been studied in more detail in the past, it would be interesting if the authors could further comment on the differences/similarities in the molecular mechanism of glucose and AA transporter downregulation in the discussion.* *

Response: Thank you for bringing up this point. We now have added the following paragraph to the discussion to speculate about the differences/similarities in the molecular mechanism of glucose and AA transporter downregulation in the discussion:

‘Moreover, in RPE1 cells entering quiescence, GLUT1/4 was not downregulated. Hence, it seems that TXNIP can discriminate, in a context dependent manner, between targeting SLC7A5-SLC3A2 or GLUT1/4 for endocytosis. Since AKT mediated phosphorylation invariably appeared to inactivate TXNIP, and dephosphorylation re-activated it, additional mechanism must confer TXNIP selectivity towards SLC7A5-SLC3A2 or GLUT1/4. We consider it likely, that the exposure of sorting motifs in cytosolic tails of SLC7A5 or GLUT1/4 could regulate the binding of activated TXNIP and thus controls selective endocytosis to adapt nutrient uptake. The exposure of these sorting motifs could be dependent on the metabolic context / state of the cell. Indeed, yeast a-arrestins can detect n- or c-terminal acidic sorting motifs in amino acid transporters, respectively, that are alternatively exposed in response to amino acid excess or starvation (Ivashov et al., 2020a) (Guiney et al, 2016). Inspection of the SLC7A5 sequence indicates a possible n-terminal acidic sorting motif (17EEKEEAREK25). Two lysine residues (K19, K25) in this sequence have been found to be ubiquitinated in an earlier study upon protein kinase C (PKC) activation and mTORC1 inhibition (Barthelemy & Andre, 2019; Rosario et al, 2016).’

Minor ____comment 5: I would recommend a colour blind-friendly colour palette for the confocal images* *

Response: Thank you for pointing this out – we have changed the color palette accordingly.

Reviewer # 2: This study establishes TXNIP as a regulator of LAT1 endocytosis and metabolic homeostasis in quiescence. The integration of KO models and a TXNIP-deficient patient strengthens the findings, though clinical characterization remains underdeveloped relative to the mechanism reported, and biochemical interactions require endogenous validation. The work expands our understanding of TXNIP beyond association studies, positioning it as a key player in nutrient sensing and metabolic regulation. Addressing the concerns will enhance its relevance across fields - particularly metabolism, cell biology, and disease research. Overall, this is a very interesting study indeed. The use of TXNIP knockout models and a loss-of-function patient variant strengthens the conclusion that TXNIP is required for LAT1 degradation. The functional consequences of TXNIP deficiency (elevated intracellular aa, sustained mTORC1 activation, and accelerated quiescence exit) are well-supported by the data. The major concerns are as follows:

Response: We thank the reviewer for the thorough evaluation of our manuscript and for the insightful, constructive comments. Reviewer 2 had three major concerns and one minor comment.

Major concern 1. The identification of a biallelic TXNIP loss-of-function variant in a patient with metabolic disease and neurological dysfunction is highly significant. However, it is problematic that the manuscript effectively presents a case report but does not explicitly frame it as such, and the clinical details are very superficial (lack of pedigree, genetics, structured disease timeline, differential diagnosis, any histology/scans/photography and broader metabolic profiling - please see best practices for case reports). Although whole-exome sequencing identified the TXNIP variant, it remains unclear whether other genetic or metabolic contributors were systematically excluded. At first glance, the clinical discovery strengthens the physiological significance of the cell biology. However, a discrepancy remains between the clear neurological presentation of the patient (intellectual disability, autism and epilepsy) and the fibroblast-based TXNIP-LAT1 mechanism described in the study. Furthermore, the metabolic phenotype described in this manuscript is significantly more severe than that reported in a previous Swedish study of TXNIP deficiency in humans, where the clinical presentation was milder. This discrepancy suggests that different TXNIP mutations may lead to a spectrum of clinical outcomes, which is highly novel (i.e. metabolic and neurological in terms of loss of function, and carcinogenesis with respect to association studies, reviewed in PMID: 37794178). Of course, this could be influenced by mutation type, genetic background, compensatory mechanisms or environmental factors - it is noteworthy that the previous siblings had mitochondrial dysfunction, and this remains unknown in the present individual. Addressing this variability and discussing potential reasons for the pronounced phenotype observed in this patient would strengthen the manuscript overall. It is noteworthy that LAT1 is highly expressed in brain endothelial cells, which can also adopt a quiescent state (PMID: 33627876), and the authors should expand beyond the single sentence in their discussion. In the absence of the above details, the title and conclusions of Figure 3 and in the discussion greatly overstate causality, implying a direct relationship between TXNIP loss and metabolic dysfunction, despite data from only one patient. his may indeed be the case, but the claims should be carefully revised to reflect an association rather than definitive causation until additional patients are identified. Additionally, while it is assumed that the authors have obtained ethical approval and informed consent, this needs to be explicitly stated for transparency, with dedicated details in the methods sections. Addressing these issues will improve the rigor and mechanistic coherence of the study - otherwise it is quite disjointed.

Response: We have addressed many these valid concerns and provide a detailed description of the patient in the partially revised manuscript (please see below).

‘The patient is a boy, born in 2014 as the first child of healthy, consanguineous parents of Turkish origin. During pregnancy, the mother was diagnosed with polyhydramnios. At 38 + 6 weeks of gestation, the baby was in a breech position, leading to a cesarean section. At birth, he weighed 3880 g (P90), measured 55 cm in length, and had a head circumference of 38 cm.

On the seventh day of life, he exhibited floppiness, recurrent hypoglycaemia, and lactic acidosis, prompting his transfer from the birth hospital to a tertiary care centre. During the first three days there, his lowest recorded blood glucose level was 30 mg/dl, lactate levels were approximately 6.5 mmol/l, and pH was 7.11. Subsequently, he developed hypertriglyceridemia, with triglyceride levels reaching 364 mg/dl. Initially stable, he began experiencing elevated pCO2 levels (up to 70 mmHg due to bradypnea) and metabolic acidosis on day 10. A glucose infusion (10 mg/kg/min) stabilized his glucose and lactate concentrations, though lactate remained elevated at around 3-4 mmol/l. Regardless, his muscular hypotonia persisted. On day 12, a skin punch biopsy for a fibroblast culture was performed.

By day 20, glucose and lactate levels had stabilized with regular feeding, allowing his transfer back to a peripheral hospital. During infancy, his blood glucose concentrations were within standard range (Supplementary Table 1), but the boy experienced recurrent hypoglycaemia in response to metabolic stress, e.g., infections. He exhibited psychomotor developmental delays and, from 18 months of age, experienced increasing epileptic seizures (up to 3-4 per month), which were managed with levetiracetam, topiramate, and lamotrigine. Currently, he remains metabolically stable but presents with significant developmental delay, muscular hypotonia, and autistic features.

Whole-exome sequencing from peripheral blood of the patient detected a homozygous single nucleotide insertion c.642_643insT in exon 5 of 8 of the TXNIP gene. This variant was not recorded in the population genetic variant database gnomAD that lists TXNIP as likely haplosufficient (pLI = 0, LOEUF = 0,709: https://gnomad.broadinstitute.org accessed Sept. 10, 2024). No other (likely) pathogenic variant in any other gene, with known function in metabolism was identified as explanation of the clinical features in the child. Potential pathogenic variants in genes required for mitochondrial functions were also not detected, although they were initially expected to cause the phenotype of the boy.

The TXNIP variant c.642_643insT caused a frameshift and a premature stop codon after 59 AA (denoted p.Ile215TyrfsTer59), likely causing nonsense-mediated decay (NMD) or the synthesis of a severely truncated TXNIP protein (Figure 3a). Both parents are healthy heterozygous carriers for the TXNIP variant. Serendipitously, this TXNIP variant was similar to the gene-edited version in the RPE1 TXNIPKO cells (p.I215TfsX11).

The patient showed consistent metabolic alterations compatible with an AA transporter deficiency. Blood plasma concentrations of several large neutral amino acids (LNAAs, including L, I, V) were elevated throughout the years 2014 – 2022 (Supplementary Table 1). The increased molar ratio of the LNAAs (L, I, V) to aromatic AAs (F, Y), resulted in an elevated Fischer’s ratio (FR, 2014: FR = 4.46; 2016: FR = 5.38, 2018: FR = 5.90; 2021; FR= 6.98; 2022: FR = 4.23; FR reference range = 2.10 - 4). The methionine levels are not dramatically altered (Supplementary Table 1).’

We also provide the following ethical statement:

__‘Ethical statement __

All patients’ data were extracted from the medical routine records. Written informed consent for molecular genetic studies and publication of data was obtained from the legal guardians of the patient. This approach was approved by the ethics committee of the Medical University of Innsbruck (UN4501-MUI). The study was conducted in accordance with the principles of the Declaration of Helsinki.’

During the revision, we will additionally address how the other known TXNIP variant (TXNIP p.Gln58His; p.Gly59*; PMID: 30755400) affects nutrient transporter endocytosis. This TXNIP variant will be expressed in TXNIPKO RPE1 cells to analyze its effect on quiescence induced SLC7A5 downregulation. The results of this experiment will allow comparing directly the effect of both known TXNIP variants (p.Gln58His; p.Gly59* and p.Ile215TyrfsTer59) on SLC7A5 downregulation in an identical genetic background. In addition, we will compare how both TXNIP variants affect mitochondrial function (using Seahorse technology).

Major concern 2. The authors report that TXNIP interacts with HECT E3 ligases to regulate substrate degradation, yet this conclusion is drawn from overexpression-based immunoprecipitation studies, which do not confirm interaction under endogenous conditions. Without direct evidence of TXNIP-HECT E3 binding at native expression levels, this mechanistic link remains unresolved. Given that the authors have already generated antibody-validated TXNIP KO models, endogenous validation should be feasible if the interactions are not super-transient.

Response: While the manuscript was under review, we have improved the stringency of our TXNIP-HECT type ubiquitin ligase interaction experiments and developed additional biochemical experiments that strengthen our original conclusions. In the course of these experiments, we found that the interaction of TXNIP with NEDD4, WWP2 and HECW1/2 (but not with WWP1 or ITCH) were particularly dependent on the PPxY331 motif.

During the revision, we will conduct additional experiments to substantiate these findings and to narrow down the list possible ubiquitin ligases that are required for the downregulation of SLC7A5. In particular, we will test if endogenous TXNIP co-immunoprecipitates (in a PPxY motif dependent manner) NEDD4, HECW1/2 or another HECT type ubiquitin ligase.

Furthermore, we will include a newly developed ‘Bead-Immobilized Prey Assay (BIPA)’, were protein-protein interactions can be analyzed by microscopy in a fast in straight forward manner. In the BIPA, ALFA-TXNIP (or mutant variants) are first captured on ALFA-beads (Bead immobilized). These TXNIP beads are then incubated with cell lysates from HEK293 expressing GFP-tagged HECT type ubiquitin ligases (Prey). The binding of the GFP-tagged ubiquitin ligases to the TXNIP beads is analyzed by fluorescence microscopy and quantified (Figure 1b, a BIPA with YFP-NEDD4). This efficient assay will also be conducted with NEDD4, WWP1, WWP2, HECW2, and ITCH to analyze how they bind to TXNIP, TXNIP-PPxY331 and the PPxY double mutant.

Together we are confident that our experiments establish that TXNIP must interact with a specific subset of HECT type ubiquitin ligase (our prime candidate are NEDD4 and HECW1/2) to trigger SLC7A5-SLC3A2 ubiquitination, endocytosis and lysosomal degradation.

Major concern 3. What are the temporal dynamics of TXNIP-associated degradation, and is this process distinct from endosomal microautophagy (as reported in PMID: 30018090)? The authors present convincing, high-quality FACS-based data supporting TXNIP-mediated turnover. If this pathway is mechanistically separate from endosomal microautophagy, it suggests a hierarchy of degradation pathways leading to quiescence. Live cell imaging studies that define the temporal dynamics of this process using the tools the authors have created may reveal the relationship between these processes and refine the broader implications of TXNIP in homeostatic adaptation.

Response: Thank you for this interesting suggestion.

During the revision, we will first investigate a potential temporal correlation of endosomal micro-autophagy of p62/SQSTM1, NBR1, TAX1BP1, NDP52, and NCOA4 (PMID: 30018090) and the downregulation of SLC7A5 as cells enter quiescence. For these experiments, we will follow the turn-over of the above-mentioned autophagy adaptors and compare it to the turnover of SLC7A5, using either WB analysis, or microscopy or both.

Next, we will test if SLC7A5-SLC3A2 endocytosis and lysosomal degradation is required to initiate endosomal micro-autophagy of p62/SQSTM1, NBR1, TAX1BP1, NDP52, and NCOA4 in TXNIPKO cells.

Together, these experiments will address if the endosomal micro-autophagy and TXNIP mediated downregulation of SLC7A5 are mechanistically linked during entry into quiescence.

Minor comment 1. In the discussion, the authors might briefly speculate on the implications of any functional redundancy that might exist between other arrestins.

We will provide this information in the fully revised version of the manuscript.

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Referee #2

Evidence, reproducibility and clarity

Summary

Cells entering quiescence must recalibrate metabolism to match lower energy demands, yet the role of endocytosis in this process remains poorly defined. In yeast, amino acid transporters undergo rapid endocytic degradation upon entry into quiescence, but whether a similar mechanism exists in human cells is unknown. Kahlhofer and colleagues demonstrate that human quiescent cells selectively degrade plasma membrane-resident amino acid (AA) transporters, particularly SLC7A5-SLC3A2 (LAT1-4F2hc) and SLC1A5 (ASCT2). TXNIP facilitates LAT1 endocytosis and lysosomal degradation, thereby limiting AA uptake and intracellular AA levels to attenuate mTORC1 signaling and protein translation. In TXNIP-deficient cells, LAT1 remains at the plasma membrane, leading to persistent AA uptake, sustained mTORC1 activation, and accelerated proliferation upon exiting quiescence. In proliferating cells, AKT phosphorylates TXNIP at Ser308, inactivating it and preventing LAT1 degradation, a process that is reversed upon entering quiescence. Notably, the authors identify a biallelic TXNIP loss-of-function variant in a patient with severe metabolic disease, recurrent hypoglycemia, and amino acid imbalances. Patient-derived fibroblasts exhibit defective LAT1 internalization, a phenotype that cannot be rescued by complementation with the pathogenic TXNIP variant, supporting an important role in disease pathology. Functionally, TXNIP-deficient cells have elevated AA levels that sustain mTORC1 activation, enhancing translation, and accelerate exit from quiescence. This study establishes TXNIP as a key regulator of amino acid transporter endocytosis in quiescent cells, linking metabolic adaptation, mTORC1 signaling, and cell cycle control through a previously unrecognized mechanism.

Major comments

Overall, this is a very interesting study indeed. The use of TXNIP knockout models and a loss-of-function patient variant strengthens the conclusion that TXNIP is required for LAT1 degradation. The functional consequences of TXNIP deficiency (elevated intracellular aa, sustained mTORC1 activation, and accelerated quiescence exit) are well-supported by the data. The major concerns are as follows:

1. The identification of a biallelic TXNIP loss-of-function variant in a patient with metabolic disease and neurological dysfunction is highly significant. However, it is problematic that the manuscript effectively presents a case report but does not explicitly frame it as such, and the clinical details are very superficial (lack of pedigree, genetics, structured disease timeline, differential diagnosis, any histology/scans/photography and broader metabolic profiling - please see best practices for case reports). Although whole-exome sequencing identified the TXNIP variant, it remains unclear whether other genetic or metabolic contributors were systematically excluded. At first glance, the clinical discovery strengthens the physiological significance of the cell biology. However, a discrepancy remains between the clear neurological presentation of the patient (intellectual disability, autism and epilepsy) and the fibroblast-based TXNIP-LAT1 mechanism described in the study. Furthermore, the metabolic phenotype described in this manuscript is significantly more severe than that reported in a previous Swedish study of TXNIP deficiency in humans, where the clinical presentation was milder. This discrepancy suggests that different TXNIP mutations may lead to a spectrum of clinical outcomes, which is highly novel (i.e. metabolic and neurological in terms of loss of function, and carcinogenesis with respect to association studies, reviewed in PMID: 37794178). Of course, this could be influenced by mutation type, genetic background, compensatory mechanisms or environmental factors - it is noteworthy that the previous siblings had mitochondrial dysfunction, and this remains unknown in the present individual. Addressing this variability and discussing potential reasons for the pronounced phenotype observed in this patient would strengthen the manuscript overall. It is noteworthy that LAT1 is highly expressed in brain endothelial cells, which can also adopt a quiescent state (PMID: 33627876), and the authors should expand beyond the single sentence in their discussion. In the absence of the above details, the title and conclusions of Figure 3 and in the discussion greatly overstate causality, implying a direct relationship between TXNIP loss and metabolic dysfunction, despite data from only one patient. his may indeed be the case, but the claims should be carefully revised to reflect an association rather than definitive causation until additional patients are identified. Additionally, while it is assumed that the authors have obtained ethical approval and informed consent, this needs to be explicitly stated for transparency, with dedicated details in the methods sections. Addressing these issues will improve the rigor and mechanistic coherence of the study - otherwise it is quite disjointed.
2. The authors report that TXNIP interacts with HECT E3 ligases to regulate substrate degradation, yet this conclusion is drawn from overexpression-based immunoprecipitation studies, which do not confirm interaction under endogenous conditions. Without direct evidence of TXNIP-HECT E3 binding at native expression levels, this mechanistic link remains unresolved. Given that the authors have already generated antibody-validated TXNIP KO models, endogenous validation should be feasible if the interactions are not super-transient.
3. What are the temporal dynamics of TXNIP-associated degradation, and is this process distinct from endosomal microautophagy (as reported in PMID: 30018090)? The authors present convincing, high-quality FACS-based data supporting TXNIP-mediated turnover. If this pathway is mechanistically separate from endosomal microautophagy, it suggests a hierarchy of degradation pathways leading to quiescence. Live cell imaging studies that define the temporal dynamics of this process using the tools the authors have created may reveal the relationship between these processes and refine the broader implications of TXNIP in homeostatic adaptation.

Minor comments

In the discussion, the authors might briefly speculate on the implications of any functional redundancy that might exist between other arrestins.

Significance

This study establishes TXNIP as a regulator of LAT1 endocytosis and metabolic homeostasis in quiescence. The integration of KO models and a TXNIP-deficient patient strengthens the findings, though clinical characterization remains underdeveloped relative to the mechanism reported, and biochemical interactions require endogenous validation. The work expands our understanding of TXNIP beyond association studies, positioning it as a key player in nutrient sensing and metabolic regulation. Addressing the concerns will enhance its relevance across fields - particularly metabolism, cell biology, and disease research.

Referees cross-commenting

The comments raised by Reviewer #1 are reasonable, well-founded and align well with the concerns I have raised.

Expertise: Organelle dynamics/degradation, metabolism, biochemistry, tissue homeostasis/disease.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In their study, Kahlhofer et al. investigate the mechanism by which cells downregulate amino acid (AA) uptake while entering quiescence. Using western blotting, immunohistochemistry and KO cell lines, the authors show that the α-arrestin family protein TXNIP acts as a regulator of specific membrane-resident AA transporters. They demonstrate that TXNIP promotes the endocytosis and degradation of SLC7A5-SLC7A3 in serum-starved cells as a result of reduced AKT signalling. They further show that the molecular mechanism involves a direct interaction between a PPCY motif in TXNIP and HECT-type ubiquitin ligases which promote AA transporter ubiquitination. Additionally, they identify a novel TXNIP loss-of-function in a patient and show that patient-derived fibroblasts fail to downregulate SLC7A5-SLC7A3 upon starvation. This dysregulation likely contributes to persistent alterations in serum AA levels observed in the patient.

Experiments are well designed and important controls have been performed. Overall, the claims and the conclusions are supported by the data.

Minor comments:

Authors should indicate how often western blot experiments were repeated with similar results. Ideally band quantification (as in Fig. 2b) for the most relevant proteins should be provided for all shown Western blots.

For confocal images no n number of experiments/analysed cells is stated. Often only 2-3 cells are shown in these images. In some figures, conclusions from these confocal images are additionally supported by cell surface FACS. For panels with missing cell surface FACS quantifications, the authors should consider using the existing imaging data to perform quantifications of the membrane signal. In this way the reader can get the right impression of the reproducibility of the phenotype described.

I appreciate that the authors have also investigated SLC2A1 endocytosis in their experimental setup. Interestingly, they found that TXNIP mediated downregulation of SLC7A5-SLC3A2 was not linked to TXNIP mediated SLC2A1 endocytosis. Since the role of TXNIP in glucose metabolism has been studied in more detail in the past, it would be interesting if the authors could further comment on the differences/similarities in the molecular mechanism of glucose and AA transporter downregulation in the discussion.

I would recommend a colour blind-friendly colour palette for the confocal images

Significance

The study is well-executed, and the claims are supported by appropriate experiments. As introduced by the authors in their introduction, ubiquitin-dependent endocytosis of AA transporters has been previously shown in S. cerevisiae and TXNIP has previously been identified as a regulator of glucose uptake by promoting endocytosis of GLUT1 and GLUT4. Here, the authors identify the molecular mechanism by which TXNIP promotes the endocytosis, and degradation of amino acid transporters (SLC7A5-SLC7A3) through its interaction with HECT-type ubiquitin ligases. This is an advance in the field and will be of interest for researchers in the fields of quiescence, metabolism and cell biology.

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Reply to the reviewers

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

Summary: This manuscript authored by Kakui and colleagues aims to understand on how mitotic chromosomes get their characteristic, condensed X shape, which is functionally important to ensure faithful chromosome segregation and genome inheritance to both daughter cells. The authors focus on the condensin complex, a central player in chromosome condensation. They ask whether it condenses chromosomes through a now broadly popular "loop-extrusion" mechanism, in which a chromatin-bound condensin complex reels chromatin into loops until it dissociates or encounters a roadblock on the polymer (another condensin or some other protein complex), or through an alternative, "diffusion-capture" mechanism, in which a chromatin-bound condensin complex forms loops by encountering another chromatin-bound condensin until they dissociate from DNA (or from each other.) The authors measured the progressive changes in the shape of mitotic chromosomes by taking samples at given time points from synchronized and mitotically arrested cells and found that while all chromosomes become more condensed and shorter, their width correlated with the length of the chromosome arms. They also observed that chromosome compaction/shortening evolves on a time scale much longer than the interval between the onset of chromosome condensation and the start of chromosome segregation, suggesting that chromatin condensation does not reach its steady-state during an unperturbed mitosis. The observed width-length correlation could be described by a power law with an exponent that increases with the time (i.e. chromosome condensation). The authors also performed polymer simulations of the diffusion-capture mechanism and found that the simulations semi-quantitatively recapitulate their experimental observations. Major Comments My most substantial comments focus on somewhat technical details of the image analysis approaches taken and the polymer models employed. However, as all reported data are derived from those details, I feel it is crucial to address them. *

We thank the reviewer for their suggestions on how to improve our image analysis and polymer modelling experiments. We are keen to develop both aspects of our manuscript with additional experiments as detailed below.

1. * Definition/measurement of chromatin arms width and length. The approach taken to manually threshold an "arm" object and then fitting it with a same-area ellipse is not an ideal approach to gauge length and width of the arm, for the following reasons: (1) An ellipse appears to do a poor job approximating many of the objects that we see in the zoom-in insets of Fig.1. Importantly, for somewhat bent shapes we see in the insets it likely strongly underestimates the length of the arms; this approach also presents potential problems for measuring width as well (see 2 and 3 here). (2) One concern is that, due to the diffraction limit, a cylindrical fluorescent object could appear somewhat wider at the mid-length than the real underlying cylinder or the poles; this effect could become more pronounced as the object gets brighter and shorter. (3) Forcing the fit to an ellipse to objects that are not truly rod-shaped can drive an overestimation of the width of the object, and I suspect that this effect also might correlate with the length and brightness of the object. (4) Given 1-3 above, I think the approach the authors used for the first two time points, while not perfect, is better suited and likely more robust while avoiding these caveats. Moreover, why the authors cannot use this same approach (but just for each arm separately) for the later (30+ min) time points as they used for first two is unclear. This point is underscored by the observation that there is a drastic difference in the results between the first two and all subsequent points. When the authors compared the two approaches at the 30 min time point (where width-length dependence is still weak) in different cell lines they did indeed see different results (Fig. S2), although they concluded that the difference was acceptable. * While the manuscript was under review, we have developed an improved pipeline to measure chromosome widths. As suggested by the reviewer, this approach is based on the method used for the first two time points. An additional improvement allows us to take automated measurements along the entire chromosome arm length, instead of being restricted to straight segments. We propose to use the improved algorithm to repeat the measurements at later time points.

* Along these lines, the difference between short and long arms for the chromosome in the insets of Fig.1 are quite subtle, except maybe at 180 and 240 min. On a related note, it might be informative to compare data for the two sister chromatid arms (as the underlying polymer has the same length) long vs long and short vs short and long vs short to help establish the robustness of the approach. *

The chromosome arm width differences are clear and measurable. We will select insets that illustrate the arm width differences in a more representative way, and we will furthermore conduct the suggested analyses on subsets of chromosome arms to test the robustness of our approach.

* Regarding the power-law distribution, it is hard to judge based on the presented data whether it is a really good description of the data or not. In Fig.1c, the points for a given time can barely be distinguished, while in Fig.1b the authors plot individual time points in the panels, but the fits and points are overlapping so much that it is challenging to the main trends described by the clouds. The most informative approach for the reader would be to provide confidence intervals of the best fit parameters for all parameters that were varied in the fit. As the authors make some conclusions based on the power-law exponent values they observed, it would be helpful to know how confident we are in those values. *

Confidence intervals of the power law exponents will be provided.

* The conclusion that short arms equilibrate faster based on Fig.3a is not fully convincing. For example, in a scenario where ~1.5 microns is the equilibrium length for all arms, and that the longest arms equilibrate the fastest - you would see the same qualitative pattern for quantiles, not much change in low percentiles, while you would observe a decrease in the values for the high percentiles. The authors might be right, but Fig. 3A does not unambiguously demonstrate that it is so based on this evidence alone. *

Our reasoning is based on the observation that the shortest percentiles do not change or do not change rapidly after 30 minutes, while the longest percentiles are clearly still relaxing towards a steady state. We will repeat this analysis with the new measurements, obtained in response to point 1.

* As for chromosome roundness, typically in image analysis, roundness is defined through the ratio of (perimeter)2/area; it might be better to use "aspect ratio" for the metrics used by the authors. And, perhaps, one should expect that shorter (measured, not necessarily by polymer contour length) arms should have a higher width/length ratio? If one selects for more round objects, there should be no surprise that the width and length get almost proportional. Given all of this, I am not sure whether width/aspect ratio serves as a good proxy for the chromatin condensation progression, which is how the authors are employing this data in the manuscript as written. *

We thank the reviewer for alerting us to an alternatively used definition of ‘roundness’. We will consider this concern, with one solution being to use ‘width-length ratio’ in its place.

* For the diffusion-capture model simulations, I think the results of the simulation would strongly depend on the assumptions of the probability to associate and the time scale of dissociation of the beads representing the condensin complex. For example, for a very strong association one might expect that all condensin will end up in one big condensate, even in the case of a long polymer. This is not explored/discussed at all. Did the authors optimize their model in any way? If not, how have they estimated the values they used? Moreover, perhaps this is an opportunity to learn/predict something about condensin properties, but the authors do not take advantage of this opportunity. *

We in fact explored the consequences of altering diffusion capture on and off rates when we initially developed the loop capture simulations, and we will report on the robustness of our model to the probability of dissociation as part of our revisions.

* In addition, the authors did some checks to show that the steady-state results of the simulations do not depend on the initial conditions. However, as some of the results reported concern the polymer evolution to the steady state (Fig.6b-c), they also need to examine whether these results depend on the chosen initial conditions (or not), and if they do, what is the rationale for the choices the authors have made? *

The current manuscript contains a comparison of steady states reached after simulations were started from elongated or random walk initial states (see Supplementary Figure 4). We will provide better justification for the choice of a 4x elongated initial state, which approximates the initial state observed in vivo.

* A more thorough discussion of other possible models, beyond diffusion-capture model considered here, would be beneficial to the reader. First, the authors practically discard the possibility of the loop-extrusion model to explain their observations (although they never explicitly state this in the abstract or discussion). However, they neither leveraged simulations to rigorously compare models nor included some other substantiated arguments to explain why they prefer their model. This is important, as one of the major findings here is that the chromatin never reaches steady state for condensation, making it challenging to intuit what one should expect in this very dynamic state. Second, the authors, while briefly mentioning that there might be some other mechanisms contributing to the mitotic chromosome reshaping, do not really discuss those possibilities in a scholarly way. For example, work by the Kleckner group has suggested an involvement of bridges between sister chromatids into their shortening dynamics (Chu et al. Mol Cell 2020). Third, the authors do not discuss how they envision the interplay between the different SMC complexes - cohesin, condensin I and condensin II - as they act on the same chromatin polymer, or at least acknowledge a possible role that this interplay might contribute to the observed time dependencies. The reviewer raises important points, which we are keen to explore by performing loop extrusion simulations, as well as in an expanded discussion section.

Reviewer #1 (Significance (Required)):

Significance: The question the authors are trying to address is fundamental and important. While loop extrusion-driven mitotic chromosome organization is a popular model, considering alternative models is always crucial, especially when one can find experimental observations that allow us to discriminate between possible models. The main limitations are: 1) the performance of the approach the authors take to measure chromosome shape is in question and 2) the main competitive model (loop extrusion) is not modeled. If all shortcomings are addressed this work may provide strong evidence for the diffusion-capture model and thus advance our mechanistic understanding of mitotic processes, which will be of broad interest to the fields of genome and chromosome biology. We are happy to hear that the reviewer agrees that our work ‘may provide strong evidence for the diffusion-capture model and thus advance our mechanistic understanding of mitotic processes’. See above for how we propose to address the two main limitations.

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

SUMMARY The authors tracked the progression of mitotic chromosome compaction over time by imaging chromatin spreads from HeLa cells that were released from G2/M arrest. By measuring the mitotic chromosome arms' width and length at different times post-release, the authors demonstrated that the speed at which the chromosome arms reach an equilibrium state is dependent on their length. The authors were able to recapitulate this observation using polymer simulations that they previously developed, supporting the model of loop capture as the mechanism for mitotic chromosome compaction.

MAIN COMMENTS This is a straightforward paper that supports an alternative mechanism (relative to the highly popular loop-extrusion) model for chromosome compaction. My comments are meant to help the manuscript reach a wider audience.

I suggest that "equilibrium" be replaced with "equilibrium length" since it is the only equilibrium parameter of concern. *

The reviewer is correct, and we will implement this change, also taking into account the reasoning of reviewer 3 that ‘steady state’ is a better term to describe a final shape that is maintained by an active process.*

In the results, it may help to describe how loop capture and loop extrusion are incorporated into the simulations, using terminology that non-experts can understand. Such a description should be accompanied by figures that can be related to the other figures (color scheme, nomenclature if possible). *

Following from the reviewer’s suggestion, we will provide schematics of the loop capture and loop extrusion mechanisms.*

OTHER COMMENTS P5: Is it possible the chromosome-spread processing may distort the structures of the chromosomes? *

We will compare chromosome dimension in live cells with those following spreading to investigate this possibility.*

Please clarify whether mitosis can complete after drug removal at the various treatment intervals. *

Drug treatment and removal is often used as an experimental tool. We will perform a control experiment to explore whether mitosis can indeed complete after drug removal under our experimental conditions.*

P6: "Our records are not, therefore, meant as an accurate absolute measure of individual arms. Rather, fitting allows us to sample all chromosome arms and deduce overall trends of chromosome shape changes over time" It would be better to state this sentence earlier in this paragraph, or earlier in the section so that readers' expectations are curbed when they're reading the detailed analysis plan. *

Note that we will employ an additional image analysis method, in response to comments from reviewer 1, which should lead to more reliable width measurements.*

P6: "As soon as individual chromosome arms become discernible (30 minutes), longer chromosome arms were wider, a trend that became more pronounced as time progressed." Implies that at early time points, when the lengths of the arms were unknown, the longer arms were equal or narrower than the short arms. I think it's more accurate to say that as soon as the arms were resolved, the longer arms appeared wider. *

We will adopt the reviewers’ more accurate wording.*

P7: Is there a functional consequence to the long arms not equilibrating before anaphase onset? *

The reviewer raises an interesting question, which we will explore in our revised discussion. One consequence of not reaching ‘steady state’ is that ‘time in mitosis’ becomes a key parameter that defines compaction at anaphase onset.*

P13: "In a loop capture scenario, we can envision how condensin II sets up a coarse rosette architecture, with condensin I inserting a layer of finer-grained rosettes." This should be illustrated in a figure. *

We will consider such a figure, though the roles of two condensin complexes is peripheral to our current study. Investigating the consequences of two distinct condensins for chromosome formation will provide fertile ground for future investigations. *

FIGURES Fig. 1: "...while insets show chromosomes at increasing magnification over time" sounds like the microscope magnification is changing over time. Please change "magnification" to "enlargement". Alternatively, if the goal of the figure is to illustrate the shape/dimensions change of the chromosomes over time, wouldn't it be better to keep all the enlargements at the same scale? *

During the revisions, we will explore whether to show the insets at the same magnification, or to adjust the wording as suggested by the reviewer.*

Fig. 2a plot: Does the distribution of normalized intensities really justify a Gaussian fit? I see a double Gaussian. *

The chosen example indeed resembles a double Gaussian. We will explore whether this is due to noise in the measurement and a poor choice of an example, or whether a double Gaussian fit is indeed merited.*

Please label the structures that resemble "rosettes". Good idea, which we will implement.

Lu Gan

Reviewer #2 (Significance (Required)):

General - This is a simulation-centric study of mammalian chromosome compaction that supports the loop-capture mechanism. It may be viewed as provocative by some readers because loop-extrusion has dominated the chromosome-compaction literature in the past decade. The only limitation, which is best addressed by future studies, is the absence of more direct molecular evidence of loop capture in situ. Though this same limitation applies to studies of the loop-extrusion mechanism.

Advance - It is valuable for the field to consider alternative mechanisms. In my opinion, the dominant one has been studied to death by indirect methods without a direct molecular-resolution readout in situ. While the field awaits better experimental tools, more mechanisms should be explored.

Audience - The chromosome-biology community (both bacterial and eukaryotic) will be interested.

Expertise - My lab uses cryo-ET to study chromatin in situ.

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

In this manuscript, Kakui et al. measured the length/width relationships of mitotic chromosomes in human cells that had entered mitosis for different durations. This simple measurement revealed very interesting behaviors of mitotic chromosomes. They found that the longer chromosome arms were wider than shorter ones. Mitotic chromosoms became progressively wider over time, with shorter ones reached the final state faster than the longer ones. They then built a loop-capture polymer model, which explained the time-dependent increase of width/length ration rather well, but did not quite explain the final roundness of chromosomes.

I suggest the following points for the authors to consider.

Major points (1) There is no experimental evidence that the loop capture mechanism is condensin-depdendent. Can the authors deplete condensin I or II or both and measure chromosome length and width in similar assays? This will link their models to molecular players. *

Such analyses have been conducted by others, and we will provide a brief survey with relevant references to the literature in our revised introduction.*

(2) It seems rather intuitive to me that if one defines the spacing the condensin-binding sites, then the loop sizes will be the same between shorter and longer chromosomes. It then follows that shorter chromosomes are rounder. Is it that simple? If not, can the authors provide a better explanation. *

The reviewer makes an interesting point that roundness (width-length ratio), is greater for shorter chromosome arms, even if chromosome width is constant. We will make this clear in the revised manuscript.*

(3) If the loop sizes are the same between shorter and longer chromosomes, why can't loop extrusion model explain this phenomenon? If one assumes that condensin is stopped by the same barrier element and has the same distrution at the loop base, this should produce the same outcome as loop capture. *

The key feature of loop extrusion is the formation of a linear condensin backbone, resulting in a bottle brush-shaped chromosome. This arrangement prevents further equilibration of loops into a wider structure, as occurs in the loop capture mechanism by rosette rearrangements. These differences will be better explained, using a schematic, in the revised manuscript.*

Minor points (1) "We are aware that this approximation underestimates the length of the longest chromosome arms and overestimates the length of the shortest arms." should be "We are aware that this approximation underestimates the length of the longer chromosome (q) arms and overestimates the length of the shorter (p) arms.". Right? *

In fact, this comparison applies to all longer and shorter arms, not only pairs of p and q arms, which we will clarify.*

(2) Some scientists argue that the final chromosome conformation might be kinetically driven. Even if the short chromosomes have reached the final roundness, this doesn't necessarily mean that they have reached equilibrium in cells. "Steady state" might be a better term to describe the chromosomes in vivo, as there are clearly energy-burning processes. *

The reviewer is right that the term ‘equilibrium’ can be seen as misleading, which we will replace with ‘steady state’.*

Reviewer #3 (Significance (Required)):

I find the paper intellectually stimulating and a pleasure to read. It suggests a plausible explanation for mitotic chromosome formation. As such, it will be of great interest to scientists in the chromatin field.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

The take home message of this study is that chromosome structure can be attained through mechanisms of looping that do not require an explicit loop extrusion function. As the authors states, alternative models of loop capture have been proposed, dating from 2015-2016. THese models show DNA chains through simply Brownian diffusion can adopt a loop structure (citation 27, 28 and similarly Entropy gives rise to topologically associating domains Vasquez et al 2016 DOI: 10.1093/nar/gkw510).*

The reviewer makes an excellent point in that entropy considerations, e.g. depletion attraction, likely contribute to the efficiency of loop capture. We will refer to this principle, including a citation to the Vasquez et al. study, in the revised manuscript.

* In this study, the authors go through careful and well-documented chromosome length measurements through prophase and metaphase. The modeling studies clearly show that loop capture provides a tenable mechanism that accounts for the biological results. The results are clearly written and propose an important alternative narrative for the foundation of chromosome organization.

Reviewer #4 (Significance (Required)):

The study is important because it takes a reductionist approach using just Brownian motion and loop capture to ask how well the fundamental processes will recapitulate the biological outcome. The fact that loop capture can account for the arm length to width relationships on biological time scales is important to report to the community. The work is extremely well done and the analysis of chromosome features is thorough and well-documented.*

• *

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Referee #4

Evidence, reproducibility and clarity

The take home message of this study is that chromosome structure can be attained through mechanisms of looping that do not require an explicit loop extrusion function. As the authors states, alternative models of loop capture have been proposed, dating from 2015-2016. THese models show DNA chains through simply Brownian diffusion can adopt a loop structure (citation 27, 28 and similarly Entropy gives rise to topologically associating domains Vasquez et al 2016 DOI: 10.1093/nar/gkw510).

In this study, the authors go through careful and well-documented chromosome length measurements through prophase and metaphase. The modeling studies clearly show that loop capture provides a tenable mechanism that accounts for the biological results. The results are clearly written and propose an important alternative narrative for the foundation of chromosome organization.

Significance

The study is important because it takes a reductionist approach using just Brownian motion and loop capture to ask how well the fundamental processes will recapitulate the biological outcome. The fact that loop capture can account for the arm length to width relationships on biological time scales is important to report to the community.

The work is extremely well done and the analysis of chromosome features is thorough and well-documented.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, Kakui et al. measured the length/width relationships of mitotic chromosomes in human cells that had entered mitosis for different durations. This simple measurement revealed very interesting behaviors of mitotic chromosomes. They found that the longer chromosome arms were wider than shorter ones. Mitotic chromosoms became progressively wider over time, with shorter ones reached the final state faster than the longer ones. They then built a loop-capture polymer model, which explained the time-dependent increase of width/length ration rather well, but did not quite explain the final roundness of chromosomes.

I suggest the following points for the authors to consider.

Major points

1. There is no experimental evidence that the loop capture mechanism is condensin-depdendent. Can the authors deplete condensin I or II or both and measure chromosome length and width in similar assays? This will link their models to molecular players.
2. It seems rather intuitive to me that if one defines the spacing the condensin-binding sites, then the loop sizes will be the same between shorter and longer chromosomes. It then follows that shorter chromosomes are rounder. Is it that simple? If not, can the authors provide a better explanation.
3. If the loop sizes are the same between shorter and longer chromosomes, why can't loop extrusion model explain this phenomenon? If one assumes that condensin is stopped by the same barrier element and has the same distrution at the loop base, this should produce the same outcome as loop capture.

Minor points

1. "We are aware that this approximation underestimates the length of the longest chromosome arms and overestimates the length of the shortest arms." should be "We are aware that this approximation underestimates the length of the longer chromosome (q) arms and overestimates the length of the shorter (p) arms.". Right?
2. Some scientists argue that the final chromosome conformation might be kinetically driven. Even if the short chromosomes have reached the final roundness, this doesn't necessarily mean that they have reached equilibrium in cells. "Steady state" might be a better term to describe the chromosomes in vivo, as there are clearly energy-burning processes.

Significance

I find the paper intellectually stimulating and a pleasure to read. It suggests a plausible explanation for mitotic chromosome formation. As such, it will be of great interest to scientists in the chromatin field.

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Referee #2

Evidence, reproducibility and clarity

Summary

The authors tracked the progression of mitotic chromosome compaction over time by imaging chromatin spreads from HeLa cells that were released from G2/M arrest. By measuring the mitotic chromosome arms' width and length at different times post-release, the authors demonstrated that the speed at which the chromosome arms reach an equilibrium state is dependent on their length. The authors were able to recapitulate this observation using polymer simulations that they previously developed, supporting the model of loop capture as the mechanism for mitotic chromosome compaction.

Main Comments

This is a straightforward paper that supports an alternative mechanism (relative to the highly popular loop-extrusion) model for chromosome compaction. My comments are meant to help the manuscript reach a wider audience.

I suggest that "equilibrium" be replaced with "equilibrium length" since it is the only equilibrium parameter of concern.

In the results, it may help to describe how loop capture and loop extrusion are incorporated into the simulations, using terminology that non-experts can understand. Such a description should be accompanied by figures that can be related to the other figures (color scheme, nomenclature if possible).

Other comments

P5: Is it possible the chromosome-spread processing may distort the structures of the chromosomes?

Please clarify whether mitosis can complete after drug removal at the various treatment intervals.

P6: "Our records are not, therefore, meant as an accurate absolute measure of individual arms. Rather, fitting allows us to sample all chromosome arms and deduce overall trends of chromosome shape changes over time" It would be better to state this sentence earlier in this paragraph, or earlier in the section so that readers' expectations are curbed when they're reading the detailed analysis plan.

P6: "As soon as individual chromosome arms become discernible (30 minutes), longer chromosome arms were wider, a trend that became more pronounced as time progressed." Implies that at early time points, when the lengths of the arms were unknown, the longer arms were equal or narrower than the short arms. I think it's more accurate to say that as soon as the arms were resolved, the longer arms appeared wider.

P7: Is there a functional consequence to the long arms not equilibrating before anaphase onset?

P13: "In a loop capture scenario, we can envision how condensin II sets up a coarse rosette architecture, with condensin I inserting a layer of finer-grained rosettes." This should be illustrated in a figure.

Figures

Fig. 1: "...while insets show chromosomes at increasing magnification over time" sounds like the microscope magnification is changing over time. Please change "magnification" to "enlargement". Alternatively, if the goal of the figure is to illustrate the shape/dimensions change of the chromosomes over time, wouldn't it be better to keep all the enlargements at the same scale?

Fig. 2a plot: Does the distribution of normalized intensities really justify a Gaussian fit? I see a double Gaussian.

Please label the structures that resemble "rosettes".

Lu Gan

Significance

General This is a simulation-centric study of mammalian chromosome compaction that supports the loop-capture mechanism. It may be viewed as provocative by some readers because loop-extrusion has dominated the chromosome-compaction literature in the past decade. The only limitation, which is best addressed by future studies, is the absence of more direct molecular evidence of loop capture in situ. Though this same limitation applies to studies of the loop-extrusion mechanism.

Advance It is valuable for the field to consider alternative mechanisms. In my opinion, the dominant one has been studied to death by indirect methods without a direct molecular-resolution readout in situ. While the field awaits better experimental tools, more mechanisms should be explored.

Audience The chromosome-biology community (both bacterial and eukaryotic) will be interested.

Expertise My lab uses cryo-ET to study chromatin in situ.

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Referee #1

Evidence, reproducibility and clarity

Summary: This manuscript authored by Kakui and colleagues aims to understand on how mitotic chromosomes get their characteristic, condensed X shape, which is functionally important to ensure faithful chromosome segregation and genome inheritance to both daughter cells. The authors focus on the condensin complex, a central player in chromosome condensation. They ask whether it condenses chromosomes through a now broadly popular "loop-extrusion" mechanism, in which a chromatin-bound condensin complex reels chromatin into loops until it dissociates or encounters a roadblock on the polymer (another condensin or some other protein complex), or through an alternative, "diffusion-capture" mechanism, in which a chromatin-bound condensin complex forms loops by encountering another chromatin-bound condensin until they dissociate from DNA (or from each other.)

The authors measured the progressive changes in the shape of mitotic chromosomes by taking samples at given time points from synchronized and mitotically arrested cells and found that while all chromosomes become more condensed and shorter, their width correlated with the length of the chromosome arms. They also observed that chromosome compaction/shortening evolves on a time scale much longer than the interval between the onset of chromosome condensation and the start of chromosome segregation, suggesting that chromatin condensation does not reach its steady-state during an unperturbed mitosis. The observed width-length correlation could be described by a power law with an exponent that increases with the time (i.e. chromosome condensation). The authors also performed polymer simulations of the diffusion-capture mechanism and found that the simulations semi-quantitatively recapitulate their experimental observations.

Major Comments

My most substantial comments focus on somewhat technical details of the image analysis approaches taken and the polymer models employed. However, as all reported data are derived from those details, I feel it is crucial to address them. 1. Definition/measurement of chromatin arms width and length. The approach taken to manually threshold an "arm" object and then fitting it with a same-area ellipse is not an ideal approach to gauge length and width of the arm, for the following reasons: (1) An ellipse appears to do a poor job approximating many of the objects that we see in the zoom-in insets of Fig.1. Importantly, for somewhat bent shapes we see in the insets it likely strongly underestimates the length of the arms; this approach also presents potential problems for measuring width as well (see 2 and 3 here). (2) One concern is that, due to the diffraction limit, a cylindrical fluorescent object could appear somewhat wider at the mid-length than the real underlying cylinder or the poles; this effect could become more pronounced as the object gets brighter and shorter. (3) Forcing the fit to an ellipse to objects that are not truly rod-shaped can drive an overestimation of the width of the object, and I suspect that this effect also might correlate with the length and brightness of the object. (4) Given 1-3 above, I think the approach the authors used for the first two time points, while not perfect, is better suited and likely more robust while avoiding these caveats. Moreover, why the authors cannot use this same approach (but just for each arm separately) for the later (30+ min) time points as they used for first two is unclear. This point is underscored by the observation that there is a drastic difference in the results between the first two and all subsequent points. When the authors compared the two approaches at the 30 min time point (where width-length dependence is still weak) in different cell lines they did indeed see different results (Fig. S2), although they concluded that the difference was acceptable. Along these lines, the difference between short and long arms for the chromosome in the insets of Fig.1 are quite subtle, except maybe at 180 and 240 min. On a related note, it might be informative to compare data for the two sister chromatid arms (as the underlying polymer has the same length) long vs long and short vs short and long vs short to help establish the robustness of the approach. 2. Regarding the power-law distribution, it is hard to judge based on the presented data whether it is a really good description of the data or not. In Fig.1c, the points for a given time can barely be distinguished, while in Fig.1b the authors plot individual time points in the panels, but the fits and points are overlapping so much that it is challenging to the main trends described by the clouds. The most informative approach for the reader would be to provide confidence intervals of the best fit parameters for all parameters that were varied in the fit. As the authors make some conclusions based on the power-law exponent values they observed, it would be helpful to know how confident we are in those values. 3. The conclusion that short arms equilibrate faster based on Fig.3a is not fully convincing. For example, in a scenario where ~1.5 microns is the equilibrium length for all arms, and that the longest arms equilibrate the fastest - you would see the same qualitative pattern for quantiles, not much change in low percentiles, while you would observe a decrease in the values for the high percentiles. The authors might be right, but Fig. 3A does not unambiguously demonstrate that it is so based on this evidence alone. 4. As for chromosome roundness, typically in image analysis, roundness is defined through the ratio of (perimeter)2/area; it might be better to use "aspect ratio" for the metrics used by the authors. And, perhaps, one should expect that shorter (measured, not necessarily by polymer contour length) arms should have a higher width/length ratio? If one selects for more round objects, there should be no surprise that the width and length get almost proportional. Given all of this, I am not sure whether width/aspect ratio serves as a good proxy for the chromatin condensation progression, which is how the authors are employing this data in the manuscript as written. 5. For the diffusion-capture model simulations, I think the results of the simulation would strongly depend on the assumptions of the probability to associate and the time scale of dissociation of the beads representing the condensin complex. For example, for a very strong association one might expect that all condensin will end up in one big condensate, even in the case of a long polymer. This is not explored/discussed at all. Did the authors optimize their model in any way? If not, how have they estimated the values they used? Moreover, perhaps this is an opportunity to learn/predict something about condensin properties, but the authors do not take advantage of this opportunity. In addition, the authors did some checks to show that the steady-state results of the simulations do not depend on the initial conditions. However, as some of the results reported concern the polymer evolution to the steady state (Fig.6b-c), they also need to examine whether these results depend on the chosen initial conditions (or not), and if they do, what is the rationale for the choices the authors have made? 6. A more thorough discussion of other possible models, beyond diffusion-capture model considered here, would be beneficial to the reader. First, the authors practically discard the possibility of the loop-extrusion model to explain their observations (although they never explicitly state this in the abstract or discussion). However, they neither leveraged simulations to rigorously compare models nor included some other substantiated arguments to explain why they prefer their model. This is important, as one of the major findings here is that the chromatin never reaches steady state for condensation, making it challenging to intuit what one should expect in this very dynamic state. Second, the authors, while briefly mentioning that there might be some other mechanisms contributing to the mitotic chromosome reshaping, do not really discuss those possibilities in a scholarly way. For example, work by the Kleckner group has suggested an involvement of bridges between sister chromatids into their shortening dynamics (Chu et al. Mol Cell 2020). Third, the authors do not discuss how they envision the interplay between the different SMC complexes - cohesin, condensin I and condensin II - as they act on the same chromatin polymer, or at least acknowledge a possible role that this interplay might contribute to the observed time dependencies.

Significance

The question the authors are trying to address is fundamental and important. While loop extrusion-driven mitotic chromosome organization is a popular model, considering alternative models is always crucial, especially when one can find experimental observations that allow us to discriminate between possible models. The main limitations are: 1) the performance of the approach the authors take to measure chromosome shape is in question and 2) the main competitive model (loop extrusion) is not modeled. If all shortcomings are addressed this work may provide strong evidence for the diffusion-capture model and thus advance our mechanistic understanding of mitotic processes, which will be of broad interest to the fields of genome and chromosome biology.

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Reply to the reviewers

Our manuscript shows that, in cycling cells, the proneural master regulator transcription factor ASCL1 binds preferentially to pro-neurogenic enhancers in G1 phase of the cell cycle but this binding does not drive gene expression. As cells move to S/G2, ASCL1 binding is now enriched at promoters of pro-proliferative genes where it activates gene expression to maintain a pro-proliferative progenitor state. However, stalling of the cell cycle in G1 allows ASCL1 binding at enhancers to facilitate H3K27ac deposition and pro-neurogenic gene expression, driving the differentiation programme. We thus show hitherto unknown cell cycle dependency of distinct transcriptional programmes driven by the same transcription factor at different cell cycle stages and reveal why a lengthening specifically of G1 can allow engagement of a differentiation programme by turning unproductive factor binding into a productive interaction.

• *

We note, Reviewer 1:

This is an interesting study and provides new insight into the dual mechanisms of proneural transcription factors in neuroblastoma proliferation and differentiation. Since ASCL1 has similar dual roles in proliferation and neural differentiation in normal CNS development, the results of this report will improve the understanding of this factor more generally.

from Reviewer 2:

This work addresses an important long-standing question: how can Ascl1 simultaneously promote cell cycle and neurogenesis? It will be of relevance for the fields of neurogenesis, stem cell biology, reprogramming, and cancer biology.

We thank the reviewers for their very positive evaluations of the paper and its implications. Where questions and concerns were raised we have addressed them fully, below.

1. Point-by-point description of the revisions

Reviewer 1:

“The authors have not done a motif analysis of the ASCL1-ChIPseq so it is not clear whether E-box motifs are enriched/dominate. This is an important control. Also, it would be very useful to compare the ASCL1-ChIP-seq with other published datasets in other neural tissues, as an additional control.”

Prompted by this comment, we have performed motif analysis on the consensus set of ASCL1 ChIP-seq peaks in the DMSO control samples (i.e. freely cycling cells). This identified the canonical ASCL1 E-box motif as the most significantly enriched, occurring in the majority of peaks:

We have now added this motif analysis output to Figure 1A.

As requested, we downloaded a previously published ASCL1 ChIP-seq dataset (Păun et al. 2023) where human iPSCs were differentiated into cortical neurons. We find that ~25% of our consensus peakset intersects with binding sites detected in cortical neurons, representing just under 50% of this latter set. This is a large intersection of 25,000 peaks, especially considering the developmental differences between the two cell types (neuroblastic progenitors of the PNS versus more differentiated cortical neurons of the CNS). We have now added this figure to Supplementary Figure 1.

“Most of the analysis is done on regions that are less than 50 kb from the nearest TSS. This restricts the analysis to about half the peaks. Since they observe a difference between the G2M peak and the G1 peaks in their distance from the TSSOur ChIP-seq protocol was very sensitive and detected even low levels/transient ASCL1 binding, giving a large number of ASCL1 peaks. Consequently, a significant fraction of the genes in the genome became associated with ASCL1 binding and so we used a stringent distance based cut-off based on the assumption that there is a higher likelihood of enhancers acting on nearby promoters, rather than those further away. When we link all peaks to their nearest TSS, irrespective of distance, we find a similar trend, namely G1 enriched ASCL1 binding is associated with neuronal developmental processes, whereas SG2M enriched binding is uniquely associated with mitotic and cell cycle processes, (although we do now see some axonal terms appear under these less stringent conditions). These two figures have now been added to Supplementary Figure 4.

“The correlate the genes that decline with ASCL1 KO and the peaks from the ChIP-seq using GO terms, but would be very useful to determine how many of these genes are direct targets. This can bve done by showing the correlaiton between the RNAseq and the ChIP-seq on a gene-by-gene basis rather than using GO.”

Thank you for this useful suggestion. To investigate any correlation between the ASCL1 ChIP-seq and ASCL1 KO RNA-seq, we quantified the log2 fold change in expression level (WT/KO) following ASCL1 KO for any gene that was associated with an ASCL1 binding site in asynchronous cycling cells. Plotting these fold changes as a histogram/density plot (left) reveals that these genes generally exhibited a positive fold change i.e. a decrease in expression level following ASCL1 KO (blue dotted line shows the mean log2 fold change for the ASCL1 bound genes, black dotted line is at 0). Looking specifically at the 1000 genes associated with the most significant ASCL1 ChIP-seq peaks confirms this (right), where more genes show large decreases in gene expression following KO, where the local polynomial regression (LOESS; locally estimated scatterplot smoothing, black line) is consistently higher than 1.

Left plot: Log2 fold change in expression level for all ASCL1 bound genes, where positive fold change indicates a reduction in expression level following ASCL1 knockout, and a negative fold change indicates an increased expression following knockout. The mean value (blue dotted line), mode and median are all greater than 0 (black dotted line) indicating general reduction in expression level following ASCL1 knockout.

Right plot: 1000 genes associated with the strongest ASCL1 peaks (normalised peak score from DiffBind) were plotted against their fold change in expression following ASCL1 knockout. There is a large amount of variability, but the local polynomial regression (LOESS, black line) is consistently greater than 1 (red dotted line; no fold change).

We have now added the right figure to Supplementary Figure 2

Reviewer 2 also raised similar concerns:

“Other minor points: In figure 2, it would be interesting to display the overlap between bound and regulated genes.”

As suggested, we looked at the overlap between genes bound by ASCL1 in DMSO treated, freely cycling cells and intersected them with genes that showed a significant change in expression level following ASCL1 KO. This reveals that the majority of bound genes are regulated by ASCL1. Put another way, the large majority of genes that exhibited differential expression following ASCL1 KO were bound by ASCL1 in WT cells.

We have now added this Venn diagram to Figure 2.

“The lack of ASCL1 dependence of the G1 neuronal genes (Fig 5B) is interesting, but may be confounded by the possibility that these sites are driven equally well by a redundant proneural trnascription factor, like NEUROD1 or NEUROG. This possibility should be addressed by carrying out ChIP for these factors at select sites (G2M vs G1). Alternatively ChIP-seq for these factors would be ideal. Without these experiments the conclusion is not supported: "This indicates that ASCL1 is capable of binding to neuronal targets in G1 phase of the cell cycle in neuroblastoma cells but is not supporting their expression under cycling conditions."

The problem of redundant TFs is also an issue with the experiments to teat the effects of long G1 arrest.”

Thank you for raising this possibility, which prompted us to look at expression of other proneural proteins in these neuroblastoma cells. Consistent with the important role for ASCL1 in neuroblastoma previously reported in contrast to lack of reports about prominent roles for other proneural transcription factors, we quantified the expression levels of other proneural proteins in parental SK-N-BE(2)-C cells and the ASCL1 KO clone. We found that the expression level of all other proneural factors was very low, especially when compared to ASCL1, and did not increase following ASCL1 KO, showing no signs of compensatory uplift. We therefore conclude that there is a very low likelihood of interference from these factors. Moreover, methodologies such as ChIP-seq for these other proneural proteins are unlikely to work given their extremely low expression levels. We now include these findings in Supplementary Figure 5.

“The finding that G1 ASCL1 sites show less accessibility than G2M sites is interesting; is thre a reduction in ASCL1 ChIP-seq signal at these sites as well? Or is ASCL1 bound but not able to open the chromaitn at these sites?”

We have shown in Supplementary Figure 3 of the original manuscript that there is a reduced level of ASCL1 binding at G1 enriched sites compared to SG2M enriched sites when looking at asynchronous, freely cycling cells SK-N-BE(2)-C, and two other neuroblastoma cell lines.

To further investigate this, we performed this same analysis on the individual SK-N-BE(2)-C asynchronous replicates independently, which showed the same trend. These freely cycling cells comprise approximately 65% G0/G1 cells and 35% SG2M cells (Figure 3C). Despite more cells being in G1 in asynchronous freely cycling cells, the ASCL1 ChIP-seq signal is markedly reduced for sites which are preferentially bound by ASCL1 during G1 phase. Addressing the Reviewer’s question, this indicates that the lower levels of accessibility at G1 enriched sites versus G2M enriched sites are a result of reduced binding of ASCL1 in G1.

We hypothesised that reduced binding in G1 could be a result of lower ASCL1 protein concentrations. To address this, we performed ASCL1 antibody-based staining and hoechst based cell cycle analysis in SK-N-BE(2)-C cells, followed by flow cytometry. This enabled us to individually quantify ASCL1 protein levels in specific cell cycle subpopulations. The relative cell size changes across the cell cycle, so to account for this we plotted the relative changes in ASCL1 protein levels with the relative changes in cell size. This revealed that ASCL1 protein levels in G2M were significantly higher than expected if solely due to changes in cell size (and the levels in S phase were lower than expected for the cell size). In contrast, when we performed the same analysis for the housekeeping gene, TBP, we observed more consistent protein levels that scaled proportionately with cell size. This reveals a degree of cell cycle-dependent regulation of ASCL1 protein levels, which may account for differences in overall binding between the two phases, and indicate that reduced ASCL1 binding in G1 may be due to a lower amount of ASCL1 protein compared to the level in other cell cycle stages (normalised for cell size).

We have now moved the SK-N-BE(2)-C plot from original Supplementary Figure 3 to Figure 4, and added the results above to Figure 4.

“The reduction in accessible sites in the ASCL1 KO for the G2M sites is consistent with the effects on proliferation, but the effect is very modest. Would this effect be greater if the analysis of the ATAC-seq data were confined to sites with E-boxes? it would be useful to know what percentage of the accessible sites have an E-box and what percent of these sites are lost in the ASCL1 KO. This might show the importance of redundant proneural TFs.”

We now undertake additional analysis to address this important point directly. Of the 14,460 peaks that exhibit enriched ASCL1 binding during SG2M, 9,228 contain a canonical ASCL1 E-box motif (NNVVCAGCTGBN, taken from HOMER motif analysis above), as determined by FIMO, MEME suite (q-value We quantified the ATAC-seq signal at these peaks containing high confidence ASCL1 E-box motifs before and after ASCL1 KO and found that this extra filtering step had no impact on the magnitude of the change in accessibility following ASCL1 KO. This suggests that ASCL1 knockout has an equal effect on the accessibility of bound sites regardless of the underlying motif, and indirectly indicates that even the peaks showing degenerate ASCL1 motifs show a reduction in accessibility following ASCL1 knockout. This latter set could include sites where ASCL1 binding is mediated or enhanced by a cofactor.

Reviewer 2:

“There is however, one important concern to be clarified before strong conclusions can be extracted from the data: are palbociclib-treated cells comparable to control cells? 7 days of G1 arrest could have led to differentiation of at least a fraction of the NSCs and therefore the increased expression of neuronal genes (and chromatin changes) could reflect a higher percentage of differentiated cells (or higher degree of differentiation) in that sample rather than increased expression of neuronal genes in NSCs. A characterization of the cultures after the 7-day treatment is therefore necessary before drawing any conclusions. This could be done through immunohistochemistry to assess the presence of differentiated cells and control for the continuous and homogeneous expression of stemness markers (some useful markers include Nestin, Sox2, DCX, Tubb3 or GFAP). The reversibility assay, as shown in Figure S2 would also be very informative for the 7-day time point.”

For ASCL1 ChIP-seq experiments on cell cycle synchronized cells, palbociclib treatment was for a short duration of 24 hours, to ensure that the cells are only stalled in G1, and not differentiating. Control cells were treated with DMSO for the same duration, and the confluency was not more than 80% to ensure that they are healthy, cycling cells.

It was not experimentally possible to directly compare cells plated at the same density and then grown with or without PB for 7 days as extreme overgrowth and extensive cell death (rather that cell cycle arrest and differentiation) occurred in the cells without PB. When we performed 7 day palbociclib treatments, we plated control cells at half the density of treated cells so that by the 7 day time point, they were not overly confluent and were still cycling, allowing us to collect control cells for the RNA-seq analysis comparison. The morphology of the 7 day PB-treated cells were markedly different from control cells, showing extended neurites and overall lower confluency due to cell cycle exit and differentiation (see below).

The morphological effects of PB treatment on neuroblastoma cells was covered in some detail in a previous publication, Ferguson et al, 2023, Dev Cell, 58:1967-1982 . In this previous study we have extensively characterised the morphology of SK-N-BE(2)-C cells plated under very similar conditions to those used here, DMSO treated (again plated on day 0 at a lower density that PB treated to limit control cell death) versus palbociclib treated, below,). These cells were stained for Tubb3 as suggested by the Reviewer. We saw extensive cell cycle inhibition morphological differentiation with PB accompanied by upregulation of Tubb3 and neurite extension. In contrast we saw very little Tubb3 upregulation or morphological change in the DMSO control cells, and cells maintain a largely uniform typical neuroblast morphology. We now describe this previous work that directly addresses the point raised more fully in the results and discussion of this manuscript.

­­­­Figure from Ferguson et al., 2023.

To further address the point raised by Reviewer 2, we undertook more interrogation of our RNAseq data to confirm that 7 days of palbociclib treatment is inducing differentiation compared to the control cells. Taking suggestions from the Reviewer, we quantified the expression of several markers of stemness and neuronal differentiation from the RNA-seq data comparing treated and untreated cells. Indeed, the stemness markers SOX2, MYCN and HES1 all decrease following treatment, while the expression of key early neuronal genes (DCX, MAP2) increases.

We have now added this plot to Supplementary Figure 4.

“Other minor points: In figure 2, it would be interesting to display the overlap between bound and regulated genes.”

As suggested, we looked at the overlap between genes bound by ASCL1 in DMSO treated, freely cycling cells and intersected them with genes that showed a significant change in expression level following ASCL1 KO. This reveals that the majority of bound genes are regulated by ASCL1. Put another way, the large majority of genes that exhibited differential expression following ASCL1 KO were bound by ASCL1 in WT cells:

We have now added this Venn diagram to Figure 2.

“Please clarify where does the number of 47,294 non-commonly regulated genes between G1 and S/G2/M come from. From the data in figure 3D the number should be roughly 30k.”

Thank you for raising this. We agree that this is not clear and have changed the text and figure legend to better explain it. Prior to DiffBind analysis, the consensus peak sets for palbociclib-treated cells and thymidine-treated cells are shown in figure 3D. A consensus peak is one that appears in two out of the three replicates for that condition. DiffBind is then run using these consensus peak sets, which takes the magnitude of the peaks into account, identifying 47,294 differentially bound regions.

“In figure 3F/G, it would be very informative to show also examples of cell cycle independent genes.”

Recognising this was a minor point, we would suggest that this is largely a control for cell cycle-dependent expression that is extensively analysed in the rest of the paper. Unfortunately we do not have any remaining ChIP’ed DNA with which to show control regions. The samples were generated from approx. 1 million FACS sorted cells and so all ChIP’ed DNA was used for the qPCR reactions shown.

“In graph 4B, please unify the way the legend is displayed (location of "count" and "p.adjust").”

Corrected in the figure.

“In figure 5A, could it be that the expression levels of neuronal genes are too low in control cells, so that it is difficult to see a difference in the cKO cells? Even if not significant, would be good to show the p value.”

It is certainly possible that expression of neuronal genes is low in the WT cells and that this is why ASCL1 KO has no significant effect, but it still raises the question of how ASCL1 can bind and not drive the expression of these genes in this context. We would expect the statistical test to identify significant differences regardless of the expression level.

Since there are multiple t tests performed in each of the right figure panels, we used the Bonferroni’s Correction for multiple testing which is equal to the p-value divided by the number of statistical tests performed (i.e. 0.05/7 = 0.0071). Thus, any test with an adjusted p-value higher than 0.0071 is considered non-statically significant.

We have now updated the figure to show the p-values, and will modify the figure legend to explain the multiple testing correction. Additional information has also been added to the methods section.

“And simply a style point: I found the color scheme for significance in the graphs confusing, as dark colors signify less significance and white/clear shades high significance.”

For all other GO analyses figures, we have used a colour to represent high significance and black to represent lower significance, and it is for this reason that the GO analyses in Figures 1 and 2 use black to represent low significance. For consistency we feel it is best to keep it the same throughout the paper.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript by Beckman et al. the authors propose that different dynamics of Ascl1 binding to promoters of cell cycle and neuronal genes could explain the known association between cell cycle lengthening and differentiation. This stems from their observation that Ascl1 binds preferentially enhancers of neuronal genes in G1, although it does not drive their expression, while it binds the promoters and regulates the expression of cell cycle associated genes in G2/M. They also show that lengthening of G1 through pharmacological means increases chromatin accessibility (shown by ATAC-seq and H3K27ac) and allows Ascl1 to induce the expression of neuronal genes. They therefore propose a system where Ascl1 binds to primed neuronal enhancers in G1 but only drives their expression when a lengthened G1 phase has previously allowed chromatin changes involving histone modification. Their data is nicely controlled using Asc1cKO cells, allowing them to show specificity to the ability of Ascl1 to promote the expression of neuronal vs cell cycle genes. Overall, the work is nicely executed and clearly presented.

There is however, one important concern to be clarified before strong conclusions can be extracted from the data: are palbociclib-treated cells comparable to control cells? 7 days of G1 arrest could have led to differentiation of at least a fraction of the NSCs and therefore the increased expression of neuronal genes (and chromatin changes) could reflect a higher percentage of differentiated cells (or higher degree of differentiation) in that sample rather than increased expression of neuronal genes in NSCs. A characterization of the cultures after the 7-day treatment is therefore necessary before drawing any conclusions. This could be done through immunohistochemistry to assess the presence of differentiated cells and control for the continuous and homogeneous expression of stemness markers (some useful markers include Nestin, Sox2, DCX, Tubb3 or GFAP). The reversibility assay, as shown in Figure S2 would also be very informative for the 7-day time point.

Other minor points:

• In figure 2, it would be interesting to display the overlap between bound and regulated genes.
• Please clarify where does the number of 47,294 non-commonly regulated genes between G1 and S/G2/M come from. From the data in figure 3D the number should be roughly 30k.
• In figure 3F/G, it would be very informative to show also examples of cell cycle independent genes.
• In graph 4B, please unify the way the legend is displayed (location of "count" and "p.adjust").
• In figure 5A, could it be that the expression levels of neuronal genes are too low in control cells, so that it is difficult to see a difference in the cKO cells? Even if not significant, would be good to show the p value.
• And simply a style point: I found the color scheme for significance in the graphs confusing, as dark colors signify less significance and white/clear shades high significance.

Significance

This work addresses an important long-standing question: how can Ascl1 simultaneously promote cell cycle and neurogenesis? It will be of relevance for the fields of neurogenesis, stem cell biology, reprogramming, and cancer biology.

Conceptually, it could be made clearer in the discussion that Ascl1 appears to be dispensable for the increased chromatin accessibility caused by G1 lengthening, and even for the expression of neuronal genes (as shown in figure 5B, where there is a similar increase in neuronal genes expression in the absence of Ascl1 than in control cells after 7 days of palbociclib). This won't compromise the significance of the work, which has the potential to explain the dual role of Ascl1 in NSCs. But will hopefully encourage the field to further investigate the mechanisms behind the effects of G1 lengthening on chromatin accessibility.

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Referee #1

Evidence, reproducibility and clarity

This is an interesting study investigating the role of the proneural transcription factor ASCL1 in neuroblastoma. Previous work has shown that over-expression of ASCL1 can drive differentiation on neuroblastoma cells, but the gene also has roles in maintaining proliferation. The authors carry out a series of genomic studies including ChIP-seq and ATAC-seq to untangle these different roles of ASCL1. While most of the work presented is well-done and the analysis is straightforward, there are some concerns with the conclusions, since some key controls have not been done.

1. The authors have not done a motif analysis of the ASCL1-ChIPseq so it is not clear whether E-box motifs are enriched/dominate. This is an important control. Also, it would be very useful to compare the ASCL1-ChIP-seq with other published datasets in other neural tissues, as an additional control.
2. Most of the analysis is done on regions that are less than 50 kb from the nearest TSS. This restricts the analysis to about half the peaks. Since they observe a difference between the G2M peak and the G1 peaks in their distance from the TSS< it would be useful to show whether the same relationship holds when all peaks are included. This may stregthen the finding.
3. The correlate the genes that decline with ASCL1 KO and the peaks from the ChIP-seq using GO terms, but would be very useful to determine how many of these genes are direct targets. This can bve done by showing the correlaiton between the RNAseq and the ChIP-seq on a gene-by-gene basis rather than using GO.
4. The cell cycle synchronization experiments are a good confirmation of the unsynchronized experiments.
5. The lack of ASCL1 dependence of the G1 neuronal genes (Fig 5B) is interesting, but may be confounded by the possibility that these sites are driven equally well by a redundant proneural trnascription factor, like NEUROD1 or NEUROG. This possibility should be addressed by carrying out ChIP for these factors at select sites (G2M vs G1). Alternatively ChIP-seq for these factors would be ideal. Without these experiments the conclusion is not supported: "This indicates that ASCL1 is capable of binding to neuronal targets in G1 phase of the cell cycle in neuroblastoma cells but is not supporting their expression under cycling conditions."
6. The problem of redundant TFs is also an issue with the experiments to teat the effects of long G1 arrest.
7. The finding that G1 ASCL1 sites show less accessibility than G2M sites is interesting; is thre a reduction in ASCL1 ChIP-seq signal at these sites as well? Or is ASCL1 bound but not able to open the chromaitn at these sites?
8. The reduction in accessible sites in the ASCL1 KO for the G2M sites is consistent with the effects on proliferation, but the effect is very modest. Would this effect be greater if the analysis of the ATAC-seq data were confined to sites with E-boxes? it would be useful to know what percentage of the accessible sites have an E-box and what percent of these sites are lost in the ASCL1 KO. This might show the importance of redundant proneural TFs.

Significance

This is an interesting study and provides new insight into the dual mechanisms of proneural transcription factors in neuroblastoma proliferation and differentiation. Since ASCL1 has similar dual roles in proliferation and neural differentiation in normal CNS development, the results of this report will improve the understanding of this factor more generally.

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Reply to the reviewers

Response to reviewers

We sincerely thank all reviewers for taking the time to review our manuscript and for providing insightful comments and suggestions. Your feedback has been invaluable in improving the quality and clarity of our work.

Reviewer #1

Evidence, reproducibility and clarity

This manuscript by Peterl and colleagues seeks to understand the long-standing observation that influenza A virus generally exhibits a filamentous phenotype in vivo which is lost upon serial passaging in vitro or in embryonated chicken eggs. In addressing this question, the authors perform a detailed quantitative comparison of how filamentous and spherical strains of influenza spread in cell culture in the presence or absence of perturbations including neutralizing antibodies, mucin, and disruption of cell-cell junctions.

The manuscript reports several observations that will be of interest to researchers in the area of influenza virus morphology and spread. Using a combination of imaging modalities, the authors convincingly demonstrate that spherical strains of influenza virus produce larger plaques than filamentous strains that are isogenic except for mutations in M1. The authors show that this is at least partly attributable to differences in entry kinetics. The authors also recapitulate a prior finding that filamentous viruses are more resistant to neutralizing antibodies than spherical ones. In most cases, the authors' claims are supported by the data presented. A few partial exceptions are noted below.

The paper would be strengthened by a clearer description of some of the experimental approaches which lack important details in some instances. The scope of the paper is also limited somewhat by the use of immortalized cell lines that lack physiological features of the airway epithelium. Although this limitation is understandable from a technical standpoint, a discussion of these limitations should be included. Specific comments are listed below.

Major Points

In Figure 4, it is not stated at what time the cell density is measured in panel B, and how this might change across the time points sampled in panel C. This would make the experiment difficult to reproduce. This could be a very important consideration if the cells reach confluency soon after the infection is initiated, since the plaque sizes seem statistically similar out to 24hpi in 4B.

Thank you for your comment on cell densities in Figure 4 B. We agree that the quantification of cell confluency across the time points is crucial in this context. Furthermore, we recognize that counting the number of nuclei within a well is not the most accurate method for comparing the two cell lines. We now provide measurements of relative cell density based on plasma membrane staining for uninfected MDCK-WT and MDCK-α-Catenin-KO cells at 24h and 48h for three biological replicates (Figure 4 A and B). These data show that MDCK-α-Catenin-KO have lower confluency (area=229.69 µm2) at 48 h compared to MDCK-WT cells (area=361.24 µm2). While the confluency of MDCK-WT cells was > 95% at both time points, MDCK-a-Catenin-KO cells did not reach 70% confluency, which reflects the lack of adherens junctions in these cells.

In Figure 4F, it appears that plaque sizes for M1Ud are less affected by mucin than M1WSN plaques at all concentrations tested. However, the authors conclude that "mucin did not show any IAV morphology-dependent inhibitory effect as indicated by the slopes of linear fits of the plaque diameters" (Line 265). I understand that the authors are looking for dose-dependent effects, but it is not clear to me why an analysis based on the slope is preferable, especially when the response to mucins may not be linear. How does the availability of IAV receptors in the porcine gastric mucin used here compare to human airway mucins? Finally, the authors should clarify the number of replicates for this experiment.

Thank you for pointing out that the data representation of IAV WSN and WSN-M1Udorn plaque growth in the presence of mucin (Figure 4 C) lacked clarity. We agree and removed the regression fitting and, instead, show all individual plaque sizes (Extended Figure 4 B). We now provide relative reduction of plaque sizes compared between WSN and WSN-M1Udorn plaques at each mucin concentration using 3 or 4 independent experiments (Figure 4 E). This did not reveal that there was a significant reduction in plaque size change between WSN and WSN-M1Udorn in the absence or presence of mucin. We changed our conclusion: "mucin did not show an IAV morphology-dependent inhibitory effect as indicated by the relative plaque size decrease of WSN-M1Udorn compared to WSN across the mucin concentrations" (Line 278).

We have included information on the mucus composition and receptor availability in the discussion: "Notably, we used porcine gastric mucin, which might differ structurally and in the sialic acid linkage types compared to human mucins (Nordman et al., 2002, doi: 10.1042/bj3640191; Zhang et al., 2021, doi: 10.1007/s10719-021-10014-y). However, both in the porcine stomach and human airway, MUC5AC molecules are the predominant gel-forming mucins." (Graigner et al., 2006, 10.1007/s11095-006-0255-0) (Line 436).

One key difference between the cells used here and the airway epithelium is the presence of multiciliated cells that could alter viral transport in ways that depend on morphology and may be difficult to predict. I appreciate that this concept is outside the scope of the current work, but it is an important point that warrants mention.

We have now included fluorescence microscopy data using anti-MUC5AC antibody to assess mucin production in Calu-3 cells. Importantly, we could demonstrate that Calu-3 cells used in our study express mucins (Figure 4 D). We acknowledge that the absence of multiciliated cells is a limitation and plan to address this in future studies by using air-liquid interface cultures and by incorporating primary human bronchial cells. We established a transwell Calu-3 cell culture under air-liquid interface (ALI) conditions, which allowed for cell polarization. The apical surface of Calu-3 cells grown in an ALI culture contains more mucin than in liquid-covered unpolarized cultures. We plan to adapt and further develop a correlative imaging workflow to be able to assess spread in transwells in a separate study, as this is technically more challenging. We have included this in the discussion (Line 440-444).

Minor Points

It is somewhat unclear what is being captured in the data in Figure 5D-I. I assume that the cell surfaces that are imaged here are from infected cells within the plaque. If this is the case, it is difficult to tell whether the particles that are being quantified are incoming viruses or viruses that are currently budding. MEDI8852 is a stalk-binding antibody which would not be expected to inhibit viral attachment. This is unlikely to change the interpretation since the data shows differences between spherical and filamentous strains. However, a clearer description of this data would be helpful.

We appreciate your constructive feedback. Figure 5 captures the effect of HA-stalk-binding MEDI8852 antibodies on IAV spread and morphology. While this antibody does not prevent receptor binding, it blocks membrane fusion and exerts pressure on the viruses, which, based on our hypothesis, can be overcome by increasing the number of HA on the surface of filamentous viruses. This is now also confirmed in Figure 5B showing that entry of spherical viruses is more sensitive to MEDI8852 than entry of filamentous viruses above concentration of 5 nM.

SEM images of IAV plaques in MDCK cells in the presence of 1 nM MEDI8852 antibody show that viral morphology is not altered by antibody pressure. We agree that this method provides information on IAV morphology but does not allow us to distinguish between incoming or budding viruses. However, virus entry is fast, and IAV release from plasma membrane is slow as obvious from transmission electron microscopy studies showing large quantities of budding virions connected to plasma membrane by budding neck (example: DOI: 10.1099/vir.0.036715-0). Hence, it can be assumed that the majority of viruses captured by SEM on the cell surface are budding viruses. We have included this in the discussion (Line 409-414).

Nevertheless, to further address this limitation, we now provide a more robust analysis of IAV particle numbers and morphologies from supernatants of serial passaging in MDCK cells under MEDI8852 antibody pressure, using cryo-EM (Fig. 5 D, E). In accordance with the SEM data, we did not observe morphological changes of IAV in the presence of the antibody.

For experiments in Calu-3 cells, is trypsin added to the culture media following infection? If not, what percentage of HA is proteolytically cleaved? I would expect these cells to express activating proteases, but if activation is less efficient, this could favor the filamentous strain (as discussed in ref 49).

Thank you for this comment. Yes, trypsin was added to the medium of Calu-3 cells during infection. We included this in the methods section.

The schematic in Figure 4D illustrates mucins as tethered to the cell surface. This does not reflect the experiments in Figure 4E and F, where secreted mucins are added to the overlay media.

We agree, and we removed the schematic representation of mucins in Figure 4D, instead we show data on mucin production in Calu-3 cells (Figure 4 D).

There are a few small typos. Line 61: "to results in" and Line 111: "neutralizing antibodies against hemagglutinin are more effectively blocking virions with spherical morphology."

We corrected the typo in line 61 and changed the phrasing of lines 111-112 for more clarity.

Significance

A strength of this manuscript is the quantitative rigor of the approaches used, which reveal interesting differences in the spread of filamentous and spherical influenza. These differences are compelling, but are limited somewhat in their significance by the difficulty of evaluating whether or not some of the observations would be preserved in differentiated airway epithelial cells. The authors do not over-generalize their conclusions, but more detailed discussion of these potential limitations is warranted.

As mentioned above, we agree that a differentiated airway is important; however, assessing determining factors responsible for inhibition might be difficult due to the high complexity of the culture composed of different cells. The presented methods allow quantitatively assessing individual factors, which provides benefits. Hence, both approaches are valid and important.

Reviewer #2

Evidence, reproducibility and clarity

Summary: This manuscript by Peteryl and colleagues explores the question of why some influenza viruses (typically those that have been recently isolated from animals, though also the Udorn strain) produce filamentous particles, while influenza viruses that have been adapted to eggs or cell culture form spherical particles. This is a long standing question in the influenza field, and the authors have used a nice set of new tools and approaches to shed light on this question. They created mScarlet labelled viruses that produce spherical (WSN) or predominantly filamentous (WSN with an M segment from Udorn) virions, but share the same glycoproteins. While this approach is not novel (the fact that the segment 7 of Udorn drives a filamentous phenotype has been previously demonstrated), the authors used these viruses in an elegant series of experiments to look at the rate of cell to cell spread within a plaque to show that the spherical viruses spread more quickly. The authors then explored the effect of cell density, inhibitors designed to inhibit different routes of viral entry, and the presence of neutralizing antibody. The experiments are thoughtfully designed, and the electron microscopy in particular is beautifully done. In general, the conclusions are supported by the data, though the specific claim that filamentous viruses have an advantage in viral entry in the presence of neutralizing antibody would be significantly strengthened by performing the specific entry assay the authors employ earlier in the manuscript.

Major comments: The key conclusions are largely convincing, though the authors should perform the entry assays they employ in figure 3 (measuring the kinetics of entry and the efficiency of entry) to determine whether the delay in cell to cell spread they observe for spherical viruses in the presence of neutralizing antibody is due specifically to the effect on entry. I also am concerned about the method used to determine that the antibody treatment in Fig 5D-H results in a difference in the number of virions produced. While I appreciate that SEM is time consuming and difficult to quantify, counting the number of virions seen in a single field of view from 7 or 12 cells does not provide a robust foundation to support the central claim of the paper, that the difference in speed of filamentous and spherical viral spread is due to a difference in their ability to support viral entry in the presence of neutralizing antibody . If the authors wish to count virions produced by the WSN/WSN M-Udorn viruses in the presence/absence of neutralizing antibody it would be sensible to perform a synchronized high MOI infection and measure infectious titer by plaque assay (as this would be able to quickly and easily measure millions of virions produced by hundreds of thousands of cells).

Thank you very much for the suggestion to perform an entry assay in the presence of a neutralizing antibody to determine whether the antibody acts at the level of viral entry. We now provide data on the entry efficiency of WSN and WSN-M1Udorn in the presence of increasing MEDI8852 concentrations (Figure 5 B). The results show that entry of the WSN spherical viruses are more affected by MEDI8852 at 5 nM and 10 nM, compared to WSN-M1Udorn, suggesting that the reduced plaque growth presented in Figure 5 C reflects an inhibition of IAV entry.

We agree that the quantification of virions at the surface of 7-12 cells in SEM images is not a robust method. Therefore, we removed the quantification as it is technically very time-consuming to obtain a large enough dataset or to perform statical power analysis on how many cells would need to be screened. We additionally performed a serial passaging experiment of WSN and WSN-M1Udorn under antibody pressure, providing a more robust analysis of IAV particle numbers and morphologies from supernatants using cryo-EM (Fig. 5 D, E). By quantifying the length/diameter ratio of at least 80 virions per condition, we observed that both IAV morphologies remained stable in the presence of the antibody after five passages.

The two entry assays could be done in parallel, and I anticipate them to take ~3 days per replicate (a day to seed, a day to infect/add NH4Cl at the indicated time points and fix, a day to image and analyze data). Similarly, infected cells at high MOI in the presence/absence of nAb, collecting viral supernatants, and tittering by plaque assay should take ~one week. The reagents to perform these experiments are already in hand, and as the costs will be limited to standard tissue culture reagents, using a microscopy set up the authors already possess. The experiments throughout the paper are well described, with appropriate methodological detail and statistical analysis.

Minor comments: • Viruses without the mScarlet spread faster, the WSN-Udorn has more viruses with mScarlet than the WSN does so how do we know that some of the difference isn't down to that?

Thank you for this important question. It is correct that viruses without mScarlet spread faster. We used WSN mScarlet viruses for CLSEM and live cell imaging of Calu-3 cells. To ensure that the observed differences in viral spread kinetics were not attributable to the presence or absence of mScarlet but to viral morphology, we conducted additional immunofluorescence staining for viral nucleoprotein (NP) or matrix protein 2 (M2) (Extended Figure 1 H-I). This allowed us to account for all viral plaques, including those that were not mScarlet-positive. This way we obtained data for our experiments with MDCK-α-Catenin-KO cells, mucin, zanamivir and MEDI8852 (Figure 4 and 5).

• While Calu3 cells are reported to make mucus the authors should verify the expression of relevant mucus proteins in their hands, and this phenotype can be variable depending on culture conditions.

Thank you for highlighting this important point. We verified the expression of MUC5AC in Calu-3 cells grown on cover slips and observed MUC5AC expression in distinct puncta (Figure 5 D).

• In 5F and I does 'mock' mean no antibody or no virus?

We apologize for the imprecise nomenclature in Figure 5 F and changed the Figure description.

• The authors should either include data to support the claim in line 410: "Our data provide further evidence that IAV filamentous morphology is lost to accelerate cell-to-cell spread by faster entry kinetics and to achieve higher entry efficiency" or reword this sentence, since at present this manuscript does not include experiments demonstrating the loss of filamentous morphology in tissue culture of the WSN-M1 Udorn virus.

Thank you, we agree and modified the sentence.

Significance

The data and conclusions presented in this manuscript are exciting and novel, and should be of high interest to virologists and cell biologists. The work builds on (and appropriately references) prior work in the field of influenza particle shape by the Lamb, Barclay, Garcia-Sastre, Vahey, Fletcher and Ivanovic groups. It provides new information and techniques to show that spherical virions spread faster than filamentous virions within plaques, and this advantage is not negated by cell density, the presence of mucus, or different entry inhibitors but is significantly reduced in the presence of neutralizing antibodies. It also includes other useful observations to the field (the fact that infected Calu3 cells migrate to the center of infected plaques, the fact that the entry kinetics and success rate of filaments is lower compared to spheres). Expertise: virology, influenza, virion morphology, cell biology

__Reviewer #3 __

Evidence, reproducibility and clarity:

The manuscript by Peterl et al. deals with the still interesting question of why influenza A viruses are filamentous in natural isolates but adopt a spherical phenotype in cell culture. The authors generated recombinant IAV reporter viruses that display identical antigenic (HA and NA) surfaces but differ in their morphology due to expression of an M1 protein that confers a spherical or filamentous phenotype. The data show that spherical viruses exhibit increased entry kinetics and spread faster in cell culture compared to filamentous viruses and that this is also the case in the presence of mucins and at a low cell density. Interestingly, the authors found that spherical viruses are more efficiently blocked by neutralizing HA antibodies than filamentous viruses, providing an interesting advantage for the filamentous phenotype of natural IAV isolates due to antibody pressure. The manuscript is of the usual excellent quality of the working group of Petr Chlanda and the data are very interesting. The experiments are well thought out and the results are comprehensible, convincing and visually very clear. The fact that a current preprint also describes that neutralizing antibodies drives filamentous virus formation (as mentioned by the authors in the discussion) does not diminish the message and quality of this work. There were a few minor open questions that came to mind that could be included in the discussion: The authors found that the filamentous morphology was stable throughout multiple rounds of infection during plaque formation. Is this still the case even with multiple passages (e.g 10x) in cell culture or does the number of spherical particles increase at some point?

Thank you for your positive feedback and this suggestion. We performed serial passaging of WSN and WSN-M1Udorn in MDCK cells in the presence of 1 nM MEDI8852 antibody and harvested supernatants from passage 1 and 5. Supernatants were plunge-frozen, and virion counts and morphologies were determined by cryo-electron microscopy. Data from at least 80 analyzed virions per condition showed that the overall number of spherical and filamentous virions was reduced after passage 5 under antibody pressure (Fig 5 D). However, both morphologies remained stable throughout five passages in the presence of MEDI8852 (Fig. 5 E). We did not observe an increase in spherical particles after five passages.

The filamentous virus spreads slower in cell culture. Does NA play a role here? NA is probably distributed differently on the surface of filamentous viruses (at the tips) than on spherical viruses?

Thank you for this comment. As correctly pointed out, NA is enriched on one side/tip of filamentous (Calder et al., 2010, doi:10.1073/pnas.1002123107) or spherical IAV as now highlighted in Figure 1 D and E (white arrowheads). This asymmetric NA distribution and the HA-NA balance have been reported to be crucial for the release of newly formed virions and their spread through the mucus layer in the airway epithelium (De Vries et al., 2019, doi: 10.1016/j.tim.2019.08.010). Additionally, we compared the role of NA in the spread of spherical and filamentous IAV by performing fluorescent plaque assays in the presence of Zanamivir, a potent NA inhibitor. Analysis of plaque growth in the presence of increasing Zanamivir concentrations showed that the spread of both IAV morphologies was inhibited to a comparable extent (Figure 4 F and extended Figure 4 C). This result suggests that the inhibition of NA enzymatic activity does not influence the IAV morphology-dependent spread. We have included this information in the results (Line 281-285) and discussion (Line 465-468).

Reviewer #3 (Significance (Required)):

The manuscript is of the usual excellent quality of the working group of Petr Chlanda and the data are very interesting. The experiments are well thought out and the results are comprehensible, convincing and visually very clear.

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Peterl et al. deals with the still interesting question of why influenza A viruses are filamentous in natural isolates but adopt a spherical phenotype in cell culture. The authors generated recombinant IAV reporter viruses that display identical antigenic (HA and NA) surfaces but differ in their morphology due to expression of an M1 protein that confers a spherical or filamentous phenotype. The data show that spherical viruses exhibit increased entry kinetics and spread faster in cell culture compared to filamentous viruses and that this is also the case in the presence of mucins and at a low cell density. Interestingly, the authors found that spherical viruses are more efficiently blocked by neutralizing HA antibodies than filamentous viruses, providing an interesting advantage for the filamentous phenotype of natural IAV isolates due to antibody pressure. The manuscript is of the usual excellent quality of the working group of Petr Chlanda and the data are very interesting. The experiments are well thought out and the results are comprehensible, convincing and visually very clear. The fact that a current preprint also describes that neutralizing antibodies drives filamentous virus formation (as mentioned by the authors in the discussion) does not diminish the message and quality of this work. There were a few minor open questions that came to mind that could be included in the discussion: The authors found that the filamentous morphology was stable throughout multiple rounds of infection during plaque formation. Is this still the case even with multiple passages (e.g 10x) in cell culture or does the number of spherical particles increase at some point? The filamentous virus spreads slower in cell culture. Does NA play a role here? NA is probably distributed differently on the surface of filamentous viruses (at the tips) than on spherical viruses?

Significance

The manuscript is of the usual excellent quality of the working group of Petr Chlanda and the data are very interesting. The experiments are well thought out and the results are comprehensible, convincing and visually very clear.

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Referee #2

Evidence, reproducibility and clarity

Summary:

This manuscript by Peteryl and colleagues explores the question of why some influenza viruses (typically those that have been recently isolated from animals, though also the Udorn strain) produce filamentous particles, while influenza viruses that have been adapted to eggs or cell culture form spherical particles. This is a long standing question in the influenza field, and the authors have used a nice set of new tools and approaches to shed light on this question. They created mScarlet labelled viruses that produce spherical (WSN) or predominantly filamentous (WSN with an M segment from Udorn) virions, but share the same glycoproteins. While this approach is not novel (the fact that the segment 7 of Udorn drives a filamentous phenotype has been previously demonstrated), the authors used these viruses in an elegant series of experiments to look at the rate of cell to cell spread within a plaque to show that the spherical viruses spread more quickly. The authors then explored the effect of cell density, inhibitors designed to inhibit different routes of viral entry, and the presence of neutralizing antibody. The experiments are thoughtfully designed, and the electron microscopy in particular is beautifully done. In general, the conclusions are supported by the data, though the specific claim that filamentous viruses have an advantage in viral entry in the presence of neutralizing antibody would be significantly strengthened by performing the specific entry assay the authors employ earlier in the manuscript.

Major comments:

The key conclusions are largely convincing, though the authors should perform the entry assays they employ in figure 3 (measuring the kinetics of entry and the efficiency of entry) to determine whether the delay in cell to cell spread they observe for spherical viruses in the presence of neutralizing antibody is due specifically to the effect on entry. I also am concerned about the method used to determine that the antibody treatment in Fig 5D-H results in a difference in the number of virions produced. While I appreciate that SEM is time consuming and difficult to quantify, counting the number of virions seen in a single field of view from 7 or 12 cells does not provide a robust foundation to support the central claim of the paper, that the difference in speed of filamentous and spherical viral spread is due to a difference in their ability to support viral entry in the presence of neutralizing antibody . If the authors wish to count virions produced by the WSN/WSN M-Udorn viruses in the presence/absence of neutralizing antibody it would be sensible to perform a synchronized high MOI infection and measure infectious titer by plaque assay (as this would be able to quickly and easily measure millions of virions produced by hundreds of thousands of cells).

The two entry assays could be done in parallel and I anticipate them to take ~3 days per replicate (a day to seed, a day to infect/add NH4Cl at the indicated time points and fix, a day to image and analyze data). Similarly, infected cells at high MOI in the presence/absence of nAb, collecting viral supernatants, and tittering by plaque assay should take ~one week. The reagents to perform these experiments are already in hand, and as the costs will be limited to standard tissue culture reagents, using a microscopy set up the authors already possess. The experiments throughout the paper are well described, with appropriate methodological detail and statistical analysis.

Minor comments:

• Viruses without the mScarlet spread faster, the WSN-Udorn has more viruses with mScarlet than the WSN does so how do we know that some of the difference isn't down to that?
• While Calu3 cells are reported to make mucus the authors should verify the expression of relevant mucus proteins in their hands, and this phenotype can be variable depending on culture conditions.
• In 5F and I does 'mock' mean no antibody or no virus?
• The authors should either include data to support the claim in line 410: "Our data provide further evidence that IAV filamentous morphology is lost to accelerate cell-to-cell spread by faster entry kinetics and to achieve higher entry efficiency" or reword this sentence, since at present this manuscript does not include experiments demonstrating the loss of filamentous morphology in tissue culture of the WSN-M1 Udorn virus.

Significance

The data and conclusions presented in this manuscript are exciting and novel, and should be of high interest to virologists and cell biologists. The work builds on (and appropriately references) prior work in the field of influenza particle shape by the Lamb, Barclay, Garcia-Sastre, Vahey, Fletcher and Ivanovic groups. It provides new information and techniques to show that spherical virions spread faster than filamentous virions within plaques, and this advantage is not negated by cell density, the presence of mucus, or different entry inhibitors but is significantly reduced in the presence of neutralizing antibodies. It also includes other useful observations to the field (the fact that infected Calu3 cells migrate to the center of infected plaques, the fact that the entry kinetics and success rate of filaments is lower compared to spheres).

Expertise: virology, influenza, virion morphology, cell biology

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Referee #1

Evidence, reproducibility and clarity

This manuscript by Peterl and colleagues seeks to understand the long-standing observation that influenza A virus generally exhibits a filamentous phenotype in vivo which is lost upon serial passaging in vitro or in embryonated chicken eggs. In addressing this question, the authors perform a detailed quantitative comparison of how filamentous and spherical strains of influenza spread in cell culture in the presence or absence of perturbations including neutralizing antibodies, mucin, and disruption of cell-cell junctions.

The manuscript reports several observations that will be of interest to researchers in the area of influenza virus morphology and spread. Using a combination of imaging modalities, the authors convincingly demonstrate that spherical strains of influenza virus produce larger plaques than filamentous strains that are isogenic except for mutations in M1. The authors show that this is at least partly attributable to differences in entry kinetics. The authors also recapitulate a prior finding that filamentous viruses are more resistant to neutralizing antibodies than spherical ones. In most cases, the authors' claims are supported by the data presented. A few partial exceptions are noted below.

The paper would be strengthened by a clearer description of some of the experimental approaches which lack important details in some instances. The scope of the paper is also limited somewhat by the use of immortalized cell lines that lack physiological features of the airway epithelium. Although this limitation is understandable from a technical standpoint, a discussion of these limitations should be included. Specific comments are listed below.

Major Points

In Figure 4, it is not stated at what time the cell density is measured in panel B, and how this might change across the time points sampled in panel C. This would make the experiment difficult to reproduce. This could be a very important consideration if the cells reach confluency soon after the infection is initiated, since the plaque sizes seem statistically similar out to 24hpi in 4B.

In Figure 4F, it appears that plaque sizes for M1Ud are less affected by mucin than M1WSN plaques at all concentrations tested. However, the authors conclude that "mucin did not show any IAV morphology-dependent inhibitory effect as indicated by the slopes of linear fits of the plaque diameters" (Line 265). I understand that the authors are looking for dose-dependent effects, but it is not clear to me why an analysis based on the slope is preferable, especially when the response to mucins may not be linear. How does the availability of IAV receptors in the porcine gastric mucin used here compare to human airway mucins? Finally, the authors should clarify the number of replicates for this experiment.

One key difference between the cells used here and the airway epithelium is the presence of multiciliated cells that could alter viral transport in ways that depend on morphology and may be difficult to predict. I appreciate that this concept is outside the scope of the current work, but it is an important point that warrants mention.

Minor Points

It is somewhat unclear what is being captured in the data in Figure 5D-I. I assume that the cell surfaces that are imaged here are from infected cells within the plaque. If this is the case, it is difficult to tell whether the particles that are being quantified are incoming viruses or viruses that are currently budding. MEDI8852 is a stalk-binding antibody which would not be expected to inhibit viral attachment. This is unlikely to change the interpretation since the data shows differences between spherical and filamentous strains. However, a clearer description of this data would be helpful.

For experiments in Calu-3 cells, is trypsin added to the culture media following infection? If not, what percentage of HA is proteolytically cleaved? I would expect these cells to express activating proteases, but if activation is less efficient, this could favor the filamentous strain (as discussed in ref 49).

The schematic in Figure 4D illustrates mucins as tethered to the cell surface. This does not reflect the experiments in Figure 4E and F, where secreted mucins are added to the overlay media.

There are a few small typos. Line 61: "to results in" and Line 111: "neutralizing antibodies against hemagglutinin are more effectively blocking virions with spherical morphology."

Significance

A strength of this manuscript is the quantitative rigor of the approaches used, which reveal interesting differences in the spread of filamentous and spherical influenza. These differences are compelling, but are limited somewhat in their significance by the difficulty of evaluating whether or not some of the observations would be preserved in differentiated airway epithelial cells. The authors do not over-generalize their conclusions, but more detailed discussion of these potential limitations is warranted.

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Reply to the reviewers

1. General Statements

We thank the reviewers for their thorough evaluation of this manuscript. We are pleased that overall, they found our work and results valuable for the scientific community. Based on their feedback, we performed additional experiments and made several changes to strengthen the manuscript and expand the target audience.

*All three reviewers pointed out that the manuscript lacked demonstration of OneSABER method applicability across sample types (i.e., its claimed versatility) and other whole-mount systems beyond the Macrostomum lignano flatworm. *

We now include an additional results section with accompanying figures (Figs. 6 and 7) that demonstrate the application of OneSABER in whole-mount samples of another flatworm, the planarian Schmidtea mediterranea (Fig. 6), which is much larger than M. lignano, and in formalin-fixed paraffin-embedded (FFPE) mouse small intestine tissue sections (Fig. 7). We believe that these additional experiments on different sample types demonstrate the versatility of the OneSABER approach.

Please note that two more authors, Jan Freark de Boer and Folkert Kuipers, have been added for their contribution to mouse FFPE sections.

Furthermore, two reviewers asked for an additional main figure with a comparison of the signal strengths between the different OneSABER methods.

We have addressed this comment by including an additional results section and its adjacent figure (Fig. 5), where we provide a comparison of fluorescent signals from the same probes and gene but different OneSABER development methods.

Additionally, to implement the revisions, we modified Fig. 1 and Supplementary Fig. 6 and broadened Supplementary Tables S1-S2, S4-S6.

2. Point-by-point description of the revisions

Reviewer #1

1) “Fig.1 seems to suggest that the protocol for in vitro swapping of 3' concatemers happens in two consecutive PCR steps. I recommend indicating in the figure that the switching can be conducted in a single in vitro reaction.”

We have changed Fig. 1 to make this clearer.

2) “Is it possible to multiplex the switching in one single reaction? For example, perform p27 to p28 and p29 to p30 simultaneously? This will be crucial for the split-probe methodology.”

We did not test it. This should be possible if there is no overlap between the 3’ initiator sequences. However, it seems counterproductive as the elongation efficiencies of switching reactions from the 3’ initiator sequences to another concatemer may vary (Supplementary Fig. S6). Running independent extension/switch reactions and performing equimolar mixing of purified extended probes could be a better solution.

3) “Did the authors encounter any switching hairpins sequence that does not work? If not, can they postulate, what are the requirements for the design of switching sequences.”

The design criteria followed the requirements postulated in the original SABER article and its Supplementary Materials (Kishi et al 2019). All switching hairpins we tested in the pairs of the 3 used 3’ initiator sequences (p27, p28 and p30) worked, but elongation efficiencies varied (see an example in Supplementary Fig. S6).

4) “Is there cross hybridization between the switched and original hairpins? For example, can the authors show that the signals from p27 and p30 do not overlaps?”

The in situ hybridization results with swapped primary probes are shown in Fig. 6B (multiplexed HCR in S. mediterranea). All probes were originally designed using a p27 PER initiator. We swapped Smed-vit-1 with p30 and Smedwi-1 with p28. We also updated Fig. S6, by adding the second section (B) showing the in vitro results after concatemer swapping, as well as hybridization specificity of the secondary imager probes.

5) “Can the authors quantify results from the direct, AP, TSA, and HCR? What do you mean by 'narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible'?”

*“I agree with reviewer #2 regarding the lack of comparison to standard SABER.” *

A comparison of fluorescent signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5.

We have rephrased the sentence for clarity. From “As a result, despite higher intracellular resolution, some narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible for the human eye after SABER HCR (Figs. 3, 4).” to “As a result, despite higher intracellular resolution, some fine anatomical structures like neural chords (syt11) or muscles (tnnt2) are less resolved by widefield fluorescence microscopy after SABER HCR FISH compared to SABER TSA FISH”

Reviewer #2

1) “This work is building on standard SABER (a set of PER-extended primary probes that serve as landing pads for secondary fluorescently-labeled readout oligos) and pSABER (the readout oligo carries HRP instead of a dye for downstream TSA). The novelty of the work presented here is introducing additional variations of signal amplification, i.e. by using an hapten-labeled oligo to recruit a tertiary readout probe (antibodies conjugated with HRP or AP) or using SABER in combination with HCR. Since SABER can be seen as the underlying platform and pSABER was (arguably) also already introduced as a new platform by Attar et al. 2023, it seems difficult to introduce OneSABER as yet another new platform, of which standard SABER and pSABER are a part of. The reviewer encourages the authors to overthink the conceptual introduction, which in view of its certainly distinct novel features might allow a clearer distinction to previous work.”

We agree with the reviewer’s comments. We have added additional information in the Introduction section to clarify the novelty and key distinct features of OneSABER that justify its separation from other SABER protocols.

2) “Although the authors take care in tributing prior work, some of the studies are only mentioned in the results section, one of such cases is pSABER by Attar et al. 2023. The close relation between pSABER and SABER TSA (HRP on readout oligo vs. hapten on readout oligo + HRP-conjugated antibody) needs to be better positioned in the introduction, clearly framing earlier work, inspirations drawn etc.. This is in line with my previous point.”

The pSABER preprint article by Attar et al. 2023 (now published in a peer-reviewed journal as Attar et al. 2025) is now mentioned in the Introduction, and its inspirational impact on our research is clearly stated.

3) “Fig. 1 lists the individual modules of the OneSABER platform: i) standard SABER, ii) AP SABER, iii) SABER TSA, iv) pSABER (TSA FISH) (would recommend leaving it with original name when introducing it and include additional explanation in parentheses) and iv) SABER HCR. The main figures feature only AP SABER, SABER TSA and SABER HCR, for standard SABER and pSABER one must look up the SI. Since the authors describe the limited performance of standard SABER for one of their targets of interest (syt11) and since they have tested this target for all five conditions, it would be valuable to include a comparative view of all five platform modules in a single figure for syt11 or even also piwi, which also seems to have been tested for all five. Comparing the signal strength would be useful for the community, at least of each SABER variation compared to standard SABER.”

We agree with the reviewer’s comments. Except for pSABER, a comparison of fluorescence signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5. To make the comparison as objective as possible, all FISH developments were re-done using available “far red” fluorophores, except for pSABER. Unfortunately, our directly labeled HRP oligonucleotides for pSABER lost their activity after a year of storage at +4oC. These conjugated oligonucleotides are very expensive and, given their limited shelf life, we cannot justify ordering a new batch for this experiment. Therefore, we only have the data for pSABER syt11 with FITC green tyramide, which is not comparable to “far red” fluorophore signals. This issue has also been discussed in the main text.

In addition, we have modified Fig. 1, as suggested.

4) “The description of how the authors designed their probes is very detailed and they also provide a nice step-by-step protocol for their individual commands using Oligominer and BLAT software. This reviewer is wondering how the authors chose their PER sequences that they appended to their mined set of homologous in situ hybridization probes (p27,p28,p30). This is a general problem of multiplexed ISH approaches with single-stranded overhang, could the author's comment on potential self-interaction of the appended sequence with the homologous part, which might limit the PER efficiency, or elaborate on their choice?”

As being ourselves novice to SABER when we started our work, we based our selection of the p27, p28, and p30 PER sequences on their multiple co-occurrences in previous publications (Amamoto et al. 2019, doi: 10.7554/eLife.51452; Saka et al. 2019, doi: 10.1038/s41587-019-0207-y; Wang et al. 2020, doi: 10.1016/j.omtm.2020.10.003; Salinas-Saavedra et al. 2023, doi: 10.1016/j.celrep.2023.112687; and Attar et al. 2023, doi: 10.1101/2023.01.30.526264). We did not consider the potential interference between PER concatemers and homologous primary probe-binding sequences. However, as all PER concatemers were specifically designed to lack G nucleotides to keep them from self-annealing (Kishi et al. 2019, doi: 10.1038/s41592-019-0404-0), we assumed that it would also reduce potential annealing to the homologous part of the probe.

5) “Fig.1 and l. 125 describe straightforward in vitro switching of the concatemer sequence for an existing set of primary probes as a central feature of the OneSABER platform. However, the authors to my knowledge do not show such experiments themselves and only cite the original SABER paper by Kishi et al. 2019. This reviewer would be grateful to be pointed toward where in Kishi et al. 2019 this was demonstrated, however in view of this central part of the swopping scheme in the OneSABER platform an experiment showing this swopping is missing.”

In the article by Kishi et al. 2019, concatemer switching/swapping is termed as “primer remapping”. We found this term confusing because it does not describe the essence of the reaction. The in situ hybridization results with swapped primary probes are shown in Fig. 6B (multiplexed HCR in S. mediterranea). All probes were originally designed using a p27 PER initiator. We swapped Smed-vit-1 with p30 and Smewi-1 with p28. We also updated Fig. S6, by adding the second section (B) showing the in vitro results after concatemer swapping, as well as hybridization specificity of the secondary imager probes.

6) “the description of Table S6 could use additional information in the legend such that the reader does not have to scroll down to Section S1 to retrieve the information (PER reaction, gel conditions, ladder is dsDNA, what are the individual bands)”

Probably, the reviewer meant Fig. S6. We now wrote a more detailed caption for the figure and extended it with a second panel (B) to illustrate the results of 3’ concatemer swapping.

7) “the manuscript features an extensive set of resources in main body, supplementary materials and protocols. It is important and usually not merited sufficiently making the effort to compare orthogonal approaches for a given aim. This reviewer particularly appreciates the detailed strengths & weaknesses discussion in Table S6.”

We thank the reviewer for the appreciation of our work.

8) “Minor comments:

-Definitions should be consistent, in Fig. 1 all approaches are defined with FISH added, but this definition is not followed consistently in the main text.”

These definitions are now made consistent throughout the text.

9) “Optional:

-The authors describe several newly developed optimization steps during sample preparation for M. lignano ISH experiments compared to established ones. If the data exists, they include a supplementary figure showing improvements of optimized protocol steps”

As almost every step and the buffer recipes were different from the original ISH protocol by Pfister et al. (2007) because of the use of liquid-exchange columns, different probes, and development chemistry, we believe that a comparison would be excessive. We think that the key difference points are already substantially highlighted in the results section.

Reviewer #3

1) “Despite including a whole figure (Figure 1) featuring the operation scheme of the OneSABER platform, the figure as well as the associated text fall short with respect to clearly stating the advantage of the different aspects of the platform. Consider a clearer and more thorough explanation of the different aspects of the platfrom.”

Details on the advantages and disadvantages of using different OneSABER methods in terms of their experimental application and cost efficiency are described in Supplementary Tables S4-S6 of the submitted manuscript. However, we agree that the description in Fig. 1 was too concise and also did not refer to these tables. We have expanded the description in Fig. 1.

2) “Related to the first comment: A more detailed description of the similarities and/or differences of this platform relative to similar applications such as the study by Hall et al, 2024”

The mere point of mentioning the preprint of Hall et al. 2024 (now peer-reviewed, https://doi.org/10.1016/j.celrep.2024.114892) was to acknowledge that in M. lignano the HCR technology has been previously applied (although only once), while all other previously published works on M. lignano utilized canonical antisense RNA probes colorimetric in situ hybridization. We have extensively mentioned the HCR approach and its working principles throughout the submitted manuscript.

3) “The authors describe the probes used as short, synthetic DNA probes targeting short RNA transcripts. Are these probes Oligopaints (Beliveau et al, 2015)? Why is that not more clearly stated in the text?”

Oligopaints use oligo libraries as a renewable source of FISH probes, and these libraries are amplified with fluorophore-conjugated PCR primers. We used synthetic DNA probes directly. In this sense, our probe sets are not oligopaints. However, we used the OligoMiner pipeline of Oligopaints for the design of the probes, and thus used the same tiling strategy as oligopaints. We believe that this has been explained in the manuscript. Please refer to comment 4 of Reviewer 2.

4) “Line 105, p5: The authors state that the number of probes depends on the target RNA length and its expression strength. This data should be in the main text and described in detail since it is a major aspect of the platform design.”

We believe that this statement is common sense, as one cannot design more than 5x 30-50 bp probes for 200 nt transcripts, while for a 2000 bp mRNA, the theoretical limit is ~50 probes. Similarly, weakly expressed genes (regardless of their length) would require either more probes to reach the detection threshold or stronger amplification through choice of concatemer length and/or signal developing techniques. We have rephrased this sentence in the main text to reflect this.

5) “Figure 2 showcases one of the most compelling data supporting the versatility of the platform. Can the signals in each panel be quantified and compared to 1. Published Ab staining? Is there a clear correlation in the intensity of the signals? 2. Between Vector Blue and NBT? 3. Chemical staining and FISH signals?”

Since M. lignano is a relatively new model, there are no published antibody stainings for M. lignano genes used in this study. Furthermore, colorimetric precipitate methods are not quantitative but rather qualitative, because their signal strength is proportional to both the target RNA level and the development time; thus, signals from weakly expressed transcripts can be “boosted” simply by longer development. Therefore, a correct quantitative comparison with colorimetric methods, as requested by the reviewer, was not possible. However, with some corrections on fluorophore differences and animal-to-animal variability, it is possible to roughly compare peak saturation intensities for FISH methods if the experiments are designed for this aim. We performed these experiments, and a comparison of fluorescent signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5.

Minor comments:

6) “The whole mount images and signals are often diffuse, can they be visualized using a DIC where the morphology of the organism is clearer?”

We are unsure which images appear to be diffused to the reviewer. The other reviewers have not pointed out similar issues. Perhaps the question resolves once full-resolution uncompressed images are uploaded.

7) “In order to support the claim that this is a universal approach for whole-mount staining, can the authors show an example of applicability to C. elegans?”

This is now addressed. We included two additional results sections with two accompanying figures (Figs. 6 and 7) that demonstrate OneSABER’s application in whole-mount samples of a much larger than M. lignano model flatworm, the planarian Schmidtea mediterranea (Fig. 6), as well as in formalin-fixed paraffin-embedded (FFPE) small intestine tissue sections of a mouse model (Fig. 7).

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Referee #3

Evidence, reproducibility and clarity

Summary:

The authors of this study feature a proof-of-concept implementation of OneSABER ISH platform, that combines single, and multiplex colorimetric and fluorescent approaches in whole-mount samples of M. lignano. This includes RNA ISH, multiplex TSA and HCR FISH. The approach is supposed to provide advantages that reduce sample loss and sample processing time and cost while being applicable to whole-mount samples of one organism, M. lignano, a powerful model that is used to study tissue regeneration. One of the more obvious advantages is the use of this tool as an alternative to antibody staining for specific proteins. However, despite claiming applicability of this approach to other whole-mount organisms, no evidence was shown to support that claim. In addition, the advantage of using this approach over other ISH protocols to study tissue regeneration in particular had not been shown.

Major comments:

• Despite including a whole figure (Figure 1) featuring the operation scheme of the OneSABER platform, the figure as well as the associated text fall short with respect to clearly stating the advantage of the different aspects of the platform. Consider a clearer and more thorough explanation of the different aspects of the platfrom.
• Related to the first comment: A more detailed description of the similarities and/or differences of this platform relative to similar applications such as the study by Hall et al, 2024.
• The authors describe the probes used as short, synthetic DNA probes targeting short RNA transcripts. Are these probes Oligopaints (Beliveau et al, 2015)? Why is that not more clearly stated in the text?
• Line 105, p5: The authors state that the number of probes depends on the target RNA length and its expression strength. This data should be in the main text and described in detail since it is a major aspect of the platform design.
• Figure 2 showcases one of the most compelling data supporting the versatility of the platform. Can the signals in each panel be quantified and compared to 1. Published Ab staining? Is there a clear correlation in the intensity of the signals? 2. Between Vector Blue and NBT? 3. Chemical staining and FISH signals?

Minor comments:

• The whole mount images and signals are often diffuse, can they be visualized using a DIC where the morphology of the organism is clearer?
• In order to support the claim that this is a universal approach for whole-mount staining, can the authors show an example of applicability to C. elegans?

Significance

The work presented by the authors is promising in its versatility to single, and multiplex colorimetric and fluorescent approaches. In particular, multiplexing several targets showcases the strength of this approach. However, the versatility, applicability to other whole-mount studies and as a tool to study tissue regeneration in this model organism are not shown in the manuscript. Additional experiments will be necessary to support several of these claims.

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Referee #2

Evidence, reproducibility and clarity

In their manuscript entitled "One probe fits all: a highly customizable modular RNA in situ hybridization platform expanding the application of SABER DNA probes" Ustyantsev et al. present combinations of the SABER (signal amplification by exchange reaction) method for RNA in situ hybridization (ISH) experiments with additional fluorescence amplification strategies such as alkaline phosphatase (AP) colorimetric-based, tyramide signal amplification-based (TSA) and hybridization chain reaction-based (HCR) ISH. All experiments are performed within whole-mount samples of M. lignano and single-plex data for a total of 7 genes and multiplexed data for up to three genes are shown. Based on an initial set of SABER probes, the OneSABER platform, standard SABER fluorescently-labeled readout oligos (imagers) can be easily replaced by oligos introducing the above mentioned alternative amplification strategies. Furthermore, the authors claim to have optimized existing sample protocols for in situ hybridization in M. lignano.

Major comments:

Overall, the study is carefully conducted and many of the author's claims are supported by data presented in their manuscript.

Please find my comments below:

• This work is building on standard SABER (a set of PER-extended primary probes that serve as landing pads for secondary fluorescently-labeled readout oligos) and pSABER (the readout oligo carries HRP instead of a dye for downstream TSA). The novelty of the work presented here is introducing additional variations of signal amplification, i.e. by using an hapten-labeled oligo to recruit a tertiary readout probe (antibodies conjugated with HRP or AP) or using SABER in combination with HCR. Since SABER can be seen as the underlying platform and pSABER was (arguably) also already introduced as a new platform by Attar et al. 2023, it seems difficult to introduce OneSABER as yet another new platform, of which standard SABER and pSABER are a part of. The reviewer encourages the authors to overthink the conceptual introduction, which in view of its certainly distinct novel features might allow a clearer distinction to previous work.
• Although the authors take care in tributing prior work, some of the studies are only mentioned in the results section, one of such cases is pSABER by Attar et al. 2023. The close relation between pSABER and SABER TSA (HRP on readout oligo vs. hapten on readout oligo + HRP-conjugated antibody) needs to be better positioned in the introduction, clearly framing earlier work, inspirations drawn etc.. This is in line with my previous point.
• Fig. 1 lists the individual modules of the OneSABER platform: i) standard SABER, ii) AP SABER, iii) SABER TSA, iv) pSABER (TSA FISH) (would recommend leaving it with original name when introducing it and include additional explanation in parentheses) and iv) SABER HCR. The main figures feature only AP SABER, SABER TSA and SABER HCR, for standard SABER and pSABER one must look up the SI. Since the authors describe the limited performance of standard SABER for one of their targets of interest (syt11) and since they have tested this target for all five conditions, it would be valuable to include a comparative view of all five platform modules in a single figure for syt11 or even also piwi, which also seems to have been tested for all five. Comparing the signal strength would be useful for the community, at least of each SABER variation compared to standard SABER.
• The description of how the authors designed their probes is very detailed and they also provide a nice step-by-step protocol for their individual commands using Oligominer and BLAT software. This reviewer is wondering how the authors chose their PER sequences that they appended to their mined set of homologous in situ hybridization probes (p27,p28,p30). This is a general problem of multiplexed ISH approaches with single-stranded overhang, could the author's comment on potential self-interaction of the appended sequence with the homologous part, which might limit the PER efficiency, or elaborate on their choice?
• Fig.1 and l. 125 describe straightforward in vitro switching of the concatemer sequence for an existing set of primary probes as a central feature of the OneSABER platform. However, the authors to my knowledge do not show such experiments themselves and only cite the original SABER paper by Kishi et al. 2019. This reviewer would be grateful to be pointed toward where in Kishi et al. 2019 this was demonstrated, however in view of this central part of the swopping scheme in the OneSABER platform an experiment showing this swopping is missing.
• the description of Table S6 could use additional information in the legend such that the reader does not have to scroll down to Section S1 to retrieve the information (PER reaction, gel conditions, ladder is dsDNA, what are the individual bands)
• The manuscript features an extensive set of resources in main body, supplementary materials and protocols. It is important and usually not merited sufficiently making the effort to compare orthogonal approaches for a given aim. This reviewer particularly appreciates the detailed strengths & weaknesses discussion in Table S6.

Minor comments:

• Definitions should be consistent, in Fig. 1 all approaches are defined with FISH added, but this definition is not followed consistently in the main text.

Optional:

• The authors describe several newly developed optimization steps during sample preparation for M. lignano ISH experiments compared to established ones. If the data exists, they include a supplementary figure showing improvements of optimized protocol steps

Referees cross-commenting

I agree with most points raised by the other reviewers, especially with the lacking demonstration and related questions regarding swapping also raised by reviewer 1 and the questioned claim of translatability of OneSABER to other whole mount systems.

I do not question the value of this work in view of enabling new biological discovery, since it might accelerate/improve optimizations for RNA ISH experiments. In line with my comments, the manuscript would strongly benefit from a comparison to standard SABER demonstrating its insufficient signal for robust target detection.

Significance

Without a doubt this method-development focused study conducted by Ustyantsev et al. is a valuable resource featuring extensive sample optimization, protocols and guidelines for RNA in situ hybridization studies in M. lignano and as such deserves publication after the points raised were addressed. The manuscript is of high interest to the M. lignano community, to researchers conducting in situ hybridization experiments in larger/challenging-to-access samples and also to other methods developers.

Field of expertise: DNA nanotechnology and DNA-based multiplexed fluorescence imaging in mammalian cell culture & tissues.

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Referee #1

Evidence, reproducibility and clarity

Authors developed a customizable PER reaction system that is able to switch between different imager probes, as well as imaging modalities (Hapten, HCR, etc). This work will be of interest to biologists looking to validate gene expression, as well as biotechnologist looking to advance imaging-based spatial transcriptomics. The paper is well written and easy to read. The protocol is also very clear and well written. However, it is unclear how the method can enable new biological discovery.

Lack of demonstration of the applicability across sample types. Can the authors show some results in mammalian cells or tissues?

Fig.1 seems to suggest that the protocol for in vitro swapping of 3' concatemers happens in two consecutive PCR steps. I recommend indicating in the figure that the switching can be conducted in a single in vitro reaction.

Is it possible to multiplex the switching in one single reaction? For example, perform p27 to p28 and p29 to p30 simultaneously? This will be crucial for the split-probe methodology.

Did the authors encounter any switching hairpins sequence that does not work? If not, can they postulate, what are the requirements for the design of switching sequences.

Is there cross hybridization between the switched and original hairpins? For example, can the authors show that the signals from p27 and p30 do not overlaps?

Can the authors quantify results from the direct, AP, TSA, and HCR? What do you mean by 'narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible'?

Referees cross-commenting

I agree with reviewer #2 regarding the lack of comparison to standard SABER.

Significance

Authors developed a customizable PER reaction system that is able to switch between different imager probes, as well as imaging modalities (Hapten, HCR, etc). This work will be of interest to biologists looking to validate gene expression, as well as biotechnologist looking to advance imaging-based spatial transcriptomics. The paper is well written and easy to read. The protocol is also very clear and well written. However, it is unclear how the method can enable new biological discovery.

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Reply to the reviewers

Reviewer #1

Evidence, reproducibility and clarity

In this manuscript, the authors highlight the importance of the Golgi apparatus during SARS-CoV-2 infection. Specifically, using different compounds able to alter Golgi structure and function, the authors show a strong reduction in SARS-CoV-2 infection rate. In particular it is interesting to observe that treatments of 24 hrs with BFA strongly impair viral infection, highlithing the importance of Golgi function for this virus. Albeit the time of treatment is different. this observation is in contrast with previous studies on related coronaviruses (Ghosh et al., 2020) that did not observe any effect upon treatment with BFA. This might imply that SARS-CoV-2 relies more on conventional trafficking pathways respect to other coronaviruses which, under certain conditions, favour different trafficking routes.

We thank the reviewer for the positive comments. Indeed, our results with BFA treatment for 24 hours are inconsistent with previous studies based on the prototype coronavirus MHV (Ghosh et al., 2020). To validate this observation, we have now performed new experiments with BFA treatment for 4, 6, and 8 hours, matching the time points used in the previous study (Ghosh et al, 2020). Our new results show that BFA treatment at these early time points significantly inhibits SARS-CoV-2 assembly and secretion, as measured by immunoblotting and TCID50 assays, without reducing intracellular viral RNA levels, which serve as a marker of genome replication. This implies that Golgi function and an intact ER-to-Golgi trafficking route are required for SARS-CoV-2 assembly and secretion. These new results are now presented as new Fig. 2C-H.

The authors additionally observed that viral infection increases TGN46 levels while decreasing GRASP55 levels. To dissect the role of TGN46 and GRASPR55, the authors performed several infection studies in cells in which the levels of the two proteins were modulated either by overexpression (GRASP55) and/or siRNA-mediated knock-down (GRASP55 and TGN46). Those approaches suggest that GRASPR55 overexpression, a protein essential for Golgi stack formation, decelerates viral trafficking and inhibits viral assembly while its depletion reverses the effects. On the other hand, TGN46 knock-down impairs viral trafficking but not assembly. Overall the study clearly shows the importance of the Golgi during SARS-CoV-2 and also shows that modulation of those two factors affect viral infection.

We appreciate the reviewer's accurate summary of our work and positive comments.

However the claims that specifically the trafficking (TGN46) and trafficking and assembly (GRASP55) are not fully substantiated. Regarding GRASP55, the authors state that viral infection decreases GRASPR55 levels and this results in Golgi fragmentation. However GRASPR55 levels decrease is shown at 24 hrs post infection while Golgi fragmentation occurs as early as 5 hrs. Thus there might be no direct casual effect between the two effects.

We agree with the reviewer that GRASP55 downregulation is unlikely to be the only reason for Golgi fragmentation in the infected cells. In our results, 5- or 8-hour post infection caused only mild Golgi fragmentation (Fig. S6D), while 24 hours post infection led to severe Golgi fragmentation. On the other hand, GRASP55 is likely to play a relevant role as SARS-CoV-2 induced Golgi fragmentation can be partially rescued by exogenous GRASP55 expression (Fig S6C). We have modified the text in lines 303-305 accordingly to acknowledge the possibility that other factors also contribute to Golgi fragmentation in infected cells.

Additionally, the authors show that overexpression of GRASP55 rescue Golgi fragmentation, as observed by imaging, however is not clear if only infected cells where quantified and if they had the same level of infection.

Yes, only infected cells with either GFP or GRASP55-GFP expression were quantified. The viral infection rate was significantly lower in GRASP55-GFP expressing cells compared to GFP expressing cells (Fig 5A-B).

The authors exclude and effect on entry based on experiment on Spike expressing pseudovirus in 293-ACE2, however they also clearly observe reduction of ACE2 on the membrane of GRASPR55 expressing cells (Fig S6B). Thus how can they explain this discrepancy and how ca defect in entry can be fully marked out in these cell lines?

We thank the reviewer for pointing this out. This discrepancy is likely due to the different systems used in the two experiments.

In the pseudovirus entry assay, ACE2 was exogenously expressed in 293T cells and GRASP55 expression did not show any effect on the viral entry efficiency. In contrast, Huh7-ACE2 cells were selected for a high surface expression of ACE2. While GRASP55 expression reduces surface ACE2 levels as shown in our cell surface biotinylation assay, we believe that the surface ACE2 levels in GRASP55-expressing cells remain sufficient to support viral entry. To further investigate whether GRASP55 expression affects viral entry using authentic SARS-CoV-2, we performed RT-qPCR analysis of intracellular RNA level of the spike, N, and RdRp in both GFP and GRASP55-GFP expressing cells 4 hours post infection (new Fig 5D). Our results show that GRASP55 expression does not affect SARS-CoV-2 entry efficiency, even though it reduces ACE2 surface expression levels.

It is not clear to which process the authors refer to when they write about "viral trafficking". Is it virion trafficking or viral proteins trafficking? The two process are linked but are not the same. This oversemplification can be misleading. For instance the authors show that overexpression of GRASP55 decreases Spike protein on the plasma membrane and its depletion increases S protein incorporation into psudoviruses. However it was shown that in infected cells S protein is mainly retained at the ERGIC by M and E (Boson et al., 2021) where viral assembly occurs. Thus an increase in S trafficking on the PM does not correlate with an increase in virion trafficking,

We agree with the reviewer that our use of the term "viral trafficking" is imprecise and we have changed this throughout the manuscript to be more specific. S trafficking to the PM may not necessarily be equal to an increase in virion trafficking and thus have rephrased these terms in our writing accordingly.

We acknowledge that our cell surface biotinylation assay results only demonstrate that GRASP55 overexpression slows down spike protein trafficking to the PM. We have accordingly also examined viral protein and infectious particle secretion into the culture medium as a more direct readout of virion trafficking (new Fig 2E, 2H, 6K, and 7P).

Finally, we have removed all of the data describing spike incorporation into pseudoviruses as we acknowledge that plasma membrane assembly of lentiviruses is not a good model for SARS-CoV-2 assembly.

...and ultimately, the data provided do not fully support the authors claim on a modulation of "virion trafficking" in response to GRASP or TGN46 changes, since no experiments clearly show a change in virions secretion.

In response to the above comment, we provide the following clarification: Our Western blotting, TCID50 assay, and plaque assay results collectively demonstrate that SARS-CoV-2 virion secretion is reduced in GRASP55 expressing cells (new Fig 5E-M) and in TGN46-depleted cells (new Fig 7F-H, 7L-N). Conversely, viral assembly and secretion appear to be increased in GRASP55-depleted cells (new Fig 6A, 6E-I) at 24 hpi. Furthermore, within a single viral secretion cycle (10 hpi), GRASP55 depletion increased viral secretion (new Fig 6K), while TGN46 depletion reduced viral secretion (new Fig 7P). These findings strongly support the conclusion that GRASP55 and TGN46 modulate viral secretion.

Importantly, the authors do not rule out potential effects of their perturbations on genome replication. The only experiment that they perform in this direction is presented in Fig. S7B, where the authors show similar percentage of infected cells at early stage upon silecing of GRASPR55. The experiment suggests that productive entry is similar in these conditions, but quantification of intracellular viral genome could exclude a change in viral replication. If no changes in viral replication are observed, the authors could verify an increase in particles secretion by collecting supernatants from the early time points and performing plaque assays and quantification of viral genomes by qRT-PCR, to prove that modulation of GRASPR55 indeed promote SARS-CoV-2 trafficking.

We thank the reviewer for the excellent suggestions. In response, we performed RT-qPCR analysis in GRASP55-expressing and TGN46-depleted cells at 4 hpi to compare the viral genome replication process. Additionally, we performed western blotting analysis and released viral titer assay of the culture media from both GRASP55-depleted and TGN46-depleted cells at 10 hpi to investigate virion release. Our new results show that GRASP55 depletion increases viral secretion (new Fig. 6K), while TGN46 depletion reduces viral secretion (new Fig. 7P). Furthermore, GRASP55 expression and TGN46 depletion do not perturb viral genome replication (new Fig. 5D and new Fig. 7R).

Finally, whenever reduction of viral infection is observed upon cell partubation, a robust analysis of cell viability should be presented to exclude pleiotropic effects. Expecially in presence of multiple pertubation that might affect cell metabolism. The authors should carefully control cell viability and growth in response to depletion of TGN46 and GRASP55.

We thank the reviewer for the excellent suggestions, which were also pointed out by reviewer #3. To address this, we performed the LDH cytotoxicity assay under SARS-CoV-2 infection conditions with TGN46 depletion and GRASP55 depletion/expression (new Fig. 5C, 6L, 7Q). Our new results show that no significant cell death was induced by TGN46 depletion, GRASP55 depletion/expression, or other perturbations.

Minor: show data on viability of the drug and add the relative section in Material and Methods.

We performed LDH assays of SARS-CoV-2 infected Huh7-ACE2 cells treated with 9 small molecules, and LDH release levels were similar across all treatments (new Fig. S3C). Additionally, a CellTiter Glo viability assay of 293T-ACE2 cells did not show any significant effect of cell viability with small molecule treatment (new Fig S3F). Detailed descriptions of these assays have been included in the Material and Methods section.

Figure 3A: should read spike and not nucleocapsid eported for SARS-CoV-2

Fig. 3A labeling is correct - cells were labeled with antibodies for GRASP65 (rabbit) and for nucleocapsid (mouse).

Lack of inhibition with camostat correlates with lack of TMPRSS2 in the Huh7. The sentence seems to be too general while in this case the effect is clearly cell specific. Similarly, the importance of the lysosome in viral entry is restricted to cells lacking TMPRSS2 and cannot be generalized since CQ, for example, does not work in Calu-3 cells that express TMPRSS2 cells.

We agree with the reviewer and have added one sentence: The relative smaller effect of camostat mesylate observed here, compared to previous studies (Hoffmann et al, 2021), might be due to the use of different cell lines across studies in lines 182-184. We also discussed the discrepancy of CQ treatment between our Huh7-ACE2 cells and Calu-3 cells (Hoffmann et al, 2020) in lines 466-473.

Typo: Fig S3B - Y axis should reat viral not vrial

Thank you - we have corrected this.

S3C: concentrations of the compound used in the assay should be reported. Was a viability assay performed also in the 293T-ACE2 cell line?

We thank the reviewer for the suggestion. We have added the concentration information to the legend in Fig. S3E "Cell entry assay of 293T or 293T-ACE2 cells by SARS-CoV-2 Spike pseudotyped lentivirus for 24h in the presence of indicated molecules at the same concentrations as in Fig. 2A." Additionally, we performed a CellTiter Glo assay to assess the viability of 293T-ACE2 cells treated with the 9 molecules. The results demonstrate that treatment with these 9 molecules does not alter cell viability (Fig. S3F).

Significance

Overall, the major strenght of the manuscript is that it has clarified the importance of the Golgi during SARS-CoV-2 infection. The drugs screening demonstrate that for SARS-CoV-2 the conventional secretion seems to have major role respect to other secretory routes observed for other coronaviruses. Also it is clear that the two factors identified by the authors have a role in viral infection, however the major limitation is that the authors failed to clearly highlight which step/s of the viral life cycle are modulated upon GRASP55 and TGN46 perturbatio. Expecially the claims on "trafficking" is not fully substantiated, since the only experiment in this direction is the transport of Spike protein on the plasma membrane upon GRASPR55 overexpression. It is risky to conclude that the trafficking of a single protein reflect the intracellular trafficking of the virions.

Several of the finding presented in the first part of the manuscript have been already previously reported (for example the fragmentation of the Golgi upon SARS-CoV-2 infection), however the role of GRASP55 and TGN46 in SARS-CoV-2 infection has been reported here for the first time. This manuscript can be of interest for a broad audience considering the topic (cell biology, host-pathogen interactions and molecular virology)

My expertise reside in the field of molecular virology, expecially in the contest of the mechanisms of viral replication and host-pathogen interactions.

We thank the reviewer for the overall positive comments and excellent suggestions. We hope that our new results have convincingly demonstrated that viral trafficking is regulated by GRASP55 and TGN46.

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

Summary: In this study, Zhang and colleagues address the impact on SARS-CoV-2 infection on the morphology of the Golgi apparatus and convincingly demonstrate a fragmentation of this organelle in infected cells. Conversely, they show that the modulation of TGN46 or GRASP55 expressions, two components of this organelle impact SARS_CoV-2 replication. By monitoring the relative levels of viral Spike and nucleocapsid in the cell supernatants, they conclude that GRASP55 regulates particle assembly and trafficking while TGN46 controls only secretion. The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were robustly quantified. I believe that this study is potentially interesting and relevant for the SARS-CoV-2 community since providing an extensive characterization of the interplay between SARS-CoV-2 and the Golgi apparatus.

We thank the reviewer for the positive comments.

However, as described below, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery used for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study, especially without clear molecular mechanisms about the interplay between SARS-CoV-2 proteins and TNG46/GRASP55.

We rephrased some sentences following the reviewer's suggestions. Although it was believed that SARS-CoV-2 is assembled at the ERGIC, there has been significant controversy surrounding the virion secretion pathway. Our results strongly support that SARS-CoV-2 virions traffic through the Golgi apparatus and that an intact ER-to-Golgi trafficking pathway is essential for SARS-CoV-2 assembly and secretion. Manipulation of two Golgi-resident proteins, GRASP55 and TGN46, significantly regulates SARS-CoV-2 secretion. Interestingly, GRASP55 regulates both assembly and secretion of SARS-CoV-2, while TGN46 exclusively modulates viral secretion. This is consistent with their subcellular localization, as GRASP55 is localized to the medial/trans Golgi, whereas TGN46 is localized to the TGN. We hope that our new experimental results (Figs. 2C-H, 5C-D, 6J-L, and 7O-R) have addressed all concerns from the reviewer. Identification of downstream protein targets involved in TGN46/GRASP55-mediated modulation of SARS-CoV-2 trafficking will be the focus of our future studies.

Major comments: -All the assays have been performed in liver-derived Huh7 cells (overexpressing SARS-CoV-2 receptor) ACE2 (for infection) or kidney 293 cells (for pseudotyped HIV entry assays). However, no conclusion was validated in lung-derived cells (like A549-ACE2, Calu-3 or primary cells), which would be important since the respiratory tract is the main target of SARS-CoV-2

In our study, Huh7-ACE2 cells are sorted for the high expression of endogenous ACE2 protein, and we did not overexpress ACE2 protein. Also, the liver has been reported to be a site of SARS-CoV-2 infection in humans (Barnes, 2022). We did use A549 and Calu-3 cells in pilot experiments; A549 cells displayed infection rates that were too low for our purposes, and Calu-3 cells showed both low infection rates and relatively disorganized Golgi in the absence of viral infection. We were able to add new IF results from Calu-3 cells. Consistent with our findings in Huh7-ACE2 cells, SARS-CoV-2 infection disrupts Golgi structure and alters protein levels of TGN46 and GRASP55 in Calu3 cells (new Fig. S5R-W). We also confirmed GRASP55 downregulation and TGN46 upregulation in VeroE6 cells (Fig S5K-N).

-Fig2: The impact of the drugs on replication was assessed by measuring the % of infected cells. At 24 hpi, I am unsure about what this value is supposed to measure (the whole life cyle, intracellular replication or spread?), especially since it is not indicated when the drugs were added to the cells. Was it during, before or after the infection? This information should be provided.

Fig. 2 refers to infection, not replication. We agree that infection encompasses multiple steps of the viral cycle. In our experiments, cells were treated with the drugs immediately before viral infection. We have added the information into the Fig. 2 legend.

If the "Golgi" drugs impact egress only (as inferred by the genetic modulation phenotypes), I would expect that at this early time point, the % of infection would not drastically change (as well as intracellular RNA) but that the extracellular infectious titers would decrease. Plaque assays (or TCID50 assays) and RT-qPCR on intracellular viral RNA should be conducted to better understand the impact of drug treatments.

This is a great suggestion! As the reviewer expected, our new BFA time-point assay shows that at early time points, the intracellular RNA levels for S, N and RdRp are not reduced. However, the extracellular N protein (measured by WB) and virial titer (measured by TCID50 assay), which serve as readouts for virion secretion, are significantly decreased (new Fig. 2C-H).

On page 10, it is said that the virus makes three cycles of replication within 24 hours following infection. On what data is this based? This seems a lot. If this is true (and shown in Huh7-ACE2 cells), does the assay of figure 2 measure spread in general? More importantly, despite mentioned, the cell viability data are not provided. It is important to show them to ensure that these concentrations of drugs are not toxic at the tested concentrations.

It has been reported that a single cycle of SARS-CoV-2 infection is approximately 8 hours (Eymieux et al, 2021). Therefore, Fig. 2 represents a multicycle infection, reflecting a composite measure of viral infection and spread. Under the microscope, we did not observe dramatic cell death at the tested concentration. To further assess cytotoxicity, we performed a cell toxicity assay for the 9 small molecules that inhibit viral infection of Huh7-ACE2 cells. The results show that no or minor cell death was observed with all these compounds (Fig. S3C).

-I appreciated the extensive confocal microscopy analysis performed by the authors, which seems of high quality and overall, very convincing. They clearly show that SARS-CoV-2 infection induces the fragmentation of the Golgi apparatus although it was reported by others before as mentioned by the authors.

We thank the reviewer for the positive comments. We agree that Golgi fragmentation was observed during SARS-CoV-2 infection, as we mentioned. However, our study provides a comprehensive and systematic analysis of the entire host cell endomembrane system in the response to viral infection.

However, it was hard for me to make the functional link between these data and those related to GRASP55 and TGN46 overexpression/knockdown. First, the authors should assess the morphology of the Golgi apparatus in Huh7-ACE2 when GRASP55 is knocked down/out or when TGN46 is overexpressed. Second, in these 2 conditions that favor replication, it should be assessed whether this correlates with Golgi fragmentation. Even if this was probably shown before, it is relevant to show that these genetic modulations induce Golgi reshaping in this particular cell type by confocal microscopy (and ideally electron microscopy).

Thank you for the suggestion. We performed IF analysis to assess Golgi morphology in Huh7-ACE2 cells under conditions of GRASP55 knockdown or TGN46 overexpression. Our results show that GRASP55 depletion disrupts Golgi structure (Fig. S7D), whereas TGN46 expression does not significantly alter the Golgi morphology (Fig. S8D).

-The fact that GRASP55-GFP expression decreases in 293T the cell surface levels of ACE2, the receptor of Spike (Fig S6), raises concern that the effect of GRASP55 is not specific to the virus and suggests that the whole secretory pathway is altered, while an impairment of virus entry should be expected in this cell line. Is there a similar trend in Huh7-ACE2?

Reviewer 1 raised a similar question regarding viral entry efficiency. Fig. S6B, performed in Huh7-ACE2 cells, shows that GRASP55-GFP expression also decreases ACE2 surface level in these cells. To further assess whether GRASP55 expression affects viral entry, we performed RT-qPCR analysis of viral RNA at early time points of infection. We found that authentic SARS-CoV-2 entry efficiency was not altered by GRASP55 expression (new Fig. 5D). Although GRASP55 overexpression does alter the secretory pathway, we want to point out that SARS-CoV-2 infection downregulates endogenous GRASP55 expression. We have used GRASP55 overexpression as a probe to assess the effects of GRASP55 on the secretory pathway and on SARS-CoV-2 virion trafficking, but this does not actually reflect what is observed in SARS-CoV-2 infection.

In addition to addressing the functionality of the secretory machinery in Huh7-ACE2, it would be relevant to repeat the cell surface labelling in the context of pseudotyped virus production with other viral envelopes such as VSV G protein or HIV gp41/gp120. If the phenotype is specific to Spike trafficking, the cell surface abundance of these alternative viral proteins should not be impacted by GRASP55 overexpression. Otherwise, this would indicate a general effect of on the secretory pathway. Besides, since HIV Gag is directed directly to the plasma membrane during particle assembly without entering the secretory pathway, I am not convinced that upstream alteration on nucleocapsid assembly at the ERGIC should be excluded. Indeed, changes on the S/N ratios are generally mild and I feel that this cannot explain the phenotypes in the extracellular infectious titers.

We have removed the original figure because we acknowledge that HIV Gag is directed directly to the plasma membrane, which is different from the trafficking of SARS-CoV-2 spike protein. We appreciate the reviewer's recognition of the difference in extracellular infectious titers between GFP and G55-GFP expressing cells. We hypothesize that GRASP55 expression not only reduces the number of spikes on each virion but also inhibits the secretion of SARS-CoV-2, resulting in a significantly lower extracellular infectious titer. We agree that it would be interesting to test whether GRASP55 expression affects viral production with other viral envelopes. However, this is beyond the scope of the current study and represents a promising direction for future research.

More generally, the comparison between trafficking and assembly should be better assessed and not simply based on extracellular N and S levels. It was hard to see the differences between the two in terms of phenotypes. The authors should at least measure the intracellular infectivity upon TGN46 and GRASP55 knock/down and overexpression as well as intracellular vRNA abundance as a readout of RNA replication (which is anticipated to remain unchanged).

We thank the reviewer for the valuable suggestions. We performed RT-qPCR analysis of Spike, N, and RdRp at early time points of infection. The new results show that neither GRASP55 expression (new Fig. 5D) nor TGN46 depletion (new Fig. 7R) affects viral RNA abundance at an early infection timepoint (4 hpi). Also, we found that GRASP55 depletion increased intracellular infectivity (new Fig. 6J) while TGN46 depletion did not affect intracellular infectivity (new Fig. 7O), suggesting that GRASP55 modulates viral assembly but TGN46 does not.

-Finally, mechanistic insight about the viral determinants regulating the morphology of the Golgi would significantly strengthen the study.

Fig S6 shows that S expression decreases ACE2 surface levels? If so, could some S mutants be tested? Does it correlate with Golgi fragmentation? Do other viral structural proteins contribute to Golgi morphological alterations?

We thank the reviewer for the suggestions. These are indeed interesting experiments, but we believe that investigating viral determinants of Golgi fragmentation should be pursued by future studies.

In the same line of idea, how GRASP55 and TGN46 regulate replication. The link with Golgi morphology is unclear. Are these proteins hijacked by SARS-COV-2?

Our new data in this revised manuscript more clearly define the stages in the viral infection cycle that are modulated by GRASP55 and TGN46. New Fig. 5D and Fig. 7R show that neither GRASP55 nor TGN46 affects viral entry or early viral replication. However, GRASP55 perturbation modulates viral assembly and secretion, while TGN46 perturbation affects virion secretion but not assembly. Fig. S6C shows that GRASP55 overexpression in the presence of the virus partially rescues Golgi fragmentation. The mechanisms by which GRASP55 and TGN46 are hijacked by SARS-CoV-2 will be explored in the future studies.

Page 13 mentions some relevant mutants that could be assessed in this context and provide mechanistic insights.

It would be interesting to investigate the effects of GRASP55 mutants or specific domains on SARS-CoV-2 trafficking, which we plan to explore in future studies.

Minor comments: -The signal of calreticulin in Fig. S1 is too low to appreciate it distribution.

We have increased the intensity of calreticulin staining for both uninfected and infected cells in parallel in Fig. S1. Thank you.

-Fig 4K, Q: The differences in LC3 forms levels are not convincing. These results do not allow to draw any conclusion about autophagy, especially considering that this was done at steady-state and that the autophagic flux was not measured. Indeed, a bafilomycin A treatment control would be required to measure the real induction of autophagosomes. Lysosomal degradation inhibition allows the detection of LC3 accumulation.

We agree that additional experiments are needed to demonstrate autophagic flux alteration by SARS-CoV-2. We observed an increase in LC3II/LC3I ratio in infected cells at steady state and did not explore this further, since this is not our main focus of this study. Therefore, we have removed the LC3 blots and quantification from Figs. 4 and S5.

-In the GRASP55 overexpression and TGN46 knockdown studies, associated cell viability should be measured to control that that these genetic manipulations do not induce any cytotoxicity which may impact viral replication.

We appreciate the reviewer's suggestions. We performed the LDH cytotoxicity assay under SARS-CoV-2 infection with TGN46 depletion or GRASP55 expression. Our new results show that TGN46 depletion or GRASP55 depletion/expression did not induce significant cell death (Figs. 5C, 6L, and 7Q).

-The authors should test the impact of GRASP55 and GRASP65 knock-out on SARS-CoV-2 replication

Investigating the genetic GRASP55 knockout effect on SARS-CoV-2 replication would be valuable. However, ACE2 protein expression in our Huh7-ACE2 cells decreases with cell passages, making knockout construction on this background impractical due to low ACE2 levels and poor viral infection rates. We believe that both our GRASP55 overexpression and depletion assays sufficiently support its role in SARS-CoV-2 trafficking. Future studies will explore GRASP55 knockout in different cell lines.

-The authors should provide more details about the USA-WA1/2020 isolate in the Methods section. Is it related to the "Wuhan" strain or the variant which spread globally in early 2020 (with D614G mutation in Spike).

USA-WA1/2020 was isolated from an oropharyngeal swab from a patient who returned from China and developed COVID-19 on January 19, 2020, in Washington, USA. It is related to the "Wuhan" strain but does not have D614G mutation in spike. Additional details have been added to the Methods section.

-Fig 8: The combined modulation of GRASP55 and TGN46 expressions does not really seem additive to me since a 70% decrease of either protein modulation is observed while the combined condition brings this value to 75% in TCID50 assays. This does not bring much insight to the study in my opinion. I would suggest that the authors consider removing this figure.

We agree with the reviewer's recommendation and have removed Fig. 8.

Reviewer #2 (Significance (Required)):

General assessment and advance: The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were quantified extensively. However, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study. In addition, the antiviral activity of several tested drugs was also reported elsewhere. A clear mechanism of how SARS-CoV-2 induces a fragmentation of the Golgi would strengthen the study. In the same line of idea, it is unclear how TGN46 and GRASP55 regulate the late steps of the life cycle. The link between SARS-CoV-2-induced Golgi fragmentation and TGN46/GRASP55 is unclear. In my opinion, the data did not allow to clearly discriminate between virion assembly and egress. I was not convinced that it was not simply due to a general disruption of the secretory pathway (as attested by ACE2 down regulation upon GRASP55 overexpression).

Targeted audience: This study will be of high interest for molecular virologists (not only working on SARS-CoV-2) but could be very well fit into the scope of molecular/cell biology-focused generalist journals

Reviewer expertise: Molecular virology, virus-host interactions (especially involving membranous organelles), SARS-CoV-2, RNA viruses

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

Summary:

Zhang et al. demonstrated in this study that the Golgi apparatus and many other organelles are disturbed by SARS-CoV-2 infection. They focused on the Golgi apparatus and especially on TGN46 and GRASP55 which are both affected differently in their level of expression by the SARS-CoV-2 infection. TGN46 is overexpressed while GRASP55 is decreased in expression. Through different methods overexpression or depletion, the authors nicely demonstrated that modulation of both proteins either increased or decreased particles production. They demonstrated that in absence of GRASP55, SARS-CoV-2 release is increased in the medium. On the contrary, depletion of TGN46 decreases the secretion of SARS-CoV-2 particles.

We thank the reviewer for the accurate summary of our work.

Major comments:

Figure 1: The authors demonstrated that SARS-CoV-2 expression affected the morphology of multiple organelles. Although the results are clear, my concern was that the MOI=1 was really high which indeed would affect the whole cell. To have a less drastic effect on the cell, I would suggest realizing the visualization of some organelles (Golgi, EEA1, Rab7 for example) at a lower MOI=0.1. In addition, it would be nice to verify with a live-dead assay with the MOI=1 if after 24h the cells are still alive, which will confirm that these disturbances are not caused by cells in process of dying.

We thank the reviewer for the excellent suggestions. Investigating how SARS-CoV-2 reshapes subcellular organelles at low MOI (e.g., 0.1) and at different time points would be interesting but is beyond the scope of our study. However, we have performed LDH assay at MOI=1, 2 and 3 for 24 hours to assess cell death. Our results show that LDH release was similar across these conditions (Fig. S5R). We also performed RT-qPCR analysis of Spike, N, and RdRp at early time points of infection. The new results show that neither GRASP55 expression (new Fig. 5D) nor TGN46 expression (Fig. 7R) affects viral RNA abundance at an early infection timepoint (4 hpi).

Figure 2: The results indicated in that panel are really nice. However, the addition of a virus with drugs could increase the proportion of cell death. For the Figure 2C, I propose that the author use a LDH assay to prove that the decrease in infection is not caused by cell death. In addition, a RT-qPCR would be more appropriate to indicate the infection rate and support the microscopy data.

We thank the reviewer for the positive feedback and suggestions. As recommended, we performed an LDH assay to assess cytotoxicity under 9 small molecules treatment of infected cells. Additionally, we performed RT-qPCR analysis for the BFA time-point treatment assay. No significant cell death was observed under these conditions (new Figs. 2D, and S3C).

Figure 3: The authors should have been consistent and add spike instead of nucleocapsid for GalT. According to the figures, Spike seemed to co-localize more with GM130 than Golgin 245. Data analysis of colocalization between Spike and GM130 should be performed to complete the observation. Are no colocalizations of Spike observed with the other Golgi markers?

We agree with the reviewer that it was ideal if spike and GalT were co-stained. Unfortunately, both our spike antibody and GalT antibody are from rabbit, so co-staining could not be done as GM130/spike. We performed colocalization analysis between Spike and GM130, and the results show that GRASP55 expression did enhance Spike and GM130 colocalization to some extent (new Fig. S6E-F). We only co-stained spike with GM130 and Golgin-245 due to the antibody availability.

Figure 4K: While all the experiments were performed at MOI=1, why is the authors using MOI=2 for the immunoblots. Did they have a different result in protein expression for MOI=1 in HuH cells? if so they should show a blot indicating this result.

We did not perform WB to assess protein expression at MOI=1, but our cell toxicity assay showed that there is no significant difference between MOI=2 and MOI=1.

Figure 5: Viral infection should be indicated using RT-qPCR data analysis to support the microscopy observations.

We performed RT-qPCR analysis (new Figs. 2F, 5D, and 7R) and found that BFA treatment did not reduce viral RNA levels at all three time points. Also, GRASP55 expression and TGN46 depletion did not inhibit viral genome RNA levels within one viral infection cycle. Additionally, our new TCID50 assay results support our microscope observation (new Fig. 7O-P). Thanks for the suggestion.

Figure 6: The authors should look at the trafficking of ACE2 and TfR in case of GRASP55 depletion like they did in case of GRASP55 overexpression. It could demonstrate if the virus is using trafficking pathways that are common to the one used by some host receptors to reach the plasma membrane.

Thanks for the excellent suggestion. We performed cell surface biotinylation assay of control and GRASP55-depleted cells. We found that ACE2 and TfR receptor displayed a similar reduction on the cell surface (Fig. S7C), consistent with previous findings that GRASP55 depletion induced Golgi fragmentation and accelerated global conventional protein secretion.

Figure 7: Viral infection assay should also be performed by RT-qPCR. Figure 7H: The immunoblots conditions were performed at MOI=3 this time. The authors should indicate why they did not keep the same MOI conditions. In that case, they should use an intracellular marker for their medium experiment to prove that they isolated proteins that are secreted and not simply released from dead cells. I will also suggest to show LDH assay at MOI=2 and 3 to monitor cell death. Is the Golgi fragmented when GRASP 55 is overexpressed in presence of the virus? Microscopy observations should be performed to reply to this question as it will support their model. The authors suggest that GRASP55 overexpression decreases spike incorporation inside the virion. Can they observe if Spike still colocalizes with GM130 when GRASP55 is overexpressed?

We showed that TGN46 depletion inhibits viral infection by both IF and WB. We further confirmed this through TCID50 assay for both cells and media (new Fig. 7O-P), strengthening our hypothesis.

As we described above, we performed morphological analysis at MOI=1 so that we could observe a significant number of infected cells but minimize cell toxicity. We performed immunoblotting (in Fig. 7H) at MOI=3 to get a good viral infection rate.

As suggested, we also performed LDH assay at MOI=2 and 3 to monitor cell death (new Fig. S2O). Fig. S6C shows that GRASP55 overexpression in the presence of the virus partially rescues Golgi fragmentation. GRASP55 expression did also enhance Spike and GM130 colocalization to some extent (new Fig. S6E-F).

Minor comments:

Figure 1P in the text: Considering that Rab7 up-regulation is equal to "growth of late endosome" is an overstatement. Rab7 is cytosolic at its inactive state and at the endosome at its active state. The authors would have to prove this statement by monitoring an increased quantity of Rab7 at the endosomes which is not enough by just monitoring protein intensity by microscopy. As Rab7 is also localized in lysosomes, and the authors used Lamp2 as a lysosomal marker, it is strange that the area of these structures is not increased. The authors should replace the term "growth" by "an increase in the area of their vesicles".

We did observe less but larger LAMP2 puncta in the infected cells. We agree with the reviewer and rephrased "growth" by an increase in the area of their vesicles". Thank you for the excellent suggestions.

Figure 1Q-T: The observations described in the text did not match the quantification, the area of lysosomes is not significantly different from the non-infected conditions.

In Fig. 1Q-T, we did observe fewer but larger LAMP2 puncta in the infected cells, which was consistent with our quantification, i.e., fewer puncta (Fig. 1R), but each punctum was larger (Fig. 1S), and total area was similar.

Figure 8: In the text, it is mentioned that there is "a dramatic reduction of spike and N in the lysate in GRASP55-expressing and TGN46 depleted cells". However, the quantification indicated that the decrease in N and S content is non-significant. Can the authors precise what was the sample of comparison in the text (siControl versus siTGN46 or siTGN46+GFP versus siTGN46+GFP-GRASP55)?

The decrease in N and S content is significant with the lysate sample comparison (siControl versus siTGN46; siControl+GFP versus siTGN46+GFP; siTGN46+GFP versus siTGN46+GFP-GRASP55). We have now removed this Figure following Reviewer #2's suggestion, since the results are consistent with single protein manipulation and more experiments are needed to confirm whether there is an additive effect.

**Referee cross-commenting**

I agree with most of the concerns of the other reviewers. I do also consider that they should have done their study on cells expressing naturally ACE2. However, at this stage, it will be a lot of work to perform all of their study in a more relevant cell type. The authors should repeat some of their key experiments in lung-derived cell types, to determine if GRASP55 and TGN46 have the same effect on SARS-CoV-2 virion secretion/production.

We thank the reviewer for the suggestions and understanding. As we mentioned before, our study utilizes Huh7-ACE2 cells, which are sorted for the high expression of endogenous ACE2 protein, without ACE2 overexpression. Actually, we also tested A549 and Calu-3 cells. While A549 cells displayed very low infection rate, Calu-3 cells displayed disorganized Golgi without viral infection. However, we did perform immunofluorescence assays in Calu-3 cells. Consistent with our findings in Huh7-ACE2 cells, SARS-CoV-2 infection disrupts Golgi structure and alters protein levels of TGN46 and GRASP55 in Calu3 cells (new Fig. S5R-W). Also, others have reported that liver can be a target for SARS-CoV-2 infection in humans. Furthermore, we confirmed GRASP55 downregulation and TGN46 upregulation in VeroE6 cells (Fig. S6K-N).

Reviewer #3 (Significance (Required)):

The study identified two Golgi proteins (TGN46 and GRASP55) that are involved in modulating the release of SARS-CoV-2 particles from the cells. As these proteins are also acting on general secretion of host proteins to the plasma membrane, the effect on SARS-CoV-2 release could just be indirect. However, it does not change the informative points of the study raised by Zhang et al. It highlights really well how the host trafficking pathway could be diverted for the purpose of the virus, which is to produce particles to maintain its survival.

Strengths: The authors performed a precise and well quantified study. Observing how SARS-CoV-2 impacts host organelles morphology and uses host trafficking proteins to produce particles, brings more clarity on some unclear parts of the life cycle of the virus. In addition, it exposes new targets for therapeutic studies.

We thank the reviewer for the positive comments.

Weakness: The paper is mostly based on microscopy analysis and need some other methods to support their data. The paper lacks some molecular mechanisms explaining the clear role of GRASP55 and TGN46 in particle production or assembly.

In the revised version, we incorporated RT-qPCR assay, cell cytotoxicity assay, and BFA time-point treatment assay. Notably, we added intracellular and extracellular viral titer assays to more precisely distinguish between effects on virion assembly and virion secretion. We also confirmed the key observation that SARS-CoV-2 infection modulates GRASP55 and TGN46 expression in the Calu-3 lung cell line. Additionally, our early time-point results clearly support the role of GRASP55 and TGN46 in viral trafficking.

• Audience: The paper will be interesting for basic research for a virology and cell biology audience.
• Field of expertise with a few keywords: Virology and host cell trafficking.

References

Barnes E (2022) Infection of liver hepatocytes with SARS-CoV-2. Nat Metab 4: 301-302

Bekier ME, 2nd, Wang L, Li J, Huang H, Tang D, Zhang X, Wang Y (2017) Knockout of the Golgi stacking proteins GRASP55 and GRASP65 impairs Golgi structure and function. Mol Biol Cell 28: 2833-2842

Eymieux S, Rouille Y, Terrier O, Seron K, Blanchard E, Rosa-Calatrava M, Dubuisson J, Belouzard S, Roingeard P (2021) Ultrastructural modifications induced by SARS-CoV-2 in Vero cells: a kinetic analysis of viral factory formation, viral particle morphogenesis and virion release. Cell Mol Life Sci 78: 3565-3576

Ghosh S, Dellibovi-Ragheb TA, Kerviel A, Pak E, Qiu Q, Fisher M, Takvorian PM, Bleck C, Hsu VW, Fehr AR et al (2020) beta-Coronaviruses Use Lysosomes for Egress Instead of the Biosynthetic Secretory Pathway. Cell 183: 1520-1535 e1514

Hoffmann M, Hofmann-Winkler H, Smith JC, Kruger N, Arora P, Sorensen LK, Sogaard OS, Hasselstrom JB, Winkler M, Hempel T et al (2021) Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity. EBioMedicine 65: 103255

Hoffmann M, Mosbauer K, Hofmann-Winkler H, Kaul A, Kleine-Weber H, Kruger N, Gassen NC, Muller MA, Drosten C, Pohlmann S (2020) Chloroquine does not inhibit infection of human lung cells with SARS-CoV-2. Nature 585: 588-590

Xiang Y, Wang Y (2010) GRASP55 and GRASP65 play complementary and essential roles in Golgi cisternal stacking. J Cell Biol 188: 237-251

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Referee #3

Evidence, reproducibility and clarity

Summary:

Zhang et al. demonstrated in this study that the Golgi apparatus and many other organelles are disturbed by SARS-CoV-2 infection. They focused on the Golgi apparatus and especially on TGN46 and GRASP55 which are both affected differently in their level of expression by the SARS-CoV-2 infection. TGN46 is overexpressed while GRASP55 is decreased in expression. Through different methods overexpression or depletion, the authors nicely demonstrated that modulation of both proteins either increased or decreased particles production. They demonstrated that in absence of GRASP55, SARS-CoV-2 release is increased in the medium. On the contrary, depletion of TGN46 decreases the secretion of SARS-CoV-2 particles.

Major comments:

Figure 1: The authors demonstrated that SARS-CoV-2 expression affected the morphology of multiple organelles. Although the results are clear, my concern was that the MOI=1 was really high which indeed would affect the whole cell. To have a less drastic effect on the cell, I would suggest realizing the visualization of some organelles (Golgi, EEA1, Rab7 for example) at a lower MOI=0.1. In addition, it would be nice to verify with a live-dead assay with the MOI=1 if after 24h the cells are still alive, which will confirm that these disturbances are not caused by cells in process of dying.

Figure 2: The results indicated in that panel are really nice. However, the addition of a virus with drugs could increase the proportion of cell death. For the Figure 2C, I propose that the author use a LDH assay to prove that the decrease in infection is not caused by cell death. In addition, a RT-qPCR would be more appropriate to indicate the infection rate and support the microscopy data.

Figure 3: The authors should have been consistent and add spike instead of nucleocapsid for GalT. According to the figures, Spike seemed to co-localize more with GM130 than Golgin 245. Data analysis of colocalization between Spike and GM130 should be performed to complete the observation. Are no colocalizations of Spike observed with the other Golgi markers?

Figure 4K: While all the experiments were performed at MOI=1, why is the authors using MOI=2 for the immunoblots. Did they have a different result in protein expression for MOI=1 in HuH cells? if so they should show a blot indicating this result.

Figure 5: Viral infection should be indicated using RT-qPCR data analysis to support the microscopy observations.

Figure 6: The authors should look at the trafficking of ACE2 and TfR in case of GRASP55 depletion like they did in case of GRASP55 overexpression. It could demonstrate if the virus is using trafficking pathways that are common to the one used by some host receptors to reach the plasma membrane.

Figure 7: Viral infection assay should also be performed by RT-qPCR. Figure 7H: The immunoblots conditions were performed at MOI=3 this time. The authors should indicate why they did not keep the same MOI conditions. In that case, they should use an intracellular marker for their medium experiment to prove that they isolated proteins that are secreted and not simply released from dead cells. I will also suggest to show LDH assay at MOI=2 and 3 to monitor cell death. Is the Golgi fragmented when GRASP 55 is overexpressed in presence of the virus? Microscopy observations should be performed to reply to this question as it will support their model. The authors suggest that GRASP55 overexpression decreases spike incorporation inside the virion. Can they observe if Spike still colocalizes with GM130 when GRASP55 is overexpressed?

Minor comments:

Figure 1P in the text: Considering that Rab7 up-regulation is equal to "growth of late endosome" is an overstatement. Rab7 is cytosolic at its inactive state and at the endosome at its active state. The authors would have to prove this statement by monitoring an increased quantity of Rab7 at the endosomes which is not enough by just monitoring protein intensity by microscopy. As Rab7 is also localized in lysosomes, and the authors used Lamp2 as a lysosomal marker, it is strange that the area of these structures is not increased. The authors should replace the term "growth" by "an increase in the area of their vesicles".

Figure 1Q-T: The observations described in the text did not match the quantification, the area of lysosomes is not significantly different from the non-infected conditions.

Figure 8: In the text, it is mentioned that there is "a dramatic reduction of spike and N in the lysate in GRASP55-expressing and TGN46 depleted cells". However, the quantification indicated that the decrease in N and S content is non-significant. Can the authors precise what was the sample of comparison in the text (siControl versus siTGN46 or siTGN46+GFP versus siTGN46+GFP-GRASP55)?

Referee cross-commenting

I agree with most of the concerns of the other reviewers. I do also consider that they should have done their study on cells expressing naturally ACE2. However, at this stage, it will be a lot of work to perform all of their study in a more relevant cell type. The authors should repeat some of their key experiments in lung-derived cell types, to determine if GRASP55 and TGN46 have the same effect on SARS-CoV-2 virion secretion/production.

Significance

The study identified two Golgi proteins (TGN46 and GRASP55) that are involved in modulating the release of SARS-CoV-2 particles from the cells. As these proteins are also acting on general secretion of host proteins to the plasma membrane, the effect on SARS-CoV-2 release could just be indirect. However, it does not change the informative points of the study raised by Zhang et al. It highlights really well how the host trafficking pathway could be diverted for the purpose of the virus, which is to produce particles to maintain its survival.

Strengths: The authors performed a precise and well quantified study. Observing how SARS-CoV-2 impacts host organelles morphology and uses host trafficking proteins to produce particles, brings more clarity on some unclear parts of the life cycle of the virus. In addition, it exposes new targets for therapeutic studies.

Weakness: The paper is mostly based on microscopy analysis and need some other methods to support their data. The paper lacks some molecular mechanisms explaining the clear role of GRASP55 and TGN46 in particle production or assembly.

Audience: The paper will be interesting for basic research for a virology and cell biology audience.

Field of expertise with a few keywords: Virology and host cell trafficking.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, Zhang and colleagues address the impact on SARS-CoV-2 infection on the morphology of the Golgi apparatus and convincingly demonstrate a fragmentation of this organelle in infected cells. Conversely, they show that the modulation of TGN46 or GRASP55 expressions, two components of this organelle impact SARS_CoV-2 replication. By monitoring the relative levels of viral Spike and nucleocapsid in the cell supernatants, they conclude that GRASP55 regulates particle assembly and trafficking while TGN46 controls only secretion. The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were robustly quantified. I believe that this study is potentially interesting and relevant for the SARS-CoV-2 community since providing an extensive characterization of the interplay between SARS-CoV-2 and the Golgi apparatus. However, as described below, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery used for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study, especially without clear molecular mechanisms about the interplay between SARS-CoV-2 proteins and TNG46/GRASP55.

Major comments:

• All the assays have been performed in liver-derived Huh7 cells (overexpressing SARS-CoV-2 receptor) ACE2 (for infection) or kidney 293 cells (for pseudotyped HIV entry assays). However, no conclusion was validated in lung-derived cells (like A549-ACE2, Calu-3 or primary cells), which would be important since the respiratory tract is the main target of SARS-CoV-2
• Fig2: The impact of the drugs on replication was assessed by measuring the % of infected cells. At 24 hpi, I am unsure about what this value is supposed to measure (the whole life cyle, intracellular replication or spread?), especially since it is not indicated when the drugs were added to the cells. Was it during, before or after the infection? This information should be provided. If the "Golgi" drugs impact egress only (as inferred by the genetic modulation phenotypes), I would expect that at this early time point, the % of infection would not drastically change (as well as intracellular RNA) but that the extracellular infectious titers would decrease. Plaque assays (or TCID50 assays) and RT-qPCR on intracellular viral RNA should be conducted to better understand the impact of drug treatments. On page 10, it is said that the virus makes three cycles of replication within 24 hours following infection. On what data is this based? This seems a lot. If this is true (and shown in Huh7-ACE2 cells), does the assay of figure 2 measure spread in general? More importantly, despite mentioned, the cell viability data are not provided. It is important to show them to ensure that these concentrations of drugs are not toxic at the tested concentrations.
• I appreciated the extensive confocal microscopy analysis performed by the authors, which seems of high quality and overall, very convincing. They clearly show that SARS-CoV-2 infection induces the fragmentation of the Golgi apparatus although it was reported by others before as mentioned by the authors. However, it was hard for me to make the functional link between these data and those related to GRASP55 and TGN46 overexpression/knockdown. First, the authors should assess the morphology of the Golgi apparatus in Huh7-ACE2 when GRASP55 is knocked down/out or when TGN46 is overexpressed. Second, in these 2 conditions that favor replication, it should be assessed whether this correlates with Golgi fragmentation. Even if this was probably shown before, it is relevant to show that these genetic modulations induce Golgi reshaping in this particular cell type by confocal microscopy (and ideally electron microscopy).
• The fact that GRASP55-GFP expression decreases in 293T the cell surface levels of ACE2, the receptor of Spike (Fig S6), raises concern that the effect of GRASP55 is not specific to the virus and suggests that the whole secretory pathway is altered, while an impairment of virus entry should be expected in this cell line. Is there a similar trend in Huh7-ACE2? In addition to addressing the functionality of the secretory machinery in Huh7-ACE2, it would be relevant to repeat the cell surface labelling in the context of pseudotyped virus production with other viral envelopes such as VSV G protein or HIV gp41/gp120. If the phenotype is specific to Spike trafficking, the cell surface abundance of these alternative viral proteins should not be impacted by GRASP55 overexpression. Otherwise, this would indicate a general effect of on the secretory pathway. Besides, since HIV Gag is directed directly to the plasma membrane during particle assembly without entering the secretory pathway, I am not convinced that upstream alteration on nucleocapsid assembly at the ERGIC should be excluded. Indeed, changes on the S/N ratios are generally mild and I feel that this cannot explain the phenotypes in the extracellular infectious titers. More generally, the comparison between trafficking and assembly should be better assessed and not simply based on extracellular N and S levels. It was hard to see the differences between the two in terms of phenotypes. The authors should at least measure the intracellular infectivity upon TGN46 and GRASP55 knock/down and overexpression as well as intracellular vRNA abundance as a readout of RNA replication (which is anticipated to remain unchanged).
• Finally, mechanistic insight about the viral determinants regulating the morphology of the Golgi would significantly strengthen the study. Fig S6 shows that S expression decreases ACE2 surface levels? If so, could some S mutants be tested? Does it correlate with Golgi fragmentation? Do other viral structural proteins contribute to Golgi morphological alterations? In the same line of idea, how GRASP55 and TGN46 regulate replication. The link with Golgi morphology is unclear. Are these proteins hijacked by SARS-COV-2? Page 13 mentions some relevant mutants that could be assessed in this context and provide mechanistic insights.

Minor comments:

• The signal of calreticulin in Fig. S1 is too low to appreciate it distribution.
• Fig 4K, Q: The differences in LC3 forms levels are not convincing. These results do not allow to draw any conclusion about autophagy, especially considering that this was done at steady-state and that the autophagic flux was not measured. Indeed, a bafilomycin A treatment control would be required to measure the real induction of autophagosomes. Lysosomal degradation inhibition allows the detection of LC3 accumulation.
• In the GRASP55 overexpression and TGN46 knockdown studies, associated cell viability should be measured to control that that these genetic manipulations do not induce any cytotoxicity which may impact viral replication.
• The authors should test the impact of GRASP55 and GRASP65 knock-out on SARS-CoV-2 replication
• The authors should provide more details about the USA-WA1/2020 isolate in the Methods section. Is it related to the "Wuhan" strain or the variant which spread globally in early 2020 (with D614G mutation in Spike).
• Fig 8: The combined modulation of GRASP55 and TGN46 expressions does not really seem additive to me since a 70% decrease of either protein modulation is observed while the combined condition brings this value to 75% in TCID50 assays. This does not bring much insight to the study in my opinion. I would suggest that the authors consider removing this figure.

Significance

General assessment and advance: The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were quantified extensively. However, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study. In addition, the antiviral activity of several tested drugs was also reported elsewhere. A clear mechanism of how SARS-CoV-2 induces a fragmentation of the Golgi would strengthen the study. In the same line of idea, it is unclear how TGN46 and GRASP55 regulate the late steps of the life cycle. The link between SARS-CoV-2-induced Golgi fragmentation and TGN46/GRASP55 is unclear. In my opinion, the data did not allow to clearly discriminate between virion assembly and egress. I was not convinced that it was not simply due to a general disruption of the secretory pathway (as attested by ACE2 down regulation upon GRASP55 overexpression).

Targeted audience: This study will be of high interest for molecular virologists (not only working on SARS-CoV-2) but could be very well fit into the scope of molecular/cell biology-focused generalist journals

Reviewer expertise: Molecular virology, virus-host interactions (especially involving membranous organelles), SARS-CoV-2, RNA viruses

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, the authors highlight the importance of the Golgi apparatus during SARS-CoV-2 infection. Specifically, using different compounds able to alter Golgi structure and function, the authors show a strong reduction in SARS-CoV-2 infection rate. In particular it is interesting to observe that treatments of 24 hrs with BFA strongly impair viral infection, highlithing the importance of Golgi function for this virus. Albeit the time of treatment is different. this observation is in contrast with previous studies on related coronaviruses (Ghosh et al., 2020) that did not observe any effect upon treatment with BFA. This might imply that SARS-CoV-2 relies more on conventional trafficking pathways respect to other coronaviruses which, under certain conditions, favour different trafficking routes. The authors additionally observed that viral infection increases TGN46 levels while decreasing GRASP55 levels. To dissect the role of TGN46 and GRASPR55, the authors performed several infection studies in cells in which the levels of the two proteins were modulated either by overexpression (GRASP55) and/or siRNA-mediated knock-down (GRASP55 and TGN46). Those approaches suggest that GRASPR55 overexpression, a protein essential for Golgi stack formation, decelerates viral trafficking and inhibits viral assembly while its depletion reverses the effects. On the other hand, TGN46 knock-down impairs viral trafficking but not assembly.

Overall the study clearly shows the importance of the Golgi during SARS-CoV-2 and also shows that modulation of those two factors affect viral infection. However the claims that specifically the trafficking (TGN46) and trafficking and assembly (GRASP55) are not fully substantiated.

Regarding GRASP55, the authors state that viral infection decreases GRASPR55 levels and this results in Golgi fragmentation. However GRASPR55 levels decrease is shown at 24 hrs post infection while Golgi fragmentation occurs as early as 5 hrs. Thus there might be no direct casual effect between the two effects. Additionally, the authors show that overexpression of GRASP55 rescue Golgi fragmentation, as observed by imaging, however is not clear if only infected cells where quantified and if they had the same level of infection.

The authors exclude and effect on entry based on experiment on Spike expressing pseudovirus in 293-ACE2, however they also clearly observe reduction of ACE2 on the membrane of GRASPR55 expressing cells (Fig S6B). Thus how can they explain this discrepancy and how ca defect in entry can be fully marked out in these cell lines? It is not clear to which process the authors refer to when they write about "viral trafficking". Is it virion trafficking or viral proteins trafficking? The two process are linked but are not the same. This oversemplification can be misleading. For instance the authors show that overexpression of GRASP55 decreases Spike protein on the plasma membrane and its depletion increases S protein incorporation into psudoviruses. However it was shown that in infected cells S protein is mainly retained at the ERGIC by M and E (Boson et al., 2021) where viral assembly occurs. Thus an increase in S trafficking on the PM does not correlate with an increase in virion trafficking, and ultimately, the data provided do not fully support the authors claim on a modulation of "virion trafficking" in response to GRASP or TGN46 changes, since no experiments clearly show a change in virions secretion. Importantly, the authors do not rule out potential effects of their perturbations on genome replication. The only experiment that they perform in this direction is presented in figure S7B, where the authors show similar percentage of infected cells at early stage upon silecing of GRASPR55. The experiment suggests that productive entry is similar in these conditions, but quantification of intracellular viral genome could exclude a change in viral replication. If no changes in viral replication are observed, the authors could verify an increase in particles secretion by collecting supernatants from the early time points and performing plaque assays and quantification of viral genomes by qRT-PCR, to prove that modulation of GRASPR55 indeed promote SARS-CoV-2 trafficking.

Finally, whenever reduction of viral infection is observed upon cell partubation, a robust analysis of cell viability should be presented to exclude pleiotropic effects. Expecially in presence of multiple pertubation that might affect cell metabolism. The authors should carefully control cell viability and growth in response to depletion of TGN46 and GRASP55.

Minor:

show data on viability of the drug and add the relative section in Material and Methods

Figure 3A: should read spike and not nucleocapsid eported for SARS-CoV-2 Lack of inhibition with camostat correlates with lack of TMPRSS2 in the Huh7. The sentence seems to be too general while in this case the effect is clearly cell specific. Similarly, the importance of the lysosome in viral entry is restricted to cells lacking TMPRSS2 and cannot be generalized since CQ, for example, does not work in Calu-3 cells that express TMPRSS2 cells. Typo: Fig S3B - Y axis should reat viral not vrial S3C: concentrations of the compound used in the assay should be reported. Was a viability assay performed also in the 293T-ACE2 cell line?

Significance

Overall, the major strenght of the manuscript is that it has clarified the importance of the Golgi during SARS-CoV-2 infection. The drugs screening demonstrate that for SARS-CoV-2 the conventional secretion seems to have major role respect to other secretory routes observed for other coronaviruses. Also it is clear that the two factors identified by the authors have a role in viral infection, however the major limitation is that the authors failed to clearly highlight which step/s of the viral life cycle are modulated upon GRASP55 and TGN46 perturbatio. Expecially the claims on "trafficking" is not fully substantiated, since the only experiment in this direction is the transport of Spike protein on the plasma membrane upon GRASPR55 overexpression. It is risky to conclude that the trafficking of a single protein reflect the intracellular trafficking of the virions.

Several of the finding presented in the first part of the manuscript have been already previously reported (for example the fragmentation of the Golgi upon SARS-CoV-2 infection), however the role of GRASP55 and TGN46 in SARS-CoV-2 infection has been reported here for the first time. This manuscript can be of interest for a broad audience considering the topic (cell biology, host-pathogen interactions and molecular virology)

My expertise reside in the field of molecular virology, expecially in the contest of the mechanisms of viral replication and host-pathogen interactions.

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The authors do not wish to provide a response at this time

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Referee #3

Evidence, reproducibility and clarity

In this study, Wasilewska and colleagues generated tmbim5-/- zebrafish line and demonstrated that tmbim5 loss of function leads to decrease in zebrafish size and induces muscle atrophy. Authors used immunohistochemistry to suggest that tmbim5-/- zebrafish shows reduced glycogen levels in muscle and liver. However, most of the immunohistochemistry is not quantitated and only qualitative differences are shown. Next, the authors measured mitochondrial calcium levels in the brain of tmbim5-/- zebrafish but there was no behavioral phenotype in the fish. It would have be better to measure mitochondrial calcium levels in the muscles of tmbim5-/- zebrafish as phenotype is muscle atrophy. Further, it is reported that the mitochondrial membrane potential and glycogen levels were perturbed in tmbim5-/- zebrafish.

Next, the authors generated a scl8b1-/- (a probable NCLX ortholog in zebrafish) zebrafish, which did not show any drastic phenotype. However, neither slc8b1 function nor the phenotype of scl8b1-/- zebrafish was well characterized. Further, authors created two double knockout zebrafish lines i.e. tmbim5-/-/mcu-/- and tmbim5-/-/slc8b1-/-. Interestingly, both these lines were viable and do not show any drastic phenotypes. The authors concluded that in these transgenic fishes compensatory and/or alternative mitochondrial Ca2+ mobilization pathways counterbalance the effects of silencing of these proteins.

Although it is an interesting study, the conclusions are not well supported with the data. At several places only qualitative images are shown and quantitative data is missing. Similarly, Ca2+ imaging in muscles of tmbim5-/- zebrafish is not performed. Finally, no molecular mechanism or molecular details are provided. Though Tmbim5's potential role in EMRE degradation is discussed, it is not experimentally investigated. The quality of the manuscript would significantly enhance if authors perform the suggested experiments.

Major Comments:

1. As a potential mechanism, Tmbim5's potential role in EMRE degradation is discussed but it is not experimentally investigated. It is very easy to test this hypothesis. If this is the case, it would be a very good contribution to the field.
2. On Page 16, authors state that slc8b1 does not constitutes the major mitochondrial Ca2+ efflux transport system. Authors should do calcium imaging experiments just like they did with tmbim5 and mcu double knockouts (data presented in Figure 4C) to make any comments on functioning of slc8b1 in mitochondrial Ca2+ transport. This is important because slc8b1 is only a predictive ortholog of human NCLX and it is not experimentally examined yet.
3. The data presented in Fig. 4C is very important but it is not fully explained and discussed in the results. Please discuss all the data sets presented in Fig4C in detail. As such, it is very difficult to follow and interpret the data.
4. In tmbim5-/- zebrafish, what happens to mitochondrial Ca2+ signaling in muscle as phenotype is muscle atrophy only?
5. Please validate the observation of decreased glycogen levels in tmbim5-/- fish by one more way. Only immunohistochemistry that too without quantitation is not convincing (Fig. 2E-H).

Minor Comments:

1. Authors state that tmbim5 loss of function leads to metabolic changes but the only data provided is decrease in glycogen levels. It would be helpful for the authors to focus comments specifically on the data presented in the manuscript to avoid potential over-interpretation.
2. While discussing Fig4., authors mention that Tmbim5 may act as a MCU independent Ca2+ uptake mechanism and therefore they crossed tmbim5 mutants with mcu KO fish. But from the data presented in Fig.3 and as concluded by the authors themselves tmbim5 mutants do not show changes in the mitochondrial Ca2+ levels. Authors may clarify this point.
3. Does tmbim5 contributes to mitochondrial Ca2+ uptake in presence or along with MCU. Further analysis of Fig4C may shed some light on this. Authors should test significance between tmbim5-/- and WT as well as between tmbim5-/- and tmbim5+/+ in mcu-/- background.
4. Please check the labeling on traces in Fig3D.
5. Please include quantitation of data presented in EV2E-F.
6. Please include quantitation of immunohistochemistry data presented in 2E-H.

Referee cross-commenting

Several comments are common between the reviewers highlighting that those experiments are critical. Secondly, I agree with the concerns raised by other two reviewers.

Significance

In this study, authors report couple of new transgenic zebrafish lines. However, further characterization of slc8b1-/- is required. This study reinforces the existing idea that there are very robust compensatory mechanisms that maintain mitochondrial Ca2+ homeostasis. While the work provides useful insights, it could benefit from a broader scope to provide substantial advancement to existing knowledge.

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Referee #2

Evidence, reproducibility and clarity

Summary: The work of Wasilewska et al. focusses on the MCU independent basal Ca2+ uptake mechanisms and the effects of MCU, NCLX, and TMBIM5 KO on Zebrafish Ca2+ homeostasis, mortality, anatomy and metabolism. The authors found evidence that tmbim5 potentially has a bidirectional mode of operation and is able to extrude Ca2+ from the matrix as well as transfer Ca2+ into mitochondria. Further, a reduced membrane potential in tmbim5-/- fish and altered metabolism was found. While the conclusion drawn are well argumented, a few points have to be addressed.

Major Points:

1. While all mitochondrial genes seem collectively reduced compared to control, it would be interesting to assess the mitochondrial mass and/or mitochondrial turnover rate in regard to e.g. mitophagy. The reduced membrane potential could lead to PINK1 accumulation on the outer mitochondrial membrane to mediate mitophagy leading overall to reduced mitochondrial count and mass.
2. The characterization of slc8b1-KO fish needs some improvement to facilitate a better understanding of the molecular interactions of slc8b1 and tmbim5. This would also greatly improve the understanding of the phenotypical characterization and behavioral response to CGP.
3. Functional Ca2+ measurements of the activity of slc8b1 gene product have to be done to ensure a KO phenotype. Especially in light of the surprising results presented in Figure 6A showing an effect of CGP on slc8b1-KO fish but not on tmbim5-KO fish I advise mitochondrial isolation to conduct mitochondrial basal and extrusion Ca2+experiments of slc8b1-KO fish, tmbim5-KO fish, and double KO-fish.

Minor Points:

The authors claim that mRNA levels of mitochondrial proteins involved in Ca2+ transport in tmbim5-/- are unaffected (Figure EV3). While the T-tests show no significant alteration, what happens if a 2-way ANOVA shows a more general effect revealed between WT and TMBIM5-/-?

Significance

This is a well-designed and carefully executed piece of work. The experimental design is thoughtfully elaborated, and the topic is worthy of investigation. The strengths of this study lie in translating our knowledge of TMBIN5 from single cells to organism and organ function. Moreover, the work provides important new information that will help the scientific community working on mitochondrial regulation AND muscle diseases to understand how ions coordinately regulate mitochondrial function.

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Referee #1

Evidence, reproducibility and clarity

Although the experimental approach is promising (see below), the results do not significantly expand our current understanding. This is partly due to the challenges of interpreting negative results, which are nonetheless worth reporting. Some of the conclusions and interpretations of the results could benefit from further clarification and contextualization to enhance their impact:

• Figure 1D: The distribution of fiber size in wt vs. Tmbim5-ko fish shows a notable difference limited to one size range. Can the authors clarify this observation? Could this indicate a switch in fiber type? Is there a correlation between this finding and the differential PAS staining?
• Figure 3: one of the advantages of the zebrafish model is its transparency, allowing for fluorescence imaging. Unfortunately, this proves to be impossible in the case of cepia2mt. The data provided by the authors show that the fluorescence of this probe does not vary following physiological stimuli. The only change is that induced by CCCP (Fig 3C-D), which according to the authors causes a discharge of mitochondrial calcium. However, the use of CCCP with GFP-based probes should be avoided, as the acidification caused by CCCP treatment leads to quenching of the fluorophore, resulting in a fluorescence decrease which is independent of Ca2+ levels. Although the experimental approach aims to detect dynamic changes in mitochondrial Ca2+ levels, the presented results in Figure 3 do not provide conclusive evidence to support this capability. While significant experimental effort is evident, these findings may require further validation or additional data to strengthen their impact. Alternatively, the authors could remove this Figure 3 and relevant text from the manuscript.
• Figure 6A: In my opinion, this dataset is impossible to understand. To my knowledge, the precise molecular target of CGP-37157 remains elusive. While CGP is often considered an NCLX inhibitor, this classification lacks definitive experimental support. Although CGP is known to inhibit mitochondrial Na+-dependent Ca2+ extrusion, direct binding of CGP to NCLX has yet to be conclusively demonstrated. With this in mind, the authors show that pharmacological intervention with CGP elicits a distinct phenotype in the fish model. While this effect appears to persist in SLC8B1-KO fish, it is absent in Tmbim5-KO fish, suggesting Tmbim5 as a potential molecular target for CGP. However, this interpretation is inconsistent with the following observations: i) CGP remains effective in Tmbim5/Slc8b1 double-KO fish and ii) Tmbim5-KO fish exhibit no discernible phenotype. A comprehensive explanation that reconciles these findings is sought.
• Figure 6B: according to the authors, the phenotype induced by CGP treatment is specific because a different substance with a completely different effect, CCCP, causes the same phenotype in both wt and Tmbim5-KO fish. Also in this case, the rationale and reasoning behind this experiment in not very evident. As I see it, CCCP blocks zebrafish motility because it is a metabolic poison, and its effect does not depend on any transporter.

Significance

The manuscript submitted by Wasilewska et al investigates the functional relationship between different mitochondrial calcium transporters using zebrafish as a model. The topic is of great interest. In the last 15 years, many mitochondrial calcium transporters have been identified. In some cases, their mechanism is not fully understood, such as in the case of TMBIM5, recently described by some as an H/Ca exchanger, or as a Ca channel by others. Furthermore, the functional relationship between different transporters has so far been studied in a partial and superficial way. I believe that this work is therefore of great interest because it aims to contribute to a fundamental problem that is still poorly studied. The idea of using zebrafish is interesting, as it is an organism that is easy to manipulate and phenotype, and because it is transparent, making it possible to use specific biosensors to characterize mitochondrial calcium dynamics, at least in principle. The paper therefore deserves attention.

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Reply to the reviewers

Reviewer #1 Evidence, reproducibility and clarity Summary: Bhatt et al. seek to define factors that influence H3.3 incorporation in the embryo. They test various hypotheses, pinpointing the nuclear/cytoplasmic ratio and Chk1, which affects cell cycle state, as influencers. The authors use a variety of clever Drosophila genetic manipulations in this comprehensive study. The data are presented well and conclusions reasonably drawn and not overblown. I have only minor comments to improve readability and clarity. I suggest two OPTIONAL experiments below. We thank the reviewer for their positive and helpful comments. Major comments: We found this manuscript well written and experimentally thorough, and the data are meticulously presented. We have one modification that we feel is essential to reader understanding and one experimental concern: The authors provide the photobleaching details in the methodology, but given how integral this measurement is to the conclusions of the paper, we feel that this should be addressed in clear prose in the body of the text. The authors explain briefly how nuclear export is assayed, but not import (line 99). Would help tremendously to clarify the methods here. This is especially important as import is again measured in Fig 4. This should also be clarified (also in the main body and not solely in the methods). We have added the following sentences to the main body of the text to clarify how photobleaching and import were assayed. “We note that these differences are not due to photobleaching as our measurements on imaged and unimaged embryos indicate that photobleaching is negligible under our experimental conditions (see methods, Figure S1G-H)” lines 98-101 and “Since nuclear export is effectively zero, we attribute the increase in total H3.3 over time solely to import and therefore the slope of total H3.3 over time corresponds to the import rate.” lines 111-113 Revision Plan In addition we have clarified how import was calculated to figure legends in Figure 5D (formerly 4D) and S1F which now read: “Initial slopes of nuclear import curves (change in total nuclear intensity over time for the first 5 timepoints) …” We also added the following explanation of how nuclear import rates were calculated to the methods section: “Import rates were calculated by using a linear regression for the total nuclear intensity over time for the first 5 timepoints in the nuclear import curves.” lines 471-473, methods If the embryos appeared "reasonably healthy" (line 113) after slbp RNAi, how do the authors know that the RNAi was effective, especially in THESE embryos, given siblings had clear and drastic phenotype? This is especially critical given that the authors find no effect on H3.3 incorporation after slbp RNAi (and presumably H3 reduction), but this result would also be observed if the slbp RNAi was just not effective in these embryos. We apologize for the confusion caused by our word choice. The “healthy” slbp-RNAi embryos had measurable phenotypes consistent with histone depletion that we have reported previously (Chari et al, 2019) including cell cycle elongation and early cell cycle arrest (Figure S4D). However, they did not have the catastrophic mitosis observed in more severely affected embryos. We agree with the reviewer that a concern of this experiment is that the less severely affected embryos likely have more remaining RD histones including H3. To address this we also tested H3.3 incorporation in the embryos that fail to progress to later cell cycles in the cycles that we could measure. Even in these more severely affected embryos we were not able to detect a change in H3.3 incorporation relative to controls (lines 240-243 and Fig S4B). Unfortunately, it is impossible to conduct the ideal experiment, which would be a complete removal of H3 since this is incompatible with oogenesis and embryo survival. To address this confusion we have added supplemental videos of control, moderately affected and severely affected SLBP-RNAi embryos as movies 3-5 and modified the text to read: “All embryos that survive through at least NC12, had elongated cell cycles in NC12 and 60% arrested in NC13 as reported previously indicating the effectiveness of the knockdown (Figure S4C, Movie 3-5)39. In these embryos, H3.3 incorporation is largely unaffected by the reduction in RD H3 (Figure 6B).” lines 236-240 Finally, to characterize the range of SLBP knockdown in the RNAi embryos we propose to do single embryo RT-qPCRs for SLBP mRNA for multiple individual embryos. This will provide a measure of the range knockdown that we observed in our H3.3 movies. Minor comments: Introduction: Revision Plan Consider using "replication dependent" (RD) rather than "replication coupled." Both are used in the field, but RD parallels RI ("replication independent"). We thank the reviewer for this suggestion. We have made the text edits to change "replication coupled" (RC) to "replication dependent" (RD) throughout the manuscript. Would help for clarity if the authors noted that H3 is equivalent to H3.2 in Drosophila. Also it is relevant that there are two H3.3 loci as the authors knock mutations into the H3.3A locus, but leave the H3.3B locus intact. The authors should clarify that there are two H3.3 genes in the Drosophila genome. We have changed the text as follows to increase clarity as suggested: “Similarly, we have previously shown that RD H3.2 (hereafter referred to as H3) is replaced by RI H3.3 during these same cycles, though the cause remains unclear29” lines 52-54 “There are ~100 copies of H3 in the Drosophila genome, but only 2 of H3.3 (H3.3A and H3.3B)26. To determine which factor controls nuclear availability and chromatin incorporation, we genetically engineered flies to express Dendra2-tagged H3/H3.3 chimeras at the endogenous H3.3A locus, keeping the H3.3B locus intact.” lines 127-131 Please add information and citation (line 58): H3.3 is required to complete development when H3.2 copy number is reduced (PMID: 37279945, McPherson et al. 2023) We have added the suggested information. The text now reads “Nonetheless, H3.3 is required to complete development when H3.2 copy number is reduced54.” lines 61-62 Results: Embryo genotype is unclear (line 147): Hira[ssm] haploid embryos inherit the Hira mutation maternally? Are Hira homozygous mothers crossed to homozygous fathers to generate these embryos, or are mothers heterozygous? This detail should be in the main text for clarity. The Hira mutants are maternal effect. We crossed homozygous Hirassm females to their hemizygous Hirassm or FM7C brothers. However, the genotype of the male is irrelevant since the Hira phenotype prevents sperm pronuclear fusion and therefore there is no paternal contribution to the embryonic genotype. We have clarified this point in the text: “We generated embryos lacking functional maternal Hira using Hirassm-185b (hereafter Hirassm) homozygous mothers which have a point mutation in the Hira locus57.” lines 160-162 Revision Plan Line 161: Shkl affects nuclear density, but it also appears from Fig 3 to affect nuclear size? The authors do not address this, but it should at least be mentioned. We thank the reviewer for the astute observation. More dense regions of the Shkl embryos do in fact have smaller nuclei. We believe that this is a direct result of the increased N/C ratio since nuclear size also falls during normal development as the N/C ratio increases. We have added a new figure 1 in which we more carefully describe the events of early embryogenesis in flies including a quantification of nuclear size and number in the pre-ZGA cell cycles (Figure 1C). We also note the correlation of nuclear size with nuclear density in the text: “During the pre-ZGA cycles (NC10-13), the maximum volume that each nucleus attains decreases in response to the doubling number of nuclei with each division (Figure 1C).” lines 86-87 “To test this, we employed mutants in the gene Shackleton (shkl) whose embryos have non-uniform nuclear densities and therefore a gradient of nuclear sizes across the anterior/posterior axis (Figure 3A-B, Movie 1-2)58.” lines 180-183 The authors often describe nuclear H3/H3.3 as chromatin incorporated, but these image-based methods do not distinguish between chromatin-incorporated and nuclear protein. To distinguish between chromatin incorporated and nuclear free histone we have exploited the fact that histones that are not incorporated into DNA freely diffuse away from the chromatin mass during mitosis while those that are bound into nucleosomes remain on chromatin during this time. In our previous study we showed that H3-Dendra2 that is photoconverted during mitosis remains stably associated with the mitotic chromatin through multiple cell cycles (Shindo and Amodeo, 2019) strengthening our use of this metric. To help clarify this point as well as other methodological details we have added a new Figure 1B which documents the time points at which we make various measurements within the lifecycle of the nucleus. We also edited the text to read: “We have previously shown that with each NC, the pool of free H3 in the nucleus is depleted and its levels on chromatin during mitosis decrease (Figure 1D, S1C-D)29. In contrast, H3.3 mitotic chromatin levels increase during the same cycles (Figure 1D, S1C-D)29.” lines 89-92 I very much appreciate how the authors laid out their model in Fig 3 and then used the same figure to explain which part of the model they are testing in Figs 4 and 5. This is not a critique- we can complement too! Thank you! Revision Plan OPTIONAL experimental suggestion: The experiments in Figure 4 and 5 are clever. One would expect that H3 levels might exhaust faster in embryos lacking all H3.2 histone genes (Gunesdogan, 2010, PMID: 20814422), allowing a comparison testing the H3 availability > H3.3 incorporation portion of the hypothesis without manipulating the N/C ratio. This might also result in a more consistent system than slbp RNAi (below). We thank the reviewer for the experimental suggestion. We also considered this experimental manipulation to decrease RD histone H3.2. We chose not to do this experiment because in the Gunesdogan paper they show that the zygotic HisC nulls have normal development until after NC14 (unlike the maternal SLBP-RNAi that we used) suggesting that maternal H3.2 supplies do not become limiting until after the stages under consideration in our paper. Maternal HisC-nulls are, of course, impossible to generate since histones are essential. O'Haren 2024 (PMID: 39661467) did not find increased Pol II at the HLB after zelda RNAi (line 227). Might also want to mention here that zelda RNAi does not result in changes to H3 at the mRNA level (O'Haren 2024), as that would confound the model. We thank the reviewer for the suggestion. We have removed the discussion of Pol II localization and replaced it with the information about histone mRNA : “zelda controls the transcription of the majority of Pol II genes during ZGA but disruption of zelda does not change RD histone mRNA levels67–70”. lines 249-251 Discussion: Should discuss results in context of McPherson et al. 2023 (PMID: 37279945), who showed that decreasing H3.2 gene numbers does not increase H3.3 production at the mRNA or protein levels. We expanded our discussion to include the following: “Given the fact that H3.3 pool size does not respond to H3 copy number in other Drosophila tissues,54 our results suggest that H3.3 incorporation dynamics are likely independent of H3 availability.” lines 278-280 The Shackleton mutation is a clever way to alter N/C ratio, but the authors should point out that it is difficult (impossible?) to directly and cleanly manipulate the N/C ratio. For example, Shkl mutants seem to also have various nuclear sizes. As discussed above, we think that nuclear size is a direct response to the N/C ratio. We have added the following sentence to the discussion as well as a citation to a paper which discusses how the N/C ratio might contribute to nuclear import in early embryos to the discussion: “This may be due to N/C ratio-dependent changes in nuclear import dynamics which may also contribute to the observed changes in nuclear size across the shkl embryo75.” lines 307-309 Revision Plan How is H3.3 expression controlled? Is it possible that H3.3 biosynthesis is affected in Chk1 mutants? To address this question we propose to perform RT-qPCR for H3.3A and H3.3B as well as Hira in the Chk1 mutant. Unfortunately, we do not have antibodies that reliably distinguish between H3 and H3.3 in our hands (despite literature reports), but we will also perform a pan-H3 immunostaining in the Chk1 embryos to measure how the total H3-type histone pool changes as a result of the loss of Chk1. Figures: While I appreciate the statistical summaries in tables, it is still helpful to display standard significance on the figures themselves. We have added statistical comparisons in Figure 3 (formerly Figure 2). We do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. Although we plot H3 on the same graph as the other proteins to allow for ease of comparison of their trends over time it is not appropriate to directly compare their normalized intensities which including statistical tests would encourage. We have added a note to the legend of Figure 1 explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” Fig 1: A: Is it possible to label panels with the nuclear cycle? We have done this. B: Statistics required - caption suggests statistics are in Table S2, but why not put on graph? Please see the explanation above for why we do not feel that it is appropriate to perform this comparison. C/D: Would be helpful if authors could plot H3/H3.3 on same graph because what we really need to compare is NC13 between H3/H3.3 (and statistics between these curves) Please see the explanation above for why we do not feel that it is appropriate to perform this comparison. These curves can be directly compared within a construct and we can evaluate their trends over time, but the normalized values should not be directly compared in the way that would be encouraged by plotting the data as suggested. E: The comparison in the text is between H3.3 and H3, but only H3.3 data is shown. I realize that it is published prior, but the comparison in figure would be helpful. We have added the previously published values to the text. Revision Plan “These changes in nuclear import and incorporation result in a less complete loss of the free nuclear H3.3 pool (~70% free in NC11 to ~30% in NC13) than previously seen for H3 (~55% free in NC11 to ~20% in NC13)” lines 116-119 Fig 2: A: A very helpful figure. Slightly unclear that the H3 that is not Dendra tagged is at the H3.3 locus. Also unclear that the H3.3A-Dendra2 line exists and used as control, as is not shown in figure. Should show H3 and H3.3 controls (Figure S2) We have edited the figure to add Dendra2 to all of the constructs and made clear the location of each construct including adding the landing site for H3-Dendra2. We have also cited Figure S1 in the legend which contains a more detailed diagram of the integration strategy. F/H- As the comparison is between H3 and ASVM, it would help to combine these data onto the same graph. As the color is currently used unnecessarily to represent nuclear cycle, the authors could use their purple/pink color coding to represent H3/ASVM. We have combined these data onto a single graph as requested and changed the colors appropriately. We have not added statistical comparisons to this graph as we again believe that they would be inappropriate. In the legend of Fig 2 the authors write "in the absence of Hira." Technically, there is only a point mutation in Hira. It is not absent. Good catch! We have changed this to “in Hirassm mutants”. Fig 3: G: Please show WT for comparison. Can use data in Fig 3A. We have added the color-coded number of neighbor embryo representations for WT and Shkl embryos underneath the example embryo images in 4A-B (formerly 3A-B,G). Model in H is very helpful (complement)! Thank you. Fig 4: B/C/F/G: The authors use a point size scale to represent the number of nuclei, but the graphs are so overlaid that it is not particularly useful. Is there a better way to display this dimension? We chose to represent the data in this way so that the visual impact of each line is representative of the amount of data (number of nuclei in each bin) that underlies it. This helps to prevent sparsely populated outlier bins at the edges of the distribution from dominating the interpretation of the data. If the reviewer has a suggestion for a better way to visualize this information we would welcome their suggestion, but we cannot think of a better way at this time. D/E/H/I: What does "min volume" mean on the X axis? Since the uneven N/C ratio in the shkl embryos results in a wavy cell cycle pattern there is no single time point where we can calculate the number of neighbors for the whole embryo (since Revision Plan not all nuclei are in the same cell cycle at a given point). Therefore, we had to choose a criterion for when we would calculate the number of neighbors for each nucleus. We chose nuclear size as a proxy for nuclear age since nuclear size increases throughout interphase (see new figure 1B). So, the minimum volume is the newly formed nucleus in a given cell cycle. We also tested other timepoints for the number of neighbors (maximum nuclear volume, just before nuclear envelope breakdown and midway between these two points) and found similar results. We chose to use minimum volume in this paper because this is the time point when the nucleus is growing most quickly and nuclear import is at its highest. We have added the following explanation to the methods: “For shkl embryos, as the nuclear cycles are asynchronous, nuclear divisions start at different timepoints within the same cell cycle and the nuclear density changes as the neighboring nuclei divide. Therefore, the total intensity traces were aligned to match their minimum volumes (as shown in Figure 1B) to T0.” lines 485-488, methods And the following detail to the figure legend: “...plotted by the number of nuclear neighbors at their minimum nuclear volume…” Figure 5 legend We also added a depiction of the lifecycle of the nucleus in which we marked the minimum volume as the new Figure 1B. Fig 5: F: OPTIONAL Experimental request: Here I would like to see H3 as a control. This is a very good suggestion, and we are currently imaging H3-Dendra2 in the Chk1 background. However, our preliminary results suggest that there may be some synthetic early lethality between the tagged H3-Dendra2 and Chk1 since these embryos are much less healthy than H3.3-Dendra2 Chk1 embryos or Chk1 with other reporters. In addition, we have observed a much higher level of background fluorescence in this cross than in the H3-Dendra2 control. We are uncertain if we will be able to obtain usable data from this experiment, but will continue to try to find conditions that allow us to analyze this data. As an orthogonal approach to answer the question, we will perform immunostaining with a pan-H3 antibody in Chk1 mutant embryos to measure total H3 levels under these conditions. Since the majority of H3-type histone is H3.2 and we know how H3.3 changes, this staining will give us insight into the dynamics of H3 in Chk1 mutant embryos. Significance General assessment: Many long-standing mysteries surround zygotic genome activation, and here the authors tackle one: what are the signals to remodel the zygotic chromatin around ZGA? This is a tricky question to answer, as basically all manipulations done to the embryo Revision Plan have widespread effects on gene expression in general, confounding any conclusions. The authors use clever novel techniques to address the question. Using photoconvertible H3 and H3.3, they can compare the nuclear dynamics of these proteins after embryo manipulation. Their model is thorough and they address most aspects of it. The hurdle this study struggles to overcome is the same that all ZGA studies have, which is that manipulation of the embryo causes cascading disasters (for example, one cannot manipulate the nuclear:cytoplasmic ratio without also altering cell cycle timing), so it's challenging to attribute molecular phenotypes to a single cause. This doesn't diminish the utility of the study. Advance: The conceptual advance of this study is that it implicates the nuclear:cytoplasmic ratio and Chk1 in H3.3 incorporation. The authors suggest these factors influence cell cycle closing, which then affects H3.3 incorporation, although directly testing the granularity of this model is beyond the scope of the study. The authors also provide technical advancement in their use of measuring histone dynamics and using changes in the dynamics upon treatment as a useful readout. I envision this strategy (and the dendra transgenes) to be broadly useful in the cell cycle and developmental fields. Audience: The basic research presented in this study will likely attract colleagues from the cell cycle and embryogenesis fields. It has broader implications beyond Drosophila and even zygotic genome activation. This reviewer's expertise: Chromatin, Drosophila, Gene Regulation Reviewer #2 (Evidence, reproducibility and clarity (Required)): This manuscript investigates the regulation of H3.3 incorporation during zygotic genome activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication. We thank the reviewers for their careful reading of this manuscript. We have sought to clarify the concerns regarding clarity through numerous text edits detailed below. We did include ANOVA analysis for all of the relevant statistical comparisons in the supplemental table. However, to increase clarity we have also added some statistical comparisons in the main figures. We note that we do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. Although we plot H3 on the same graph as the other proteins to allow for ease of comparison of their trends over time it is not appropriate to directly compare their normalized intensities which including statistical tests would encourage. We have added a note to the legend of the new Figure 1 Revision Plan explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” Major Concerns The manuscript would benefit from a clearer introduction that explicitly distinguishes between previously known mechanisms of histone regulation during ZGA and the novel contributions of this study. Currently, the introduction lacks sufficient background on early embryonic chromatin regulation, making it difficult for readers unfamiliar with the field to grasp the significance of the findings. The authors should also be more precise when discussing the timing of ZGA. While they state that ZGA occurs after 13 nuclear divisions, it is well established that a minor wave of ZGA begins at nuclear cycle 7-8, whereas the major wave occurs after cycle 13. Clarifying this distinction will improve the manuscript's accessibility to a broader audience. We have added a new figure 1 to make the timing and nuclear behaviors of the embryo during ZGA in Drosophila more clear. We have also added information about how the chromatin changes during Drosophila ZGA in the following sentence: “ In Drosophila, these changes include refinement of nucleosomal positioning, partitioning of euchromatin and heterochromatin and formation of topologically associated domains20–22,24.” lines 39-41 We have clarified the major and minor waves of ZGA in the introduction and results by adding the following sentences to the introduction and results respectively: “In most organisms ZGA happens in multiple waves but the chromatin undergoes extensive remodeling to facilitate bulk transcription during the major wave of ZGA (hereafter referred to as ZGA)18–20,22–25..” lines 36-39 “In Drosophila, ZGA occurs in 2 waves. The minor wave starts as early as the 7th cycle, while major ZGA occurs after 13 rapid syncytial nuclear cycles (NCs) and is accompanied by cell cycle slowing and cellularization (Figure 1A-B).” lines 83-85 We hope that these changes help to reduce confusion and make the paper more accessible. However, we are happy to add additional information if the reviewer can provide specific points which require further attention. One of the primary weaknesses of this study is the lack of adequate control experiments. In Figure 1, the authors suggest that the levels of H3 and H3.3 are influenced by the N/C ratio, but Revision Plan it is unclear whether transcription itself plays a role in these dynamics. To properly test this, RNA-seq or Western blot analyses should be performed at nuclear cycles 10 and 13-14 to compare the levels of newly transcribed H3 or H3.3 against maternally supplied histones. Without such data, the authors cannot rule out transcriptional regulation as a contributing factor. In the pre-ZGA cell cycles the vast majority of protein including histones is maternally loaded. Gunesdogan et al. (2010) showed that the zygotic RD histone cluster nulls survive past NC14 (well past ZGA) with no discernible defects indicating that maternal RD histone supplies are sufficient for normal development during the cell cycles under consideration. Therefore, new transcription of replication coupled histones is not needed for apparently normal development during this period. Moreover, we have done the western blot analysis using a Pan-H3 antibody as suggested by the reviewer in our previously published paper (Shindo and Amodeo, 2019 supplemental figure S3A-B) and found that total H3-type histone proteins only increase moderately during this period of development, nowhere near the rate of the nuclear doublings. We have added the following sentence to clarify this point. “These divisions are driven by maternally provided components and the total amount of H3 type histones do not keep up with the pace of new DNA produced29.” lines 88-89 We have also previously done RNA-seq on wild-type embryos (and those with altered maternal histone levels) (Chari et al 2019). In this RNA-seq (like most RNA-seq in flies) we used poly-A selection and therefore cannot detect the RD histone mRNAs (which have a stem-loop instead of a poly-A tail). We have plotted the mRNA concentrations for both H3.3 variants from that dataset below for the reviewers reference (we have not included this in the revised manuscript). The total H3.3 mRNA levels are nearly constant from egg laying (NC0- these are from unfertilized embryos) until after ZGA (NC14). These data combined with the westerns discussed above give us confidence that what we are observing is the partitioning of large pools of maternally provided histones with only a relatively small contribution of new histone synthesis. Revision Plan In Figure 2, the manuscript introduces chimeric embryos expressing modified histone variants, but their developmental viability is not addressed. It is essential to determine whether these embryos survive and whether they exhibit any phenotypic consequences such as altered hatching rates, defects in nuclear division, or developmental arrest. Tagging histones is often deleterious to organismal health. In Drosophila there are two H3.3 loci (H3.3A and H3.3B). In all of our chimera experiments we have left the H3.3B and one copy of the H3.3A locus unperturbed to provide a supply of untagged H3.3. This allows us to study H3.3 and chimera dynamics without compromising organism health. All of our chimeras are viable and fertile with no obvious morphological defects. We have added the following sentences to the text to clarify this point: “There are ~100 copies of H3 in the Drosophila genome, but only 2 of H3.3 (H3.3A and H3.3B)26. To determine which factor controls nuclear availability and chromatin incorporation, we genetically engineered flies to express Dendra2-tagged H3/H3.3 chimeras at the endogenous H3.3A locus, keeping the H3.3B locus intact….These chimeras were all viable and fertile. ” lines 127-131, 136 In addition we propose performing hatch rate assays for embryos from the chimeric embryos of S31A, SVM and ASVM to assess if there is any decrease in fecundity due to the presence of the chimeras. Moreover, given that H3.3 is associated with actively transcribed genes, an RNA-seq analysis of chimeric embryos should be included to assess transcriptional changes linked to H3.3 incorporation. This is an excellent suggestion and will definitely be a future project for the lab. However, to do this experiment correctly we will need to generate untagged chimeric lines that will (hopefully) allow for the full replacement of H3.3 with the chimeric histones instead of a single copy among 4. This is beyond the scope of this paper. Figures 3 and 4 raise additional concerns about whether histone cluster transcription is altered in shkl mutant embryos. The authors propose that the shkl mutation affects the N/C ratio, yet it remains unclear whether this leads to changes in the transcription of histone clusters. Furthermore, since HIRA is a key chaperone for H3.3, it would be important to assess whether its levels or function are compromised in shkl mutants. To address these gaps, RT-qPCR or RNA-seq should be performed to quantify histone cluster transcription, and Western blot analysis should be used to determine if HIRA protein levels are affected. The changes in the N/C ratio that are observed in the shkl mutant are within SINGLE embryo (differences in nuclear spacing). In these experiments we are comparing nuclei within a common cytoplasm that have different local nuclear densities (N/C ratios). Therefore, if Shkl Revision Plan were somehow affecting the transcription of histones or their chaperones we would expect all of the nuclei within the same mutant embryo to be equally affected since they are genetically identical and share a common cytoplasm. We do not directly compare the behavior of shkl embryos to wildtype except to demonstrate that there is no positional effect on the import of H3 and H3.3 across the length of the embryo in wildtype. To clarify our experimental system for these experiments we have added additional panels to Figure 4A and B that depict the number of neighbors for both control and Shkl embryos. Nonetheless, to address the reviewer’s concern that shkl may change the amount of H3 present in the embryo, we propose to conduct a western blot comparison of wildtype and shkl embryos using a pan-H3 antibody. There are no tools (antibodies or fluorescently tagged proteins) to assess HIRA protein levels in Drosophila. We therefore propose to perform RT-qPCR for HIRA in wildtype and shkl embryos. A similar issue arises in Figure 5, where the authors claim that H3.3 incorporation is dependent on cell cycle state but do not sufficiently test whether this is linked to changes in HIRA levels. Given the importance of HIRA in H3.3 deposition, its levels should be examined in Slbp, Zelda, and Chk1 RNAi embryos to verify whether changes in H3.3 incorporation correlate with HIRA function. Without this, it is difficult to conclude that the observed effects are strictly due to cell cycle regulation rather than histone chaperone dynamics. Since H3.3 incorporation is unaffected in the Slbp and Zelda-RNAi lines there is no reason to suspect a change in HIRA function. There are no available tools (antibodies or fluorescently tagged proteins) to directly measure HIRA protein in Drosophila. To test if changes in HIRA loading might contribute to the decreased H3.3 incorporation in the Chk1 mutant we propose to perform RT-qPCR for HIRA in wildtype and Chk1 embryos. Several figures require additional statistical analyses to support the claims made. In Figure 1B, statistical testing should be included to validate the reported differences. Figure 1C-D states that "H3.3 accumulation reduces more slowly than H3," yet there is no quantitative comparison to substantiate this claim. Similarly, Figure 1E presents the conclusion that "These changes in nuclear import and incorporation result in a less dramatic loss of the free nuclear H3.3 pool than previously seen for H3," despite the fact that H3 data are not included in this figure. The conclusions drawn from these data need to be supported with appropriate statistical comparisons and more precise descriptions of what is being measured. For Figure 1B (now 2B) we do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings and therefore we do not feel that direct statistical tests are appropriate. Rather, we plot the two histones on the same graph normalized to their own NC10 values so that the trend in their decrease over time may be compared. The statistical tests for H3.3 compared to the chimeras which were originally in the supplemental table have been added to Figure 3 (formerly figure 2). Revision Plan It is important to note that in this directly comparable situation the ASVM mutant (whose trends closely mirror H3) is highly statistically distinct from H3.3. We have added a note to the legend of the new Figure 1 explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” For Figure 1C-D (now 2C-D) we have removed this claim from the text. We were referring to the plateau in nuclear import for H3 that is less dramatic in H3.3, but this is more carefully discussed in the next paragraph and its addition at that point generated confusion. The text now reads: “To further assess how nuclear uptake dynamics changed during these cycles, we tracked total nuclear H3 and H3.3 in each cycle (Figure 2C-D). Since nuclear export is effectively zero, we attribute the increase in total H3.3 over time solely to import and therefore the slope of total H3.3 over time corresponds to the import rate. Though the change in initial import rates between NC10 and NC13 are similar between the two histones (Figure S1F), we observed a notable difference in their behavior in NC13. H3 nuclear accumulation plateaus ~5 minutes into NC13, whereas H3.3 nuclear accumulation merely slows (Figure 2C-D).” lines 109-116 For Figure 1E (now 2E), to address the difference between H3 and H3.3 free pools we have added the previously published values to the text and changed the phrasing from “less dramatic” to “less complete”. The sentence now reads: “These changes in nuclear import and incorporation result in a less complete loss of the free nuclear H3.3 pool (~70% free in NC11 to ~30% in NC13) than previously seen for H3 (~55% free in NC11 to ~20% in NC13)” lines 116-119 Figure 2 presents additional concerns regarding data interpretation. The comparisons between H3.3 and H3.3S31A to H3 and H3.3SVM/ASVM lack statistical analysis, making it difficult to determine the significance of the observed differences. As discussed above, it is not appropriate to directly compare H3 to H3.3 and the chimeras at the H3.3A locus since they are expressed from different promoters and imaged with different settings. The ANOVA comparisons between all of the constructs in the H3.3A locus can be found in the supplemental table. We have also added the statistical significance between each chimera and H3.3 within a cell cycle to the figure. Including the full set of comparisons for all genotypes and timepoints makes the figure nearly impossible to interpret, but they remain available in the supplemental table. Revision Plan The disappearance of H3.3 from mitotic chromosomes in Figure 2E is also not explained. If this phenomenon is functionally relevant, the authors should provide a mechanistic interpretation, or at the very least, discuss potential explanations in the text. In Figures 2F-H, the reasoning behind comparing the nuclear intensity of H3.3 to H3 in Hira mutants is unclear. To properly assess the role of HIRA in H3.3 chromatin accumulation, a more appropriate comparison would be between wild-type H3.3 and H3.3 levels in Hira knockdown embryos. As explained in the text and depicted in Figure 3D (formerly 2D), the HIRAssm mutant is a point mutation that prevents observable H3.3 chromatin incorporation, but not nuclear import. This is what is depicted in Figure 3E (formerly 2E). The loss of H3.3 from mitotic chromatin is due to the inability to incorporate H3.3 into chromatin as expected for a HIRA mutant. We have edited the figure 3 legend to make this more clear. It now reads: “Hirassm mutation nearly abolishes the observable H3.3 on mitotic chromatin (E).” In Figure 3F (formerly 2F-H) we ask what happens to H3 chromatin incorporation when there is almost no incorporation of H3.3 due to the HIRA mutation. In this mutant there is so little H3.3 incorporation that we cannot quantify H3.3 levels on mitotic chromatin (see the new Figure 1B for the stage where chromatin levels are quantified). This experiment was done to test if H3.3ASVM (expressed at the H3.3A locus) is incorporated into chromatin in embryos lacking the function of H3.3’s canonical chaperone. We have edited the text to make this more clear: “Since the chromatin incorporation of the H3/H3.3 chimeras appears to depend on their chaperone binding sites, we asked if impairing the canonical H3.3 chaperone, Hira, would affect the incorporation of H3.3ASVMexpressed from the H3.3A locus.”lines 158-160 A broader concern is that the authors only test HIRA as a histone chaperone but do not consider alternative chaperones that could influence H3.3 deposition. Since multiple chaperone systems regulate histone incorporation, it would strengthen the conclusions if additional chaperones were tested. Since HIRAssm reduced H3.3-Dendra2 incorporation to nearly undetectable levels (Figure 3E) we believe that it is the primary H3.3 incorporation pathway during this period of development. Therefore, we believe that removing HIRA function is a sufficient test of the dependance of H3.3ASVM on the major H3.3 chaperone at this time. Although it would be interesting to fully map how all H3 and H3.3 chimera constructs respond to all histone chaperone pathways, we believe that this is beyond the scope of this manuscript. Additionally, the manuscript does not include any validation of the RNAi knockdown efficiencies used throughout the study. This raises concerns about whether the observed phenotypes are truly due to target gene depletion or off-target effects. RT-qPCR or Western blot analyses should be performed to confirm knockdown efficiency. Revision Plan Both the Zelda and slbp-RNAi lines used for knockdowns have been used and validated in the early fly embryo in previously published works ((Yamada et al., 2019), (Duan et al., 2021), (O’Haren et al., 2025), (Chari et al, 2019)) and the phenotypes that we observe in our embryos are consistent with the published data including altered cell cycle durations (Figure S4C) and lack of cellularization/gastrulation. We note that the zelda RNAi phenotypes are also highly consistent with the effects of Zelda germline clones. To validate that slbp-RNAi knocks down histones we included a western blot for Pan-H3 in slbp-RNAi embryos that demonstrates a large effect on total H3 levels (Figure S4A). To further demonstrate the phenotypic effects of the slbp-RNAi we have added supplemental movies (Videos 4 and 5). To fully characterize the RNAi efficiency under our conditions we propose to perform RT-qPCR for slbp in slbp-RNAi and Zelda in Zelda-RNAi compared to control (w) RNAi embryos. Finally, the section discussing "H3.3 incorporation depends on cell cycle state, but not cell cycle duration" is unclear. The term "cell cycle state" is vague and should be explicitly defined. Does this refer to a specific phase of the cell cycle, changes in chromatin accessibility, or another regulatory mechanism? The term cell cycle state is deliberately vague. We know that Chk1 regulates many aspects of cell cycle progression and cannot determine from our data which aspect(s) of cell cycle regulation by Chk1 are important for H3.3 incorporation. Our data indicate that it is not simply interphase duration as we originally hypothesized. We have expanded our discussion section to underscore some aspects of Chk1 regulation that we speculate may be responsible for the change in H3.3 behavior. “Chk1 mutants decrease H3.3 incorporation even before the cell cycle is significantly slowed. Cell cycle slowing has been previously reported to regulate the incorporation of other histone variants in Drosophila15. However, our results indicate that cell cycle state and not duration per se, regulates H3.3 incorporation. In most cell types, the primary role of Chk1 is to stall the cell cycle to protect chromatin in response to DNA damage. Therefore, Chk1 activity directly or indirectly affects the chromatin state in a variety of ways. We speculate that Chk1’s role in regulating origin firing may be particularly important in this context73,74. Late replicating regions and heterochromatin first emerge during ZGA, and Chk1 mutants proceed into mitosis before the chromatin is fully replicated22,23,25,71. Since H3.3 is often associated with heterochromatin, the decreased H3.3 incorporation in Chk1 mutants may be an indirect result of increased origin firing and decreased heterochromatin formation73,74.” lines 287-298 Reviewer #2 (Significance (Required)): This manuscript investigates the regulation of H3.3 incorporation during zygotic genome Revision Plan activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication. Reviewer #3 (Evidence, reproducibility and clarity (Required)): Summary: Based on previous findings of the changing ratios of histone H3 to its variant H3.3, the authors test how H3.3 incorporation into chromatin is regulated for ZGA. They demonstrate here that H3 nuclear availability drops and replacement by H3.3 relies on chaperone binding, though not on its typical chaperone Hira. Furthermore, they show that nuclear-cytoplasmic (N/C) ratios can influence this histone exchange likely by influencing cell cycle state. We thank the reviewer for their thoughtful comments. We note that our data ARE consistent with H3.3 incorporation depending on Hira through its chaperone binding site. Major comments: 1. The claims are largely supported by the data but I think a couple more experiments could help bolster the claims about cell cycle and chk1 regulation. a. Creating a phosphomimetic of the chk1 phosphorylation site on H3.3 to see if it can overcome the defects seen in chk1 mutants b. Assessing heterochromatin of embryos without chk1 (or ASVM mutants) for example, by looking at H3K9me3 levels The first experiments could take several months if the flies haven't already been generated by the authors but the second should be quicker. a. This is an excellent experimental suggestion which is bolstered by the fact that in frogs H3.3 S31A cannot rescue H3.3 morpholino during gastrulation, but H3.3S31D can (Sitbon et al, 2020). However, to correctly conduct this experiment would require generating and validating multiple additional endogenous H3.3 replacement lines, likely without a fluorescent tag as they can interfere with histone rescue constructs in most species. As the reviewer notes, this would take several months of work (we have not generated the critical flies yet) and may not yield a satisfying answer since there are reports that H3.3 may be dispensable in flies aside from as a source of H3-type histone outside of S-phase (Hödl and Bassler, 2012). While we hope to continue experiments along these lines in the future we feel that this is beyond the scope of the current manuscript. Revision Plan b. To address this we propose to stain for H3K9me3 in wildtype and Chk1-/- embryos. Since the ASVM line is not a full replacement of all H3.3 we think that staining for H3K9me3 in this line is unlikely to yield a detectable difference. 2. It would also be interesting to see what the health of the flies with some mutations in this paper are beyond the embryo stage if they are viable (e.g., development to adulthood, fertility etc.) a. the SVM, ASVM mutations b. the hira + ASVM mutations The authors might already have this data but if not they have the flies and it shouldn't take long to get these data. a. To address this concern we propose to conduct hatch rate assays for embryos from the Dendra tagged H3.3, S31A, SVM, ASVM flies. However, we do note that in our experiments only one copy of the H3.3A locus was mutated and tagged with Dendra2 leaving one copy of H3.3A and both copies of H3.3B untouched to ensure normal development as tagging all copies of histone genes can lead to lethality. b. All Hira mutants develop as haploids due to the inability to decondense the sperm chromatin (which is dependent on Hira). This leads to one extra division to restore the N/C ratio prior to cell cycle slowing and ZGA. These embryos go on to gastralate and die late in development after cuticle formation (presumably due to their decreased ploidy) (Loppin et al., 2000). The addition of ASVM into the Hira background does not appear to rescue the ploidy defect as these embryos also undergo the extra division (Figure 3H). We are therefore confident that these embryos will not hatch. We have added the information about the development of Hira mutant to the text as follow: “These embryos develop as haploids and undergo one additional syncytial division before ZGA (NC14). Hirassmembryos develop otherwise phenotypically normally through organogenesis and cuticle formation, but die before hatching57.” lines 164-167 3. In the discussion section, can the authors speculate on how they think H3.3 ASVM is getting incorporated if not through Hira. Are there other known H3 variant chaperones, or can the core histone chaperone substitute? We have expanded our discussion to include the the following: “In the case of the chimeric histone proteins the incorporation behavior was dependent on the chaperone binding site. For example, H3.3ASVM import and incorporation was similar to H3 in control embryos and H3.3ASVM was still incorporated in Hirassm mutants. This is consistent with the chaperone binding site determining the chromatin incorporation pathway and suggests that H3.3ASVM likely interacts with H3 chaperones such as Caf1.” lines 280-285 Revision Plan Minor comments: While the paper is well written, I found the figures very confusing and difficult to interpret. Comments here are meant to make it easier to interpret. 1. Fig 1 and most of the paper would benefit from a schematic of early embryo transitions labelled with time and stages of cell cycle to make interpreting data easier This is an excellent suggestion! We have added a new figure (Figure 1) to explain both the biological system and the way that we measured many properties in this paper. 2. Fig 1- same green color is used for nuclear cycle 12 and for H3.3 making it confusing when reading graphs. Please check other figures where there is a similar use of color for two different things We have changed the colors so that they are more distinct. 3. Fig 1C,D might benefit more from being split up into 3 graphs by cell cycle with H3 and H3.3 plotted on the same graphs rather than the way it is now We do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. These curves can be directly compared within a construct and we can evaluate their trends over time, but the normalized values should not be directly compared in the way that would be encouraged by plotting the data as suggested. 4. Line 130-133: can they also comment on the different between SVM and ASVM. It seems like SVM might be even worse than ASVM (Fig 2C). Is this related to chk1 phosphorylation? We think that this is a property of the mixed chimeras since S31A is also imported less efficiently than H3.3 (though we cannot be sure without further experiments). We have added this explanation to the text: “We speculate that chimeric histone proteins (H3.3S31A and H3.3SVM) are not as efficiently handled by the chaperone machinery as species that are normally found in the organism including H3.3ASVM which is protein-identical to H3.” lines 150-152 5. Fig 2F-G: It is very difficult to compare between histones when they are on different graphs, please consider putting H3, H3.3 and H3.3ASVM in a hirassm background on the same graph. We have done this in the new Figure 3F. Revision Plan 6. Fig 3- move G to become A and then have A and B. We have restructured this figure to include the nuclear density map of control in response to a comment from Reviewer 1. Although not exactly what the reviewer has envisioned, we hope that this adds clarity to the figure. 7. The initial slope graphs in 4D, E, H and I are not easy to understand and would benefit from an explanation in the legend. We have edited the legend of Figure 5D (formerly 4D) and S1F which now read: “Initial slopes of nuclear import curves (change in total nuclear intensity over time for the first 5 timepoints) …” In addition we have updated the methods to include: “Import rates were calculated by using a linear regression for the total nuclear intensity over time for the first 5 timepoints in the nuclear import curves.” lines 471-473, methods Reviewer #3 (Significance (Required)): This paper addresses an important and understudied question- how do histones and their variants mediate chromatin regulation in the early embryo before zygotic genome activation? The authors follow up on some previous findings and provide new insights using clever genetics and cell biology in Drosophila melanogaster. However, the authors do not directly look at chromatin structural changes using existing genomic tools. This may be beyond the scope of this work but would make for a nice addition to strengthen their claims if they can implement these chromatin accessibility techniques in the early embryo. Histones affect a majority of biological processes and understanding their role in the early embryo is key to understanding development. I believe this study applies to a broad audience interested in basic science. However, I do think the authors might benefit from a more broad discussion of their results to attract a broad readership.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Based on previous findings of the changing ratios of histone H3 to its variant H3.3, the authors test how H3.3 incorporation into chromatin is regulated for ZGA. They demonstrate here that H3 nuclear availability drops and replacement by H3.3 relies on chaperone binding, though not on its typical chaperone Hira. Furthermore, they show that nuclear-cytoplasmic (N/C) ratios can influence this histone exchange likely by influencing cell cycle state.

Major comments:

1. The claims are largely supported by the data but I think a couple more experiments could help bolster the claims about cell cycle and chk1 regulation.

a. Creating a phosphomimetic of the chk1 phosphorylation site on H3.3 to see if it can overcome the defects seen in chk1 mutants

b. Assessing heterochromatin of embryos without chk1 (or ASVM mutants) for example, by looking at H3K9me3 levels The first experiments could take several months if the flies haven't already been generated by the authors but the second should be quicker. 2. It would also be interesting to see what the health of the flies with some mutations in this paper are beyond the embryo stage if they are viable (e.g., development to adulthood, fertility etc.)

a. the SVM, ASVM mutations

b. the hira + ASVM mutations The authors might already have this data but if not they have the flies and it shouldn't take long to get these data. 3. In the discussion section, can the authors speculate on how they think H3.3 ASVM is getting incorporated if not through Hira. Are there other known H3 variant chaperones, or can the core histone chaperone substitute?

Minor comments:

While the paper is well written, I found the figures very confusing and difficult to interpret. Comments here are meant to make it easier to interpret.

1. Fig 1 and most of the paper would benefit from a schematic of early embryo transitions labelled with time and stages of cell cycle to make interpreting data easier
2. Fig 1- same green color is used for nuclear cycle 12 and for H3.3 making it confusing when reading graphs. Please check other figures where there is a similar use of color for two different things
3. Fig 1C,D might benefit more from being split up into 3 graphs by cell cycle with H3 and H3.3 plotted on the same graphs rather than the way it is now
4. Line 130-133: can they also comment on the different between SVM and ASVM. It seems like SVM might be even worse than ASVM (Fig 2C). Is this related to chk1 phosphorylation?
5. Fig 2F-G: It is very difficult to compare between histones when they are on different graphs, please consider putting H3, H3.3 and H3.3ASVM in a hirassm background on the same graph.
6. Fig 3- move G to become A and then have A and B.
7. The initial slope graphs in 4D, E, H and I are not easy to understand and would benefit from an explanation in the legend.

Significance

This paper addresses an important and understudied question- how do histones and their variants mediate chromatin regulation in the early embryo before zygotic genome activation? The authors follow up on some previous findings and provide new insights using clever genetics and cell biology in Drosophila melanogaster. However, the authors do not directly look at chromatin structural changes using existing genomic tools. This may be beyond the scope of this work but would make for a nice addition to strengthen their claims if they can implement these chromatin accessibility techniques in the early embryo.

Histones affect a majority of biological processes and understanding their role in the early embryo is key to understanding development. I believe this study applies to a broad audience interested in basic science. However, I do think the authors might benefit from a more broad discussion of their results to attract a broad readership.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

This manuscript investigates the regulation of H3.3 incorporation during zygotic genome activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication.

Major Concerns

The manuscript would benefit from a clearer introduction that explicitly distinguishes between previously known mechanisms of histone regulation during ZGA and the novel contributions of this study. Currently, the introduction lacks sufficient background on early embryonic chromatin regulation, making it difficult for readers unfamiliar with the field to grasp the significance of the findings. The authors should also be more precise when discussing the timing of ZGA. While they state that ZGA occurs after 13 nuclear divisions, it is well established that a minor wave of ZGA begins at nuclear cycle 7-8, whereas the major wave occurs after cycle 13. Clarifying this distinction will improve the manuscript's accessibility to a broader audience. One of the primary weaknesses of this study is the lack of adequate control experiments. In Figure 1, the authors suggest that the levels of H3 and H3.3 are influenced by the N/C ratio, but it is unclear whether transcription itself plays a role in these dynamics. To properly test this, RNA-seq or Western blot analyses should be performed at nuclear cycles 10 and 13-14 to compare the levels of newly transcribed H3 or H3.3 against maternally supplied histones. Without such data, the authors cannot rule out transcriptional regulation as a contributing factor. In Figure 2, the manuscript introduces chimeric embryos expressing modified histone variants, but their developmental viability is not addressed. It is essential to determine whether these embryos survive and whether they exhibit any phenotypic consequences such as altered hatching rates, defects in nuclear division, or developmental arrest. Moreover, given that H3.3 is associated with actively transcribed genes, an RNA-seq analysis of chimeric embryos should be included to assess transcriptional changes linked to H3.3 incorporation. Figures 3 and 4 raise additional concerns about whether histone cluster transcription is altered in shkl mutant embryos. The authors propose that the shkl mutation affects the N/C ratio, yet it remains unclear whether this leads to changes in the transcription of histone clusters. Furthermore, since HIRA is a key chaperone for H3.3, it would be important to assess whether its levels or function are compromised in shkl mutants. To address these gaps, RT-qPCR or RNA-seq should be performed to quantify histone cluster transcription, and Western blot analysis should be used to determine if HIRA protein levels are affected. A similar issue arises in Figure 5, where the authors claim that H3.3 incorporation is dependent on cell cycle state but do not sufficiently test whether this is linked to changes in HIRA levels. Given the importance of HIRA in H3.3 deposition, its levels should be examined in Slbp, Zelda, and Chk1 RNAi embryos to verify whether changes in H3.3 incorporation correlate with HIRA function. Without this, it is difficult to conclude that the observed effects are strictly due to cell cycle regulation rather than histone chaperone dynamics. Several figures require additional statistical analyses to support the claims made. In Figure 1B, statistical testing should be included to validate the reported differences. Figure 1C-D states that "H3.3 accumulation reduces more slowly than H3," yet there is no quantitative comparison to substantiate this claim. Similarly, Figure 1E presents the conclusion that "These changes in nuclear import and incorporation result in a less dramatic loss of the free nuclear H3.3 pool than previously seen for H3," despite the fact that H3 data are not included in this figure. The conclusions drawn from these data need to be supported with appropriate statistical comparisons and more precise descriptions of what is being measured.

Figure 2 presents additional concerns regarding data interpretation. The comparisons between H3.3 and H3.3S31A to H3 and H3.3SVM/ASVM lack statistical analysis, making it difficult to determine the significance of the observed differences. The disappearance of H3.3 from mitotic chromosomes in Figure 2E is also not explained. If this phenomenon is functionally relevant, the authors should provide a mechanistic interpretation, or at the very least, discuss potential explanations in the text. In Figures 2F-H, the reasoning behind comparing the nuclear intensity of H3.3 to H3 in Hira mutants is unclear. To properly assess the role of HIRA in H3.3 chromatin accumulation, a more appropriate comparison would be between wild-type H3.3 and H3.3 levels in Hira knockdown embryos. A broader concern is that the authors only test HIRA as a histone chaperone but do not consider alternative chaperones that could influence H3.3 deposition. Since multiple chaperone systems regulate histone incorporation, it would strengthen the conclusions if additional chaperones were tested. Additionally, the manuscript does not include any validation of the RNAi knockdown efficiencies used throughout the study. This raises concerns about whether the observed phenotypes are truly due to target gene depletion or off-target effects. RT-qPCR or Western blot analyses should be performed to confirm knockdown efficiency. Finally, the section discussing "H3.3 incorporation depends on cell cycle state, but not cell cycle duration" is unclear. The term "cell cycle state" is vague and should be explicitly defined. Does this refer to a specific phase of the cell cycle, changes in chromatin accessibility, or another regulatory mechanism?

Significance

This manuscript investigates the regulation of H3.3 incorporation during zygotic genome activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Bhatt et al. seek to define factors that influence H3.3 incorporation in the embryo. They test various hypotheses, pinpointing the nuclear/cytoplasmic ratio and Chk1, which affects cell cycle state, as influencers. The authors use a variety of clever Drosophila genetic manipulations in this comprehensive study. The data are presented well and conclusions reasonably drawn and not overblown. I have only minor comments to improve readability and clarity. I suggest two OPTIONAL experiments below.

Major comments:

We found this manuscript well written and experimentally thorough, and the data are meticulously presented. We have one modification that we feel is essential to reader understanding and one experimental concern: The authors provide the photobleaching details in the methodology, but given how integral this measurement is to the conclusions of the paper, we feel that this should be addressed in clear prose in the body of the text. The authors explain briefly how nuclear export is assayed, but not import (line 99). Would help tremendously to clarify the methods here. This is especially important as import is again measured in Fig 4. This should also be clarified (also in the main body and not solely in the methods).

If the embryos appeared "reasonably healthy" (line 113) after slbp RNAi, how do the authors know that the RNAi was effective, especially in THESE embryos, given siblings had clear and drastic phenotype? This is especially critical given that the authors find no effect on H3.3 incorporation after slbp RNAi (and presumably H3 reduction), but this result would also be observed if the slbp RNAi was just not effective in these embryos.

Minor comments:

Introduction:

Consider using "replication dependent" (RD) rather than "replication coupled." Both are used in the field, but RD parallels RI ("replication independent"). Would help for clarity if the authors noted that H3 is equivalent to H3.2 in Drosophila. Also it is relevant that there are two H3.3 loci as the authors knock mutations into the H3.3A locus, but leave the H3.3B locus intact. The authors should clarify that there are two H3.3 genes in the Drosophila genome. Please add information and citation (line 58): H3.3 is required to complete development when H3.2 copy number is reduced (PMID: 37279945, McPherson et al. 2023)

Results:

Embryo genotype is unclear (line 147): Hira[ssm] haploid embryos inherit the Hira mutation maternally? Are Hira homozygous mothers crossed to homozygous fathers to generate these embryos, or are mothers heterozygous? This detail should be in the main text for clarity. Line 161: Shkl affects nuclear density, but it also appears from Fig 3 to affect nuclear size? The authors do not address this, but it should at least be mentioned. The authors often describe nuclear H3/H3.3 as chromatin incorporated, but these image-based methods do not distinguish between chromatin-incorporated and nuclear protein. I very much appreciate how the authors laid out their model in Fig 3 and then used the same figure to explain which part of the model they are testing in Figs 4 and 5. This is not a critique- we can complement too! OPTIONAL experimental suggestion: The experiments in Figure 4 and 5 are clever. One would expect that H3 levels might exhaust faster in embryos lacking all H3.2 histone genes (Gunesdogan, 2010, PMID: 20814422), allowing a comparison testing the H3 availability > H3.3 incorporation portion of the hypothesis without manipulating the N/C ratio. This might also result in a more consistent system than slbp RNAi (below). O'Haren 2024 (PMID: 39661467) did not find increased Pol II at the HLB after zelda RNAi (line 227). Might also want to mention here that zelda RNAi does not result in changes to H3 at the mRNA level (O'Haren 2024), as that would confound the model.

Discussion:

Should discuss results in context of McPherson et al. 2023 (PMID: 37279945), who showed that decreasing H3.2 gene numbers does not increase H3.3 production at the mRNA or protein levels. The Shackleton mutation is a clever way to alter N/C ratio, but the authors should point out that it is difficult (impossible?) to directly and cleanly manipulate the N/C ratio. For example, Shkl mutants seem to also have various nuclear sizes. How is H3.3 expression controlled? Is it possible that H3.3 biosynthesis is affected in Chk1 mutants? Figures:

While I appreciate the statistical summaries in tables, it is still helpful to display standard significance on the figures themselves.

Fig 1:

A: Is it possible to label panels with the nuclear cycle? B: Statistics required - caption suggests statistics are in Table S2, but why not put on graph? C/D: Would be helpful if authors could plot H3/H3.3 on same graph because what we really need to compare is NC13 between H3/H3.3 (and statistics between these curves) E: The comparison in the text is between H3.3 and H3, but only H3.3 data is shown. I realize that it is published prior, but the comparison in figure would be helpful.

Fig 2:

A: A very helpful figure. Slightly unclear that the H3 that is not Dendra tagged is at the H3.3 locus. Also unclear that the H3.3A-Dendra2 line exists and used as control, as is not shown in figure. Should show H3 and H3.3 controls (Figure S2) F/H- As the comparison is between H3 and ASVM, it would help to combine these data onto the same graph. As the color is currently used unnecessarily to represent nuclear cycle, the authors could use their purple/pink color coding to represent H3/ASVM. In the legend of Fig 2 the authors write "in the absence of Hira." Technically, there is only a point mutation in Hira. It is not absent.

Fig 3:

G: Please show WT for comparison. Can use data in Fig 3A. Model in H is very helpful (complement)!

Fig 4:

B/C/F/G: The authors use a point size scale to represent the number of nuclei, but the graphs are so overlaid that it is not particularly useful. Is there a better way to display this dimension? D/E/H/I: What does "min volume" mean on the X axis?

Fig 5:

F: OPTIONAL Experimental request: Here I would like to see H3 as a control.

Significance

General assessment: Many long-standing mysteries surround zygotic genome activation, and here the authors tackle one: what are the signals to remodel the zygotic chromatin around ZGA? This is a tricky question to answer, as basically all manipulations done to the embryo have widespread effects on gene expression in general, confounding any conclusions. The authors use clever novel techniques to address the question. Using photoconvertible H3 and H3.3, they can compare the nuclear dynamics of these proteins after embryo manipulation. Their model is thorough and they address most aspects of it. The hurdle this study struggles to overcome is the same that all ZGA studies have, which is that manipulation of the embryo causes cascading disasters (for example, one cannot manipulate the nuclear:cytoplasmic ratio without also altering cell cycle timing), so it's challenging to attribute molecular phenotypes to a single cause. This doesn't diminish the utility of the study.

Advance: The conceptual advance of this study is that it implicates the nuclear:cytoplasmic ratio and Chk1 in H3.3 incorporation. The authors suggest these factors influence cell cycle closing, which then affects H3.3 incorporation, although directly testing the granularity of this model is beyond the scope of the study. The authors also provide technical advancement in their use of measuring histone dynamics and using changes in the dynamics upon treatment as a useful readout. I envision this strategy (and the dendra transgenes) to be broadly useful in the cell cycle and developmental fields.

Audience: The basic research presented in this study will likely attract colleagues from the cell cycle and embryogenesis fields. It has broader implications beyond Drosophila and even zygotic genome activation.

This reviewer's expertise: Chromatin, Drosophila, Gene Regulation

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Reply to the reviewers

Reviewer 1

Major issue #1. Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities (https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S. cerevisiae, do the authors observe HAC1 mRNA splicing?

We have not tested whether HAC1 mRNA is processed in S. cerevisiae.

In addition, as requested by the reviewers, we reassessed our RNA-seq data and compared it with data from (Kimmig et al., 2012) (UPR activation in S. pombe), which added a new layer of data that reinforces the differences between the transcriptomic responses induced by HU and DIA and the canonical UPR. The following information is now included in the paper (page 26, highlighted in blue):

“We further compared our transcriptomic data with that obtained by Kimmig et al. from DTT- treated S. pombe cells. When we compared the genes that were downregulated in our conditions with the ones described by Kimmig et al. (FC≤-1), we found no similarities between HU treatment (75 mM HU for 150 minutes) and UPR-induced downregulation, and only three genes ( ist2, efn1 and xpa1) all of them encode for transmembrane proteins, were common with DIA treatment (3 mM DIA for 60 minutes). Additionally, ist2 and xpa1, but not efn1, are considered Ire1-dependent downregulated genes and are located in the ER. These results show that HU- or DIA- induced transcriptomic programs are different from UPR, as they do not heavily rely on mRNA decay and favor gene overexpression. Interestingly, we found similarities between genes showed to be upregulated more that twofold by DTT in Kimmig et al., and HU and DIA conditions. When the two N-Cap-inducing conditions were compared with DTT, we found eight common upregulated genes (frp1, plr1, SPCC663.08c, srx1, gst2, str3, caf5 and hsp16) mostly involved in reduction processes and the chaperone Hsp16 which suggests folding stress”.

Major issue #2. The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S. pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status.

We agree with the reviewer that it is important to determine the redox and the functional state of the secretory pathway in our conditions to fully understand the cellular consequences of these treatments, especially in the case of HU, as it is routinely used in clinics. In this context, we have already included new data showing that HU or DIA treatment leads to alterations in the Golgi apparatus and in the distribution of secretory proteins (Figures 3A-B). In addition, we are currently performing mass spectrometry experiment to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. Finally, we plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (See below, Reviewer 2, Major issue #1).

What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?

We have tested whether the addition of this antioxidant could prevent and/or revert the N-Cap phenotype. We found that NAC in combination with HU increased N-Cap incidence (Figure 5H). As NAC is a GSH precursor and we find that GSH is required to develop the phenotype of N-Cap (Figure 5A-B, D, G), this result further supports that the HU-induced cellular damage might involve ectopic glutathionylation of proteins.

Unfortunately, we have not tested NAC in combination with DIA, as NAC seems to reduce DIA as soon as they get in contact, as judged by the change in the characteristic orange color of DIA, the same that happens when we combine GSH and DIA (Supplementary Figure 5A-B).

In this regard, the following information has been added to the manuscript (page 30, highlighted in blue):

“We also tested GSH addition to the medium in combination with either HU or DIA. When mixed with DIA, we noticed that the color of the culture changed after GSH addition (Figure S5A), which suggests that GSH and DIA can interact extracellularly, thus preventing us from being able to draw conclusions from those experiments. On the other hand, combining GSH with HU increased N-Cap incidence (Figure 5G), as expected based on our previous observations. Additionally, we checked whether the addition of the antioxidant N-acetyl cysteine (NAC), a GSH precursor, impacted upon the N-Cap phenotype. The results were the same as with GSH addition: when combined with HU, NAC increased N-Cap incidence (Figure 5H), whereas in combination, the two compounds interacted extracellularly (Figure S5B). These data align with NAC being a precursor of GSH, as incrementing GSH levels augments the penetrance of the HU-induced phenotype”.

Major issue #3. The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates?

DIA is a strong oxidant, and HU treatment results in the production of reactive oxygen species (ROS). Therefore, one hypothesis would be that cytoplasmic chaperone foci represent oxidized and/or misfolded soluble proteins. Indeed, in this revised version of the manuscript we have included data showing that guk1-9-GFP and Rho1.C17R-GFP soluble reporters of misfolding accumulate in cytoplasmic foci upon HU or DIA treatment that colocalize with Hsp104 (Figure 4I-J, pages 23-24 and 29), which demonstrate that cytoplasmic chaperone foci contain misfolded proteins. We have also tested if they contain Vgl1, which is one of the main components of heat shock induced stress granules in S. pombe (Wen et al., 2010). However, we found that HU or DIA-induced foci lacked this stress granule marker, and indeed Vgl1 did not form any foci in response to these treatments. Therefore, our aggregates differ from the canonical stress-induced granules.

Are those resulting from proficient retrotranslocation or reflux of misfolded proteins from the ER?

To test whether these cytosolic aggregates result from retrotranslocation from the ER, we plan to use the vacuolar Carboxipeptidase Y mutant reporter CPY*, which is misfolded. This misfolded protein is imported into the ER lumen but does not reach the vacuole. Instead, it is retrotranslocated to the cytoplasm, where it is ubiquitinated and degraded by the proteasome (Mukaiyama et al., 2012). We will analyze by fluorescence microscopy the localization of CPY*´-GFP and Hsp104-containing aggregates upon HU or DIA treatment and with or without proteasome inhibitors. We can also test the levels, processing and ubiquitination of CPY*-GFP by western blot, as ubiquitination of retrotranslocated proteins occurs once they are in the cytoplasm.

Are those aggregates membrane bound or do they correspond to aggresomes as initially defined? The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol.

Our results suggest that these aggregates are not bound to ER membranes, as they do not appear in close proximity to the ER area marked by mCherry-AHDL in fluorescence microscopy images.

To fully rule out this possibility, we have tested whether these Hsp104-aggregates colocalized with ER transmembrane proteins Rtn1 and Yop1, and with Gma12-GFP that marks the Golgi apparatus. In none of the cases the Hsp104-containing aggregates colocalized or were surrounded by membranes. This information will be added to the final version of the manuscript.

With respect to autophagy, we have tested whether deletion of key genes involved in autophagy affected the N-Cap phenotype. To this end, we used deletions of vac8 and atg8 in strains expressing Cut11-GFP and/or mCherry-AHDL and found that none of them affected N-Cap formation. These data suggest that the core machinery of autophagy is not critical for HU/DIA-induced ER expansion. We plan to include this data in the final version of the manuscript along with the rest of experiments proposed.

To get deeper insights and to fully rule out a possible contribution of macro-autophagy to the HU- and DIA-induced phenotypes, we plan to analyze by western blot whether GFP-Atg8 is induced and cleaved upon HU or DIA treatments which would be indicative of macroautophagy activation.

To test whether the cytoplasmic aggregates are the result of an imbalance between ER-expansion and ER-phagy we plan to analyze the localization of GFP-Atg8 and Hsp104-RFP in the atg7Δ mutant, impaired in the core macro-autophagy machinery. In these conditions, the number or size of the cytoplasmic aggregates might be impacted.

On the other hand, it has been recently shown that an ER-selective microautophagy occurs in yeasts upon ER stress (Schäfer et al., 2020; Schuck et al., 2014). This micro-ER-phagy involves the direct uptake of ER membranes into lysosomes, is independent of the core autophagy machinery and depends on the ESCRT system and is influenced by the Nem1-Spo7 phosphatase. ESCRT directly functions in scission of the lysosomal membrane to complete the uptake of the ER membrane. Interestingly, N-Caps are fragmented in the absence of cmp7 and specially in the absence of vps4 or lem2, the nuclear adaptor of the ESCRT (Figure 3E), We had initially interpreted these results as the need to maintain nuclear membrane identity during the process of ER expansion (Kume et al., 2019); however, the appearance of fragmented ER upon HU treatment in the absence of ESCRT might also be due to an inability to complete microautophagic uptake of ER membranes. To test this hypothesis, we plan to analyze whether the fragmented ER in these conditions co-localize with lysosome/vacuole markers.

Major issue #4. Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (https://academic.oup.com/nar/article/29/14/3030/2383924), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.

As stated in (Taricani et al., 2001), hsp16 expression is strongly induced in a cdc22-M45 mutant background. We performed experiments in this mutant that were included in the original version of the manuscript and remain in the current version (Sup. Fig. 2C) and, under restrictive conditions, we do not see spontaneous N-Cap formation. If Hsp16 overexpression and nucleotide depletion were key to the mechanism triggering N-Cap appearance, we would expect this mutant to eventually form N-Caps when placed at restrictive temperature. Furthermore, Taricani et al. show that Hsp16 expression was abolished in a Δatf1 mutant background in the presence of HU, and we found that this mutant is still able to produce N-Caps in HU; therefore, our results strongly suggest that the phenotype of N-cap is independent on the MAPK pathway and on the expression of hsp16.

Minor issues

1. __P1 - UPR = Unfolded Protein Response: __Corrected in the manuscript
2. 2__. P22 - HSP upregulation "might" be indicative of a folding stress:__ Corrected in the manuscript
3. __ The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors revise the storytelling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.__ We have modified the abstract to better reflect our findings and will further revise our arguments in the final version of the manuscript once we have the results of the experiments proposed

Reviewer 2

Major issue #1. The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?

We have addressed the status of secretion in cells treated with HU or DIA by assessing the morphology of the Golgi apparatus and the localization of several secretory proteins by fluorescence microscopy and found that both HU and DIA treatments impact the secretion system. In addition, we plan on addressing the redox status of ER proteins (Bip1, Pdi or Ero1) by biochemical approaches. Please see the answer to major issue #2 from reviewer 1.

We will also analyze by western blot the biogenesis and processing of the wildtype vacuolar Carboxypeptidase Y (Cpy1-GFP) and/or alkaline phosphase (Pho8-GFP), two widely used markers to test the functionality of the ER/endomembrane system.

Major issue #2. Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.

This same issue arose with reviewer 3, so we decided to change the image of the western blot showing another one with less exposure and added a quantification showing that Bip1-GFP levels remain mostly constant between control conditions and treatments with HU and DIA.

We have also performed the suggested photobleaching experiment to analyze potential changes in crowding and mobility in Bip1-GFP upon HU treatment. We found that Bip1-GFP signal recovers after photobleaching the perinuclear ER in HU-treated cells that had not yet expanded the ER, showing that Bip1-GFP is dynamic in these conditions. However, Bip1-GFP signal did not recover after photobleaching the whole N-Cap in cells that had fully developed the expanded perinuclear ER phenotype, whereas it did recover when only half of the N-Cap region was bleached. This suggests that Bip1-GFP is mobile within the expanded perinuclear ER but cannot freely diffuse between the cortical and the perinuclear ER once the N-Cap is formed.

These data have been included in the revised version of the manuscript, in figure 4B, sup. figures 4A-B, and in page 22.

Major issue #3. It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?

As all three reviewers had comments about the CHX and Pm-related data, we revised those experiments and noticed a phenotype occurring upon HU+CHX treatment that had gone unnoticed previously and that changed our understanding about the effect of these drugs on the ER. Briefly, we noticed that, although CHX treatment decreases the HU-induced expansion of the perinuclear ER, it indeed induced expansion but in this case in the cortical area of the ER. This means that the phenotype of ER expansion in HU is not being suppressed by addition of CHX, but rather taking place in another area of the ER (cortical ER). We do not understand why this happens; however, these results show that ER expansion is exacerbated both in DIA and HU when combined with CHX. We have included this data in Figures 3C-D and in page 21.

We also examined the trafficking of secretory proteins that go from the ER to the cell tips and noticed that this transit was affected under both drugs (Figures 3A-B). This suggests that, although there is still protein synthesis when cells are exposed to the drugs (as can be seen by the higher levels of chaperones induced by both stresses (Figure 4C-E)), their protein synthesis capacity is possibly impinged on to certain degree. All this information is now included in the manuscript (page 18).

Major issue #4. While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).

Although we have only included experiments using one redox sensor in the manuscript, we had tested the oxidation of several biosensors during HU and DIA exposure monitoring cytoplasmic, mitochondrial and glutathione-specific probes. We have tried to use ER directed probes however, we have not been successful due to oversaturation of the probe in the highly oxidative environment of the ER lumen.

Although so far we have not been able to directly test the redox status of the ER with optical probes, we plan to test the folding and redox status of several ER proteins and secretory markers by biochemical approaches, so hopefully these experiments will give us more information on this question (See answer to Reviewer 1, Main Issue #2 and Reviewer 2, Main issue #1).

Major Issue #5. What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci?

Pm causes premature termination of translation, leading to the release of truncated, misfolded, or incomplete polypeptides into the cytosol and the re-engagement of ribosomes in a new cycle of unproductive translation, as puromycin does not block ribosomes (Aviner, 2020; Azzam & Algranati, 1973). This likely decreases the number of peptides entering the ER that can be targeted by either HU or DIA, decreasing in turn ER expansion. Indeed, we have found that Pm treatment alone results in the formation of multiple cytoplasmic protein aggregates marked by Hsp104-GFP (Figure 4K), consistent with a continuous release of incomplete and misfolded nascent peptides to the cytoplasm. This would explain why Pm treatment suppresses N-Cap formation when cells are treated with either HU or DIA.

To further test this idea, we analyzed the number and size of Hsp104-containing cytoplasmic aggregates in cells treated with HU or DIA and Pm, where N-Caps are suppressed. As expected, we found an increase in the accumulation of proteotoxicity in the cytoplasm in these conditions. This information has now been added to the paper (Figure 4K, pages 23-24 and 29).

On the other hand, CHX inhibits translation elongation by stalling ribosomes on mRNAs, preventing further peptide elongation but leaving incomplete polypeptides tethered to the blocked ribosomes. This reduces overall protein load entering the ER by blocking new protein synthesis and stabilizes misfolded proteins bound to ribosomes. Accordingly, it has been shown previously that blocking translation with CHX abolishes cytoplasmic protein aggregation (Cabrera et al., 2020; Zhou et al., 2014). Similarly, we have found that Hsp104 foci are not observed when we add CHX alone or in combination with HU or DIA (Figures 4K-L). These results suggest that cytoplasmic foci that we observe upon HU or DIA treatment likely contain misfolded proteins derived from ongoing translation.

As this question had also been raised by reviewer 1, we further explored the nature of these cytoplasmic foci (please see answer to Reviewer1, Issue 3). Briefly:

• We tested whether they colocalize with the foci of Guk1-9-GFP and Rho1.C17R-GFP reporters of misfolding that appear upon HU or DIA treatments and, indeed, Hsp104-containing aggregates colocalize with Guk1-9-GFP and Rho1.C17R-GFP. This information has now been added to the paper (Figure 4I-J, pages 23-24 and 29).
• We tested whether these foci were membrane bound with several ER transmembrane proteins (Tts1, Yop1, Rtn1) and integral membrane protein Ish1, and in none of the cases we detected membranes surrounding the aggregates. This information will be included in the final version of the paper.
• We plan to test whether the cytoplasmic foci represent proteins retro-translocated from the ER.
• We will also test whether autophagy or an imbalance between ER expansion and ER-phagy might contribute to the accumulation of cytoplasmic protein foci. The new data regarding the suppression of cytoplasmic foci by CHX treatment has already been included in the current version of the manuscript in Figure 4K and in the text (page 29).

The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.

We agree with the reviewer. We have toned down our statements about the relationship between thiol stress, the cytoplasmic chaperone foci and their relationship with ER expansion. We have removed from the text the statement that cytoplasmic foci are independent from ER expansion and thiol stress and have further revised our claims about CHX and Pm in the main text and the discussion to address these and the other reviewers’ concerns.

Major Issue #6. Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.

To address this issue, we plan to analyze the localization of proteins involved in iron-sulfur cluster assembly and/or containing iron-sulfur clusters by in vivo fluorescence microscopy, such as DNA polymerase Dna2 or Grx5, during HU or DIA treatments.

Related to this, we have found that a subunit of the ribonucleotide reductase (RNR) aggregated in the cytoplasm upon HU exposure (Figure S2B). It is worth noting that RNR is an iron-containing protein whose maturation needs cytosolic Grxs (Cotruvo & Stubbe, 2011; Mühlenhoff et al., 2020). The catalytic site, the activity site (which governs overall RNR activity through interactions with ATP) and the specificity site (which determines substrate choice) are located in the R1 (Cdc22) subunits, which are the ones that aggregate, while the R2 subunits (Suc22) contain the di-nuclear iron center and a tyrosyl radical that can be transferred to the catalytic site during RNR activity (Aye et al., 2015). The fact that a subunit of RNR aggregates could be related to an impingement on its synthesis and/or maturation due to defects in iron-sulfur cluster formation, as it has been recently published that RNR cofactor biosynthesis shares components with cytosolic iron-sulfur protein biogenesis and that the iron-sulfur cluster assembly machinery is essential for iron loading and cofactor assembly in RNR in yeast (Li et al., 2017). This information has been added to the discussion.

Major Issue #7. The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.

We modified the language used to describe the experiment in the manuscript, as suggested by the reviewer, to clarify that while DTT is kept in the medium, N-Caps never form. In addition, we have also performed a pre-treatment with DTT; adding 1 mM DTT one hour before, washing the reducing agent out and adding HU to the medium then. The result indicates that pre-treating cells with DTT significantly reduces N-Cap formation after a 4-hour incubation with HU, which suggests that triggering reducing stress “protects” cells from the oxidative damage induced by HU and DIA. This information has been also added to the manuscript (Figure 2J).

Major Issue #8. For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).

We have revised and expanded our discussion. In addition, in the final revision of our work we will increase the discussion in the context of the new results obtained.

Minor points

1. __ Figure numbering goes from figure 4 to S6 to 5.__ We have updated the numbering of the figures after merging several supplementary figures, so now this issue is fixed.

__ It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters__.

Both the Guk1-9-GFP and Rho1.C17R-GFP are two thermosensitive mutants in guanylate kinase and Rho1 GTPase respectively, that have been previously used in S. pombe as soluble reporters of misfolding in conditions of heat stress. During mild heat shock, both mutants aggregate into reversible protein aggregate centers (Cabrera et al., 2020). This information has now been added to the manuscript.

__ Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?__

We thank the reviewer for pointing out this mistake. The experiment was performed in 75 mM HU, the legend was correct. It has now been corrected in the manuscript.

__ The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signaling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.__

We have included pertinent clarification in the new discussion.

Reviewer 3

Major issue #1. It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.

Regarding the levels of Bip1, we now show in Figure 4 a less exposed image of the western blot, and a quantification of Bip1-GFP intensity from three independent experiments. We find that, in our experimental conditions, neither HU nor DIA treatments significantly altered Bip1 levels.

With respect to the RNA-Seq, as we mentioned in the major issue 1 from reviewer 1, we reassessed our data to further clarify and add information about ER stress markers induced or repressed by HU and DIA.

Major issue #2. The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all.

We have found that puromycin treatment alone results in the formation of cytoplasmic foci containing Hsp104, suggesting that puromycin indeed increases folding stress in the cytoplasm. We have now included this data in Figure 4K (please see Main Issue #5 from Reviewer 2). Pm suppresses the formation of N-caps induced by HU or DIA; however, we have not addressed cell survival or fitness in these conditions and therefore we cannot conclude about being protective.

In addition, upon the reevaluation of our data, we have realized that CHX treatment suppresses HU-induced perinuclear expansion, although it does not suppress but instead enhances ER expansion in the cortical region. This data has been added to the present version of the manuscript in Figure 3C-D (pages 20-21).

Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.

Thapsigargin has been described to be ineffective in yeasts as they lack a (SERCA)‐type Ca2+ pump which is the target of this drug (Strayle et al., 1999). However, deletion of the P5A-type ATPase Cta4, which is required for calcium transport into ER membranes (Lustoza et al., 2011), reduced but did not abolish ER expansion. We also tested the effect of anisomycin. We found that anisomycin in combination with HU or DIA mimicked CHX behavior (ER expansion occurrs in both conditions, exacerbating perinuclear ER expansion in combination with DIA and cortical ER expansion when combined with HU). It is difficult to correlate this result with a role of Ca leakage in ER expansion, as there is no recent information regarding CHX and Ca leakage, although it has been indicated that CHX treatment does not increase cytoplasmic Ca levels (Moses & Kline, 1995). As anisomycin, like CHX, blocks protein synthesis and stabilizes polysomes, what we can conclude from this information is that nascent peptides attached to ribosomes during protein synthesis do promote ER expansion when combined with HU or DIA. This information will be added to the final version of the paper.

Regarding the downstream effects of HU or DIA treatment on ER proteostasis, we plan to further explore the effect of these drugs on the secretory system (please see major issue #2 from Reviewer 1) and to evaluate the redox state and processing of several key ER and secretory proteins. We have also further explored the nature of the aggregates that appear in the cytoplasm in our experimental conditions, which also shed light into the downstream effects of these drugs in cytoplasmic proteostasis (please see answer to issue #5 from Reviewer 2).

Major issue #3. The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction?

We readdressed this topic by analyzing the genes that have been described to be differentially expressed during UPR activation in S. pombe and comparing them with our data by reevaluating our transcriptomic data.. The re-analysis of our RNA-Seq data have allowed us to infer the mechanisms that modulate the ER response to HU or DIA treatment and further separate them from UPR. This information has been added to the paper (page 26). As an alternative approach, we will also analyse the levels of UPR targets by western blot upon HU or DIA treatment

Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.

We thank the reviewer for pointing this out. We forgot to include this information which now appears in the M&M section as follows:

“A gene was considered as differentially expressed when it showed an absolute value of log2FC(LFC)≥1 and an adjusted p-valueIn this regard, we are currently performing proteome-wide mass spectrometry experiments to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. We also plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (see below, and Reviewer 2, Major issue #1).

We have already tested whether mutant strains with deletions of key enzymes in both cytoplasmic and ER redox systems are able to expand the ER upon HU or DIA treatment. We have found that only pgr1Δ (glutathione reductase), gsa1Δ (glutathione synthetase) and gcs1Δ (glutamate-cysteine ligase) mutants fully suppressed N-Cap formation, which suggests that glutathione has an important role in the phenotype of ER expansion. We have now added the pgr1Δ mutant strain to the main text of the manuscript (Figure 5C, page 30).

Major issue #5. Figure S5 presents weak ER expansion in fibrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.

We not only investigated the effects of HU on the ER in mammalian cells, but also of DIA. The results from this experiment mimicked the effect of HU (an increase in ER-ID fluorescence intensity in DIA). We merely excluded this information from the manuscript because we were focusing on HU at that point due to its importance as it is used currently in clinics. In this new version of the manuscript, we have included an extra panel in supplementary figure 5 to show the results from DIA in mammalian cells.

Minor concerns

1) Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.

Although we initially changed the graph, we believe the bar plot disposition facilitates its comprehension and went back to the initial one. Also, as the rest of the graphs similar to 1A are all expressed as bar plots. Therefore, we preferred keeping the figure as it was in the original version. However, we include here the graph with each of the averages of the independent experiments.

2) It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.

We have changed the image to show a whole population of cells, with several of them having NPC clusters, and we have indicated the position of SPB in each of them (all colocalizing with the N-Cap).

3) Figures 1B through 1D do not indicate the HU concentration.

We thank the reviewer for pointing out this mistake. Figures 1B and 1C represent cells exposed to 15 mM HU for 4 hours, while the graph in 1D shows the results from cells exposed to 75 mM HU over a 4-hour period. This information has been now added to the corresponding figure legend.

4) I was confused by the photobleaching experiments of Figure S1. How do the authors know that there is complete photobleaching of the cytoplasm or nucleus in the absence of a positive control? If photobleaching is incomplete, they could be measuring motility without compartments rather than transport between compartments, and hence the conclusion that trafficking is unaffected could be wrong.

Our control is the background of each microscopy image; we make sure that after the laser bleaches a cell, the bleached area coincides with the background noise. That way, we make sure that fluorescence from any remaining GFP is completely removed from the bleached area.

5) On page 8, they say "exposure to DIA" when they intend HU.

This has been corrected in the manuscript.

6) In Figure S3A, the colocalization of INM proteins with the ER are presented. It is not clearly explained what conclusions are meant to be drawn from this figure, but it seems it would have been more useful to compare INM and Cut11, to see whether the NPCs are localizing at the INM or ONM.

We have added an explanation in the main text to clarify the main conclusions derived from this figure. We think that NPCs localize in a section of the nucleus where the two membranes (INM and ONM) are still bound together.

7) I had to read Figure 2C's description and caption several times to understand the experiment. A schematic would be helpful. 20 mM HU is low compared to most conditions used. Does repositioning eventually take place for 75 mM HU or 3 mM DIA treatment, or do the cells just die before they get a chance?

20 mM HU was used in this experiment to provide a time frame suitable for analysis after HU addition, as a higher HU concentration increases the repositioning time. We found that both HU (75mM 4h) and DIA (3mM 4h)-induced ER expansions are reversible upon drug washout. If HU is kept in the media, ER expansions are eventually resolved. However, DIA is a strong oxidant and if it is kept in the media ER expansions are not resolved and cells do not survive.

8) Figure 2D shows little oxidative consequence from 75 mM HU treatment until 40 min., the same time that phenotypes are observed (Figure 1D). Is this relationship consistent with the kinetics of other concentrations of HU, or of DIA? Seems like a pretty important mechanistic consideration that can rationalize the effects of the two oxidants.

Thanks to this comment we realized that the numbering underneath Figure 1D (1E in the new version of the manuscript) was wrongly annotated. The original timings shown in the figure were “random”, meaning that the time stablished as 40 minutes was not measuring the passing of 40 minutes since the beginning of the experiment. We have now corrected this panel: the timings are now normalized to the moment when NPCs cluster. The fact that, before, that moment coincided with “40 minutes” does not mean N-Caps appear at that time point in HU (they indeed appear after a >2 hour incubation).

9) Figure S4 is missing the asterisk on the lower left cell.

Fixed in the corresponding figure.

10) How is roundness determined in Figure S4B?

Roundness in Figure S4B (now S2E) is determined the same way as in Figure 1D, and as is described in the Method section (copied below). A clarification has been added to the legend to address that.

The ‘roundness’ parameter in the ‘Shape Descriptors’ plugin of Fiji/ImageJ was used after applying a threshold to the image in order to select only the more intense regions and subtract background noise (Schindelin et al., 2012). Roundness descriptor follows the function:

where [Area] constitutes the area of an ellipse fitted to the selected region in the image and [Major axis] is the diameter of the round shape that in this case would fit the perimeter of the nucleus.

11) What threshold is used to determine whether cells analyzed in Figures S4C have "small ER" or "large ER"?

Large ER are considered when their area along the projection of a 3-Z section is over 4 μm2 (more than twice the mean area of the ER in cells with N-Caps in milder conditions). This has now been clarified in the legend of the corresponding figure.

__12) The authors interpret Figure 4K as indicating that ER expansion is not involved in the generation of punctal misfolded protein aggregates. However, the washout occurs only after the proteins have already aggregated. The proper interpretation is that the aggregates are not reversible by resolution of the stress, and hence are not physically reliant on disulfide bonds. __

We agree with the reviewer and have modified the interpretation of the indicated figure accordingly (page 29).

The speculation that these proteins are iron dependent is a stretch; there is no reason to believe that losses of iron metabolism are the most important stress in these cells. It seems at least as likely that oxidizing cysteine-containing proteins in the cytosol or messing with the GSH/GSSG ratio in the cytosol would make plenty of proteins misfold; oxidative stress in budding yeast does activate hsf1. However, this point could be addresses by centrifugation and mass spectrometry to identify the aggregated proteome. It is also surprising that the authors did not investigate ER protein aggregation, perhaps by looking at puncta formation of chaperones beyond BiP. By contrast, the fact that gcs1 deletion prevents ER expansion but does not prevent Hsp104 puncta does support the idea that cytoplasmic aggregation is not dependent on ER expansion.

To address this suggestion, we plan to analyze the localization of other chaperones and components of the protein quality control such as the ER Hsp40 Scj1 or the ribosome-associated Hsp70 Sks2.

13) Figure 4L is cited on page 28 when Figure 4K is intended.

This has been corrected in the text, although new panels have been added and now it is 4N.

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Referee #3

Evidence, reproducibility and clarity

This article makes the following claims, using S. pombe as their model system. Hydroxyurea (HU) and diamide (DIA) induce ER stress, an atypical UPR, and cytoplasmic protein aggregation. HU and DIA induce IRE1-independent and GSH-dependent reversible ER perinuclear expansion which causes nuclear pore clustering with no effect on protein trafficking, and can be reversed by DTT.

Major concerns:

1. It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.
2. The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all. Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.
3. The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction? Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.
4. Mechanistically, one would expect effects to be mediated by PDIs and oxidoreductases. No effort is made to characterize the redox state of these molecules, nor how that relates to the kinetics of ER expansion and resolution under HU/DIA treatment. No discussion is made of the existing literature on oxidants and ER stress. A few papers: PMID: 29504610, PMID: 31595201.
5. Figure S5 presents weak ER expansion in fribrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.

Minor concerns:

1. Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.
2. It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.
3. Figures 1B through 1D do not indicate the HU concentration.
4. I was confused by the photobleaching experiments of Figure S1. How do the authors know that there is complete photobleaching of the cytoplasm or nucleus in the absence of a positive control? If photobleaching is incomplete, they could be measuring motility without compartments rather than transport between compartments, and hence the conclusion that trafficking is unaffected could be wrong.
5. On page 8, they say "exposure to DIA" when they intend HU.
6. In Figure S3A, the colocalization of INM proteins with the ER are presented. It is not clearly explained what conclusions are meant to be drawn from this figure, but it seems it would have been more useful to compare INM and Cut11, to see whether the NPCs are localizing at the INM or ONM.
7. I had to read Figure 2C's description and caption several times to understand the experiment. A schematic would be helpful. 20 mM HU is low compared to most conditions used. Does repositioning eventually take place for 75 mM HU or 3 mM DIA treatment, or do the cells just die before they get a chance?
8. Figure 2D shows little oxidative consequence from 75 mM HU treatment until 40 min., the same time that phenotypes are observed (Figure 1D). Is this relationship consistent with the kinetics of other concentrations of HU, or of DIA? Seems like a pretty important mechanistic consideration that can rationalize the effects of the two oxidants.
9. Figure S4 is missing the asterisk on the lower left cell.
10. How is roundness determine in Figure S4B?
11. What threshold is used to determine whether cells analyzed in Figures S4C have "small ER" or "large ER"?
12. The authors interpret Figure 4K as indicating that ER expansion is not involved in the generation of punctal misfolded protein aggregates. However, the washout occurs only after the proteins have already aggregated. The proper interpretation is that the aggregates are not reversible by resolution of the stress, and hence are not physically reliant on disulfide bonds. The speculation that these proteins are iron dependent is a stretch; there is no reason to believe that losses of iron metabolism are the most important stress in these cells. It seems at least as likely that oxidizing cysteine-containing proteins in the cytosol or messing with the GSH/GSSG ratio in the cytosol would make plenty of proteins misfold; oxidative stress in budding yeast does activate hsf1. However, this point could be addresses by centrifugation and mass spectrometry to identify the aggregated proteome. It is also surprising that the authors did not investigate ER protein aggregation, perhaps by looking at puncta formation of chaperones beyond BiP. By contrast, the fact that gcs1 deletion prevents ER expansion but does not prevent Hsp104 puncta does support the idea that cytoplasmic aggregation is not dependent on ER expansion.
13. Figure 4L is cited on page 28 when Figure 4K is intended.

Significance

This paper is for the most part well-written, presenting a logical chain of experiments that fully support the most important claims that have been made. Specifically, they show that HU and DIA induce reversible perinuclear expansion and nuclear pore clustering in an IRE1-independent and GSH-dependent manner, and that DTT can prevent and accelerate recovery of this phenotype. Both oxidants clearly induce protein aggregation in the cytosol. The evidence that perinuclear expansion is responsible for nuclear pore clustering is compelling, with strong support from the kinetics and the nup120 deletion experiments. Some conclusions are not supported, including the claim of an atypical UPR and of ER stress, but the validity of these claims does not substantively affect the overall importance of the paper and could be handled by withdrawal or tempering of the claims. The lack of a molecular mechanism connecting oxidation with ER expansion moderately detracts from the potential impact. Adequate experimental detail is provided unless otherwise noted

This paper is likely to be important for cell biologists interested in interorganelle communication and how the cell responds to oxidative stress. Modulating ER oxidoreductase activity has been shown to be a powerful way to regulate ER stress and proteostasis, and this paper shows how specific oxidative stresses that have not widely been investigated in this context, as opposed to the more commonly studied reductive and electrophilic stresses, can remodel the ER with cell-wide consequences. More specifically, the nuclear pore and nuclear morphology phenotypes, while not yet functionally significant in yeast, could be significant in other unexplored ways identified in the future. Towards that end, it would be valuable to see if these gross phenotypes reproduce in any metazoan cell or tissue, rather than just looking at ER expansion as in the current manuscript. My expertise is centered around ER proteostasis and chaperones, and as such I consider this paper important to my field.

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Referee #2

Evidence, reproducibility and clarity

The manuscript by Sánchez-Molina et al describes a striking time and dose-dependent clustering of nuclear pores and perinuclear ER expansion in response to hydroxyurea (HU) or diamide (DIA) treatment in S. pombe. Using microscopy, the authors establish clustering is reversible upon drug washout or extended drug treatment. Pretreatment or post-treatment with the reductant DTT prevents or reverses the clustering and expansion effects, as does the release of translating polypeptides from ribosomes (with puromycin). The phenotypes were established to occur independent of the established impact of HU on RNR activity and the cell cycle. The authors suggest instead that the phenotypes (referred to as nuclear-cap (N-Cap) formation) are associated with disulfide-based folding stress. Overlapping transcriptional responses for HU and DIA treatment suggest that cells are experiencing folding stress (based on chaperone induction) and/or a disruption in iron homeostasis (induction of genes involved in iron homeostasis). The observed clustering, ER expansion, and transcriptional profiles are independent of the well-established ER stress response pathway: the UPR.

The manuscript outlines several interesting phenotypic observations, and they establish the potential for conserved of this ER expansion and nuclear pore clustering from yeast (S. cerevisiae) and mammals (HT1080 fibrosarcoma cells). Data clearly establish the time and dose-dependent formation of these interesting structures. Additional experiments with combined drug treatments points towards a role for changes in the redox environment in cells, an impact on cytoplasmic protein aggregation, and a potential impact on the ER folding environment / ER redox environment.

Data obtained with thiol oxidants and reductants, alongside translation inhibitors, suggest a potential connection between the N-Cap phenotype and oxidative folding within the ER. Yet, this latter observation remains a suggestive link with less clear mechanistic connections. Some experiments that would more directly assess the suggested changes within the nuclear/ER region are outlined below.

1. The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?
2. Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.
3. It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?
4. While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).

Addition suggestions / comments:

1. What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci? The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.
2. Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.
3. The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.
4. For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).

Minor points

1. Figure numbering goes from figure 4 to S6 to 5.
2. It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters.
3. Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?
4. The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signalling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.

Significance

An interesting finding that is well-supported as a phenotype. What would raise the impact would be data that connect these observations more directly with a mechanism. In particular, there are suggestions of a disruption in ER folding and/or the ER redox environment that are logical but not directly tested. How one viewed these additional experiments will depend on what journal is considering the manuscript.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Sanchez-Molina describe the impact of hydroxyurea on the remodeling of the nuclear pore complex (clustering) and the expansion of both cortical and perinuclear ER. The study is carried out in S. pombe, and the observations confirmed in S. cerevisiae. Results are clear and analyzed properly, however considering the differences in UPR signaling in both yeast strains the conclusions raised may remain to be fully documented.

Major issues

Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S cerevisiae, do the authors observe HAC1 mRNA splicing?

The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status. What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?

The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates? Are those resulting from proficient retrotranslocation (or reflux of misfolded proteins from the ER? Are those aggregates membrane bound or do they correspond to aggresomes as initially defined?

The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol. Does HU impact on the regulation of autophagy?

Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (10.1093/nar/29.14.3030), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.

Minor issues

P1 - UPR = Unfolded Protein Response

P22 - HSP upregulation "might" be indicative of a folding stress

The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors to revise the story telling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.

Significance

This is a nice manuscript describing the likely effects of HU on protein misfolding and several consequences including the remodeling of the nuclear pore complex, the expansion of both cortical and perinuclear ER. The underlying mechanisms remain however unclear (for each parameter evaluated) and the manuscript would definitely benefit from the elucidation of one of those (if not more).

The work in yeast is novel and might bring light on mechanisms existing in mammalian systems. Since HU is used as a therapeutic, the characterization of the molecular mechanisms associated with its mode(s) of action will definitely be useful for better (targeted) efficiency.

The audience for this work is more targeted towards people working on yeast cell biology, however, the authors could expand the discussion section to make it of a broader scope.

I am expert on ER stress signaling

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Manuscript number: RC-2024-02605

Corresponding author: Woo Jae, Kim

1. ____Point-by-point description of the revisions

Reviewer #1

General Comment: This study investigates the role of the foraging gene in modulating interval timing behaviors in flies, with a particular focus on mating duration. Using single-cell RNA sequencing and gene knockdown experiments, the research demonstrates the crucial role of foraging gene expression in Pdfr-positive cells for achieving longer mating duration (LMD). The study further identifies key neurons in the ellipsoid body (EB) as essential when the foraging gene is overexpressed, highlighting its specific influence on LMD. The findings suggest that a small subset of EB neurons must express the foraging gene to modulate LMD effectively.

__Answer:____ __We would like to express our gratitude to the reviewer for their insightful comments and positive feedback on our manuscript. During the revision process, we serendipitously discovered that the heart-specific expression of the foraging gene plays a crucial role in regulating LMD behavior. We have elaborated on the significance of this finding in the revised manuscript and have addressed the reviewer's comments accordingly.

Comment 1. *(optional) Integration of Neuronal Subsets into a Pathway: The knockdown experiments indicate that a small subset of neurons must express the foraging gene to influence LMD. Could these neurons be integrated into a potential signaling pathway, or being treated as separate components within the brain circuit? How might this integration provide a more cohesive understanding of their role in LMD? *

Answer: We sincerely thank the reviewer for her/his insightful comments regarding the integration of neuronal subsets into a signaling pathway and their potential role in modulating LMD behavior. During the revision process, we conducted further experiments to address this question. While we were unable to identify a specific small subset of EB neurons expressing foraging, we utilized the recently developed EB-split GAL4 driver line (SS00096), which is restricted to the EB region of the brain, to confirm that foraging expression in the EB is indeed crucial for generating LMD behavior (Fig. 4L-M). This finding underscores the importance of foraging in specific neural circuits within the EB for interval timing.

Additionally, we discovered that foraging expression in Hand-GAL4-labeled pericardial cells (PCs) of the heart is essential for LMD behavior. These PCs are also partially labeled by fru-GAL4 and 30y-GAL4 drivers, indicating that foraging functions in both neuronal and non-neuronal tissues to regulate interval timing. Importantly, we observed that group-reared males exhibit higher calcium activity in PCs compared to socially isolated males, suggesting that social context-dependent calcium dynamics in the heart play a critical role in modulating LMD behavior.

These findings highlight a novel integration of neuronal and cardiac mechanisms, where foraging expression in both the EB and heart coordinates calcium dynamics to regulate interval timing. This dual-tissue involvement provides a more cohesive understanding of how foraging integrates social cues with internal physiological states to modulate complex behaviors like LMD. We believe this integration of neuronal and cardiac pathways offers a comprehensive framework for understanding the gene’s pleiotropic roles in behavior. We have included these new findings in the revised manuscript to better address the reviewer’s question and to strengthen the discussion of how foraging functions across tissues to regulate interval timing behaviors.

Comment 2. Genetic Considerations in Gal4 System Usage (Fig. 1D): In the study, the elavc155-Gal4 transgene, located on chromosome I, produces hemizygous males after crossing, while the repo-Gal4 transgene, located on chromosome III, results in heterozygous males. Is there any evidence suggesting that this genetic configuration could impact the experimental outcomes? If so, what steps could be taken to address potential issues?

Answer: We appreciate the reviewer’s thoughtful consideration of potential genetic confounds related to the chromosomal locations of the elavc155 and repo-GAL4 transgenes. To address this concern, we conducted additional experiments using the nSyb-GAL4 driver, which is located on the third chromosome, and observed that knockdown of foraging with this driver also disrupts LMD behavior (Fig. S1G). This result aligns with our findings using elavc155 (chromosome I) and repo-GAL4 (chromosome III), indicating that the chromosomal location of the GAL4 transgene does not significantly impact the experimental outcomes.

Furthermore, our extensive tissue-specific GAL4 screening, which included drivers on different chromosomes, consistently demonstrated that foraging knockdown effects on LMD are robust and reproducible across various genetic configurations. These results suggest that the observed behavioral deficits are due to the loss of foraging function rather than positional effects of the GAL4 transgenes. We thank the Reviewer for raising this important point and have taken care to address it thoroughly in our revised manuscript.

Comment 3. Discrepancies in lacZ Signal Intensity (Fig. 5A): The observed discrepancies in lacZ signal intensity on the surface of the male brain have been attributed to the dissection procedure. Is it feasible to replace the current data with a new, more consistent dataset? How might improved dissection techniques mitigate these discrepancies?

Answer____: We thank the reviewer for her/his observation regarding the discrepancies in lacZ signal intensity on the surface of the male brain, which we attributed to variations in the dissection procedure. While replacing the current dataset with a new one is feasible, we have instead shifted our focus to address this concern by leveraging more reliable and validated tissue-specific GAL4 drivers combined with foraging-RNAi.

During the revision process, we extensively examined multiple foraging-GAL4 lines and found that foraging expression in the brain is limited and often inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To circumvent this issue, we utilized well-characterized tissue-GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior.

Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment.

We believe this new analysis addresses the reviewer’s concerns by providing a more robust and consistent approach to studying foraging function, focusing on its role in the heart rather than relying on potentially unreliable brain expression data. We hope these findings meet the reviewer’s expectations and provide a clearer understanding of foraging’s role in mating duration.

Comment ____4. Rescue Experiment Data (Fig. S2L): Could additional data be provided to demonstrate the rescue effect using the c61-Gal4 driver, similar to what was observed with the 30y-Gal4 driver? How would such data enhance the study's conclusions regarding the specificity and robustness of the foraging gene's role in LMD?

Answer: We appreciate the reviewer’s suggestion to provide additional rescue experiment data using the c61-GAL4 driver, similar to the results obtained with the 30y-GAL4 driver. While we do not currently have a UAS-for line to perform direct rescue experiments with c61-GAL4, we have conducted extensive follow-up experiments using both 30y-GAL4 driver to further validate the role of foraging in LMD behavior. These experiments consistently demonstrated that foraging knockdown in cells targeted by these drivers disrupts LMD, reinforcing the specificity and robustness of foraging’s role in interval timing.

Additionally, our revised manuscript includes new findings that highlight the critical role of foraging expression in fru-positive heart neurons for generating male-specific mating investment. These heart neurons exhibit dynamic calcium activity changes in response to social context, further supporting the idea that foraging modulates LMD through both neuronal and non-neuronal mechanisms. While we acknowledge that direct rescue data with c61-GAL4 would strengthen the study, we believe the combination of 30y-GAL4 and c61-GAL4 knockdown results, along with the newly identified role of heart neurons, provides compelling evidence for foraging’s role in LMD.

In addition, we have confirmed that the 30y-GAL4 driver labels fru-positive heart cells, further supporting the critical role of foraging expression in these cells for generating male-specific mating investment. This finding aligns with our broader results, demonstrating that foraging function in fru-positive heart neurons is essential for modulating interval timing behaviors, particularly LMD. We hope these additional analyses address the reviewer’s concerns and enhance the study’s conclusions regarding the specificity and robustness of foraging function in interval timing behaviors. We have incorporated the following findings into the main text:

“Therefore, we conclude that the knockdown and genetic rescue effects observed with the Pdfr3A-GAL4 driver (Fig. 3J and 3N) and the 30y-GAL4 driver (Fig. 4A, S2A, and S2L) are attributable to their expression in the heart. In summary, our findings demonstrate that fru-positive heart cells expressing foraging and Pdfr play a critical role in mediating LMD behavior.”

Reviewer #2

General Comment: The authors nicely demonstrated that the Drosophila for gene is involved in the plastic LMD behavior that serves as a model for interval timing. For is widely expressed in the body, they have tentatively localized the LMD-relevant for functioning to the ellipsoid body of the central complex.

Answer: We sincerely thank the reviewer for their positive feedback on our manuscript and their recognition of our findings regarding the role of the foraging gene in modulating plastic LMD behavior as a model for interval timing. In addition to its function in the ellipsoid body (EB) of the central complex, we have identified a novel and critical role for foraging in fru-positive heart neurons. These neurons are essential for regulating male-specific mating investment, as demonstrated by dynamic calcium activity changes in response to social context. This discovery expands our understanding of foraging’s pleiotropic roles, highlighting its function not only in neural circuits but also in non-neuronal tissues, particularly the heart, to modulate interval timing behaviors. We believe these findings provide a more comprehensive view of how *foraging* integrates genetic, neural, and physiological mechanisms to regulate complex behaviors. We hope this additional insight into the role of fru-positive heart neurons further strengthens the manuscript and aligns with the reviewer’s interest in the broader implications of foraging function.

Major concerns: __ Comment 1.__ Please clarify how a loss-of-function forS allele can be dominant in the presence of overactive forR allele? In the same vein, please clarify how does the forR/forS transgeterozygote supports your hypothesis that high levels of PKG activity disrupt SMD and low levels of it disrupt LMD?

Answer: We thank the reviewer for her/his insightful questions regarding the dominance of the forS allele in the presence of the overactive forR allele and the implications of the forR/forS transheterozygote phenotype. As the Reviewer noted, the forR allele is associated with higher PKG activity, while the forS allele exhibits lower PKG activity. The disruption of SMD in the presence of a single forR allele can be explained by the excessive PKG activity, which may hyperactivate or desensitize neural circuits required for SMD. Conversely, the forS homozygote disrupts LMD, suggesting that a minimum threshold of PKG activity is necessary for LMD generation.

The forR/forS transheterozygote, which disrupts both LMD and SMD, presents an intriguing case. Unlike forR/+ or forS/+ heterozygotes, which show intact behaviors due to intermediate PKG activity levels, the forR/forS combination results in conflicting PKG activity levels that likely destabilize shared pathways required for both behaviors. We propose two hypotheses to explain this phenomenon:

1. Metabolic Disruption: The foraginggene mediates adult plasticity and gene-environment interactions, particularly under conditions of food deprivation (Kent 2009). It influences body fat, carbohydrate metabolism, and gene expression levels, leading to metabolic and behavioral gene-environment interactions (GEI). In forR/forStransheterozygotes, the metabolic changes induced by each allele may accumulate without proper regulatory mechanisms, disrupting the male’s internal metabolic state and impairing the ability to accurately measure interval timing.

Neuronal Polymorphism: The foraginggene regulates neuronal excitability, synaptic transmission, and nerve connectivity (Renger 1999). The forRand forS alleles may induce distinct neuronal polymorphisms, such as altered synaptic terminal morphology, which could lead to conflicting circuit dynamics in transheterozygotes. This neuronal mismatch may explain why forR/forS flies exhibit disrupted behaviors, unlike heterozygotes with a wild-type allele.

These findings align with prior studies showing that PKG activity must be tightly regulated within context-dependent ranges for optimal behavior. The foraging gene’s pleiotropic roles, including its influence on metabolic and neural pathways, highlight the importance of allelic balance in maintaining behavioral robustness. The forR/forS transheterozygote phenotype underscores the complexity of foraging’s role in interval timing, where extreme or mismatched PKG activity levels disrupt circuit-specific thresholds critical for distinct behaviors. We hope this explanation clarifies the dominance effects and the role of PKG activity in LMD and SMD, and we have incorporated these insights into the revised manuscript to strengthen our discussion of foraging’s pleiotropic functions.

We provide a concise explanation of this hypothesis in the Discussion section, as outlined below:

“The foraging gene plays a critical role in regulating interval timing behaviors, with its allelic variants, rover and sitter, exhibiting distinct effects on LMD and SMD. These differences are primarily driven by their opposing impacts on cGMP-dependent protein kinase (PKG) activity. The forR allele, associated with higher PKG activity, disrupts SMD while maintaining normal LMD (Fig. 1A), suggesting that elevated PKG levels may hyperactivate or desensitize neural circuits specific to SMD processes. Conversely, the forS allele, characterized by lower PKG activity, impairs LMD but not SMD (Fig. 1B), indicating that reduced PKG activity fails to meet the neuromodulatory thresholds required for LMD coordination. The forR/forS transheterozygotes, which disrupt both LMD and SMD (Fig. 1C), reveal a complex interaction between these alleles, likely due to conflicting PKG activity levels or metabolic and neuronal polymorphisms that destabilize shared pathways. This phenomenon underscores the foraging gene’s pleiotropic roles, where allelic balance fine-tunes PKG activity to maintain behavioral robustness, while extreme or mismatched levels disrupt circuit-specific thresholds critical for distinct memory processes [6,10] .

The foraging gene’s influence on interval timing behaviors extends beyond neural circuits to include metabolic and synaptic regulation. The intact behaviors observed in forR/+ or forS/+ heterozygotes suggest that intermediate PKG activity levels balance circuit dynamics, allowing for normal LMD and SMD. However, the dual deficits in forR/forS transheterozygotes highlight the importance of allelic balance, as conflicting PKG levels may lead to systemic disruptions in both metabolic and neural pathways. This aligns with previous studies showing that foraging mediates adult plasticity and gene-environment interactions, particularly under stress conditions, and regulates synaptic terminal morphology and neuronal excitability [29,77]. The gene’s role in integrating genetic and environmental cues further emphasizes its central role in adaptive behaviors. Collectively, these findings illustrate the complex interplay between PKG activity, neural circuits, and metabolic regulation in shaping interval timing behaviors, highlighting the foraging gene as a key modulator of behavioral plasticity in Drosophila [3,6,77].”

Comment 2. Please consider removing lines 193-201 & Fig 3G,H, since abruptly and briefly returning to SMD could distract the reader and hinder the flow.

Answer: We sincerely thank the reviewer for her/his suggestion to improve the flow of the manuscript. In response to reviewer’s feedback, we have removed Figure 3G-H and the related text (lines 193-201) from the main text. While the data on SMD behavior provided additional insights into the role of foraging in gustatory modulation via sNPF-expressing peptidergic neurons, we agree that its inclusion at this point in the manuscript could distract from the primary focus on LMD behavior and interval timing.

Comment 3. Please use more specific Gal4 drivers to identify the exact subset of the EB-RNs where for function is necessary for LMD. Please note that Taghert lab already identified Pdfr+ EB-RN subset, and in contradiction to your findings, demonstrated that Cry is expressed in these Pdfr+ EB neurons

Answer: We thank the reviewer for their suggestion to use more specific GAL4 drivers to identify the exact subset of EB ring neurons (EB-RNs) where foraging function is necessary for LMD. In response, we utilized the EB-split-GAL4 driver SS00096, which has been previously employed to map the neuroanatomical ultrastructure of the EB (Turner-Evans 2020). Knockdown of foraging using this refined EB driver disrupted LMD behavior, confirming that foraging function in the EB is indeed crucial for interval timing.

Regarding the reviewer’s observation about the Taghert lab’s findings on Pdfr+ EB-RNs and the expression of Cry in these neurons, we acknowledge this discrepancy. However, during the revision process, we discovered that foraging and Pdfr are co-expressed not only in EB neurons but also in fru-positive heart neurons, which play a complementary role in modulating LMD behavior. This finding suggests that the apparent contradiction may arise from the dual-tissue involvement of foraging in both EB neurons and heart cells. While foraging function in the EB is critical, its role in heart neurons may provide an additional layer of regulation for interval timing behaviors, potentially compensating for or interacting with EB-related mechanisms.

We have incorporated these insights into the revised manuscript, emphasizing the importance of both EB and heart neurons in mediating LMD behavior. This dual-tissue perspective offers a more comprehensive understanding of foraging’s role in interval timing and addresses the potential discrepancies highlighted by the reviewer. We hope this clarification resolves the reviewer’s concerns and strengthens the manuscript’s conclusions regarding the neural and non-neural mechanisms underlying foraging function.

Comment 4. Please clarify how do you think for and Pdfr signaling molecularly interact in these neurons? Since your work doesn't implicate the for+ AL neurons, please remove lines 260-269.Please clarify if the Pdfr+ for+ EB neurons are also fru+.The lacZ staining in Fig5A-B is atypical in having a mosaic-like pattern. Please replace the image.

Answer: We thank the reviewer for her/his thoughtful questions regarding the molecular interaction between foraging and Pdfr signaling, as well as their observations on the atypical lacZ staining pattern. Below, we address each point in detail:

1. Molecular Interaction Between foragingand PdfrSignaling: Our tissue-specific driver screening indicates that Pdfr and foraging do not co-express in the same neurons within the brain. Instead, we found that Pdfr and foraging are co-expressed in fru-positive heart cells, suggesting that PDF-Pdfr signaling in these cells modulates calcium activity in pericardial cells (PCs) in a social context-dependent manner. This finding aligns with our previous work showing that PDF signaling is crucial for LMD behavior (Kim 2013). We propose that PDF-Pdfr signaling operates not only through the brain’s sLNv to LNd neuronal circuit but also through a brain-to-heart signaling axis, influencing behaviors and physiological processes across multiple tissues.

Removal of Lines 260-269: As suggested, we have removed lines 260-269, which discussed for+ AL neurons, as our findings do not implicate these neurons in LMD regulation. This revision helps streamline the manuscript and maintain focus on the relevant neural and cardiac mechanisms.

Clarification on Pdfr+for+EB Neurons and fru Expression: While our data do not directly address whether Pdfr+ for+ EB neurons are also fru+, we have confirmed that foraging and Pdfr co-express in fru-positive heart cells. This suggests that fru may play a role in integrating foraging and Pdfr signaling in non-neuronal tissues, particularly in the heart, to regulate LMD behavior.

Replacement of lacZ Staining Images: During the revision process, we extensively examined multiple foraging-GAL4lines and found that foragingexpression in the brain is limited and often inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To circumvent this issue, we utilized well-characterized tissue-GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior. Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment. We believe this new analysis addresses the reviewer’s concerns by providing a more robust and consistent approach to studying foraging function, focusing on its role in the heart rather than relying on potentially unreliable brain expression data. We hope these findings meet the reviewer’s expectations and provide a clearer understanding of foraging’s role in mating duration.

We hope these revisions meet the Reviewer’s expectations and provide a clearer understanding of the interplay between foraging and Pdfr signaling in interval timing behaviors.

Comment 5. Please consider removing lines 303-312, since this negative result may dilute your final conclusions without adding strong factual value.

Answer: We appreciate the reviewer's suggestion regarding lines 303-312. Upon careful consideration, we believe this paragraph provides important context about the roles of dsx-positive and fru-positive cells in foraging behavior. Specifically, it highlights that the foraging function is associated with fru-positive cells rather than dsx-positive cells, which is a key distinction in our study. This information is relevant to understanding the broader implications of our findings, as it underscores the functional specificity of these genes in regulating behavior. However, to address the reviewer's concern, we have revised the paragraph to ensure it is more concise and directly tied to the study's conclusions. We have also integrated additional data from the new manuscript to further strengthen the factual value of this section. We hope this adjustment strikes the right balance between maintaining necessary context and avoiding any dilution of the final conclusions. Thank you for this thoughtful feedback.

__Minor concerns: __

__Comment 6. __Minor points: In the intro please mention other interval timing mechanisms and their underlying molecular mechanisms (e.g., CREB work of Crickmore lab). Please provide a better rationale for why you thought for is a good candidate for LMD? In line 124, when you start to talk about larval neurons - please specify which neurons you are referring to. In Fig 2E,G,H - 'glia' should be replaced with 'neurons'.

Answer: We appreciate the reviewer’s insightful comments regarding our conclusion linking LMD to interval timing behavior. Current research by Crickmore et al. has shed light on how mating duration in Drosophila serves as a powerful model for exploring changes in motivation over time as behavioral goals are achieved. For instance, at approximately six minutes into mating, sperm transfer occurs, leading to a significant shift in the male's nervous system: he no longer prioritizes sustaining the mating at the expense of his own survival. This change is driven by the output of four male-specific neurons that produce the neuropeptide Corazonin (Crz). When these Crz neurons are inhibited, sperm transfer does not occur, and the male fails to downregulate his motivation, resulting in matings that can last for hours instead of the typical ~23 minutes (Thornquist 2020).

Recent research by Crickmore et al. has received NIH R01 funding (Mechanisms of Interval Timing, 1R01GM134222-01) to explore mating duration in Drosophila as a genetic model for interval timing. Their work highlights how changes in motivation over time can influence mating behavior, particularly noting that significant behavioral shifts occur during mating, such as the transfer of sperm at approximately six minutes, which correlates with a decrease in the male's motivation to continue mating (Thornquist 2020). These findings suggest that mating duration is not only a behavioral endpoint but may also reflect underlying mechanisms related to interval timing.

In addition to the efforts of Crickmore's group to connect mating duration with a straightforward genetic model for interval timing, we have previously published several papers demonstrating that LMD and SMD can serve as effective genetic models for interval timing within the fly research community. For instance, we have successfully connected SMD to an interval timing model in a recently published paper (Lee 2023), as detailed below:

"We hypothesize that SMD can serve as a straightforward genetic model system through which we can investigate "interval timing," the capacity of animals to distinguish between periods ranging from minutes to hours in duration.....

In summary, we report a novel sensory pathway that controls mating investment related to sexual experiences in Drosophila. Since both LMD and SMD behaviors are involved in controlling male investment by varying the interval of mating, these two behavioral paradigms will provide a new avenue to study how the brain computes the ‘interval timing’ that allows an animal to subjectively experience the passage of physical time (Buhusi & Meck, 2005; Merchant et al, 2012; Allman et al, 2013; Rammsayer & Troche, 2014; Golombek et al, 2014; Jazayeri & Shadlen, 2015)."

Lee, S. G., Sun, D., Miao, H., Wu, Z., Kang, C., Saad, B., ... & Kim, W. J. (2023). Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. PLoS Genetics, 19(5), e1010753.

We have also successfully linked LMD behavior to an interval timing model and have published several papers on this topic recently (Huang 2024,Zhang 2024,Sun 2024).

Sun, Y., Zhang, X., Wu, Z., Li, W., & Kim, W. J. (2024). Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. G3: Genes, Genomes, Genetics, jkae255.

Zhang, T., Zhang, X., Sun, D., & Kim, W. J. (2024). Exploring the Asymmetric Body’s Influence on Interval Timing Behaviors of Drosophila melanogaster. Behavior Genetics, 54(5), 416-425.

Huang, Y., Kwan, A., & Kim, W. J. (2024). Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. Gene Reports, 36, 101999.

Finally, in this context, we have outlined in our INTRODUCTION section below how our LMD and SMD models are related to interval timing, aiming to persuade readers of their relevance. We hope that the reviewer and readers are convinced that mating duration and its associated motivational changes such as LMD and SMD provide a compelling model for studying the genetic basis of interval timing in Drosophila.

“The mating duration (MD) of male fruit flies, Drosophila melanogaster, serves as an excellent model for studying interval timing behaviors. In Drosophila, two notable interval timing behaviors related to mating duration have been identified: Longer-Mating-Duration (LMD), which is observed when males are in the presence of competitors and extends their mating duration [15–17] and Shorter-Mating-Duration (SMD), which is characterized by a reduction in mating time and is exhibited by sexually experienced males [18,19]. The MD of male fruit flies serves as an excellent model for studying interval timing, a process that can be modulated by internal states and environmental contexts. Previous studies by our group (Kim 2013,Kim 2012,Zhang 2024,Lee 2023,Huang 2024) and others (Thornquist 2020,Crickmore 2013,Zhang 2019,Zhang 2021) have established robust frameworks for investigating MD using advanced genetic tools, enabling the dissection of neural circuits and molecular mechanisms that govern interval timing.

The foraging gene emerged as a strong candidate for regulating LMD due to its well-documented role in behavioral plasticity and decision-making processes (Kent 2009,Alwash 2021,Anreiter 2019). The foraging gene encodes a cGMP-dependent protein kinase (PKG), which has been implicated in modulating foraging behavior, aggression, and other context-dependent behaviors in Drosophila. Its involvement in these processes suggests a potential role in integrating environmental cues and internal states to regulate interval timing, such as LMD. Furthermore, the molecular mechanisms underlying interval timing have been explored in other contexts, such as the work of the Crickmore et al., which has demonstrated the critical role of CREB (cAMP response element-binding protein) in regulating behavioral timing and plasticity. CREB-dependent signaling pathways, along with other molecular players like PKG, provide a broader framework for understanding how interval timing is orchestrated at the neural and molecular levels (Thornquist 2020,Zhang 2016,Zhang 2021,Zhang 2019,Crickmore 2013,Zhang 2023). By investigating foraging in the context of LMD, we aim to uncover how specific genetic and neural mechanisms fine-tune interval timing in response to social and environmental cues, contributing to a deeper understanding of the principles governing behavioral adaptation.”

When describing larval neurons, we provide specific references to ensure clarity and accuracy, as outlined below:

“Moreover, the cultured giant neural characteristics of these phenotypes are distinctly different [29].”

We thank the reviewer for catching this error. We have corrected the incorrect label "Glia" to "Neuron" in Figures 2E, 2G, and 2H.

Reviewer #3

General Comment: This manuscript explores the foraging gene's role in mediating interval timing behaviors, particularly mating duration, in Drosophila melanogaster. The two distinct alleles of the foraging gene-rover and sitter-demonstrate differential impacts on mating behaviors. Rovers show deficiencies in shorter mating duration (SMD), while sitters are impaired in longer mating duration (LMD). The gene's expression in specific neuronal populations, particularly those expressing Pdfr (a critical regulator of circadian rhythms), is crucial for LMD. The study further identifies sexually dimorphic patterns of foraging gene expression, with male-biased expression possibly in the ellipsoid body (EB) being responsible for regulating LMD behavior. The findings suggest that the foraging gene operates through a complex neural circuitry that integrates genetic and environmental factors to influence mating behaviors in a time-dependent manner. Additionally, restoring foraging expression in Pdfr-positive cells rescues LMD behavior, confirming its central role in interval timing related to mating.

Answer: We sincerely thank the reviewer for her/his thoughtful and comprehensive synthesis of our work, as well as their recognition of its key contributions. We are grateful that the reviewer highlighted the central findings of our study, including the allele-specific roles of forR (rover) and forS (sitter) in regulating distinct interval timing behaviors—specifically, the deficiencies of rovers in SMD and sitters in LMD. We also appreciate the reviewer’s emphasis on the sexually dimorphic expression of the *foraging* gene, particularly its male-biased expression in the ellipsoid body (EB), and its critical role in Pdfr-positive neurons for mediating LMD.

We agree with the reviewer that the interplay between genetic factors (e.g., allelic variation in foraging) and environmental cues (e.g., circadian rhythms via Pdfr pathways) underscores the complexity of interval timing regulation. The rescue of LMD behavior by restoring foraging expression in Pdfr cells further supports our hypothesis that foraging operates through specialized neural circuits to integrate temporal and environmental inputs. This finding aligns with broader studies on interval timing mechanisms, such as the work of the Crickmore lab on CREB-dependent pathways, which have demonstrated how molecular and neural mechanisms converge to regulate behavioral plasticity and timing.

In the revised manuscript, we will expand on these points to strengthen the discussion of foraging’s pleiotropic roles in time-dependent mating strategies and its potential links to evolutionary fitness. Specifically, we will incorporate additional insights from the new manuscript, including further evidence of how foraging balances behavioral plasticity with metabolic and neural demands, and how its expression in specific neuronal populations, such as the EB, contributes to adaptive behaviors. These updates will provide a more comprehensive understanding of the gene’s role in interval timing and its broader implications for behavioral adaptation. Once again, we thank the Reviewer for their valuable feedback, which has helped us refine and enhance the presentation of our findings.

__Major concerns: __

Comment 1. The sexually dimorphic expression of the foraging gene is not convincing. Specifically, the lacZ signal in the male brain is not representative.

__Answer:____ __We sincerely thank the reviewer for her/his insightful comment regarding the sexually dimorphic expression of the foraging gene. We agree that the lacZ signal in the male brain, as presented, may not be fully representative, and we appreciate the reviewer’s observation regarding the discrepancies in signal intensity, which we attribute to variations in dissection procedures. While replacing the current dataset with a new one is feasible, we have chosen to address this concern by shifting our focus to a more reliable and validated approach using tissue-specific GAL4 drivers combined with foraging-RNAi.

During the revision process, we conducted an extensive examination of multiple foraging-GAL4 lines and found that foraging expression in the brain is often limited and inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To overcome this limitation, we employed well-characterized tissue-specific GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior.

Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment.

By focusing on the heart and leveraging more reliable genetic tools, we believe this new analysis addresses the Reviewer’s concerns and provides a more robust and consistent approach to studying foraging function. We hope these findings meet the reviewer’s expectations and offer a clearer understanding of foraging’s role in mating duration. We are grateful for the Reviewer’s constructive feedback, which has significantly strengthened our study.

Comment 2____. Key control genotypes are missing.

Answer: We thank the Reviewer for raising this important point regarding control genotypes. We would like to clarify that all necessary control experiments have indeed been conducted, and the results are included in the manuscript. Detailed descriptions of these controls, including the specific genotypes and experimental conditions, are provided in the Methods section. For example, control experiments were performed to account for genetic background effects, GAL4 driver activity, and RNAi efficiency, ensuring the reliability and specificity of our findings. In the revised manuscript, we have further emphasized these control experiments and their outcomes to ensure transparency and reproducibility. We have also included additional details in the Results section to highlight how these controls validate our key findings. For instance, control genotypes lacking the foraging-RNAi or GAL4 drivers were used to confirm that the observed phenotypes are specifically due to the manipulation of foraging expression.

We appreciate the Reviewer’s attention to this critical aspect of our study and hope that the additional clarification and emphasis on control experiments in the revised manuscript address their concerns. If there are specific control genotypes or experiments the reviewer would like us to include or elaborate on further, we would be happy to do so. Thank you for this valuable feedback.

Comment 3____.fru is not expressed in the EB, so the authors may need to reconcile their model in figure 5G.

Answer: We thank the reviewer for her/his insightful comment regarding the expression of fru in the ellipsoid body (EB) and its relevance to our model in Figure 5G. We agree that fru is not expressed in the EB, and we acknowledge the need to reconcile this aspect of our model. While initial evidence suggested a potential role for the EB in regulating foraging-dependent LMD behavior, further investigation has revealed that neurons outside the EB are more likely to be involved in this process.

During our revision, we identified fru-positive heart neurons that coexpress Pdfr and foraging, which appear to play a critical role in modulating LMD behavior. These findings suggest that the heart, rather than the EB, may be a key site for foraging function in the context of interval timing and mating duration. Specifically, we demonstrated that calcium activity in these fru+ heart cells is dynamically regulated by social context, further supporting their role in modulating male mating investment.

In light of these new findings, we revised Figure 5G as new Figure 6H and the accompanying model to reflect the updated understanding that fru+ heart neurons, rather than EB neurons, are central to the regulation of LMD behavior. This adjustment aligns with our broader goal of accurately representing the neural and molecular mechanisms underlying foraging’s role in interval timing. We appreciate the Reviewer’s feedback, which has helped us refine our model and strengthen the manuscript. We hope these revisions address their concerns and provide a clearer and more accurate representation of our findings. Thank you for this valuable input.

Minor concerns: Comment 4____.

Line 32, what do you mean by "overall success of the collective"

Line 124-126: I suggest not using "sitter neurons" or "rover neurons". Line 301, typo with "male-specific".

Answer: We thank the Reviewer for their careful reading and constructive feedback. We have addressed each of their comments as follows:

1. Line 32: We agree with the reviewer that the phrase "overall success of the collective" was unclear and have completely revised the Abstract to remove this expression. The updated Abstract now provides a clearer and more concise summary of our findings.

Lines 124-126: We appreciate the reviewer’s suggestion to avoid using the terms "sitter neurons" or "rover neurons," as they could be misleading. We have revised this phrasing to "neurons of sitter/rover allele" to more accurately reflect the genetic context of our study.

Line 301: We have corrected the typo with "male-specific" to ensure accuracy and clarity in the text.

We hope these revisions address the Reviewer’s concerns and improve the overall quality of the manuscript. Thank you for your valuable input, which has helped us refine our work.

__Strengths and limitations of the study:______ This study presents a significant advancement in understanding the foraging gene's role in regulating mating behaviors through interval timing, and identifies the critical role of Pdfr-expressing neurons in the ellipsoid body for LMD. However, it does not fully explain how these neurons specifically modulate timing mechanisms. The lack of in-depth mechanistic exploration of how these neurons interact with other circuits involved in memory and decision-making leaves gaps in the understanding of the exact pathways influencing interval timing. Also, the study focuses more on LMD behaviors and the neural circuits involved, leaving the mechanisms underlying SMD comparatively underexplored.

__Answer:____ __We thank the reviewer for her/his thoughtful assessment of the strengths and limitations of our study. We agree that our work represents a significant advancement in understanding the role of the foraging gene in regulating mating behaviors through interval timing, particularly in identifying the critical role of Pdfr-expressing neurons in the ellipsoid body (EB) for long mating duration (LMD). However, we acknowledge that the initial manuscript did not fully elucidate how these neurons specifically modulate timing mechanisms or interact with other neural circuits involved in memory and decision-making.

In response to this feedback, we have conducted additional experiments and analyses, which are now included in the revised manuscript. Specifically, we identified fru-positive heart neurons that coexpress Pdfr and foraging, and we demonstrated their essential role in LMD using calcium imaging (CaLexA). These findings provide a more comprehensive mechanistic understanding of how foraging influences interval timing through cardiac activity, which is dynamically regulated by social context. This new evidence addresses the reviewer’s concern by offering a clearer picture of the neural and molecular pathways underlying LMD.

Regarding SMD behavior, we agree that it was comparatively underexplored in the initial manuscript. However, we have extensively studied SMD in other contexts, as highlighted in several of our previously published papers. These studies have investigated the sensory mechanisms, memory processes, peptidergic signaling, and clock gene functions associated with SMD (Zhang 2024,Zhang 2024,Sun 2024,Wong 2019,Kim 2024,Lee 2023). While the current manuscript focuses primarily on LMD, we will include a discussion of these findings to provide a more balanced perspective on the mechanisms underlying both LMD and SMD.

We believe these revisions address the Reviewer’s concerns and significantly strengthen the manuscript by providing a more detailed mechanistic understanding of foraging’s role in interval timing and mating behaviors. We are grateful for the Reviewer’s constructive feedback, which has helped us improve the depth and clarity of our study. Thank you for your valuable input.

__Advance:______ This study brings a novel perspective to the foraging gene, previously known for its role in regulating food-search behavior. It demonstrates that foraging is also involved in interval timing, a cognitive process integral to mating behaviors in Drosophila. This discovery challenges the assumption that foraging is solely related to foraging strategies, revealing a broader function in time-based decision-making processes.

Answer: We sincerely thank the reviewer for her/his insightful comments and for recognizing the novel contributions of our study. We are pleased that the reviewer highlighted how our work expands the understanding of the foraging gene, which was previously primarily associated with food-search behavior. By demonstrating its role in interval timing—a cognitive process critical to mating behaviors in Drosophila—we challenge the conventional assumption that foraging is solely related to foraging strategies. Instead, our findings reveal its broader function in time-based decision-making processes, particularly in the context of mating duration.

This discovery not only advances our understanding of the pleiotropic roles of foraging but also opens new avenues for exploring how genetic and neural mechanisms integrate temporal and environmental cues to regulate complex behaviors. We are grateful for the reviewer’s support and acknowledgment of the significance of our findings. Thank you for this valuable feedback.

__Audience:______ The study offers significant value to several specialized research communities, including behavioral genetics and evolutionary biology, especially those using the Drosophila model. This could inform future research on other behaviors that depend on precise timing and decision-making.

Answer: We sincerely thank the reviewer for her/his thoughtful comment and for recognizing the broad relevance of our study. We are pleased that the reviewer highlighted the significant value our work offers to be specialized research communities, particularly in behavioral genetics and evolutionary biology, as well as to researchers using the Drosophila model. By elucidating the role of the foraging gene in interval timing and its impact on mating behaviors, our findings provide a foundation for future research on other behaviors that rely on precise timing and decision-making. This study not only advances our understanding of the genetic and neural mechanisms underlying interval timing but also opens new avenues for exploring how similar processes may operate in other species or contexts. We hope our work will inspire further investigations into the interplay between genetic variation, neural circuits, and environmental cues in shaping adaptive behaviors. Thank you for your valuable feedback and for acknowledging the potential impact of our research.

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Referee #3

Evidence, reproducibility and clarity

Summary：

This manuscript explores the foraging gene's role in mediating interval timing behaviors, particularly mating duration, in Drosophila melanogaster. The two distinct alleles of the foraging gene-rover and sitter-demonstrate differential impacts on mating behaviors. Rovers show deficiencies in shorter mating duration (SMD), while sitters are impaired in longer mating duration (LMD). The gene's expression in specific neuronal populations, particularly those expressing Pdfr (a critical regulator of circadian rhythms), is crucial for LMD. The study further identifies sexually dimorphic patterns of foraging gene expression, with male-biased expression possibly in the ellipsoid body (EB) being responsible for regulating LMD behavior. The findings suggest that the foraging gene operates through a complex neural circuitry that integrates genetic and environmental factors to influence mating behaviors in a time-dependent manner. Additionally, restoring foraging expression in Pdfr-positive cells rescues LMD behavior, confirming its central role in interval timing related to mating.

Major comments：

1. The sexually dimorphic expression of the foraging gene is not convincing. Specifically, the lacZ signal in the male brain is not representative.
2. Key control genotypes are missing.
3. fru is not expressed in the EB, so the authors may need to reconcile their model in figure 5G.

Minor comments:

1. Line 32, what do you mean by "overall success of the collective"
2. line 124-126: I suggest not using "sitter neurons" or "rover neurons".
3. line 301, typo with "male-specific".

Significance

Strengths and limitations of the study:

This study presents a significant advancement in understanding the foraging gene's role in regulating mating behaviors through interval timing, and identifies the critical role of Pdfr-expressing neurons in the ellipsoid body for LMD. However, it does not fully explain how these neurons specifically modulate timing mechanisms. The lack of in-depth mechanistic exploration of how these neurons interact with other circuits involved in memory and decision-making leaves gaps in the understanding of the exact pathways influencing interval timing. Also, the study focuses more on LMD behaviors and the neural circuits involved, leaving the mechanisms underlying SMD comparatively underexplored.

Advance:

This study brings a novel perspective to the foraging gene, previously known for its role in regulating food-search behavior. It demonstrates that foraging is also involved in interval timing, a cognitive process integral to mating behaviors in Drosophila. This discovery challenges the assumption that foraging is solely related to foraging strategies, revealing a broader function in time-based decision-making processes.

Audience:

The study offers significant value to several specialized research communities, including behavioral genetics and evolutionary biology, especially those using the Drosophila model. This could inform future research on other behaviors that depend on precise timing and decision-making.

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Referee #2

Evidence, reproducibility and clarity

The authors nicely demonstrated that the Drosophila for gene is involved in the plastic LMD behavior that serves as a model for interval timing. For is widely expressed in the body, they have tentatively localized the LMD-relevant for functioning to the ellipsoid body of the central complex.

Major points:

Please clarify how a loss-of-function forS allele can be dominant in the presence of overactive forR allele? In the same vein, please clarify how does the forR/forS transgeterozygote supports your hypothesis that high levels of PKG activity disrupt SMD and low levels of it disrupt LMD?

Please consider removing lines 193-201 & Fig 3G,H, since abruptly and briefly returning to SMD could distract the reader and hinder the flow.

Please use more specific Gal4 drivers to identify the exact subset of the EB-RNs where for function is necessary for LMD. Please note that Taghert lab already identified Pdfr+ EB-RN subset, and in contradiction to your findings, demonstrated that Cry is expressed in these Pdfr+ EB neurons.

Please clarify how do you think for and Pdfr signaling molecularly interact in these neurons? Since your work doesn't implicate the for+ AL neurons, please remove lines 260-269.

Please clarify if the Pdfr+ for+ EB neurons are also fru+. The lacZ staining in Fig5A-B is atypical in having a mosaic-like pattern. Please replace the image.

Please consider removing lines 303-312, since this negative result may dilute your final conclusions without adding strong factual value.

Minor points: In the intro please mention other interval timing mechanisms and their underlying molecular mechanisms (e.g., CREB work of Crickmore lab). Please provide a better rationale for why you thought for is a good candidate for LMD? In line 124, when you start to talk about larval neurons - please specify which neurons you are referring to. In Fig 2E,G,H - 'glia' should be replaced with 'neurons'.

Referees cross-commenting

I find the two other reviewers' comments constructive & insightful. All the reviewers are unanimously urging the authors to change the lacZ figure (5) panel, and to provide a more integrated, coherent model indicating how For, Pdfr, and Fru genes are interacting through a specific neural circuit.

Significance

The comprehensive work is based on many behavioral assays, combined with genetics. It shows a novel function of the for gene. However, how for contributes to interval timing is not studied. Nevertheless, their work represents an incremental conceptual advance to the genetic underpinnings of interval timing. The work is primarily targeted to Drosophila neurogeneticists.

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Referee #1

Evidence, reproducibility and clarity

This study investigates the role of the foraging gene in modulating interval timing behaviors in flies, with a particular focus on mating duration. Using single-cell RNA sequencing and gene knockdown experiments, the research demonstrates the crucial role of foraging gene expression in Pdfr-positive cells for achieving longer mating duration (LMD). The study further identifies key neurons in the ellipsoid body (EB) as essential when the foraging gene is overexpressed, highlighting its specific influence on LMD. The findings suggest that a small subset of EB neurons must express the foraging gene to modulate LMD effectively.

Questions for Further Exploration:

(optional) Integration of Neuronal Subsets into a Pathway: The knockdown experiments indicate that a small subset of neurons must express the foraging gene to influence LMD. Could these neurons be integrated into a potential signaling pathway, or being treated as separate components within the brain circuit? How might this integration provide a more cohesive understanding of their role in LMD?

Genetic Considerations in Gal4 System Usage (Fig. 1D): In the study, the elavc155-Gal4 transgene, located on chromosome I, produces hemizygous males after crossing, while the repo-Gal4 transgene, located on chromosome III, results in heterozygous males. Is there any evidence suggesting that this genetic configuration could impact the experimental outcomes? If so, what steps could be taken to address potential issues?

Discrepancies in lacZ Signal Intensity (Fig. 5A): The observed discrepancies in lacZ signal intensity on the surface of the male brain have been attributed to the dissection procedure. Is it feasible to replace the current data with a new, more consistent dataset? How might improved dissection techniques mitigate these discrepancies?

Rescue Experiment Data (Fig. S2L): Could additional data be provided to demonstrate the rescue effect using the c61-Gal4 driver, similar to what was observed with the 30y-Gal4 driver? How would such data enhance the study's conclusions regarding the specificity and robustness of the foraging gene's role in LMD?

Referees cross-commenting

The two other reviewers provided important and valuable feedback on this topic. The LacZ figure (5) panel should be replaced as a control. The interaction between For, Pdfr, and Fru could form a circuit involved in fly mating behaviors, even as a hypothesis.

Significance

The research highlights the pivotal role of the foragine gene in regulating complex interval timing behaviors in Drosophila. This finding offers valuable insights into the interplay between genetics, environment, and behavior across species. Additionally, it suggests potential multifunctional implications for the ellipsoid body.

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The authors do not wish to provide a response at this time

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Referee #3

Evidence, reproducibility and clarity

In this study, Wasilewska and colleagues generated tmbim5-/- zebrafish line and demonstrated that tmbim5 loss of function leads to decrease in zebrafish size and induces muscle atrophy. Authors used immunohistochemistry to suggest that tmbim5-/- zebrafish shows reduced glycogen levels in muscle and liver. However, most of the immunohistochemistry is not quantitated and only qualitative differences are shown. Next, the authors measured mitochondrial calcium levels in the brain of tmbim5-/- zebrafish but there was no behavioral phenotype in the fish. It would have be better to measure mitochondrial calcium levels in the muscles of tmbim5-/- zebrafish as phenotype is muscle atrophy. Further, it is reported that the mitochondrial membrane potential and glycogen levels were perturbed in tmbim5-/- zebrafish.

Next, the authors generated a scl8b1-/- (a probable NCLX ortholog in zebrafish) zebrafish, which did not show any drastic phenotype. However, neither slc8b1 function nor the phenotype of scl8b1-/- zebrafish was well characterized. Further, authors created two double knockout zebrafish lines i.e. tmbim5-/-/mcu-/- and tmbim5-/-/slc8b1-/-. Interestingly, both these lines were viable and do not show any drastic phenotypes. The authors concluded that in these transgenic fishes compensatory and/or alternative mitochondrial Ca2+ mobilization pathways counterbalance the effects of silencing of these proteins.

Although it is an interesting study, the conclusions are not well supported with the data. At several places only qualitative images are shown and quantitative data is missing. Similarly, Ca2+ imaging in muscles of tmbim5-/- zebrafish is not performed. Finally, no molecular mechanism or molecular details are provided. Though Tmbim5's potential role in EMRE degradation is discussed, it is not experimentally investigated. The quality of the manuscript would significantly enhance if authors perform the suggested experiments.

Major Comments:

1. As a potential mechanism, Tmbim5's potential role in EMRE degradation is discussed but it is not experimentally investigated. It is very easy to test this hypothesis. If this is the case, it would be a very good contribution to the field.
2. On Page 16, authors state that slc8b1 does not constitutes the major mitochondrial Ca2+ efflux transport system. Authors should do calcium imaging experiments just like they did with tmbim5 and mcu double knockouts (data presented in Figure 4C) to make any comments on functioning of slc8b1 in mitochondrial Ca2+ transport. This is important because slc8b1 is only a predictive ortholog of human NCLX and it is not experimentally examined yet.
3. The data presented in Fig. 4C is very important but it is not fully explained and discussed in the results. Please discuss all the data sets presented in Fig4C in detail. As such, it is very difficult to follow and interpret the data.
4. In tmbim5-/- zebrafish, what happens to mitochondrial Ca2+ signaling in muscle as phenotype is muscle atrophy only?
5. Please validate the observation of decreased glycogen levels in tmbim5-/- fish by one more way. Only immunohistochemistry that too without quantitation is not convincing (Fig. 2E-H).

Minor Comments:

1. Authors state that tmbim5 loss of function leads to metabolic changes but the only data provided is decrease in glycogen levels. It would be helpful for the authors to focus comments specifically on the data presented in the manuscript to avoid potential over-interpretation.
2. While discussing Fig4., authors mention that Tmbim5 may act as a MCU independent Ca2+ uptake mechanism and therefore they crossed tmbim5 mutants with mcu KO fish. But from the data presented in Fig.3 and as concluded by the authors themselves tmbim5 mutants do not show changes in the mitochondrial Ca2+ levels. Authors may clarify this point.
3. Does tmbim5 contributes to mitochondrial Ca2+ uptake in presence or along with MCU. Further analysis of Fig4C may shed some light on this. Authors should test significance between tmbim5-/- and WT as well as between tmbim5-/- and tmbim5+/+ in mcu-/- background.
4. Please check the labeling on traces in Fig3D.
5. Please include quantitation of data presented in EV2E-F.
6. Please include quantitation of immunohistochemistry data presented in 2E-H.

Referee cross-commenting

Several comments are common between the reviewers highlighting that those experiments are critical. Secondly, I agree with the concerns raised by other two reviewers.

Significance

In this study, authors report couple of new transgenic zebrafish lines. However, further characterization of slc8b1-/- is required. This study reinforces the existing idea that there are very robust compensatory mechanisms that maintain mitochondrial Ca2+ homeostasis. While the work provides useful insights, it could benefit from a broader scope to provide substantial advancement to existing knowledge.

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Referee #2

Evidence, reproducibility and clarity

Summary: The work of Wasilewska et al. focusses on the MCU independent basal Ca2+ uptake mechanisms and the effects of MCU, NCLX, and TMBIM5 KO on Zebrafish Ca2+ homeostasis, mortality, anatomy and metabolism. The authors found evidence that tmbim5 potentially has a bidirectional mode of operation and is able to extrude Ca2+ from the matrix as well as transfer Ca2+ into mitochondria. Further, a reduced membrane potential in tmbim5-/- fish and altered metabolism was found. While the conclusion drawn are well argumented, a few points have to be addressed.

Major Points:

1. While all mitochondrial genes seem collectively reduced compared to control, it would be interesting to assess the mitochondrial mass and/or mitochondrial turnover rate in regard to e.g. mitophagy. The reduced membrane potential could lead to PINK1 accumulation on the outer mitochondrial membrane to mediate mitophagy leading overall to reduced mitochondrial count and mass.
2. The characterization of slc8b1-KO fish needs some improvement to facilitate a better understanding of the molecular interactions of slc8b1 and tmbim5. This would also greatly improve the understanding of the phenotypical characterization and behavioral response to CGP.
3. Functional Ca2+ measurements of the activity of slc8b1 gene product have to be done to ensure a KO phenotype. Especially in light of the surprising results presented in Figure 6A showing an effect of CGP on slc8b1-KO fish but not on tmbim5-KO fish I advise mitochondrial isolation to conduct mitochondrial basal and extrusion Ca2+experiments of slc8b1-KO fish, tmbim5-KO fish, and double KO-fish.

Minor Points:

The authors claim that mRNA levels of mitochondrial proteins involved in Ca2+ transport in tmbim5-/- are unaffected (Figure EV3). While the T-tests show no significant alteration, what happens if a 2-way ANOVA shows a more general effect revealed between WT and TMBIM5-/-?

Significance

This is a well-designed and carefully executed piece of work. The experimental design is thoughtfully elaborated, and the topic is worthy of investigation. The strengths of this study lie in translating our knowledge of TMBIN5 from single cells to organism and organ function. Moreover, the work provides important new information that will help the scientific community working on mitochondrial regulation AND muscle diseases to understand how ions coordinately regulate mitochondrial function.

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Referee #1

Evidence, reproducibility and clarity

Although the experimental approach is promising (see below), the results do not significantly expand our current understanding. This is partly due to the challenges of interpreting negative results, which are nonetheless worth reporting. Some of the conclusions and interpretations of the results could benefit from further clarification and contextualization to enhance their impact:

• Figure 1D: The distribution of fiber size in wt vs. Tmbim5-ko fish shows a notable difference limited to one size range. Can the authors clarify this observation? Could this indicate a switch in fiber type? Is there a correlation between this finding and the differential PAS staining?
• Figure 3: one of the advantages of the zebrafish model is its transparency, allowing for fluorescence imaging. Unfortunately, this proves to be impossible in the case of cepia2mt. The data provided by the authors show that the fluorescence of this probe does not vary following physiological stimuli. The only change is that induced by CCCP (Fig 3C-D), which according to the authors causes a discharge of mitochondrial calcium. However, the use of CCCP with GFP-based probes should be avoided, as the acidification caused by CCCP treatment leads to quenching of the fluorophore, resulting in a fluorescence decrease which is independent of Ca2+ levels. Although the experimental approach aims to detect dynamic changes in mitochondrial Ca2+ levels, the presented results in Figure 3 do not provide conclusive evidence to support this capability. While significant experimental effort is evident, these findings may require further validation or additional data to strengthen their impact. Alternatively, the authors could remove this Figure 3 and relevant text from the manuscript.
• Figure 6A: In my opinion, this dataset is impossible to understand. To my knowledge, the precise molecular target of CGP-37157 remains elusive. While CGP is often considered an NCLX inhibitor, this classification lacks definitive experimental support. Although CGP is known to inhibit mitochondrial Na+-dependent Ca2+ extrusion, direct binding of CGP to NCLX has yet to be conclusively demonstrated. With this in mind, the authors show that pharmacological intervention with CGP elicits a distinct phenotype in the fish model. While this effect appears to persist in SLC8B1-KO fish, it is absent in Tmbim5-KO fish, suggesting Tmbim5 as a potential molecular target for CGP. However, this interpretation is inconsistent with the following observations: i) CGP remains effective in Tmbim5/Slc8b1 double-KO fish and ii) Tmbim5-KO fish exhibit no discernible phenotype. A comprehensive explanation that reconciles these findings is sought.
• Figure 6B: according to the authors, the phenotype induced by CGP treatment is specific because a different substance with a completely different effect, CCCP, causes the same phenotype in both wt and Tmbim5-KO fish. Also in this case, the rationale and reasoning behind this experiment in not very evident. As I see it, CCCP blocks zebrafish motility because it is a metabolic poison, and its effect does not depend on any transporter.

Significance

The manuscript submitted by Wasilewska et al investigates the functional relationship between different mitochondrial calcium transporters using zebrafish as a model. The topic is of great interest. In the last 15 years, many mitochondrial calcium transporters have been identified. In some cases, their mechanism is not fully understood, such as in the case of TMBIM5, recently described by some as an H/Ca exchanger, or as a Ca channel by others. Furthermore, the functional relationship between different transporters has so far been studied in a partial and superficial way. I believe that this work is therefore of great interest because it aims to contribute to a fundamental problem that is still poorly studied. The idea of using zebrafish is interesting, as it is an organism that is easy to manipulate and phenotype, and because it is transparent, making it possible to use specific biosensors to characterize mitochondrial calcium dynamics, at least in principle. The paper therefore deserves attention.

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Reply to the reviewers

Reviewer #1 comments

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Major comments:

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- Neither data nor code was made available for review. There's only a mention of them being in Figshare with no link. As a consequence and a matter of principle, this study is not publishable without both public data and code. I would recommend using adequate repositories for data and code. Image data can be deposited in a public image data repository such as the BioImage Archive which would ensure that minimal metadata are provided and code could go to a public code repository (e.g. GitLab...) so that it is discoverable and eventual changes can be tracked and visible (for example should any bug be fixed after publication). Also consider depositing the models into the BioImage Model Zoo (https://bioimage.io).

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We will upload all the code used in the article in GitHub while image data will be deposited in BioImage Archive as suggested by the referee. Method section will be also rewritten.

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- The use of the term morphology is misleading. Like I expect most readers would, I understand morphology in this context as being related to shape. However, there is no indication that any specific type of information (like shape, texture, size/scale...) is used or learned by the described method. To understand what information the classifiers rely on, it would be interesting to compare with human engineered features extracted from the same ROIs.

All references to morphology in the text must be removed unless indication can be provided as to what type of information is used by the models.

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We understand the concern regarding the use of "morphology" and will revise the manuscript to be more precise. Instead of referring broadly to "morphology," we will specify "image-derived features" or "texture and structural features" where applicable.

Additionally, to address this concern directly, we have performed an analysis comparing our learned features to classical human-engineered features (such as texture and shape descriptors) to better understand what type of information is utilized by the model. These results will be incorporated into the revised manuscript.

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- The method should be described with more details:

- How are the window sizes to use determined? Are the two sizes listed in the methods section used simultaneously? What is the effect of this parameter on the performance?

- How are the ROIs determined? In a grid pattern? Do they overlap? i.e. how does the windowing function work?

- Predictions seem to be made at the ROI level but it isn't clear if this is always the case. Can inference be made at the level of individual cells?

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Window Sizes: We will clarify that the two window sizes were chosen based on empirical performance assessments. We will include a specific figure evaluating the impact of window size on classification performance, by expanding the analysis to multiple window sizes and number of training regions.

ROI Determination: We will describe thoroughly the ROI selection in the Method Section. We will include a comparison between overlapped and non-overlapping grid selection.

Inference at the Cell Level: While predictions are made at the ROI level (we will clarify the text), we will discuss an additional approach that aggregates ROI-level predictions into a final cell-level classification, which we will add as an optional post-processing step.

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- What would be the advantages of the proposed subcellular approach compared to learning to classify whole images?

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We will detail a comparison between subcellular and cellular or whole image classification; the main advantage of this subcellular technique (that will be remarked in the text) is the reduction in the number of images that are required to learn to classify cell types. Nevertheless, other advantages are the robustness to confluency variations (whole-image classification can be biased by confluency differences, while subcellular regions focus on individual cell features) and a fine-grained feature learning.

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- When fluorescent markers are used, the text isn't clear on what measures have been taken to prevent these markers from bleeding through into the brightfield image. To rule out the possibility that the models learn from bleed-through of the marker into the brightfield image, the staining should be performed after the brightfield image acquisition. Without this, conclusions of the related experiments are fatally flawed.

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__We appreciate this important point and confirm that all fluorescent staining was performed after brightfield image acquisition, ensuring that no fluorescence contamination influenced model training. We will have explicitly stated this in the Methods section. __

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- How robust are the models e.g. with respect to culture age and batch effects? Use of a different microscope is mentioned in the methods section. This should be shown, i.e. can a model trained on one microscope accurately predict on data acquired from a different microscope? Does mixing images from different sources for training improve robustness?

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We have used different cellular batches without any effect on accuracy. We will also include the experiment using another microscope, and we will add new data with/without combination of mixed images from different figures. In summary, we include a new supplementary figure that address the use of distinct and mixed cellular batches and microscopies in terms of accuracy and trained models.

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- Why not use the Mahalanobis distance in feature space? This would be the natural choice given that PCA has been selected for visualization and would allow to show uncertainty regions in the PCA plots. Could other dimensionality reduction methods show better separation of the groups? Why not train the network for further dimensionality reduction if the goal is to learn a useful feature space?

We appreciate this suggestion and will include a comparison of Mahalanobis distance-based classification with our existing approach. Regarding dimensionality reduction, we will test additional methods including t-SNE and UMAP as supplementary figures. Finally, while training a network specifically for dimensionality reduction is an interesting alternative, our current pipeline was focused on simplicity and the ample range of techniques that allow to address. However, we include include a discussion on potential future directions where such an approach could be explored.

Minor comments:

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- Make sure the language used is clear, e.g. The text describes the method as involving a transformation to black and white followed by thresholding. This doesn't make sense. What is meant by "the set of 300 genes was subjected to Gene Ontology"? Use percent instead of permille in the text for easier reading.

These minor changes will be addressed in the text, including the percent instead of permille as it was a common point suggested by the referees.

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- To provide more context, cite previous work that indicates that brightfield images contain exploitable information, e.g.

- Cross-Zamirski, J.O., Mouchet, E., Williams, G. et al. Label-free prediction of cell painting from brightfield images. Sci Rep 12, 10001 (2022). https://doi.org/10.1038/s41598-022-12914-x

- Harrison PJ, Gupta A, Rietdijk J, Wieslander H, Carreras-Puigvert J, et al. (2023) Evaluating the utility of brightfield image data for mechanism of action prediction. PLOS Computational Biology 19(7): e1011323. https://doi.org/10.1371/journal.pcbi.1011323

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We will cite these references in the introduction of the paper.

Reviewer #2

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Major comments:

• *

• • Place this study in context of previous studies that classify cell types. Here are two relevant recent papers, which could provide a good start for properly crediting previous work and placing your contribution in context: PMID: 39819559 (note the "Nucleocentric" approach) and PMID: 38837346. Please seek for papers that use label free for similar applications (which is the main contribution of the current manuscript).*
• *

We appreciate this suggestion (shared by reviewer #1) and we will include references to these and other relevant studies on label-free cell classification. We specifically discuss how our approach differs from the "nucleocentric" method in PMID: 39819559 and how our method complements existing work in label-free imaging. We will update both the Introduction and Discussion sections to reflect this improved contextualization.

• *

• Many experiments were performed, but we found it hard to follow them and the logic behind each experiment. Please include a Table summarizing the experiments and their full statistics (see below) and also please provide more comprehensive justifications for designing these specific experiments and regarding the experimental details. This will make the reading more fluent.*

• *

We will include a summary table in the Methods section that provides an overview of all experiments, detailing:

-The purpose of each experiment

-The dataset used

-The number of images/cells

-Objective used

-Cellular confluence

-Reference to BioImage Archive

-Model used (reference to Github)

-Technical / Biological replicates

-The main conclusions drawn

-Figure that presents the data

Additionally, we will revise the Results section to provide clearer justifications for each experiment, improving the logical flow of the manuscript.

• *

• The experiments, data acquisition and data reporting details are lacking. 10x objective is reported in the Results and 20x in the Methods. Please explain how the co-culturing (mixed) experiments were performed including co-culturing experiments with varying fractions of each cell type and on what data were the models trained on (Fig. 2F). Differential confluency experiments are not described in the Methods (and not on what confluency levels were the models trained on), this is also true for the detachment experiment. How many cells were acquired in each experiment (it says "20 and 40 images per cell line" but this is a wide range + it is not clear how many cells appear in each image)? How many biological/technical replicates were performed for each experiment? Please report these for each experiment in the corresponding figure legend and show the results on replicates (can be included as Supplementary). "Using a different microscope with the same objective produced similar results (data not shown)" (lines #370-371), please report these results (including what is the "different microscope") in the SI.*

• *

We will carefully review and expand the Methods section to provide complete details, as with the Table that we will prepare to address the previous comment and this one. In addition, the co-culturing experiments will explicitly describe how cell fractions were varied and how training data were generated for Fig. 2F. The differential confluency and detachment experiments will be fully described, including confluency levels used during model training. The secondary microscopy data will be added as part of a new figure that was commented for reviewer #1.

• *

• The machine learning details are lacking. The train-validation-test strategy is not described, which could be critical in excluding concerns for data leakage (e.g., batch effects) which could be a major concern in this study. It is not always clear what network architecture was used. What were the parameters used for training? Accuracy is reported in % (and sometimes in an awkward representation, 990‰). Proper evaluation will use measurements that are not sensitive to unbalanced data (e.g., ROC-AUC). What are the controls (i.e., could the accuracy reported be by chance?). Reporting accuracy at the pixel/patch level and not at the cell level is a weakness. Estimation of cell numbers (in methods) is helpful but I did not see when it was used in the Results - a better alternative is using fluorescent nuclear markers to move to a cell level (not necessary to implement if it was not imaged).*

• *

We will significantly expand the Machine Learning method and result sections, providing:

-A detailed description of the train-validation-test split strategy, (that explicitly rules out batch effects as a confounding factor). A clarification of the network architecture used for different tasks and their parameters (always the same one).

-We will expand the evaluation metrics, including ROC-AUC scores to account for class imbalances, and baseline models as controls, ensuring that model performance is not due to chance as a new supplementary figure.

- Accuracy will be reported to use percentage instead of permille as suggested by other referees.

- We will clarify the use of cell number estimation in the specific figures in which we use it, including new data in the first figure for the generalization of patch-to-cell estimation.

• *

• Downstream analyses lacking sufficient information to enable us to follow and interpret the results, please provide more information.*

• The PCA ellipses visualizations reference to previous papers. Please explain what was done, how the ellipses were calculated and from how much data? If they are computed from a small number of data points - please show the actual data. It would also be useful to briefly include the information regarding the representation and dimensionality reduction in the Results and not only in the Methods. No biologically-meaningful interpretation is provided - perhaps providing cell images along the PCs projections can help interpret what are the features that distinguish between different experimental conditions.*

• *

We will include a clearer explanation in methods as well as results for PCA and dimensionality reduction, as well as the use of Mahalanobis distance as another metric, another visualization for improved interpretation, and a supplementary figure related to tSNE reduction. We will update the figure for inclusion of real subcelullar images that help the biological interpretation of the results.

• *

• • How were the pairwise accuracies calculated? How did the authors avoid potential batch effects driving classification.*
• *

We have used different cellular batches without any effect on accuracy. In the new revised manuscript, we will clarify batch normalization techniques used in training and include additional control analyses ensuring that batch effects are not driving classification results (new figure as suggested by reviewer #1 with mixed and separate cellular batches).

• *

• • "suggesting that the current workflow can handle four cell lines simultaneously" (lines #126-127) - how were the cell lines determined for each analysis? We assume that the performance will depend on the cell types (e.g., two similar morphology cell types will be hard to distinguish). Fig. 2F is not clear: the legend should report a mixture of four cell types, and this should be translated to clear visualization in the figure panel itself: what do the data points mean? Where are the different cell types?*
• *

We will include additional experiments with other cell lines, and we will explicitly describe the rationale for cell line pairings, considering morphological similarities. Fig. 2F will be redesigned for clarity, ensuring data points are clearly labeled by cell type.

• *

• • Lines 232 and onwards use #pixels as a subcellular size measurement when referring to cell nucleus, cytoplasm and membrane, please report the actual physical size and show specific examples of these patches. This visualization and analysis of patch sizes should appear much earlier in the manuscript because it relates to the method's robustness and interpretability.*
• *

We will explicitly report patch sizes in microns and include a supplementary figure illustrating different subcellular regions to enhance interpretability.

• *

• • Analysis of co-cultured (mixed) experiments is not clear. Was the fluorescent marker used to define ground truth? Was the model trained and evaluated on co-cultures or trained on cultures of a single cell type and evaluated on mixed cultures? We assume that the models were still evaluated on the label-free data? "...obtain subcellular ROIs only from regions positive in the red channel. Using these labeled ROIs,.." (138-139) - shouldn't both positive and negative ROIs be used to have both cell types? What are the two quantifications in the bottom of Fig. 1E? Did the "labeled cells" trained another classifier for the fluorescent labels?*
• *

We will clarify both the method and results section regarding the co-culture experiment from the first figure. In that specific case, the model learned from positive ROIs in order to demonstrate that this approach can also be used from a mixed culture. In order to become clearer, we will transfer this experiment to a supplementary figure.

• *

• • Please interpret the results from Fig. 3C-D - should we expect to see passage-related changes in cells (that lead to deterioration in classification) or is it a limitation of the current study?*
• *

We will explicitly discuss whether passage-related changes affect cell morphology. In addition, we will include novel RNA-seq data comparing passage and batch effects, in order to correlate them to the image-based deterioration as part of the figure.

• *

• • In general, as we mentioned a couple of times. It would be useful to visualize different predictions (or use explainability methods such as GradCam) to try to interpret what the model has learned.*
• *

We will perform a GradCAM analysis, highlighting which subcellular regions contribute most to classification, improving interpretability.

• *

• • The correlation analysis between transcriptional profiles and morphological profiles is not clear. There are not sufficient details to follow the genetic algorithm (and its justification). What was the control for this analysis? Would shuffling the cells' labels (identities) and repeating the analysis will not yield a correlation?*
• *

We agree with the concern of the reviewer. We will expand the Methods section to clarify how the correlation was calculated, as well as the genetic algorithm. We will perform a control analysis using shuffled cell identities, trying to demonstrate that correlations do not arise by chance.

• *

• Please use proper scientific terms. For example, "white-light microscopy" and "live cell red marker".*

• *

We will change the text accordingly, making a global review of the manuscript.

• *

• This is a "Methods" manuscript and thus should open the source code and data, along with some examples on how to use it in order to enable others to replicate the results and to enable others to use it.*

• *

We acknowledge that our manuscript is more a ‘Methods’ manuscript instead of a general article (that it was conceptualized by us). Probably most of the critical points arose by the referees at the end are explained by this reason. We will deposit image data in the BioImage Archive with proper metadata, and we will published our code in GitHub as well as the models.

• *

• Please improve the figures. Fonts are tiny and in some places even clipped (e.g., Fig. 1D,E, Fig.2 E, E', and many more), some labels are missing (e.g., units of the color bar in Fig. 1B).*

• *

Figures will be redesigned accordingly.

• *

• Discussion. Please place this work in context of other studies that tackled a similar challenge of classifying cell types and discuss cons and pros of the different measurements. For example, there are clear benefits of using label-free data to reduce the number of fluorescent labels and enable long-term live cell imaging following a process without photobleaching and phototoxicity (Fig. 2G) but it is more difficult to interpret these differences in label-free image patches rather than fluorescently labeled single cells. One solution to bridge this gap that could be discussed is using silico labeling (PMID: 38838549).*

• *

The Discussion will be significantly expanded to compare our work with other methods, including in silico labeling (PMID: 38838549).

• *

• The idea of using the pairwise correlation distance of different cell types to model unseen cell types is interesting and promising. Why did these specific pairwise networks were used? How robust is this representation to inclusion of other/additional models?*

• *

As the referees are very interested in pairwise correlation distance, we will include a sensitivity analysis, testing alternative model selections to assess robustness.

Reviewer #3

• *

## General

• *

- It is often unclear if a sample in the particular experiment is a patch or a pixel. Please be more specific on this in the text.

• *

Manuscript will be rewritten for clarification of pixel/patch.

• *

- It is unclear which patch size was used and if it was consistent throughout the experiments. Please add this information.

• *

We will include a new figure with comparison between different patch sizes, as suggested by reviewer #1.

• *

- It is often unclear which data was used for training/validation and final readout. Did you do train/val splits? Did you predict on the same data or new samples? This should be stated more specifically.

• *

We will clarify in the Methods section the strategy of training/testing (90% - 10%, same data) with new samples used for final readout. All reported classification results come from that set, ensuring that the model was evaluated on unseen data.

• *

- Also, it is a little bit unclear what you mean by patch or by ROI or by region, please be more consistent and explain what you mean by adding definitions.

• *

We will standardize the use of these terms, leaving only ROI.

• *

- Please compare your method to other approaches and to baselines (see also our comment above).

• *

We will compare our approach with whole-image classification, showing that our subcellular approach provides better generalization. A new supplementary analysis will explore the feasibility of alternative feature extraction techniques and their relative performance. Several baselines will be incorporated in order to assess random accuracies (following the suggestions of other reviewers).

• *

- In general, if possible, please add more concrete examples of how you envision your method to be used in practice. There are general ideas presented in the discussion section, but we feel those could be substantiated by more concrete implementation suggestions.

• *

We will provide three specific case studies in the Discussion section, demonstrating how our approach can be applied in real-world scenarios:

-Drug Screening: Identifying cellular responses to drug treatments in high-throughput screening pipelines.

-Stem Cell Differentiation Monitoring: Tracking changes in subcellular morphology during differentiation to assess developmental stages.

-Cancer Cell Classification: Distinguishing between different subtypes of cancer cells in heterogeneous populations.

• *

Minor comments (grouped and summarized for clarification):

• *

General Clarifications & Wording Improvements

• *

Line 18: Clarify if the study is based on morphological features and specify the novelty (e.g., subcellular features).

Lines 25 & 29: The wording suggests that the workflow was extended before being validated. Improve clarity.

Line 92: Add a brief explanation of "subcellular region."

• *

We will clarify in the Introduction that our study is based on morphological features but specifically focuses on subcellular features, which distinguishes it from whole-cell analysis. We will rephrase the relevant sentences to make it clear that the workflow was first validated and then extended. We will provide a brief definition of "subcellular region" and ensure consistency throughout the manuscript.

• *

Experimental Setup & Methodological Details

• *

Lines 100-141: Clarify the use of validation and test sets, and discuss potential batch effects.

Line 113: Missing training details (loss function, data volume, epochs).

Line 117: Clarify if "pairwise classification" is meant.

Line 119: Accuracy should be reported in percent instead of permille.

Lines 136-141: Justify why two cell lines were mixed but only one was analyzed.

• *

We will add a clear explanation of the train-validation-test split, ensuring reproducibility and ruling out batch effects. Additional batch effect control experiments will be performed and included in Supplementary Figures as suggested by other reviewers.

We will include training details (e.g., loss function, number of epochs, data volume) in the Methods section and referenced it in the Results section for clarity. The terminology will be updated to "pairwise classification" where appropriate. We will report accuracy in percent (%) as suggested by other reviewers. The rationale for mixing two cell lines but analyzing only one is now explicitly stated: we used a mixed culture to simulate realistic conditions but focused on one cell type to test classification specificity. Nevertheless, following other reviewer suggestion this experiment will be placed in a supplementary figure in order to become clearer.

• *

Technical & Experimental Design Clarifications

• *

Line 105: Replace "white light microscopy" with "brightfield microscopy."

Line 107: Be specific about "transformation to black and white" and "contrast thresholding algorithm."

Line 125: Explain why performance dropped—did you try a larger network?

Line 133: Clarify how confluency was estimated.

• *

"White light microscopy" will be replaced with "brightfield microscopy." The thresholding method will be explicitly described, with a reference to the Methods section where details are provided. We will discuss the possible reasons for performance drop. Confluency estimation will be described, explaining that it was calculated using automated image segmentation and validated manually.

• *

Data Representation & Interpretation

• *

Line 143-158: Clarify the ground truth—was it based on dye labeling, thresholding, or human annotation?

Line 156: What is meant by "magnification"? Higher resolution? Different microscope? Crops?

Lines 163-166: Sudden switch to pixels instead of ROIs—explain why.

Line 191 & 192: If a strong correlation is claimed, include a statistical test.

Lines 211-214: If differences are claimed, add a quantitative analysis.

Lines 396-404: Clarify how the test set was chosen and what "in situ prediction" means.

Lines 407-409: What do you mean by "binarizing the image"? What threshold was used?

• *

We will clearly explain terms like “ground truth”, "magnification", “in situ prediction” and ‘binarization”. Consistent terminology will be ensured, regarding ROIs throughout the text. Statistical analyses will be added to correlation results and morphological feature comparisons to support claims.

• *

Biological Interpretation & Feature Space Analysis

• *

Line 226-228: You show classification in feature space but not whether distances in feature space correlate with real-world differences between cell types.

Line 234-236: What do you mean by "detect potentially more informative subcellular regions"?

Line 302-303: The claimed application (estimating cell types in an unseen culture) was not shown—please add an experiment.

• *

We now include an experiment comparing three cell types, where two are closely related and one is more distinct, to test if feature space distance corresponds to real-world differences. The concept of "informative subcellular regions" will be rephrased. We will add an experiment demonstrating the ability of our model to estimate the number of cell types in an unseen culture, as suggested.

• *

Figure & Visualization Improvements

• *

• Improve figure readability (tiny fonts, clipped text).*

Line 653-655: Show actual data points in PCA ellipses, not just ellipses.

Line 672-677: Add a quantification of performance differences between different categories.

• *

All figures will be revised for better readability, ensuring that text is legible, axes are labeled, and color bars are clear. We will overlay data points onto PCA ellipses for better visualization of feature distribution, as suggested by other reviewers. Performance differences between different experimental conditions will be quantified, with statistical comparisons provided.

• *

Model Training & Data Reproducibility

• *

Lines 386-392: Add exact details on model architecture, loss function, number of images used per experiment.

• *

A complete breakdown of model architecture, loss function, training set size, and validation details will be included in the Methods section, ensuring full reproducibility.

Dimensionality Reduction & Feature Space Interpretation

• *

Line 438-439: Consider using UMAP or t-SNE in addition to PCA. Report variance explained by PCA components.

Line 439-440: Provide more details on how eigenvectors were used to calculate ellipses.

Line 442-443: Clarify which correlation method was used.

• *

We will include t-SNE visualizations in Supplementary Figures and report the variance explained by PCA components, as well as Mahalanobis distance, as suggested by other reviewers. The eigenvector-based ellipse calculation will be described in more detail in the Methods section, and the specific correlation metric used will be explicitly stated.

• *

Code & Data Accessibility

• *

Line 491: Provide a direct URL to the code and data. Consider using GitHub for code and BioImage Archive for data.

• *

We will include the code to GitHub and image data to the BioImage Archive, following the reviewers recommendation, with direct URLs.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

Summary

The authors present a computational workflow that automatically classifies patches of transmission microscopy images of cultured cells into different cell types.

Comments to the Manuscript

General

• It is often unclear if a sample in the particular experiment is a patch or a pixel. Please be more specific on this in the text.
• It is unclear which patch size was used and if it was consistent throughout the experiments. Please add this information.
• It is often unclear which data was used for training/validation and final readout. Did you do train/val splits? Did you predict on the same data or new samples? This should be stated more specifically.
• Also, it is a little bit unclear what you mean by patch or by ROI or by region, please be more consistent and explain what you mean by adding definitions.
• Please compare your method to other approaches and to baselines (see also our comment above).
• In general, if possible, please add more concrete examples of how you envision your method to be used in practice. There are general ideas presented in the discussion section, but we feel those could be substantiated by more concrete implementation suggestions.

Specific

Line 18

• Isn't this study also based on morphological features? Eventually, you could be more specific what the novelty is, it might be the fact that your features are subcellular?

Lines 25 & 29

• In general one would expect a workflow to be validated first and extended afterwards. You could improve the wording here to make this clear for the reader.

Line 92

• Please add a short explanation of what is meant by "subcellular region".

Lines 100-141

• Did you validate the classification results with a validation and test set? Maybe with cross validation? Please add more details on how this was done.
• It could be that the model exploits batch effects of different imaging runs (e.g. different overall intensity in patches). It would be nice if this could be checked by an additional experiment.

Line 105

• "white light microscopy" is an unusual term, can you be more specific, e.g. bright-field?

Line 107

• It is unclear what a "transformation to black and white" and a "contrast thresholding algorithm" are, please be more specific (and potentially point the reader to a corresponding Methods section).

Line 113

• How does training work? Which loss is used? How much data? How many epochs? ... All of this information is missing which makes the study non-reproducible. Please add this here or point to an appropriate method section.

Line 117

• Do you mean pairwise classification?

Line 119

• It is unusual to use permille as a unit to report, percent is more common
• Also, it is unclear if accuracy is the correct read-out here, are all the data sets balanced?
• more information about the data sets could be added in a methods section and the decision to use accuracy as a measure could be explained

Line 121

• this hypothesis was never stated before, please explain this to the reader first and then check your hypothesis by experiments

Line 125

• do you have a hypothesis why the performance dropped? Did you for example try a larger network?

Line 133

• how is the confluence estimated?

Lines 136-141

• It is unclear why two cell lines were mixed when only data of one of them is used for analysis afterwards. Could you explain this in more detail or specify why this approach is used?

Lines 143-158

• We think you are trying to establish a ground truth here. Unfortunately, there are two things mixed here, the labeling with an additional dye combined with thresholding and human annotation. It is unclear which is considered the ground truth or if both are considered true. Could you explain this in more detail or be more specific?

Line 156

• What do you mean by magnification? Images with a higher resolution (different microscope with higher magnification)? Crops of the same data? Something else? Could you explain this in more detail or be more specific?

Lines 163-166

• Suddenly you talk about pixels instead of ROIs, where are they coming from? Maybe point the reader to a method section and explain the switch here.
• Also, why is the pixel size cell line dependent, didn't you use the same microscope for all of them? Could you define what you mean by pixel size?
• You say you compared different cell lines, how is this summarized in one plot? Please explain in more detail.

Lines 177-214

• Again, it is unclear which data was used for training, validation and analysis in the end. Please add this.

Lines 191 & 192

• If you claim a strong correlation please add a statistical analysis that shows this.

Lines 211-214

• If you claim these differences you should add a quantitative read-out with a statistical analysis. You could use distances in your representation space as a basis for this.

Lines 226-228

• What is shown here is that the morphological features can be used to classify cell types. You show that these classes are distant in feature space. But you don't show any correlation between the distance in feature space and the distance in real space (a.k.a how different the cell types are). It would be nice to have an experiment with at least three classes where 2 are closer to each other than to the third one. This would be a stronger claim that your features actually capture meaningful distances/differences.

Lines 234-236

• What do you mean by "detect potentially more informative subcellular regions within the cell"? Please describe in more detail what the training task was for the model and how you interpret the results.

Lines 296-298

• It is a little bit confusing what you mean here since you do train a network for each pair of cell lines. What you are describing is a foundation model. Please explain in more detail what you mean.

Lines 302-303

• The application you are claiming here was never shown in the experiments. Could you please add this experiment where a model estimates the number of cell types in an unseen culture.

Line 323

• Could you please elaborate how you would identify "specific cellular compartments"?

Lines 323-326

• Are there other studies that suggest that such malignant cells show features that are recognizable by your approach?

Lines 342-365

• Did you use biological replicates? This would be interesting and also a nice way to validate your models.

Lines 369-373

• Why do you claim that a similar microscope produces similar images? Can you give more details why this is relevant. And if that is the case it would be nice to show them. Maybe in some supplementary material.
• How big is one image? How many cells can you see in one image? What is the resolution? What is the pixel size? ... Also, for which experiment did you use how many images? Please add all these details.
• Also, please show some example images to make it clear for the reader what the data looks like. Could be done in supplementary material.

Lines 386-392

• Again, please add details. As it is right now the study is not reproducible. How many images were used for each experiment? How many for training, validation, analysis? Give the exact architecture of the model used. Which loss was used for training?

Lines 396-404

• Please add more details and clarify. How was the test set chosen? What do you mean by "in situ prediction"? What do you mean by "running ROIs"? What do you mean by "if the cell type was predicted to be more than 50 % of the times"? Was the human annotation or the life cell marker used for the final accuracy? Humans are never unbiased.

Lines 404-407

• This sounds like the ground truth for a segmentation task - is this what you mean? Since you are solving a classification task this is confusing. Please clarify.

Lines 407-409

• This sentence is confusing and it is unclear what was done. Please clarify. Do you mean the image was binarized? If yes, which threshold was used? What do you mean by "accuracy was estimated as with the prediction"? The accuracy should be estimated by comparing the prediction to the ground truth.

Lines 413-422

• Please give more details. What are these specific numbers? What do you mean by "pixel size of each cell type"? The pixel size is metadata given by the microscope/image and should not be cell type specific. We also did not understand what is meant by "fitting the percentages" and what the aim of this is. Please consider rewriting this to make it more clear.

Lines 426-430

• Please provide the oligo sequence.

Line 435

• Please consider rephrasing to: "the output of the last max pooling layer"

Lines 438-439

• It would be interesting to visualize the data based on a different dimensionality reduction algorithm that is non-linear like UMAP or t-SNE. If you use PCA, could you give a measure on how much of the variance is captured in the first two PCs.

Lines 439-440

• Please give some more details on how you use eigenvectors to calculate ellipses.

Lines 442-443

• Please give more details on which correlation you calculated.

Lines 447-457

• It would be nice if you could rephrase this a little bit to make clear that the preprocessing itself stays the same but you basically establish different data sets by separating ROIs based on their distance to the closest nucleus.

Lines 455-457

• Please be more precise here. The networks still learn to classify patches and are not aware of the fact that these ROIs fall in a certain category. You exploit this fact afterwards for your analysis.

Lines 464-474

• Please add more details why this experiment is done. Why is a genetic algorithm needed? Could not the same analysis be done on the original transcriptomics data?

Line 486

• Do you mean technical or biological replicates? If that is the case, could you please clearly state that you report mean values and also give the standard deviation.
• "test" should be experiment

Line 491

• Could you please provide a URL to the code and the data.
• Also, it is common practice to upload code to GitHub and image data to the Bioimage Archive. Please consider doing this.

Lines 627-633

• Panel A could be improved by making the ROIs larger since it is hard to see them.
• Also, please make sure that it is clear that one ROI at a time is given to the model.

Line 638

• What does "magnification" mean here - see above.
• Why do you not show the same region?

Line 640-642

• This basically shows that your approach is as good as simple thresholding. What do you want to show with this?

Lines 643-644

• Please clarify. It is unclear what percentage you present here.

Lines 652-653 (Fig. 3C)

• Please clarify. It is unclear what statistical analysis was performed here and to what end.

Lines 653-655

• It would be interesting to see not only the ellipses but also the actual data points plotted.

LInes 658-661

• Please add a statistical analysis of what you want to show here.
• It is clear that the correlation is not as clear for higher values on the x-axis, why is this?

Lines 661-662

• Please clarify. It is unclear what statistical analysis was performed here.

Lines 662-664

• Please add a statistical analysis of what you want to show here.

Lines 672-677

• Please also plot the actual data points
• Also, if possible it would be nice to quantify the differences in performance between the different categories.

Code and data availability

We could not see how to access example image data. To our best knowledge it is current best practice to upload image data to the Bioimage Archive: https://www.ebi.ac.uk/bioimage-archive/

Specifically for this kind of study the reader should have access to the training and test data that was used to train the classifier.

We also could not see how to reproduce the analysis. To our best knowledge it is current best practice to make all code publicly accessible, e.g. in a GitHub repository.

Please see https://www.nature.com/articles/s41592-023-01987-9 for general guidelines of publishing bioimage data and analysis.

Significance

The ability to use label free microscopy for extracting biologically meaningful information is very valuable and it is very interesting to learn that simple transmission microscopy contains enough information to reveal cell types. In this study the authors trained a neural network for this task and demonstrated that it works with rather high accuracy.

In its current form, we could not access the data nor the code. We could thus not fully judge the quality of the presented work. For a future revision, access to data and code will be essential.

We also found it difficult to judge how difficult the classification task is, because the size of the cells in the current figures does not allow one to see texture detail in the images. Since we did not manage to access the image data, we could not assess whether the classification task is very hard (and indeed requires an AI approach) or whether the differences are rather obvious and could be quantified with classical image analysis. To enable the interested reader to better assess this important information we would like to recommend to (a) add figures that allow one to better see the cells and their texture, at least for some of the cell types, and (b) provide easy download access to the raw image data.

Along those lines, we think it would be very interesting to actually test whether training a neural network is required or whether other methods would yield similar results. For instance, we would recommend to simply compute the mean and variance of the intensities in each patch and check whether this information also can perform some of the classification tasks. Depending on the outcome of this analysis this could be either added to some of the main figures of the article or to the supplemental material.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Automatic classification of single cell types and cell states in heterogeneous mixed cell populations has many applications in cell biology and screening. The authors present a machine learning workflow to distinguish between different cell types or cell states from label-free microscopy image patches of subcellular size. The authors evaluate their ability to identify different cell types and molecular profiles on many applications.

Major comments:

The application of classifying cell type and states from label-free data is promising and useful, but this manuscript requires major rewriting to enable us comprehensive assessment. Specifically, provide all technical details necessary for its evaluation, improve clarity and justification for the methodology used and the results obtained, and to better place this study in context of other studies in the field. Two crucial points are excluding the concern of the possibility that batch effects are contributing to the classification results and providing stronger evidence for a link between transcriptional and morphological profiles. Some efforts to interpret the classification decision making could help understand what morphological information was used for classification and reduce the concerns for the model using non-biologically meaningful information for the classification (e.g., illumination changes due to batch effects). Finally, making the source code and data publicly available would be important to enable others to apply the method (code) and to benchmark other methods (data).

1. Place this study in context of previous studies that classify cell types. Here are two relevant recent papers, which could provide a good start for properly crediting previous work and placing your contribution in context: PMID: 39819559 (note the "Nucleocentric" approach) and PMID: 38837346. Please seek for papers that use label free for similar applications (which is the main contribution of the current manuscript).
2. Many experiments were performed, but we found it hard to follow them and the logic behind each experiment. Please include a Table summarizing the experiments and their full statistics (see below) and also please provide more comprehensive justifications for designing these specific experiments and regarding the experimental details. This will make the reading more fluent.
3. The experiments, data acquisition and data reporting details are lacking. 10x objective is reported in the Results and 20x in the Methods. Please explain how the co-culturing (mixed) experiments were performed including co-culturing experiments with varying fractions of each cell type and on what data were the models trained on (Fig. 2F). Differential confluency experiments are not described in the Methods (and not on what confluency levels were the models trained on), this is also true for the detachment experiment. How many cells were acquired in each experiment (it says "20 and 40 images per cell line" but this is a wide range + it is not clear how many cells appear in each image)? How many biological/technical replicates were performed for each experiment? Please report these for each experiment in the corresponding figure legend and show the results on replicates (can be included as Supplementary). "Using a different microscope with the same objective produced similar results (data not shown)" (lines #370-371), please report these results (including what is the "different microscope") in the SI.
4. The machine learning details are lacking. The train-validation-test strategy is not described, which could be critical in excluding concerns for data leakage (e.g., batch effects) which could be a major concern in this study. It is not always clear what network architecture was used. What were the parameters used for training? Accuracy is reported in % (and sometimes in an awkward representation, 990‰). Proper evaluation will use measurements that are not sensitive to unbalanced data (e.g., ROC-AUC). What are the controls (i.e., could the accuracy reported be by chance?). Reporting accuracy at the pixel/patch level and not at the cell level is a weakness. Estimation of cell numbers (in methods) is helpful but I did not see when it was used in the Results - a better alternative is using fluorescent nuclear markers to move to a cell level (not necessary to implement if it was not imaged).
5. Downstream analyses lacking sufficient information to enable us to follow and interpret the results, please provide more information.

a. The PCA ellipses visualizations reference to previous papers. Please explain what was done, how the ellipses were calculated and from how much data? If they are computed from a small number of data points - please show the actual data. It would also be useful to briefly include the information regarding the representation and dimensionality reduction in the Results and not only in the Methods. No biologically-meaningful interpretation is provided - perhaps providing cell images along the PCs projections can help interpret what are the features that distinguish between different experimental conditions.

b. How were the pairwise accuracies calculated? How did the authors avoid potential batch effects driving classification.

c. "suggesting that the current workflow can handle four cell lines simultaneously" (lines #126-127) - how were the cell lines determined for each analysis? We assume that the performance will depend on the cell types (e.g., two similar morphology cell types will be hard to distinguish). Fig. 2F is not clear: the legend should report a mixture of four cell types, and this should be translated to clear visualization in the figure panel itself: what do the data points mean? Where are the different cell types?

d. Lines 232 and onwards use #pixels as a subcellular size measurement when referring to cell nucleus, cytoplasm and membrane, please report the actual physical size and show specific examples of these patches. This visualization and analysis of patch sizes should appear much earlier in the manuscript because it relates to the method's robustness and interpretability.

e. Analysis of co-cultured (mixed) experiments is not clear. Was the fluorescent marker used to define ground truth? Was the model trained and evaluated on co-cultures or trained on cultures of a single cell type and evaluated on mixed cultures? We assume that the models were still evaluated on the label-free data? "...obtain subcellular ROIs only from regions positive in the red channel. Using these labeled ROIs,.." (138-139) - shouldn't both positive and negative ROIs be used to have both cell types? What are the two quantifications in the bottom of Fig. 1E? Did the "labeled cells" trained another classifier for the fluorescent labels?

f. Please interpret the results from Fig. 3C-D - should we expect to see passage-related changes in cells (that lead to deterioration in classification) or is it a limitation of the current study?

g. In general, as we mentioned a couple of times. It would be useful to visualize different predictions (or use explainability methods such as GradCam) to try to interpret what the model has learned.

h. The correlation analysis between transcriptional profiles and morphological profiles is not clear. There are not sufficient details to follow the genetic algorithm (and its justification). What was the control for this analysis? Would shuffling the cells' labels (identities) and repeating the analysis will not yield a correlation? 6. Please use proper scientific terms. For example, "white-light microscopy" and "live cell red marker". 7. This is a "Methods" manuscript and thus should open the source code and data, along with some examples on how to use it in order to enable others to replicate the results and to enable others to use it. 8. Please improve the figures. Fonts are tiny and in some places even clipped (e.g., Fig. 1D,E, Fig.2 E, E', and many more), some labels are missing (e.g., units of the color bar in Fig. 1B). 9. Discussion. Please place this work in context of other studies that tackled a similar challenge of classifying cell types and discuss cons and pros of the different measurements. For example, there are clear benefits of using label-free data to reduce the number of fluorescent labels and enable long-term live cell imaging following a process without photobleaching and phototoxicity (Fig. 2G) but it is more difficult to interpret these differences in label-free image patches rather than fluorescently labeled single cells. One solution to bridge this gap that could be discussed is using silico labeling (PMID: 38838549).<br /> 10. The idea of using the pairwise correlation distance of different cell types to model unseen cell types is interesting and promising. Why did these specific pairwise networks were used? How robust is this representation to inclusion of other/additional models?

Significance

Automated classification of cell types and cell states in mixed cell populations using label-free images has important applications in academic research and in industry (e.g., cell profiling). This paper applies standard machine learning toward this technical goal, and demonstrates it on many different experimental systems, exceeding the common standard in terms of quantity and variability, and with the potential of being a nice contribution to the field. However, we were not able to properly evaluate these results due to lacking experimental and methodological details as detailed above and thus can not make a strong point regarding validity and significance before a major revision. Our expertise is in computational biology, and specifically applications of machine learning in microscopy. We are not familiar with the specific cell types, states and perturbations used in this manuscript.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This paper presents a method to classify cells in brightfield images using information from subcellular regions. The approach consists in first thresholding a brightfield image then splitting the resulting binary image into small ROIs which are then fed to a CNN-based classifier. The authors demonstrate application to the identification of cell types in pure cultures and in cultures with mixed types. They then show that features learned by the classifier correlate with expression of cell type-specific genes and explore what information can be learned from networks trained on subcellular regions selected based on distance from the nucleus. The authors conclude that subcellular ROIs extracted from brightfield images contain useful information about the identity and state of the cells in the image.

Major comments:

• Neither data nor code was made available for review. There's only a mention of them being in Figshare with no link. As a consequence and a matter of principle, this study is not publishable without both public data and code.
• I would recommend using adequate repositories for data and code. Image data can be deposited in a public image data repository such as the BioImage Archive which would ensure that minimal metadata are provided and code could go to a public code repository (e.g. GitLab...) so that it is discoverable and eventual changes can be tracked and visible (for example should any bug be fixed after publication). Also consider depositing the models into the BioImage Model Zoo (https://bioimage.io).
• The use of the term morphology is misleading. Like I expect most readers would, I understand morphology in this context as being related to shape. However, there is no indication that any specific type of information (like shape, texture, size/scale...) is used or learned by the described method. To understand what information the classifiers rely on, it would be interesting to compare with human engineered features extracted from the same ROIs. All references to morphology in the text must be removed unless indication can be provided as to what type of information is used by the models.
• The method should be described with more details:
• How are the window sizes to use determined? Are the two sizes listed in the methods section used simultaneously? What is the effect of this parameter on the performance?
• How are the ROIs determined? In a grid pattern? Do they overlap? i.e. how does the windowing function work?
• Predictions seem to be made at the ROI level but it isn't clear if this is always the case. Can inference be made at the level of individual cells?
• What would be the advantages of the proposed subcellular approach compared to learning to classify whole images?
• When fluorescent markers are used, the text isn't clear on what measures have been taken to prevent these markers from bleeding through into the brightfield image. To rule out the possibility that the models learn from bleed-through of the marker into the brightfield image, the staining should be performed after the brightfield image acquisition. Without this, conclusions of the related experiments are fatally flawed.
• How robust are the models e.g. with respect to culture age and batch effects? Use of a different microscope is mentioned in the methods section. This should be shown, i.e. can a model trained on one microscope accurately predict on data acquired from a different microscope? Does mixing images from different sources for training improve robustness?
• Why not use the Mahalanobis distance in feature space? This would be the natural choice given that PCA has been selected for visualization and would allow to show uncertainty regions in the PCA plots. Could other dimensionality reduction methods show better separation of the groups? Why not train the network for further dimensionality reduction if the goal is to learn a useful feature space?

Minor comments:

• Make sure the language used is clear, e.g.
• The text describes the method as involving a transformation to black and white followed by thresholding. This doesn't make sense.
• What is meant by "the set of 300 genes was subjected to Gene Ontology"?
• Use percent instead of permille in the text for easier reading.
• To provide more context, cite previous work that indicates that brightfield images contain exploitable information, e.g.
• Cross-Zamirski, J.O., Mouchet, E., Williams, G. et al. Label-free prediction of cell painting from brightfield images. Sci Rep 12, 10001 (2022). https://doi.org/10.1038/s41598-022-12914-x
• Harrison PJ, Gupta A, Rietdijk J, Wieslander H, Carreras-Puigvert J, et al. (2023) Evaluating the utility of brightfield image data for mechanism of action prediction. PLOS Computational Biology 19(7): e1011323. https://doi.org/10.1371/journal.pcbi.1011323

Referees cross-commenting

I support comments from reviewers 2 and 3 around the lack of sufficient details fro interpretability and reproducibility. Some of the necessary information could be communicated through well documented re-usable code and computational workflows as well as properly documented data sets.

Jean-Karim Hériché (heriche@embl.de)

Significance

This is an interesting study that adds to a growing body of evidence showing that information contained in brightfield images can be usefully exploited, potentially replacing the expensive and time-consuming use of fluorescent markers and is therefore of interest to a broad audience of cell biologists.

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #2

Evidence, reproducibility and clarity

Critique

In this manuscript, the authors examine the biochemistry of two protein domains that are, on the basis of sequence similarity, predicted to function autonomously as binders of histone H3 tails or methylated DNA. They present solid data to suggest that neither domain in fact has this function, but that they act as protein interaction domains that form a heterodimer mediated by the presence of a zinc ion (two ligands from each protein).

In the first part of the Results, the authors note that ASXL PHD doesn't contain aromatics that are characteristic of methylated lysine binding. I would just note that they don't mention at this point that some PHDs bind unmethylated H3 - and that aromatics are not required for that binding activity. The lack of H3K4me3-binding aromatics doesn't at all make a case the domain doesn't bind histones. The lack of the Ala1 binding residues does make this case, but that's separate...

Anyway, they then go on to show convincingly by ITC that ASXL doesn't bind the N-terminal H3 tail - unmodified or methylated. They also show modified-H3 ELISA data that make the same point (though it would be nice to know what the points were on the single ELISA that exceeded 2 SDs, even if they weren't reproduced - especially given there is a lot of scatter in the ELISA). I note in passing that I don't think I could find a Supp table 1).

The authors then use AF3 to show that what would typically be the N-terminal zinc-binding site is not well predicted by the software (and the site ends up being square planar), suggesting that something might be amiss. (They were also unable to obtain an experimental structure.) It would have been helpful to gain more insight into what led them to the conclusion that the protein forms a weak homodimer based on the NMR data. Typically, it can be challenging to determine by NMR whether a dimer is forming or if non-specific soluble aggregates or other factors are contributing to line broadening.

Next, the authors show nicely that MBD5/6 - two proteins shown in a previous paper to form a complex with ASXL - are predicted by AF3 to dimerize with ASXL - and form an intermolecular zinc-binding module in doing so. This is a nice result and there are very few examples of this in the literature (eg the zinc hook formed by Rad50 proteins). They confirm the zinc-binding prediction biochemically. They also show an HSQC of the complex (both subunits 15N labelled) and they count what they say is roughly the right number of peaks. To me, the lineshapes in the HSQC look good and, as the authors say, there are no clearly disordered resigies. I do make some additional comments below about the NMR data - suggesting what I think would be some valuable follow-up experiments. Overall, this study is a nice piece of biochemistry that recognizes an anomaly in the classification of examples of not one, but two, domain types well-known in the field of epigenetics. Going further than that, they not only show that the domains are mis-annotated but also demonstrate what their real function is and put forward a very likely model for their structure.

The work is a good combination of AF based computational prediction with corroborating biochemistry and the experiments look technically well done to me. It is definitely of publishable quality and represents an advance in our understanding both of the particular proteins that they have studied and of the quirkiness of protein structure in general - there is always a new wrinkle to be discovered. I would make a couple of comments and suggestions that I think could improve the manuscript. I also have a number of minor comments below.

Regarding the NMR data, the HSQC of the heterodimer that they show has nice lineshapes, as I mentioned above. However, the spectrum looks a little curious and closer inspection makes me wonder whether we are actually looking at two or more species with related structures. Many of the peaks appear to have a second peak nearby and it looks to me as if there is a consistent intensity ratio between the two forms (maybe 3:1 or 4:1?). It would be beneficial to explore this further, as understanding this aspect more clearly could have important implications for their analysis. I think the overall conclusions would probably still hold, but there would be far fewer signals than expected, suggesting likely some sort of slow-intermediate conformational exchange process that is giving two signals for a chunk of the residues and giving no signals for some of the others. Some comparison with the HSQC of the PHD domain alone might be helpful here.

Some simple backbone triple resonance experiments would also be very helpful. Not only would they allow assignments to be made - and therefore a comparison of predicted secondary structure with the AF3-predicted fold - but also would help confirm whether there are two conformers. Often in these cases, the Ca and Cb chemical shifts for an exchanging system are much more similar than the HN and 15N signals, and it is therefore often clear that two peaks are actually the same residue in two different conformations. ZZ exchange experiments could help too, though these can sometimes be challenging.

Finally, it would be reassuring to see SEC-MALS data for the heterodimer. Given that the interaction is mediated by covalent bonds, I'd expect to see a dimer molecular weight. It would also be reassuring to see a nice-looking SEC peak - and it would be useful data to have as part of the interrogation of possible chemical exchange mentioned above.

Specific points

• Intro: A nucleosome wraps less than two turns of DNA
• I'm not a fan of this sentence: "The quaternary structure of the nucleosome forces the N- and C-terminal tails from histone proteins to protrude for covalent chemical modification". Not clear to me that the nucleosome 'forces' the tails to protrude...
• The authors state that "Attachment of ubiquitin to histone H2A at K119 limits gene expression" - but they don't give any context. Which genes are limited in their expression? Nearby ones? Ones on the same chromosome? Just the gene that has an H2A-Ub in a specific position?
• No need for capital Z in zinc.
• "After purification, the protein solution was concentrated to 42.5 uM". The authors would not know the protein concentration to three significant figures. They would be unlikely to know it to 2 figures, given the inherent uncertainty in protein concentration measurement.
• I like that they show purification gels for their proteins - almost no one does...
• The authors state that "The domain, however, proved too small and flexible to produce crystals". However, the authors don't (as far as I can see) have any data to support the notion that either of these was the reason that no suitable crystals were obtained. I bet there are plenty of large, well-ordered proteins that haven't been able to have their crystal structures determined...
• Supp fig 3 - the authors could label N and C termini.
• "The 1H15N HSQC spectra revealed the presence of about 95 backbone amide peaks, which is in agreement with the overall protein complex." The authors could tell us how many peaks are expected, to make the comparison more useful! (and it should be spectrum).
• "and form a tight, stable protein complex". Too many adjectives... The data don't show that the complex is tight, nor really say anything about its stability (is the Tm 35 degrees or 95 degrees - can't really say). The data do show that the two proteins form a complex.
• I'd say that 633 A2 buried surface area isn't 'large'. It's small by protein complex standards, I think. But still perfectly reasonable.
• Figure S6 - would be good to label N and C termini.

Significance

In this manuscript, the authors examine the biochemistry of two protein domains that are, on the basis of sequence similarity, predicted to function autonomously as binders of histone H3 tails or methylated DNA. They present solid data to suggest that neither domain in fact has this function, but that they act as protein interaction domains that form a heterodimer mediated by the presence of a zinc ion (two ligands from each protein).

I am a structural biologist and biochemist who has worked on zinc-binding domains - including PHD domains - on and off over 30 years.

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Referee #1

Evidence, reproducibility and clarity

Summary: The Polycomb Repressive-Deubiquitinase (PR-DUB) complex catalyzes histone H2AK119Ub deubiquitinylation, regulating gene expression and chromatin dynamics. It comprises the BAP1 deubiquitinylase and one of three ASXL proteins (ASXL1-3). ASXL proteins contain a highly conserved C-terminal Plant Homeodomain (PHD), which was proposed to recognize epigenetic marks on histone H3 tail and other proteins. Mutations and truncations in the PHD domain are frequently observed in cancer. The authors propose that the ASXL PHD domain does not target histone H3 PTM marks. They model the PHD domain using AlphaFold3 and identified a non-canonical fold that can apparently chelate one Zinc ion only in vitro, instead of the two ions typically bound by PHD domains. They also investigated the methyl CpG-binding domain proteins MBD5 and MBD6, known to interact with the ASXL PHDs and found that the complexes contain a composite Zinc-binding site at the interface between the two proteins. While the overall concept is interesting, the data do not justify conclusions. The authors should also reefer properly to the citations

Major comments:

• Are the key conclusions convincing?

No. The final model is not substantiated by a robust experimental system The conclusions about Zinc binding and that PHD of ASXLs does not bind histone tails are based on a rather weak experimental system. There is a need for structural evidence and validation with mutagenesis. Also, comparing the sequence of the ASXL PHD to ING2 is insufficient and the PhD might bind other known or unknown peptide sequences on histones. The authors can not state or imply, based on their data, that the ASXL PHD does not recognize histone H3 epigenetic modifications. The methods are not sensitive enough and other peptides with an apparent fold enrichment have not been considered. It is not adequate to compare the Zinc-binding assay ASXL2 (residues 1375-1435) and Asx (residues 1610-1668) PHD domains with the RING domain of cIAP1 (residues 551-618) and a GST-only control. Why not other PHD domains? - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

Yes - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Need solid evidence through experimental structure validation The ASXL PHD forms a composite Zinc-binding site with MBD5 and MBD6 is not well developed. There is a need structural validation - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

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

Need to provide more technical details - Are the experiments adequately replicated and statistical analysis adequate?

It is unclear whether the data are mostly technical replicates in the same experiment as opposed to independent experiments

Minor comments:

• Specific experimental issues that are easily addressable.

Need to validate Zinc chelation and composite interface for Zinc binding with other methods - Are prior studies referenced appropriately?

Largely No Examples: - Attachment of ubiquitin to histone H2A at K119 limits gene expression (Cao & Yan, 2012) - Ubiquitin is attached to H2AK119 by the Really Interesting New Gene (RING) E3 ubiquitin ligase Polycomb Repressive Complex 1 (PRC1, Cohen et al., 2020) and is removed by the PR-DUB (Reddington et al., 2020; Scheuermann et al., 2010) - The PR-DUB has regulatory functions in the cell cycle, cellular development and DNA damageresponse, and determines short-term changes to gene expression (reviewed in Di Croce &Helin, 2013; Mozgova & Hennig, 2015; Parreno & Martinez, 2022; Schuettengruber et al., 2017). - Are the text and figures clear and accurate?

Yes - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Validate the conclusions with robust methods.

Other issues

• In the Abstract: Need to include MBD5, MBD6 in the initial statement A Plant Homeodomain (PHD) at the C-terminus of ASXL proteins is recurrently truncated in cancer, and was previously proposed to recognise epigenetic modifications on the N-terminal tail of histone H3.

Referees cross-commenting

This session contains comments from both Reviewers

Rev 1

I believe that the manuscript needs substantial improvement. This involves experiments and this would require at least 6 months.

Rev 2

I don't agree that the authors need to determine an experimental structure for this work to be publishable. I think that the methods used, as is, are sufficient to draw a conclusion about the likely zinc ligation geometry. A structure would of course be great, but is a 'next level' experiment.

Rev 1

Ok, for not doing the structure, but this is just part of several comments. They have to address very carefully the comments and better control the study overall.

Significance

Could be significant and new if adequately demonstrated. The study is preliminary Could be significant in the filed of biochemistry and epigenetics.

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The authors do not wish to provide a response at this time.

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Uttley et al., describes the identification of a candidate sequence for enhancing craniofacial sox9 expression in Neanderthals and offers functional genomics evidence towards identification of candidate sequence variants in a cis regulatory element (CRE) responsible for jaw morphology variation in hominin evolution. They generated a transgenic zebrafish model for testing the activity of a previously characterised regulatory element in human, which when mutated causes Pierre Robin developmental disorder and its neanderthal counterpart which has been identified as a candidate enhancer by sequence similarity and by being a DMR in the Neanderthal genome. They show that the Neanderthal CRE is active similarly in distribution to its human counterpart but with elevated activity in anatomically loosely or unspecified cell types in zebrafish cartilaginous neural crest candidates, which they argue are matching the cells where the same enhancer is active in mammalian development. They then show by single cell transcriptomics the cell distribution for the enhancer activity in relation to neural crest subpopulations and trasncription factors involved in craniofacial development. Finally they carry out overexpression of SOX9 with the human enhancer variant in zebrafish and demonstrate morphology changes which they interpret as evidence towards the capacity of the enhancer to broaden mesenchymal condensations leading to change in jaw morphology.<br /> Taken together, the paper provides evidence for a predicted neanderthal regulatory element candidate to function as enhancer in a zebrafish model and evidence for this enhancer to carry sequence variation which can lead to overactivation in craniofacial cell types relevant to jaw morphology, which the authors interpret as the source of the cis regulatory mechanism for jaw morphology evolution in hominin evolution.

Main comments:

I found the conclusion on the functional divergence of sequence variants of Neanderthal v human enhancer convincing as they were provided by an elegant double reporter approach which offers internal control for variant comparison. However, i found the argument about the role of the sequence variant in craniofacial development less convincing

1. Setting the aims I found the introduction to the topic and the setting of aims somewhat sketchy. It is not clear from the introduction, why the Neanderthal element was chosen for further study and why the SNVs in this one element were worth pursuing in the lack of broader understanding of the potentially complex regulatory element complexity at the Neanderthal Sox9 locus. While it is a very reasonable assumption, that a key CRE found and well characterised in human (by the authors in their seminal paper) is a worthy candidate for functional assessment, without better understanding of the overall locus conservation between human and Neanderthal this element may be one of many functionally redundant elements.
2. Justification of the fish model in hominin gene regulation

2.1. For the neanderthal element function to be compared to human in a valuable and informative fashion, one would expect that the host system i.e. the zebrafish is sufficiently conserved by offering a similar developmental context both in terms of gene regulation and in terms of anatomy. From the gene regulation perspective, i would expect that the analysis of the EC1.45 is based on expectation of similar regulatory information content to that in the fish homolog thus one can expect similar TF network activities on them and as a result one an test sequence variation effects relevant to endogenous regulatory interactions both in fish and hominins. However, there is no data shown for the relevance of fish regulatory background as a test system. No information is provided on the fish sox9 locus and its activity, or whether the fish homolog enhancer (or any sox9 enhancer that is expressed in the expected domains of craniofacial lineages and structures) has been identified and how it compares to the hominins. One expects that the hominin enhancers are active in domains of the zebrafish sox9 for the anatomical structures to give relevant readout. I would expect a comparison and match of the EC1.45 activity to ether endogenous sox9 by WISH or (although less accurate) a cross to one of the several sox9 reporter transgenic lines available on ZFIN.

2.2. There is an argument about the regulatory networks being conserved (without references), this would need more arguments particularly in the context of Sox9/SOX9 regulation. 3. Further to the justification of the fish model, from the anatomical perspective, the assessment of the parallels of zebrafish and mammalian craniofacial development need strengthening.

3.1. While indeed transparency and external development helps the reporter transgenesis and argues for the fish model, but the generation time is actually comparable to mouse (in contrast to the statement in the introduction), however the understanding of zebrafish craniofacial development and its similarity to human is not well argued, and indeed very superficially compared in the manuscript. I found the anatomical analyses to be rather imprecise and difficult to compare. In the lack of direct comparisons and diagrams comparing mammalian and fish developmental structures and their origins, the statement of 'EC1.45 activity matches expression domains from mammalian development' or 'broadly recapitulate' to be an oversimplification and overstatement. The lineage tracing is an important evidence but again the anatomical homologies need to be more clearly visualized and the lineage history better explained.

3.2. In a similar vein, direct comparison of human and Neanderthal adult morphologies (Figure 1B) would be very helpful.

3.3. I was also confused why the sox10 reporter is used as reference (with no direct overlap of activity to the SOX9 associated EC1.45 reporter) rather than or alongside a sox9a reporter line or even comparison to endogenous sox9a activity by WISH (Figure 2). The anatomical details in Figure 2 would need to be extended with more precisely describing the cell types, where the transgene is active and how the homology to mammalian anatomies are established.

3.4. Overall, the use of the fluorescence reporter is helpful for initial assessments but accurate enhancer activity profiling and comparison should be done by WISH, as mRNA is far more likely to follow the temporal activation dynamics and may explain fluorescence signal intensity differences, the latter important for correct interpretation of sequence variant effects (e.g. is the perceived higher expression by the Ne element is perhaps due to longer expression or earlier activation). 4. Single cell transcriptomics This experiment was not only used to characterise transgenic reporter active cell types, but to establish transcription factor candidates relevant to neural crest differentiation regulated by EC1.45. What is somewhat confusing, is that the EC1.45 element activity domain is only partially and not predominantly overlapping with the twist1a expressing cells. The authors previously established Twist1 as key regulator of EC1.45 in craniofacial development. How do the authors explain the apparent little relevance of twist1a in regulating the enhancer in fish? Overall the lack of any attempt to link the SNVs to TFBS (including, if available that of the fish homolog sequences) is making the interpretation of the sequence variation harder. BTW, even of the fish elements are not directly identifiable by direct sequence alignment it may be possible to identify the fish homolog through phylogenetic footprinting with stepping stone species such as the non-duplicated paddlefish. 5. Sox9 overexpression This experiment seems not to add too much to the main claim of the paper. While not essential, for this data to add more value, a comparison to that using the Neanderthal element would be more interesting and not a difficult experiment to carry out. 6. Throughout the paper there is a lack of data on reproducibility of reporter activities. As random integration often leads to position effects, it is expected that more than one lines showing the same patterns is used to identify cell type and tissue specificities. This is lacking in the paper and is a concern, as for example, the human element activity in Fig. 1 appears to be different from that by in the dual reporter shown in Fig. 3.

Minor points

A request to the editor as much as the authors: please make sure that legends are on the same page with figures, it is very hard to follow manuscripts when one needs to scroll between 3 pages at the same time (text, figure, legend). This archaic separation inherited from decades ago when physical prints used to be submitted has no justification in the digital era but continues to make reviewer's life difficult. Similarly, there should be no limit, and it should be encouraged to label anatomical structures directly on panels to point out expression domains, highlight expression variation, or to make a panel more self-explanatory, while making sure that clarity is not lost.

Figure 1A does not support the statement it is referenced to

Figure 1B should include human anatomy in comparison and perhaps a schematic diagram of the hypothesized developmental morphogenesis divergence modelled in this paper

Figure 1D should show why the authors argue the neanderthal is not the ancestral state (BTW, what does the fish homolog look like?)

Figure 4A,B are better suited in Supplemental

Significance

Conceptual: identifying sequence variants in Neanderthal cis-regulatory element as potential source of evolutionary change in morphology.

Technologically mostly following prior art, use of single cell in reporter analysis is technologically improvement on current standards, albeit somewhat rudimentary.

The use of a tractable embryo model to explore a regulatory sequence change leading to morphology change has often been applied for carious aspects of evolutionary changes during development pioneering examples include the shh ZRA enhancer in fin/limb morphogenesis, or balean fin evolution (PMID: 9860988) or human versus ape hand evolution (PMID: 18772437), but this is the first for applying it to hominin evolution. This will be of interest to human geneticists, evolutionary geneticists and developmental geneticists.

My expertise is in developmental gene regulation with the zebrafish model.

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Referee #2

Evidence, reproducibility and clarity

The authors provide evidence that nucleotide sequence variants in a remote enhancer, E1.45, which is located 1.45 Mb upstream of the Sox9 promoter, probably contributed to subtle morphological differences in the lower jaws of Neanderthals and modern humans. The study employs the use of a cleverly-designed dual reporter gene for directly comparing the activities of the Neanderthal and modern human enhancers in transgenic zebrafish. The results are clear and convincing: the Neanderthal enhancer is significantly more active than the modern human enhancer.

Here are a few minor recommendations that might help clarify aspects of the study:

1. Is it possible to quantify the different enhancer activities in the zebrafish assays? Is it strictly a question of levels or are there also subtle differences in the timing and/or sites of expression during development?
2. Is the Neanderthal form of the E1.45 enhancer ancestral for the hominids? If so, then reduced expression in modern humans is a derived trait. This could be stated more clearly.
3. Are there potential transcription factor binding motifs associated with the SNVs?

Significance

The authors address one of the most compelling problems in biology: the evolutionary origins of modern humans. This study addresses the role of regulatory DNAs in the divergence of Neanderthals and modern humans. Sox9 is a good focus of study since it has been implicated in the development of craniofacial features in humans. The authors identified three SNVs (single nucleotide variants) in Neanderthal vs. modern human E1.45 enhancer sequences. Direct comparison of these enhancers provide compelling evidence that these SNVs cause upregulation of the Sox9 in Neanderthals. I think this is a very interesting finding and strongly endorse publication.

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Referee #1

Evidence, reproducibility and clarity

This is an interesting paper that is logical continuation of authors previous work characterizing a human enhancer mutation implicated in Pierre Robin malformations that alters Sox9 expression. Here using zebrafish as a convenient model organism, the authors test the activity of the human enhancer compared to its Neanderthal ortholog. The results show that both enhancers drive reporter expression in the vicinity of forming cartilage condensations of the jaw. While both enhancers mediate reporter expression in neural crest derived cells, the Neanderthal sequence drives quantitatively higher expression than the orthologous human enhancer. Consistent with this, overexpression of Sox9 using the human enhancer caused an increase in cartilage volume. Altogether, this is a nicely done study that would be appropriate for publication after some revisions as detailed below.

Major Revisions:

1. The introduction seems overly long and a bit rambling so diminishes from the excitement of the work. It should be half the length and focus on the novelty of this question and findings.
2. The authors should demonstrate that that human EC1.45 activity overlaps with Sox9 expression. This should be included in Figure 2.
3. There are differences in level of enhancer activity signal between figures (e.g. seems lower in Fig. 3 than Fig. 2). Does enhancer activity vary between embryos or was the imaging protocol different?
4. Some co-staining should be performed to show whether or not the enhancers are active in the same cells but at different levels or if they are actually in different cells.
5. There is an important issue with the single cell RNA seq. Given that the cells were FACS sorted for +GFP and +Cherry, there seem to be many negative cells in their scRNAseq data. Perhaps the FACS gates (figure 4B) were not conservative enough? Did negative cells get included? Authors should verify that their clusters express both GFP and Cherry transcripts.
6. From their scRNAseq data, they talk about enhancer activity in PA1, but this isn't discussed/shown in the enhancer reporter embryos. It would be appropriate to annotate PA1 in figures 2 and 3.
7. Authors should quantify how many Sox9+ cells also have enhancer activity. Looking at the UMAPs in figure 4E and 4F, it actually looks like there is less enhancer activity in the Sox9 dense regions of the clusters.
8. For the over-expression of Sox9 driven by EC1.45, it is important to first establish that EC1.45 activity does indeed overlap with Sox9 gene expression. Does Sox9 itself drive EC1.45?
9. Importantly the authors do not discuss if the Neanderthal SNVs lie in TF binding sites? Which TF motifs? Are they conserved? Are those TF's expressed in the same cells as both enhancers?
10. If you introduce the Neanderthal SNVs into the human sequence, do you gain enhancer activity?
11. The over-expression experiments are tricky as they cause major developmental defects. Would it be possible to drive Sox9 expression at levels that better reflect those driven endogenously by the human versus Neanderthal enhancer?

Minor Revisions:

1. Figure 1 - authors should highlight that panel C is a zoom in of panel A.
2. Figure 3 - Why does Human EC1.45 activity looks weaker here than it does in Figure 2.
3. The first sentence of the last paragraph in the Introduction is unclear: "spatiotemporal developmental expression patterns for the human EC1.45 cluster during zebrafish development". Instead should read "reporter expression driven by the human EC1.45 enhancer over developmental time"

Significance

This is a nice paper that advances understanding of jaw development and has disease relevance as well as some evolutionary implications. Thus it is novel and would appeal to developmental biologist, the craniofacial community, and to some extent to evolutionary biologists.

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Reply to the reviewers

Manuscript number: RC-2024-02588

Corresponding author(s): Frederic SALTEL

__1. __Point-by-point description of the revisions

Reviewer #1:

Invadosomes are dynamic, actin-based structures that enable cells to interact with and remodel the extracellular matrix (ECM), playing a crucial role in tumor cell invasion and metastasis. Prior studies by the authors and other groups have established the formation, activation, and appearance of invadosomes. This study demonstrates the following:

1. Key elements of the translation machinery and endoplasmic reticulum (ER) proteins are constituents of the invadosome structure.
2. Specific proteins are associated with distinct invadosome structures.

The researchers utilized two cellular models (NIH3T3-Src and A431 melanoma cell line) and Tks5, a specific invadosome marker, for immunoprecipitation and mass spectrometry, validating the results through fluorescent images, electron microscopy, and time-lapse live imaging.

Major Comments

The manuscript is well-written, with a clear and detailed experimental workflow. Compared to their previous seminal work that first demonstrated invadosomes concentrate mRNA and exhibit translational activity using NIH3T3-Src cells, this study adds details about the specific enrichment of translation proteins for each type of invadosome and the presence of ribosomal and ER proteins. However, the experiments do not further enhance our understanding of the intricate mechanisms linking invadosome structures, function, and translation factors.

Further experiments are needed to better demonstrate the hypothesis of active translation within these structures, including the use of additional cellular models.

To demonstrate the hypothesis of active translation within these structures, we performed the same translation inhibition experiments, using CHX in additional cellular models. Indeed, these experiments were performed on MDA-MB-231 breast cancer cell lines, as well as on Huh6 liver cancer cell lines. Degradation experiments showed the same results as for NIH-3T3-Tks5-GFP and A431-Tks5-GFP, since we were able to observe a significant decrease in the degradation capacities of cells in the absence of translation (see graphs below).

Left: Quantification and representative images of ECM degradation properties of Huh6 cells on gelatin treated (CHX) or not (DMSO) with cycloheximide. Gelatin is stained in green and nuclei in blue. Values represent the mean +/- SEM of n=4 independent experiments (15 images per condition and per replicate) and were analyzed using student t-test.

Right: Left: Quantification and representative images of ECM degradation properties of MDA-MB-231 cells on gelatin treated (CHX) or not (DMSO) with cycloheximide. Gelatin is stained in green and nuclei in blue. Values represent the mean +/- SEM of n=4 independent experiments (15 images per condition and per replicate) and were analyzed using student t-test.

The authors should also investigate the effects of Tks5 silencing on ER-associated translational machinery.

The effects of Tks5 silencing on the ER-associated translation machinery were investigated using a SunSET experiment. We were able to demonstrate that Tks5 silencing had no significant impact on translation in both cellular models since no translation modification was observed between control and siTks5 conditions.

Quantification and relative western blot analysis of the effect of Tks5-targeting siRNA treatment on A431 and NIH-3T3-Src cells by using puromycin quantification. Values represent the mean +/- SEM of n=4 independent experiments and were analyzed using Anova.

How do the authors propose Tks5 is linked to these proteins? Directly or indirectly? Focusing on specific proteins night provide an opportunity to study the molecular mechanisms in greater depth.

Tks5 is a scaffold protein, a multi-domain “bridging molecule” that serve as regulators by simultnneously binding multipe molecular partners. TKs5 contain a PX domain and 5 SAH Domains. Consequently, Tks5 can bind different partners. Moreover, as focal adhesion, invadosome are large macromolecular assemblies. Here, in this study, Tks5 serve as a specific molecular hook, to precipitate partners. At this step, there is no evidence of a direct or indirect link of the translational machineray with Tks5. Even if we can hypothetize un indirect connection. In this version we focused more precisely on a specific and common Tks5 partners, such as EIF4B.

They used chemical inhibitors and siRNA approaches to assess the role of specific players, such as EIF4B, in the proteolytic activity of invadosomes, which can be considered proof of concept. Additional experiments aligning the results with the involved pathways would add molecular details and enhance the manuscript's significance. Resolving these issues is crucial for the manuscript to meet the publication standards for contributing novel and impactful insights to the field.

To better understand the variation of the pathways involved, we first wanted to observe the impact of Eif4b silencing on active translation in both cellular models. To do this, we performed SunSET experiments in both cell models. An experiment was performed for the A431 cell line and the results seem to show little difference between control conditions and conditions in the presence of siEIF4B. Conversely, SunSET experiments in the NIH 3T3 Src cell line show an increase in translation in the presence of siEIF4B.

__ __

Quantification of the effect of cycloheximide (CHX) and EIF4B-targeting siRNA (siEIF4B #1 and #2) treatment on A431 and NIH-3T3-Src cells by using puromycin quantification. Values represent the mean +/- SEM of n=1 independent experiment for A431 or n=2 independent experiments for NIH-3T3-Src.

In order to better understand the variation of the signaling pathways involved, spectrometry experiments were performed to compare the variation of the pathways in control conditions and in the presence of siRNA against EIF4B. These results allowed us to provide a better understanding of the variability of the pathways and therefore of the mechanism of action.

Volcano plot of overexpressed and underexpressed proteins after silencing of the EIF4B protein identified by mass spectrometry analysis.

These mass spectrometry experiments allowed us to highlight that the pathway mainly impacted during Eif4b depletion was the Hras pathway. However, this information is given for information purposes only. It would be necessary to look more closely at the Hras pathway to understand what the link with EIF4B and therefore the link with the formation of invadosomes could be.

Table of translation-related proteins or proteins involved in the formation or function of invadosomes that are overexpressed or underexpressed in at least one siRNA of EIF4B.

These experiments also allowed us to highlight that the depletion of EIF4B directly impacts the translation pathway by modulating translation initiation factors as well as ribosomal proteins but also proteins involved in the formation and function of invadosomes such as ADAM17, ACTR5, IGFBP6 RPL22 and RPS6KA5 proteins (see table below). It will be necessary to validate these data and determine their specificity due to the fact that some other proteins appear under-expressed like IGFBP3 and ADAM19. To conclude, to fully understand the exact impact of EIF4B into this process, additional investigations are necessary.

__ __Minor Comments :

A more detailed discussion of the implications of their findings within the broader context of cancer cell signaling and the potential impact on related cancer research areas would further advance our understanding in this area.

This part was added in the new version of the discussion. Indeed, deregulation of the translation is now a hallmark of cancer. This notion is now present in the manuscript and concluded the discussion (see page 12).

Reviewer #1 (Significance (Required)):

General Assessment:

This study offers novel insights into a new function of the invadosome-specific player Tks5 as a molecular crossroad between ER-related translation proteins and invadosomes. The authors suggest that Tks5 could act as a scaffold, supporting the rapid clustering of translation-related proteins during invadosome formation or proteolytic activity. However, a major limitation is the lack of mechanistic exploration. The results do not elucidate how Tks5 mediates the recruitment of these proteins or the specific molecular mechanisms involved.

Advances: The study extends knowledge in the field by confirming the presence of specific markers linked to different invadosome structures and demonstrating the Tks5 interactome's association with translation machinery.

Audience: This study will primarily interest specialists working on invadosomes and, secondarily, those interested in cancer cell signaling, invasion, and metastasis.

Field of Expertise: Invadosome and related signaling pathways in cancer.

__ __

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

Summary In this work, Normand and her colleagues analyze and compare the interactome of the key invadopodia component, TKS5 (overexpressed as a GFP-tagged protein), in two transformed cell models cultured on different substrates. Potential TKS5 interacting partners are identified including previously known and validated TKS5 interactors, some known to contribute to the mechanism of invadopodia formation and function. Bioinformatic (GSEA) analysis reveals a specific enrichment for proteins related to protein translation and interaction with ER-associated ribosome machinery. Evidence is presented that some of these proteins (RPS6, a component of the 40S ribosomal subunit, and translation factor, EIF4B) localize to TKS5-positive invadopodia in Src-transformed cells. Experiments based on translation inhibitor, cycloheximide, and silencing of EIF4B factor could demonstrate a link between overall protein translation and invadosome formation. Live cell imaging and microscopy analysis of fixed samples could document some proximity between the endoplasmic reticulum network and invadosome rosettes.

Major comments

__ __1- In the Results Section, the IP/proteomics-based pipeline used by Normand and colleagues to identify TKS5 partners is not clearly described and is confusing. Cut-off used to select the proteins in the different classes summarized in Table S1 should be better described. In addition, the nomenclature of the different protein subgroups used in Table S1 is confusing (see minor point#5).

Details have been added in the results section regarding the IP/proteomics section to complete the materials and methods section. As described in the materials and methods section, control versus IP data were quantified by an enrichment ratio ≥ 2. These criteria are the most classically used in the practices analyzed.

For clarity, additional tables have been added for each category (A431/NIH plastic or collagen) and gene names, protein descriptions and abundance ratios have been indicated (Supp table 2, 3, 4 and 5).

2- The effects of cycloheximide treatment or EIF4B silencing on gelatin degradation are clear and convincing. However, these are correlative evidence, and they may reflect a general implication of protein translation in the control of invadopodia function. A direct link between the observed interactions of TKS5 with the protein translation machinery and the formation and/or function of invadopodia is missing.

To demonstrate the direct links between Tks5 and the translation machinery, a fluorophore was used to visualize active translation within invadopodia. We were able to highlight an active translation localized in the rosettes (see figure below). Indeed, we can observe a localized translation within the rosettes. However, these same results were not observed in linear invadosomes where we could not observe any localized translation. We can however hypothesize that it is more difficult to observe a localized translation in linear invadosomes which are much smaller structures than rosettes.

Confocal microscopy images of NIH-3T3-Src cells. The cells were stained for B-actin RNA in green, B-actin in red, nuclei in blue and actin in grey. Scale bar: 20µm, zoom: 5µm.

In order to provide additional elements to show the link between Tks5 and the translation machinery, we performed immunofluorescence experiments by labeling the Sec61 protein. Sec61 is a well-described ER marker that allows the insertion of proteins into the ER but is also a key player in the docking of ribosomes to the ER. We were able to highlight the colocalization between Tks5 and Sec61 in all types of invadosomes, allowing to show the link between the Tks5 protein and the translation machinery. These images were inserted in the manuscript (see Figure 6b).

Confocal microscopy images of NIH-3T3-Src and A431 cells. The cells were seeded on gelatin or type I collagen and stained for Sec61 in red, nuclei in blue and Actin in grey. Scale bar: 20µm, zoom: 5µm.

__ __3- Images showing the interrelations between the ER and the adhesive podosome rosettes are striking (Figure 5). Src-transformed cells forming invadosome rosettes when in contact with the collagen substratum change shape and produce adhesive protrusions towards the substratum. As the ER is a huge compartment that fills the entire cytoplasm, it is maybe not so surprising to observe the ER filling the protrusions and getting close to the rosettes at the tip of these membrane extensions. Again, these observations are essentially correlative and there is no prove of some direct contact between some ER regions and the invadosomes.

For clarity, the contrast of the images has been improved. Thus, time-lapse imaging clearly demonstrate that the ER is not present in all the cytoplasm but is enriched in the destination of the rosettes as well as in the rosettes. Moreover, this is not systematic with all invadosome rosettes (see video 1)

4- Overall, this report is lacking a clear hypothesis or model of what could be the consequence of the interaction of TKS5 and the translation machinery on the formation and/or the activity of the invadosomes in transformed cells.

We performed a sunset experiment to analyze the impact of Tks5 depletion into translation. No variation of global translation was observable in the absence of Tks5 (see results below). Tks5 depletion block invadosome formation. So, the impact on total translation activity cannot be measurable at the cell level, suggesting that invadosome recruit a specific translation machinery. Indeed, even if we obtained a good percentage of Tks5 depletion, around 90%, the impact in total translation activity is not quantifiable. However, we noticed that some specific translation actors are modulated and specifically localized into invadosome structures suggesting that it is more a question of localization and local translation of specific mRNAs, and not a global modification. This is consistent with the fact that Tks5 expression is not altered during tumor cell invasion, and it is just recruited and activated at specific sites to form these invasive structures.

Thus, in this paper, Tks5 only served as an anchor point in order to be able to extract the specific molecular machinery and specific translational actors.

Quantification and relative western blot analysis of the effect of Tks5-targeting siRNA treatment on A431 and NIH-3T3-Src cells by using puromycin quantification. Values represent the mean +/- SEM of n=4 independent experiments and were analyzed using Anova.

Minor comments

1- Discussion Section (page 2). The statement that TKS4 is involved in ECM degradation in podosomes only and not in invadopodia is not correct. TKS4 knock down has been shown to interfere with ECM degradation in Human DLD1 colon cancer cells (Gianni et al. SCIENCESIGNALING Vol 2 Issue 88, 2009) and in in mouse and human melanoma cell lines (Iizuka et al. Oncotarget, Vol. 7, 2016). In addition, an unphosphorylable mutant form of Tks4 blocked invadopodia formation and ECM degradation in Src-transformed DLD1 cells (Gianni et al. Molecular Biology of the Cell Vol. 21, 4287- 4298, 2010). We (this reviewer's team) reported that TKS4 was associated with cortactin-positive invadopodia in MDA-MB-231 and Hs578T triple-negative breast cancer cell lines (Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

The involvement of TKS4 protein in extracellular matrix degradation has been changed in the text (page 2).

2- Discussion Section (page 3). A431 is wrongly referred to as a melanoma cell line; it is a human epidermoid carcinoma cell line.

The text has been modified according to the recommendations, the A431 cell line has been designated as a human epidermoid carcinoma cell line.

3- Results Section (page 4 & 5). The authors compare the proteins they identified as potential TKS5 partners to previously published data by Stilly et al. (based on TKS5 IP like in the present study) and Thuault et al. (TKS5 bioIB). Additionally, authors should mention and discuss previously published data based on TKS5 coIP experiment and Mass Spec analysis similar to the present study, identifying potential TKS5 partners; some of which were similarly found in the present study including proteins involved in translation and ribosome function although these were not the focus of this work (several 40S and 60S ribosomal proteins, see Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

This comparison is now present int the text of the manuscript (page 10).

4- Figure 1b. Matrix degradation is not visible in association with the invadopodia in selected high magnification images in Figure 1a and 1b.

Matrix degradation is indeed not visible in association with invadopodia in the selected high magnification images. Indeed, the imaging techniques used, Interference Refection Microscopy (IRM) do not allow us to observe matrix degradation at the invadosomes, since the reflection also highlights the cells. The aim here was to show only the presence collagen fibers that correspond to inducer of linear invadosome reorganization. It is widely accepted that all these structures are capable of degrading the extracellular matrix.

5- Supplemental table 1. The names of the different lists of proteins in the summary table is not clear and is rather confusing.

For clarity, additional tables have been added for each category (A431/NIH plastic or collagen) and gene names, protein descriptions and abundance ratios have been indicated (Supp table 2, 3, 4 and 5).

6- Supp Figure 1. Please define what is the sample named 'D' (Delta).

The Delta sample corresponds to the material that was not attached to the bead.

7- Results Section (page 5). 'These experiments confirm the correct co-localization between Tks5 and the proteins identified in Tks5 interactome by mass spectrometry analysis.' This statement is too general; in fact, data validate only colocalization between TKS5 and some identified partners, namely CD44 and MAP4.

To be less general, this statement has been modified in the text to show that the data only validate colocalization between TKS5 and certain identified partners, namely CD44 and MAP4.

8- Figure 2e and Figure 3. It would have been nice to show the colocalization of selected proteins and TKS5 in association with collagen fibers to validate that enrichment occurs at matrix/cell contact sites and corresponds to bona fide invadopodia.

As commented above, the reflection highlights the collagen fibers but also the cells. Thus, it is complex in this case to show the colocalization of the selected proteins in association with the collagen fibers with this approach. The other possibility is to stain collagen fibrils, however this kind of approach reduce the quality of interaction between fibers and associated receptors inducing a decrease of linear invadosome formation.

9- Figure 3c (high mag insets). TKS5 and EIF4b do not seem particularly enriched in invadopodia rosettes as compared to the rest of the cytoplasm.

Indeed, we can observe on this image a colocalization of Tks5 and EIF4B in the rosettes without showing an enrichment.

However, the enrichment of EIF4B remains clearly visible in the linear invadosomes and the dots.

10- Figure 4c-f. Treatments (i.e. CHX, siEIF4b) affect gelatin degradation. It would be interesting to assess the capacity of cells to form invadopodia under these conditions.

As demonstrated in this study, the CHX treatment and EIF4B depletion affect the degradation of gelatin. In addition, we were able to show that CHX only impacts the formation of rosettes on gelatin (Figure 4a, 4b and Supp 3).

Moreover, we added in the manuscript the impact of siEIF4B on invadosome formation (Supp Figure 3g). We show that it affects the formation of rosettes as CHX, but also affects the formation of linear invadosomes on collagen by A431 cells.

Quantification of the numbers of invadosomes per cell on gelatin and collagen silencing (siEIF4B) or not (DMSO) for EIF4B in A431-Tks5-GFP and NIH3T3-Src-Tks5-GFP cells. Values represent the mean +/- SEM of n=4 independent experiments (10 images per condition and per replicate) and were analyzed using student t-test.

Reviewer #2 (Significance (Required)):

This study confirms and adds to a previously published report by this research group based on invadosome laser capture microdissection and proteomics revealing that invadosomes contain specific components of the translational machinery, and that protein translation activity is required to maintain invadosome structure and activity (Ezzoukhry et al. Nat Commun 2018). It also adds to a recent study that established a crucial role for ribosome biogenesis in promoting cell invasion in the C. elegans anchor cell invasion model (Development. 2023).

The experimentation presented in this paper is of good quality and convincingly support the authors conclusions of a link between the ER-associated translation machinery and invadosome function in transformed cells. Overall, although this study adds to the emerging idea of an evolutionary-conserved translational control of cell invasion through the extracellular matrix it is mostly correlative and lacking a direct prove that the interaction of TKS5 with components of the translation machinery has a direct contribution to invadopodia function.

__ __

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

Summary: To invade the surrounding extracellular matrix (ECM), cells organize actin-rich cellular membrane structures capable of ECM degradation, called invadosomes. Depending on the composition and organization of the ECM, cells organize their invadosomes differently. The authors aimed to identify specific and common components of different types of invadosomes: rosettes formed by NIH3T3-Src cells seeded on gelatin, dots formed by A431 cells seeded on gelatin, and linear invadosomes formed by NIH3T3-Src and A431 cells when seeded on fibrillar collagen I. For this, they generated cells stably expressing GFP-Tks5, a ubiquitous constituent of invadosomes, and determined its interactome. They identified 88 common proteins, among which the protein translation machinery was enriched. Whereas general protein inhibition impaired only rosette formation and impaired every type of invadosome-associated degradation, EIF4B inhibition inhibited the formation of every type of invadosomes. They then analyzed the impact of the ER on invadosome formation and degradation activity. First, they documented the presence of the ER in the center of the NIH3T3-Src rosettes and correlated ER presence with rosette initiation and persistence. They then demonstrated that chemical inhibition of Sec61 translocon decreased formation of invadosomes in general.

Major comments:

1- The authors use cells overexpressing GFP-Tks5 for their analysis of Tks5 interactome in the different invadosomes (Fig. 2). The impact of GFP-Tks5 overexpression on invadosome formation and degradation activity should be mentioned.

Depending the cell type the TKS5-GFP overexpression do not increase the number of invadosomes but increase the matrix degradation activity (Di Martino et al 2014); or could impact the number of invadosomes as in B16 cell line (Shinji Iizuka et al, 2016). This point was added in the introduction.

However, the Tks5 overexpression was used fo immunoprecipitation and mass spectrometry analysis. The rest of the study and targets validation are done on wild type cells.

2- Concerning the analysis of the mass spectrometry (MS) data, clarifications would be appreciated:

a. The authors first "determined the specific molecular signature associated with each invadosome organization" (p.4). As I understand it, the proteins in each of these signatures correspond to proteins identified only in a particular type of invadosomes, not in the others. Could the authors indicate the percentage of the total proteins identified for each type of invadosomes that corresponds to the specific molecular signature?

The meaning of the sentence has been changed in the paper to provide more understanding. The term "molecular signature" has been replaced by "specific proteins". Percentages have been added to the tables in Figure 1 Supp.

1. __ __ The GSEA pathways related to each of the specific molecular signature were then analyzed and the authors "commonly identified an enrichment in mitochondrial, ER and Golgi proteins" (page 4) (Supp Fig 1c,e,g). Could the authors provide numbers/percentage/statistics? It is not clear to me whether the biological processes (Supp Fig 1b,d,f) are derived from the analysis of the specific molecular signature or of the total proteins identified for each type of invadosomes. Could the authors clarify this point? The percentages of each specific protein category have been added in Figure 1 Supp.

The biological processes (Supp Fig 1b, d, f) arise from the analysis of the molecular signature common to the 4 invadosomes conditions, namely the dots, rosettes and linear invadosomes of A431 and NIH-3T3-Src. Thus, the biological processes arise here from the 88 proteins commonly identified for all types of invadosomes.

1. The authors also identified "translation proteins" enriched in the specific molecular signature of each type of invadosomes (p.4). They commented on this category, indicating that each type of invadosome contains a specific set of translation-related proteins. This is true, but according to my analysis of the provided tables, the same applies to the other categories as well. Could the authors comment this point? Indeed, some proteins involved in translation can appear specific or common depending the type of invadosome. Our comment is at this step, only suggest that some of this protein should be specific for invadosome and some could be associated to only one organization. Of course, the role of each protein needs to be investigated.

2. Would similar categories of proteins (translation, ER, Golgi, mitochondrial) appear as enriched if the Tks5 interactome was analyzed as a whole for each type of invadosomes? (the authors may disregard this comment if comment a. is inaccurate). Protein pathways enriched in the different type of invadosome differ, for example, Protein activity GTPase activity, vs cell adhesion molecule binding or hydrolase activity acting on Acid Anyhdrides. This analysis demonstrates and highlights differences between the different invadosome organization. However, we focus on translational proteins, ER proteins for example and calculated the percentage of protein identified and associated with this different structure. We can notice important difference as 3% of translation proteins for rosette vs 9 % for dots in A431 cells. This point suggests that the part of each element can differ.

3. __ __ The authors identified that "cell adhesion proteins" are specifically enriched in linear invadosomes (page 4) (Supp Fig 1f). This conclusion appears to be based on the analysis of NIH3T3-Src and A431 cells. Could the authors provide more details on how this analysis was performed? Specifically, was the analysis conducted on a mixture of the specific signatures of each of the 2 cell models, or on their shared proteins? Additionally, is this category still enriched if each linear invadosome model is analyzed separately? The analysis was performed on common proteins of linear invadosomes, grouping the two cellular models. The category "cell adhesion protein" is not specifically enriched in linear invadosomes because adhesion proteins are also found in the other groups. However, this category represents a larger percentage in linear invadosomes, thus justifying our choice to highlight it for this category.

4. __ __ The authors identified 88 proteins common to all types of invadosomes (Fig. 2b) and classified them as validated or not in invadosomes. Could the authors give details on the criteria used for this classification? References for the already validated proteins should also be provided. RTN4 has been described as partially localized at invadopodia formed by MDA-MB-231 cells in Thuault et al., yet the authors classified it as not validated in invadosomes. The RTN4 protein has been moved to the category of proteins identified as localized in at least one invadosomes organization, thank you for this precision.

Please find below the list of papers having among the proteins classification as identified in at least one invadosomes organization, based on literature searches.

ADAM15 : Aspartate β-hydroxylase promotes pancreatic ductal adenocarcinoma metastasis through activation of SRC signaling pathway - Ogawa et al 2019

ADAM19 : The Adaptor Protein Fish Associates with Members of the ADAMs Family and Localizes to Podosomes of Src-transformed Cells - Abram et al 2003

ASPH : Aspartate β-hydroxylase promotes pancreatic ductal adenocarcinoma metastasis through activation of SRC signaling pathway - Ogawa et al, 2019

BAG3 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

CALD1 :

• Caldesmon is an integral component of podosomes in smooth muscle cells - Eves et al, 2006
• Caldesmon is an integral component of podosomes in smooth muscle cells, Gu et al 2007

• Changes in the balance between caldesmon regulated by p21‐activated kinases and the Arp2/3 complex govern podosome formation, Morita et al 2007 CD44 :

• The CD44s splice isoform is a central mediator for invadopodia activity, Zhao et al

• CD147, CD44, and the Epidermal Growth Factor Receptor (EGFR) Signaling Pathway Cooperate to Regulate Breast Epithelial Cell Invasiveness, Grass et al, 2013
• CD44 and beta3 integrin organize two functionally distinct actin-based domains in osteoclasts, Chabadel et al, 2007
• Macrophages podosomes go 3, Goethem et al 2011 CTTN : ERβ promoted invadopodia formation-mediated non-small cell lung cancer metastasis via the ICAM1/p-Src/p-Cortactin signaling pathway - Wang et al, 2023

EIF4B : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

FNBP1L : Transducer of Cdc42-dependent actin assembly promotes breast cancer invasion and metastasis - Chander et al, 2013

FXR1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

G3BP1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

HNRNPA1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

IGF2BP2 : IMP2 and IMP3 cooperate to promote the metastasis of triple-negative breast cancer through destabilization of progesterone receptor - Kim et al, 2018

ITGA5 : Membrane Proteome Analysis of Glioblastoma Cell Invasion, Mallawaaratchy et al, 2015

LAMP1 : Lysosomal cathepsin B participates in the podosome-mediated extracellular matrix degradation and invasion via secreted lysosomes in v-Src fibroblasts - Chun Tu et al, 2008

MAP4 : A proximity-labeling proteomic approach to investigate invadopodia molecular landscape in breast cancer cells, Thuault et al, 2020

MMP14 :

• Receptor-type protein tyrosine phosphatase alpha (PTPα) mediates MMP14 localization and facilitates triple-negative breast cancer cell invasion - Decotret 2021

• Deciphering the involvement of the Hippo pathway co-regulators, YAP/TAZ in invadopodia formation and matrix degradation - Venghateri 2023 MYH9 :

• TRPM7, a novel regulator of actomyosin contractility and cell adhesion 6 Clarck et al, 2006

• Bradykinin promotes migration and invasion of hepatocellular carcinoma cells through TRPM7 and MMP2, Chen et al, 2016 NONO : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

NPM1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

PABPC1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

PPP1CA : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

PRKAA1 : A proximity-labeling proteomic approach to investigate invadopodia molecular landscape in breast cancer cells, Thuault et al, 2020

PTBP1 : The lncRNA MIR99AHG directs alternative splicing of SMARCA1 by PTBP1 to enable invadopodia formation in colorectal cancer cells - Li et al, 2023

RPL10A : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

RPL34 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

RPS4X : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

RRBP1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

RTN4 : A proximity-labeling proteomic approach to investigate invadopodia molecular landscape in breast cancer cells, Thuault et al, 2020

SSB : The PDGFRα-laminin B1-keratin 19 cascade drives tumor progression at the invasive front of human hepatocellular carcinoma - Govaere 2017

STX7 : Syntaxin 7 contributes to breast cancer cell invasion by promoting invadopodia formation, Parveen et al, 2022

SYNCRIP : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

THBD : VEGF-Induced Endothelial Podosomes via ROCK2-Dependent Thrombomodulin Expression Initiate Sprouting Angiogenesis - Cheng-Hsiang Kuo - 2021

YBX3 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

1. __ __ Page 7, "In addition to translation proteins, the MS analysis highlighted the presence of ER-related proteins such as RTN4, LRRC59 or RRBP1 in all invadosomes linked with Tks5 (Figure 2c)". Is the "ER proteins" category enriched among the 88 common proteins? GSEA analysis on the 88 proteins showed an enrichment in proteins related to ribosomes and mRNA binding.

2. __ __ The comparative analysis of the TKS5 interactome from NIH3T3-Src-GFP-TKS5 on gelatin (this study) with the proteome of NIH3T3-Src rosettes from Ezzoukhry et al. (Fig 5a and Supp Table 2) should be included in the analysis of the MS data obtained in this study (Fig 2), rather than in the paragraph "Recruitment of ER into invadosome rosettes". Are "ER proteins" enriched? Comparative analysis of the TKS5 interactome of NIH3T3-Src-GFP-TKS5 on gelatin (this study) with the proteome of NIH3T3-Src rosettes from Ezzoukhry et al. was included in Supp Figure 2.

The proteins related to translation are enriched, but not those of the ER.__ __3- Was the localization of the newly identified Tks5 partners, such as RPS6 and EIF4B, but also MAP4 and CD44, to invadosomes analyzed in cells expressing endogenous levels of Tks5? If not, this should be addressed to rule out the possibility that their localization in invadosomes is linked to Tks5 overexpression. Through the figures, it is important to indicate whether cells overexpressing or not Tks5 were used.

The precision on the overexpression of Tks5 has been added in the figures.

The experiments were also carried out on cells not overexpressing Tks5 (see results below). Clarifications have been added in the article to specify that these experiments were carried out on cell lines overexpressing Tks5 but also on WT cell lines not overexpressing Tks5 (data not shown in the paper).

Confocal microscopy images of A431 and NIH-3T36Src cells. The cells were seeded on gelatin or type I collagen and stained for Tks5 in green, actin in red, nuclei in blue and Eif4b in grey. Scale bar: 40µm, zoom: 10µm.

Confocal microscopy images of A431 and NIH-3T3-Src cells. The cells were seeded on gelatin or type I collagen and stained for Tks5 in green, actin in red, nuclei in blue and RPS6 in grey. Scale bar: 40µm, zoom: 10µm.

Confocal microscopy images of A431 and NIH-3T3-Src cells. The cells were seeded on gelatin or type I collagen and stained for Tks5 in green, actin in red, nuclei in blue and MAP4 in grey. Scale bar: 40µm, zoom: 10µm.

4- EIF4B depletion inhibits ECM degradation (Fig 4e-f). The authors should address the impact of EIF4B depletion on invadosome formation. In other words, does EIF4B depletion corroborate the results obtained with CHX treatment, where only rosette formation is inhibited (Fig. 4a and Supp Fig. 3d).

The impact of EIF4B depletion on invadosome formation was studied. We were able to show that EIF4B depletion partly corroborates with the results obtained with CHX treatment, since rosette formation is also inhibited by EIF4B depletion but linear invadosomes formed on collagen by A431 are also inhibited by EIF4B depletion.

These results have been added to the paper (see Figure 3g).

Quantification of the numbers of invadosomes per cell on gelatin and collagen silencing (siEIF4B) or not (DMSO) for EIF4B in A431-Tks5-GFP and NIH3T3-Src-Tks5-GFP cells. Values represent the mean +/- SEM of n=4 independent experiments (10 images per condition and per replicate) and were analyzed using student t-test.

__ __5- The authors treated NIH3T3-Src-KDEL-GFP and LifeAct-Ruby cells with CHX and conclude that "translation inhibition led to the collapse of the rosette structure (Fig 6a, Video 4)" (page 8): could extra time points be added before T300 to appreciate the collapse of actin before the retraction of ER from the center of the rosette. No video 4 is provided. A video 5 is provided but does not correspond to a rosette collapse. The lifetime/dissociation rate of rosettes with and without CHX treatment should be determined.

Live cell imaging has been performed by recording one image every 2 minutes as described in methods. Graphs represent all recorded points along the experiment however we modified scale of original graph included into the manuscript to better appreciate the dissociation of fluorescence intensity curves revealing the collapse of actin before the retractation of ER. We also added a second graph which confirmed our first interpretation.

For video 4, we submitted the videos to make sure there were no errors. So, we can now clearly see the collapse of the rosette in video 4.

Lifeact-mRuby and KDEL-GFP signals were recorded in NIH-3T3-Src cells treated with cycloheximide (CHX; 35µM)

__ __6- Sec61 translocon inhibition by the chemical inhibitor ES1 decreases formation of dots by A431 and rosettes and linear invadosomes by NIH3T3-Src (Fig. 6b). Sec61 siRNA should be analyzed. Does Sec61 localize at invadosomes?

Immunofluorescence on NIH-3T3-Src and A431 WT cell lines were performed and added in the paper showing the localization of Sec61 in invadosomes (Figure 6b). Currently, we did not test siRNA targeting Sec61.

Confocal microscopy images of NIH-3T3-Src and A431 cells. The cells were seeded on gelatin or type I collagen and stained for Sec61 in red, nuclei in blue and Actin in grey. Scale bar: 20µm, zoom: 5µm.

__ __Minor comments:

1- The data of Figure 1 is not totally new, at least plasticity of NIH3T3-Src invadosomes has already been described in Juin A., MBoC, 2012. References to original work should be mentioned.

Indeed, the reference has been added to the text at Figure 1.

2- Page 4 "We realized immunoprecipitation against GFP in both cell lines on plastic and type I collagen conditions": the authors should show/mention that on plastic, cells behave has on gelatin coating.

A sentence has been added to the text to mention this: "Indeed, on plastic, the cells behave as on a gelatin coating and thus form the same types of invadosomes, i.e. dots for A431 cells and rosettes for NIH-3T3-Src cells." (see page 4).

3- The authors compared their MS data to previously published Tks5 interactomes (page 4) (Supp Fig 2a). A study from Zagryakhskaya-Masson et al (PMID: 32673397) identified Tks5 interactome of MDA-MB-231 cells generating linear invadosomes. Could the authors comment this study?

This study shows that FGD1, a guanine nucleotide exchange factor for the Rho-GTPase CDC42 interacts with Tks5 and plays a role in the formation of linear invadosomes. We have added this reference in the manuscript, but we have not found FGD1 in our data. It is possible that the GEF of Cdc42 varies from one cell type to another. This study has been added to the discussion.

4- The comparison of translation proteins found in this study with the ones found in other studies (Supp. Fig. 3 a) should be combined with the paragraph commenting the 88 common proteins (Fig. 2c-d).

For clarity, we decided to separate these two parts. There is indeed a lot of information, so it seemed clearer to us to keep the structure of the figures in this sense.

5- The table Supp Fig 2c listing the proteins present in each of the functional categories enriched among the 88 common Tks5 partners should be included as main figure or a color code representing the different biological processes should be included in Fig 2c.

A color code has been added between the two tables. A sentence has been added in the legends for clarity: "Color codes are according to Table Supp Figure 2c: orange: translation, green: actin cytoskeleton, and blue: adhesion."

__ __6- The SUnSET assay is not correctly untitled and described in the Material and Methods. Indeed, the paragraph refering to it is entitled "Inhibition of translation machinery present in invadosomes" and is a mixture of immunofluorescence and SUnSET protocols.

The SunSET assay materials and methods were modified in the paper in the "Sunset Assay" section as described below:

Sunset assay

Cells were treated with puromycin (10mg/ml) during 10min at 37°C then washed twice in ice-cold PBS for protein extraction as described above in Western Blot section. For negative control we pre-treated cells with the translation inhibitor cycloheximide (35mM) during 10min at 37°C.

7- Figure 4, the decrease in ECM degradation of A431 (GFP-Tks5) cells seeded on gelatin by CHX is not statistically different. The affirmation that "CHX treatment limited degradation activity by A431 and NIH3T3-Src cells on gelatin and collagen matrices" (page 6) should be modulated.

Indeed, thank you for your observation. We realized that incorrect values had been reported. Statistical tests (t-tests) were redone for each CHX condition, and significant results were found for each condition.

8- Page 8, "These results therefore confirm the presence but also the involvement of the ER in the rosette formation and maintenance over time". At this point in the study, there is a correlation between the presence of the ER and rosette persistence but no direct evidence of ER involvement is provided. The authors should moderate their conclusion.

That's absolutely right, the sentence has been modified accordingly (page 8).

9- Fig 5d: the authors should specify in the figure legend what are the red head arrows.

The red arrows show membranes of the endoplasmic reticulum, present at the level of the invadosome rosette. This point was added in the figure legend.

10- Some references are not correct. For example p.10, "MAP4 and LAMP1 were described in podosomes": ref 23 and 26 are studies on invadopodia, not on podosomes.

Corrections have been made to the text, the term podosomes has been replaced by invadopodia (see section references).

11- The authors indicate p.10, "Thanks to mass spectrometry experiments, we were able to show for the first time the presence of translation proteins in linear invadosomes". In their previous study Ezzoukry et al, they showed the localization of overexpressed Caprin1, eEF2 and eEF1A1 translation machinery components in linear invadosomes formed by NIH3T3-Src seeded on fibrillar collagen I. The authors should modulate their affirmations.

Indeed, this sentence has been modulated in the text (see page 10).

12- Could the authors refer to figures in the Discussion.

References to figures were added in the discussion.

Reviewer #3 (Significance (Required)):

This work extends their previous work, Ezzoukhry et al, in which the proteome of rosettes of NIH3T3-Src was identified after laser microdissection. In this work, they had identified protein translation machinery as components of rosettes and its implication in the degradation activity and/or the formation of rosettes and linear invadosomes.

The present study extends the presence of protein translation machinery to other types of invadosomes and the implication of protein translation in invadosome activity and/or formation. It also confirms the presence of ER in the center of rosettes. It suggests that ER-associated translation is required for invadosomes formation and activity. This knowledge will be of interest for the invadosome researcher community.

My expertise is in: cellular biology, invadopodia, ECM degradation, cancer. I do not have sufficient expertise to evaluate the accuracy of the analysis of mass spectrometry data and the quantification of videomicroscopy experiments.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

Summary:

To invade the surrounding extracellular matrix (ECM), cells organize actin-rich cellular membrane structures capable of ECM degradation, called invadosomes. Depending on the composition and organization of the ECM, cells organize their invadosomes differently. The authors aimed to identify specific and common components of different types of invadosomes: rosettes formed by NIH3T3-Src cells seeded on gelatin, dots formed by A431 cells seeded on gelatin, and linear invadosomes formed by NIH3T3-Src and A431 cells when seeded on fibrillar collagen I. For this, they generated cells stably expressing GFP-Tks5, a ubiquitous constituent of invadosomes, and determined its interactome. They identified 88 common proteins, among which the protein translation machinery was enriched. Whereas general protein inhibition impaired only rosette formation and impaired every type of invadosome-associated degradation, EIF4B inhibition inhibited the formation of every type of invadosomes. They then analyzed the impact of the ER on invadosome formation and degradation activity. First, they documented the presence of the ER in the center of the NIH3T3-Src rosettes and correlated ER presence with rosette initiation and persistence. They then demonstrated that chemical inhibition of Sec61 translocon decreased formation of invadosomes in general.

Major comments:

1- The authors use cells overexpressing GFP-Tks5 for their analysis of Tks5 interactome in the different invadosomes (Fig. 2). The impact of GFP-Tks5 overexpression on invadosome formation and degradation activity should be mentioned.

2- Concerning the analysis of the mass spectrometry (MS) data, clarifications would be appreciated:

a. The authors first "determined the specific molecular signature associated with each invadosome organization" (p.4). As I understand it, the proteins in each of these signatures correspond to proteins identified only in a particular type of invadosomes, not in the others. Could the authors indicate the percentage of the total proteins identified for each type of invadosomes that corresponds to the specific molecular signature?

b. The GSEA pathways related to each of the specific molecular signature were then analyzed and the authors "commonly identified an enrichment in mitochondrial, ER and Golgi proteins" (page 4) (Supp Fig 1c,e,g). Could the authors provide numbers/percentage/statistics? It is not clear to me whether the biological processes (Supp Fig 1b,d,f) are derived from the analysis of the specific molecular signature or of the total proteins identified for each type of invadosomes. Could the authors clarify this point?

c. The authors also identified "translation proteins" enriched in the specific molecular signature of each type of invadosomes (p.4). They commented on this category, indicating that each type of invadosome contains a specific set of translation-related proteins. This is true, but according to my analysis of the provided tables, the same applies to the other categories as well. Could the authors comment this point?

d. Would similar categories of proteins (translation, ER, Golgi, mitochondrial) appear as enriched if the Tks5 interactome was analyzed as a whole for each type of invadosomes? (the authors may disregard this comment if comment a. is inaccurate)

e. The authors identified that "cell adhesion proteins" are specifically enriched in linear invadosomes (page 4) (Supp Fig 1f). This conclusion appears to be based on the analysis of NIH3T3-Src and A431 cells. Could the authors provide more details on how this analysis was performed? Specifically, was the analysis conducted on a mixture of the specific signatures of each of the 2 cell models, or on their shared proteins? Additionally, is this category still enriched if each linear invadosome model is analyzed separately?

f. The authors identified 88 proteins common to all types of invadosomes (Fig. 2b) and classified them as validated or not in invadosomes. Could the authors give details on the criteria used for this classification? References for the already validated proteins should also be provided. RTN4 has been described as partially localized at invadopodia formed by MDA-MB-231 cells in Thuault et al., yet the authors classified it as not validated in invadosomes.

g. Page 7, "In addition to translation proteins, the MS analysis highlighted the presence of ER-related proteins such as RTN4, LRRC59 or RRBP1 in all invadosomes linked with Tks5 (Figure 2c)". Is the "ER proteins" category enriched among the 88 common proteins?

h. The comparative analysis of the TKS5 interactome from NIH3T3-Src-GFP-TKS5 on gelatin (this study) with the proteome of NIH3T3-Src rosettes from Ezzoukhry et al. (Fig 5a and Supp Table 2) should be included in the analysis of the MS data obtained in this study (Fig 2), rather than in the paragraph "Recruitment of ER into invadosome rosettes". Are "ER proteins" enriched?

3- Was the localization of the newly identified Tks5 partners, such as RPS6 and EIF4B, but also MAP4 and CD44, to invadosomes analyzed in cells expressing endogenous levels of Tks5? If not, this should be addressed to rule out the possibility that their localization in invadosomes is linked to Tks5 overexpression. Through the figures, it is important to indicate whether cells overexpressing or not Tks5 were used.

4- EIF4B depletion inhibits ECM degradation (Fig 4e-f). The authors should address the impact of EIF4B depletion on invadosome formation. In other words, does EIF4B depletion corroborate the results obtained with CHX treatment, where only rosette formation is inhibited (Fig. 4a and Supp Fig. 3d).

5- The authors treated NIH3T3-Src-KDEL-GFP and LifeAct-Ruby cells with CHX and conclude that "translation inhibition led to the collapse of the rosette structure (Fig 6a, Video 4)" (page 8): could extra time points be added before T300 to appreciate the collapse of actin before the retraction of ER from the center of the rosette. No video 4 is provided. A video 5 is provided but does not correspond to a rosette collapse. The lifetime/dissociation rate of rosettes with and without CHX treatment should be determined.

6- Sec61 translocon inhibition by the chemical inhibitor ES1 decreases formation of dots by A431 and rosettes and linear invadosomes by NIH3T3-Src (Fig. 6b). Sec61 siRNA should be analyzed. Does Sec61 localize at invadosomes?

Minor comments:

1- The data of Figure 1 is not totally new, at least plasticity of NIH3T3-Src invadosomes has already been described in Juin A., MBoC, 2012. References to original work should be mentioned.

2- Page 4 "We realized immunoprecipitation against GFP in both cell lines on plastic and type I collagen conditions": the authors should show/mention that on plastic, cells behave has on gelatin coating.

3- The authors compared their MS data to previously published Tks5 interactomes (page 4) (Supp Fig 2a). A study from Zagryakhskaya-Masson et al (PMID: 32673397) identified Tks5 interactome of MDA-MB-231 cells generating linear invadosomes. Could the authors comment this study?

4- The comparison of translation proteins found in this study with the ones found in other studies (Supp. Fig. 3 a) should be combined with the paragraph commenting the 88 common proteins (Fig. 2c-d).

5- The table Supp Fig 2c listing the proteins present in each of the functional categories enriched among the 88 common Tks5 partners should be included as main figure or a color code representing the different biological processes should be included in Fig 2c.

6- The SUnSET assay is not correctly untitled and described in the Material and Methods. Indeed, the paragraph refering to it is entitled "Inhibition of translation machinery present in invadosomes" and is a mixture of immunofluorescence and SUnSET protocols.

7- Figure 4, the decrease in ECM degradation of A431 (GFP-Tks5) cells seeded on gelatin by CHX is not statistically different. The affirmation that "CHX treatment limited degradation activity by A431 and NIH3T3-Src cells on gelatin and collagen matrices" (page 6) should be modulated.

8- Page 8, "These results therefore confirm the presence but also the involvement of the ER in the rosette formation and maintenance over time". At this point in the study, there is a correlation between the presence of the ER and rosette persistence but no direct evidence of ER involvement is provided. The authors should moderate their conclusion.

9- Fig 5d: the authors should specify in the figure legend what are the red head arrows.

10- Some references are not correct. For example p.10, "MAP4 and LAMP1 were described in podosomes": ref 23 and 26 are studies on invadopodia, not on podosomes.

11- The authors indicate p.10, "Thanks to mass spectrometry experiments, we were able to show for the first time the presence of translation proteins in linear invadosomes". In their previous study Ezzoukry et al, they showed the localization of overexpressed Caprin1, eEF2 and eEF1A1 translation machinery components in linear invadosomes formed by NIH3T3-Src seeded on fibrillar collagen I. The authors should modulate their affirmations.

12- Could the authors refer to figures in the Discussion.

Significance

This work extends their previous work, Ezzoukhry et al, in which the proteome of rosettes of NIH3T3-Src was identified after laser microdissection. In this work, they had identified protein translation machinery as components of rosettes and its implication in the degradation activity and/or the formation of rosettes and linear invadosomes.

The present study extends the presence of protein translation machinery to other types of invadosomes and the implication of protein translation in invadosome activity and/or formation. It also confirms the presence of ER in the center of rosettes. It suggests that ER-associated translation is required for invadosomes formation and activity. This knowledge will be of interest for the invadosome researcher community.

My expertise is in: cellular biology, invadopodia, ECM degradation, cancer. I do not have sufficient expertise to evaluate the accuracy of the analysis of mass spectrometry data and the quantification of videomicroscopy experiments.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Summary:

In this work, Normand and her colleagues analyze and compare the interactome of the key invadopodia component, TKS5 (overexpressed as a GFP-tagged protein), in two transformed cell models cultured on different substrates. Potential TKS5 interacting partners are identified including previously known and validated TKS5 interactors, some known to contribute to the mechanism of invadopodia formation and function. Bioinformatic (GSEA) analysis reveals a specific enrichment for proteins related to protein translation and interaction with ER-associated ribosome machinery. Evidence is presented that some of these proteins (RPS6, a component of the 40S ribosomal subunit, and translation factor, EIF4B) localize to TKS5-positive invadopodia in Src-transformed cells. Experiments based on translation inhibitor, cycloheximide, and silencing of EIF4B factor could demonstrate a link between overall protein translation and invadosome formation. Live cell imaging and microscopy analysis of fixed samples could document some proximity between the endoplasmic reticulum network and invadosome rosettes.

Major comments:

1- In the Results Section, the IP/proteomics-based pipeline used by Normand and colleagues to identify TKS5 partners is not clearly described and is confusing. Cut-off used to select te proteins in the different classes summarized in Table S1 should be better described. In addition, the nomenclature of the different protein subgroups used in Table S1 is confusing (see minor point#5).

2- The effects of cycloheximide treatment or EIF4B silencing on gelatin degradation are clear and convincing. However, these are correlative evidence, and they may reflect a general implication of protein translation in the control of invadopodia function. A direct link between the observed interactions of TKS5 with the protein translation machinery and the formation and/or function of invadopodia is missing.

3- Images showing the interrelations between the ER and the adhesive podosome rosettes are striking (Figure 5). Src-transformed cells forming invadosome rosettes when in contact with the collagen substratum change shape and produce adhesive protrusions towards the substratum. As the ER is a huge compartment that fills the entire cytoplasm, it is maybe not so surprising to observe the ER filling the protrusions and getting close to the rosettes at the tip of these membrane extensions. Again, these observations are essentially correlative and there is no prove of some direct contact between some ER regions and the invadosomes.

4- Overall, this report is lacking a clear hypothesis or model of what could be the consequence of the interaction of TKS5 and the translation machinery on the formation and/or the activity of the invadosomes in transformed cells.

Minor comments:

1- Discussion Section (page 2). The statement that TKS4 is involved in ECM degradation in podosomes only and not in invadopodia is not correct. TKS4 knock down has been shown to interfere with ECM degradation in Human DLD1 colon cancer cells (Gianni et al. SCIENCESIGNALING Vol 2 Issue 88, 2009) and in in mouse and human melanoma cell lines (Iizuka et al. Oncotarget, Vol. 7, 2016). In addition, an unphosphorylable mutant form of Tks4 blocked invadopodia formation and ECM degradation in Src-transformed DLD1 cells (Gianni et al. Molecular Biology of the Cell Vol. 21, 4287- 4298, 2010). We (this reviewer's team) reported that TKS4 was associated with cortactin-positive invadopodia in MDA-MB-231 and Hs578T triple-negative breast cancer cell lines (Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

2- Discussion Section (page 3). A431 is wrongly referred as to a melanoma cell line; it is a human epidermoid carcinoma cell line.

3- Results Section (page 4 & 5). The authors compare the proteins they identified as potential TKS5 partners to previously published data by Stilly et al. (based on TKS5 IP like in the present study) and Thuault et al. (TKS5 bioIB). Additionally, authors should mention and discuss previously published data based on TKS5 coIP experiment and Mass Spec analysis similar to the present study, identifying potential TKS5 partners; some of which were similarly found in the present study including proteins involved in translation and ribosome function although these were not the focus of this work (several 40S and 60S ribosomal proteins, see Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

4- Figure 1b. Matrix degradation is not visible in association with the invadopodia in selected high magnification images in Figure 1a and 1b.

5- Supplemental table 1. The names of the different lists of proteins in the summary table is not clear and is rather confusing.

6- Supp Figure 1. Please define what is the sample named '' (Delta).

7- Results Section (page 5). 'These experiments confirm the correct co-localization between Tks5 and the proteins identified in Tks5 interactome by mass spectrometry analysis.' This statement is too general; in fact, data validate only colocalization between TKS5 and some identified partners, namely CD44 and MAP4.

8- Figure 2e and Figure 3. It would have been nice to show the colocalization of selected proteins and TKS5 in association with collagen fibers to validate that enrichment occurs at matrix/cell contact sites and corresponds to bona fide invadopodia.

9- Figure 3c (high mag insets). TKS5 and EIF4b do not seem particularly enriched in invadopodia rosettes as compared to the rest of the cytoplasm.

10- Figure 4c-f. Treatments (i.e. CHX, siEIF4b) affect gelatin degradation. It would be interesting to assess the capacity of cells to form invadopodia under these conditions.

Significance

This study confirms and adds to a previously published report by this research group based on invadosome laser capture microdissection and proteomics revealing that invadosomes contain specific components of the translational machinery, and that protein translation activity is required to maintain invadosome structure and activity (Ezzoukhry et al. Nat Commun 2018). It also adds to a recent study that established a crucial role for ribosome biogenesis in promoting cell invasion in the C. elegans anchor cell invasion model (Development. 2023).

The experimentation presented in this paper is of good quality and convincingly support the authors conclusions of a link between the ER-associated translation machinery and invadosome function in transformed cells. Overall, although this study adds to the emerging idea of an evolutionary-conserved translational control of cell invasion through the extracellular matrix it is mostly correlative and lacking a direct prove that the interaction of TKS5 with components of the translation machinery has a direct contribution to invadopodia function.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #1

Evidence, reproducibility and clarity

Invadosomes are dynamic, actin-based structures that enable cells to interact with and remodel the extracellular matrix, playing a crucial role in tumor cell invasion and metastasis. Prior studies by the authors and other groups have established the formation, activation, and appearance of invadosomes. This study demonstrates the following:

1. Key elements of the translation machinery and endoplasmic reticulum (ER) proteins are constituents of the invadosome structure.

2. Specific proteins are associated with distinct invadosome structures. The researchers utilized two cellular models (NIH3T3-Src and A431 melanoma cell line) and Tks5, a specific invadosome marker, for immunoprecipitation and mass spectrometry, validating the results through fluorescent images, electron microscopy, and time-lapse live imaging.

Major Comments:

• The manuscript is well-written, with a clear and detailed experimental workflow. Compared to their previous seminal work that first demonstrated invadosomes concentrate mRNA and exhibit translational activity using NIH3T3-Src cells, this study adds details about the specific enrichment of translation proteins for each type of invadosome and the presence of ribosomal and ER proteins. However, the experiments do not further enhance our understanding of the intricate mechanisms linking invadosome structures, function, and translation factors.

• Further experiments are needed to better demonstrate the hypothesis of active translation within these structures, including the use of additional cellular models. The authors should also investigate the effects of Tks5 silencing on ER-associated translational machinery.

• How do the authors propose Tks5 is linked to these proteins? Directly or indirectly? Focusing on specific proteins might provide an opportunity to study the molecular mechanisms in greater depth.

• They used chemical inhibitors and siRNA approaches to assess the role of specific players, such as EIF4B, in the proteolytic activity of invadosomes, which can be considered proof of concept. Additional experiments aligning the results with the involved pathways would add molecular details and enhance the manuscript's significance. Resolving these issues is crucial for the manuscript to meet the publication standards for contributing novel and impactful insights to the field.

Minor Comments:

• A more detailed discussion of the implications of their findings within the broader context of cancer cell signaling and the potential impact on related cancer research areas would further advance our understanding in this area.

Significance

General Assessment:

This study offers novel insights into a new function of the invadosome-specific player Tks5 as a molecular crossroad between ER-related translation proteins and invadosomes. The authors suggest that Tks5 could act as a scaffold, supporting the rapid clustering of translation-related proteins during invadosome formation or proteolytic activity. However, a major limitation is the lack of mechanistic exploration. The results do not elucidate how Tks5 mediates the recruitment of these proteins or the specific molecular mechanisms involved.

Advances:

The study extends knowledge in the field by confirming the presence of specific markers linked to different invadosome structures and demonstrating the Tks5 interactome's association with translation machinery.

Audience:

This study will primarily interest specialists working on invadosomes and, secondarily, those interested in cancer cell signaling, invasion, and metastasis.

Field of Expertise:

Invadosome and related signaling pathways in cancer.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Below is a point-by-point response to reviewers comments. We appreciate the reviewers' thoughtful consideration of the manuscript and __suggestions

Reviewer #1

Evidence, reproducibility and clarity

In this study, Parkinson et al. investigated lung extracellular matrix using imaging mass cytometry (IMC) in mouse models. Overall, the paper is well-written, and the data are clear, although major points outlined below need to be addressed.

In its current form, the paper appears more like a methods-focused study since, to my understanding, no new biological responses are described. The methods employed are very interesting, particularly the extracellular matrix analysis.

As the reviewer points out a large part of this manuscript is the development of a novel methodology for analyzing the spatial ECM changes in a model of allergic airway inflammation. However, there are several novel responses described in the manuscript. Firstly, differing spatial organisation of immune cells across different mouse strains has not been shown before, particularly in a model of chronic allergic pathology that shares features of severe steroid-resistant asthma in people. Secondly, we show that specific macrophage-fibroblast interactions are occurring in the subepithelial region during DRA-induced allergic airway inflammation. Finally, we integrate all these established and novel findings with detailed spatial analysis of the cellular ECM environment, something which is sorely needed in the field.

However, the scope of the study is quite limited, as all the experiments were performed with mouse samples, which are relatively easy to work with, and the cell organisation is simple compared to humans.

Whilst we appreciate that the dataset in this study is limited, imaging mass cytometry studies, especially when optimizing reagents, are costly, time consuming, and have limited throughput, not to mention the time required to develop new computational tools for data analysis. Investigating cell-matrix changes in mouse data is vitally important for understanding the mechanistic role of pathways and interactions during disease processes. Whilst we have not provided human datasets in this study, staining, data acquisition and analysis has been performed on FFPE samples, making our pipelines applicable to archival tissue banks. Regardless, we are currently preparing a publication showing the applicability of this technique to human samples. Many ECM components are well conserved between humans and mice and the cellular structure and architecture of the lung shares a lot of similarities. Many papers (PMID: 39437149, 38758780, 38581685, and 38142637) have used this imaging technology in the analysis of human cancer, which shows an even more complicated and dense cellular organisation.

The authors do not discuss how this analysis pipeline could be applied to human samples. Furthermore, the entire paper relies on imaging mass cytometry, and additional techniques could have been used to confirm some of the observations, especially given the availability of mouse samples.

As mentioned above, we have taken steps to show that this technology is applicable to humans, though this is outside the scope of this already lengthy manuscript. Additionally, Steinbock, the main analysis pipeline, is well published in human datasets (PMID: 38758780, 39905080, 39759522, and 39761010) and the homology between ECM components is strong between mouse and human. The technology itself is completely species agnostic, so there is no reason to think that there would be issues when applying to humans, other than some differences in the marker expression of certain populations, which is well characterised in many cases.

The reviewer’s comment regarding the use of additional techniques is valid. Firstly, these murine lung pathology samples are derived from the same mouse experiments used in our previous publication (PMID: 33587776), where we have analysed histology, immune mediators and cells using a variety of techniques including flow cytometry and ELISA. We will ensure this point is made clearer in the manuscript. In addition, for revision we plan to compliment IMC data presented with fluorescent immuno-staining to characterize cell populations in greater resolution and also using 3D precision cut lung slices to better characterize and visualize cell populations of interest in greater depth, directly addressing the reviewer’s concerns.

The introduction mentions the DRA model without providing an explanation of what it involves. Non-specialist readers may not be familiar with this abbreviation, and further clarification should be provided.

As the DRA model has been characterized previously, we provided references in the text in order to save space. However, we agree with the reviewer and will provide this information up front in the introduction to make the manuscript more approachable for a non-specialist.

In the methods section, it is not mentioned whether the lungs were inflated before tissue collection, which is crucial for preserving normal cellular organization. The authors should clarify whether this was performed.

Lungs were inflated prior to tissue collection. We agree that this is important information to include in the methods and we will update the manuscript to reflect this.

Figure 1 provides a brief summary of the methods employed in the study but could be enriched with additional information. In its current state, it does not provide meaningful insights beyond what is described in the methods section. It would be helpful if the authors clarified whether the mice used were adults and whether both male and female animals were included.

We agree with the reviewer. The idea behind this figure was to have an approachable introduction to the manuscript. However, in line with the reviewer’s previous comments about focusing more on the biology we will move this to supplementary to keep the importance focused on the biological results. Mouse age and gender were included in the methods of the paper, aligning to the ARRIVE guidelines for reporting animal research. We will additionally clarify that these are adult mice

Additionally, they could present examples of the cell segmentation approach with zoomed-in images at the cellular level to illustrate the analysis.

This is a great idea and appreciate the reviewer’s suggestion. We will provide maps (with zoomed inserts) of the cell segmentation and cell classification across representative ROIs to show not only the segmentation but to provide an overview of how the cell types localise across the lung. This addition will also highlight the caveat of IMC around image resolution of 1μm2 which limits the sensitivity of cell segmentation. We will discuss such limitations of the technique in general in the manuscript in response to this and later reviewer comments.

The first set of data in Figure 2 suggests that C57Bl/6 mice did not respond to allergen treatment, as shown by the non-significant increase in cell numbers. The authors should provide evidence that their model induced inflammation through alternative methods, such as assessing eosinophil counts or pathology.

We know that these exact animals are allergic as their immunological responses were characterized in a previous publication (PMID: 33587776) demonstrating eosinophil counts and cytokine responses measured by flow cytometry. However, in light of the reviewer’s comment, we will add histological images of the lung to this current manuscript. Such data, together with enhanced expression of RELMα and Ym2 from airway epithelial cells (Sup Fig 6) and the shift from ATI to ATII cells in both C57BL/6 and BALB/c mice after DRA treatment (Fig 5 g) will provide thorough evidence that the DRA model induces allergic airway inflammation and pathology in both mouse strains.

The UMAP representation indicates significant overlap between cell clusters, which raises concerns about the accuracy of cell segmentation. For example, the heatmap in Supplementary Figure 1 shows endothelial cells expressing markers such as VWF, aSMA, Vimentin, and PDGFRα, suggesting that the cell cluster may contain a mixture of endothelial cells, vascular smooth muscle cells, and fibroblasts.

UMAP reductions of IMC do not separate as clearly as those from single cell RNAseq or flow cytometry. This is because the staining intensity from IMC is much lower. Rather than being on a log scale, as for single cell or flow cytometry, the values are much closer to linear. Additionally, due to the limitations in IMC resolution and the fact that we did not have distinct membrane markers in our panel, cell mask generation is often non-optimal. This is particularly evident in regions where cells are in close proximity and where the limitations of, an effectively, two-dimensional 5-micron thick tissue section mean that there can be overlap between one cell and another. Whilst we acknowledge that some populations will be a mix of cell types we are limited by the number of markers we can use in IMC, as well as the limitations mentioned above. We have accounted for this by using methodologies to identify and focus on tissue regions (lisaClust) and correlate changes to differences in these regions rather than single cells per se.

Examples of segmented cells should be shown to validate this approach.

As per the reviewers comment above, we will provide maps (with zoomed inserts) of the cell segmentation and cell classification across representative ROIs to show not only the segmentation but to provide an overview of how the cell types localised across the lung.

It is unclear what Figure 2e represents. If it is simply to show that certain clusters can be grouped together, such as AEC, AT1, and AT2 as epithelial cells, this could be conveyed in a simpler way.

We apologise that the reviewer found Figure 2e confusing. The aim of this figure was to provide a simple diagram to highlight how different classifications of cell types aligned. This was required because there were variations in the specificity of some clusters and to address specific questions it made more sense to analyse cells at a broader level. i.e. merging resting and activated ATI/II cells or grouping specific immune cell clusters into larger groups. We did consider a table, but we did not feel this was a “simpler” way to do it. As it is simply for reference, we will move Figure 2e to supplemental.

The analysis of extracellular matrix components presented in Figure 3 provides a novel method for studying these acellular structures, which is a challenge in the field. The authors should be commended for their efforts in this area.

We thank the reviewer for their comment here. We agree that this is a vital area that needs to be addressed as the immunomatrix becomes ever more important in understanding disease pathogenesis. We developed this novel method to begin to understand key spatial interactions between cells and ECM molecules, something missing from the majority of high-dimensional imaging datasets.

However, the parameters investigated in Figures 4-6 do not report any novel findings. While IMC appears to work effectively to analyse these parameters simultaneously, the induction of immune foci and changes in tissue organisation following allergen challenges are already well-documented in both mouse models and human samples.

We disagree with the reviewer on this point. Figure 4 shows that immune cell infiltration in the adventitial cuff is different between BALB/c and C57BL/6 mice. This is a new discovery and provides nuance to our previously published data (PMID: 33587776), which showed that in the bronchoalveolar lavage from these same mice there were no differences in immune cell populations at these chronic time points. Therefore, analysis of lavage cells or lung histology in isolation does not provide a full picture of allergic immune responses.

Figure 5 shows neutrophils localised with alveolar macrophages in the alveolar parenchyma in this chronic DRA model completely distinct from the spatial advential cuff region occupied by other CD11b+ cells. In addition, we show that we can identify perturbations in the alveolar parenchyma by IMC and these correlate with known differences in allergy and asthma such as alterations in ATI/ATII balance, which has also not been shown in this model.

Figure 6 demonstrates that we can identify a tissue region termed “subepithelial cells” which is the site of where remodelling events are known to occur in asthma and allergic pathology. This ECM-rich region is strongly associated with fibroblasts and immune cells which leads in to figure 7 showing that these cells are interacting.

In addition to all of this the main focus of this manuscript is to link these analysis parameters to changes in the ECM environment and we have included this in each of these figures showing how these correlates with allergic changes and how they may be important in understanding these processes. In response to this reviewer’s point, we will highlight and make these novel findings clearer within the text of the manuscript.

In Figure 5, the authors show a decrease in neutrophil numbers in challenged mice. This is unexpected, as this model is widely known to induce strong neutrophil recruitment. The authors should clarify this finding and investigate whether neutrophil chemoattractants are increased in these samples.

This is a keen observation by the reviewer. We were interested in this finding however as it was not the focus of the paper we did not investigate further. In our previous publication we show that there are increased neutrophil numbers in the BAL of these animals (PMID: 33587776) and as mentioned above, we show in figure 5 that neutrophils are found mainly in the alveolar parenchyma. This perhaps means that they are more sensitive to being washed out in the BAL and perhaps there are differences in their “stickiness” in BALB/c and C57BL/6 animals or during DRA-induced allergy. This is in contrast to eosinophils (likely within our CD11b+ cells) which are found in the adventitial cuff, a region is not likely to be captured by BAL wash, though we know that these cells are actively present in the BAL. Overall, though this is an interesting result it was not the focus of this already lengthy paper and is best investigated in another project.

When analysing epithelial cells, the authors separate AT1 and AT2 cells based on podoplanin expression. However, data in Supplementary Figure 4b suggest that both cell types express similar levels of podoplanin. The authors do not provide statistical validation for the claim that AT1 cells express higher levels. Additionally, as podoplanin is expressed by various cell types, such as lymphatic endothelial cells, additional markers are required to confirm the identity of AT1 cells.

Again, the reviewer is entirely correct here. The cells we have identified are labelled as ATI as a best guess and correlate with ATII cells based on anatomical location – though this is likely shared by some of the populations mentioned by the reviewer. The majority of cells in this population are likely ATIs, as they are localized in the alveolar parenchyma and are cells that are not SPC+, though we cannot say for sure without more markers and we were already at the limit of the number of markers that we can run with one IMC panel. It is likely that there are contaminating lymphatic endothelial cells in this cluster. However, these will be a relatively minor population and do not change the main findings presented in the paper. To address this and other comments by the reviewer we plan to include a limitation section to the discussion that highlights exactly these points for future studies.

The last set of data in Figure 7 is interesting and shows that immune cells interact with a population of S100a4 fibroblasts. This finding could be expanded further, as CD11b and Ly6C are expressed by a variety of immune cells. The authors should include additional staining to identify the specific cell types involved, such as monocytes, eosinophils, or airway macrophages. Furthermore, the authors should speculate on why these fibroblast regions attract immune cells. Are these regions enriched in chemokines or other factors?

We thank them for this suggestion. To answer this point, we will conduct immunofluorescent imaging to provide further characterization of these cells in greater depth, as we agree, this will be important to consider. To best visualize cells and their interactions in this adventitial region, we plan to use 3D precision cut lung slices from PBS versus DRA mice in combination with confocal imaging. This method will allow us to utilize antibodies and markers that do not work in the FFPE sections such as SiglecF (eosinophils), CD11c (DCs, macrophages), CD64 and CD169 (macrophages).

The discussion is engaging but focuses more on methodological aspects than new biological insights. Without mechanistic links, it is challenging to draw meaningful biological conclusions.

We agree that the discussion could be used to reinforce the importance of the biological discoveries we have made (listed previously) in the discussion. However, we also believe that it is important to discuss the methodology as this is a novel way to explore ECM-cell interactions in the tissue as highlighted by the reviewer. There are many limitations to using IMC and similar techniques that should be highlighted for future studies so that we can develop better ways of quantifying the ECM environment during disease.

Significance

The study of Parkinson et al. provides interesting methodological insights into the use of imaging mass cytometry (IMC) to analyse lung architecture following inflammation. The application of multiplex antibody staining will leverage important information related to how tissues are adapting to chronic immune response. Here the authors rely entirely on mouse models for their studies and compared two lines of WT animals and the same allergen model. This limits the scope of the study, additional timepoints, sex or age would have improved the manuscript.

Whilst we appreciate the reviewers points here, we would like to highlight the time involved in generating such datasets, with a lot of careful optimization and experimental design aspects going into each study. Whilst we have also performed staining and analysis using our described method in human FFPE tissue, we are currently looking to further develop analysis tools to assess ECM-cell interactions. Additionally, data acquisition using IMC takes considerable time, and hence it is not feasible run and analysis the number of samples required to address some of the questions proposed by the reviewer.

We believe our manuscript provides novel methodology to analyse ECM environments within spatial datasets, something that no other spatial datasets have explored to date. Furthermore, we provide numerous new biological findings in relation to how cells are organized within the tissue during allergic pathology and propose immune-fibroblast interactions that may be key for driving ECM remodelling in the lung. Integrating these analyses will be key for further understanding the role of the ECM in disease pathogenesis.

The applicability of this analysis pipeline to human tissue samples is not discussed, which would significantly enhance the impact of the study. Additionally, complementary techniques, such as flow cytometry or immunohistochemistry, could be used to validate the findings and improve reproducibility. A specialised audience of immunology researchers would be interested by the image analysis approach.

As mentioned above, this analysis pipeline is easily applied to human samples or any other species as ECM molecule organization is largely conserved across species. Moreover, we have already explored this using human samples. However, adding human data to this manuscript is beyond the scope of this manuscript which was aiming to build one of the first methodologies for incorporating the ECM into this kind spatial analysis from the start in order to make biological discoveries. Regardless, we will add a discussion point on utilizing these pipelines to other species within the discussion of the manuscript.

Flow cytometry has been published on this model and the exact samples used within this study as mentioned previously (PMID: 33587776), validating some of these findings – we will make this point more clearly in the manuscript. We do appreciate that it would be good to further expand on some findings presented in the manuscript. As such we will expand our immunostaining (as mentioned above) to give more detail on the infiltrating immune cell populations and their interactions with fibroblasts.

__Reviewer #2 (Evidence, reproducibility and clarity (Required): __

Summary Parkinson and colleagues provided a highly intriguing manuscript on spatial resolution of cell-ECM interaction in mouse models of allergic airway inflammation. They used IMH to analyse two common mouse strains for allergic airway inflammation with a human relevant allergen mix. The study implements a novel technique to better segment tissue stainings (DeepThresh) and modifies existing tools to assess non-cellular seqmentation, ECM or fibrous structures that is. The study identifies region-specific ECM and confirms cellular proximity with canonical cell markers. Furthermore, clear ECM and cellular differences between the two mouse strains are found. The study concludes that this IMH approach is superior to existing methods as it provides a high spatial resolution of ECM protein - cell interaction.

Major: ECM Isoform Annotation - The manuscript lacks precise annotation of ECM isoforms, particularly for Collagen I, IV, and VI. This impacts the accuracy of reported associations between ECM environments and cellular interactions.

We thank the reviewer for this excellent comment and pointing this out. We agree that this is very important and will add this data to the manuscript. All information was included by reference of the antibody clones. However, it is an important point to make and we will account for this during interpretation of the results.

Spatial Annotation Consistency- The manuscript inconsistently defines and annotates ECM environments (e.g., adventitial collagen, subepithelial & vascular ECM), leading to confusion in spatial correlation analyses.

We are unsure what the reviewer is exactly referring to here. We have maintained a consistent nomenclature for these annotations throughout the manuscript. If the reviewer has an issue with the names we have provided for the regions; names were chosen these to be more informative than just naming them “region 1, 2, 3…”. Names in the manuscript were based on taking the lung tissue region and the prominent ECM molecules present. Whilst some level of detail will naturally be lost, we considered this the best way to keep data clear and consistent throughout the manuscript. For example, adventitial collagen describes the region predominantly around the adventitial cuff (fig 3c and d; shown in dark blue) that has high levels of Collagen I, III and VI. Yes, HA, laminin and fibronectin are also expressed, but at much lower levels. Regardless, all the information is present within the figures with readers to observe and make their own interpretations. We are happy to consider alternative names if the reviewer were to provide some guidance on what they thought was more appropriate.

Lack of Supplemental Data- Activated cell types and regions are not clearly defined, and no supplemental data is provided to verify classifications. DeepThresh Validation - The method for removing staining artifacts via DeepThresh lacks clear validation. Complexity - Overlapping marker definitions (e.g., CD11b+ cells and infiltrating cells) need clarification for accurate immune cell characterization.

We provide heatmaps in the supplementary data which shows the exact marker expression pattern for all of the clusters we define (Sup Fig 1a). Additionally, we provide graphs showing the cellular contribution and spatial distribution of all the regions we defined with lisaClust (Fig 2h & I; Sup Fig 1d). Most activated cells are a feature of a specific clustered cell type only being present in either PBS or DRA treated animals. However, the features which have led to separation these cell types are available in the heatmaps as mentioned (Sup Fig 1a).

We believe the reviewer may be confused about the purpose of DeepThresh. This algorithm is not for removing staining artifacts. Instead it uses expert annotation of a small training set to generate a method of accurately thresholding images for positive staining in relatively small ROIs which may have diverse structural features with different staining properties. We did not have space in the manuscript to go into this in more detail. However, we appreciate this may not be as clear as needed for readers, and hence, will provide supplementary data showing some example thresholding alongside the original staining in a new edit of the manuscript.

CD11b+ and infiltrating cells are not an overlapping population, they were separately clustered by the algorithm, but we take the reviewers point that further characterisation could be done. As mentioned in comments from reviewer 1, there is a limitation in the number of markers we can use in IMC, especially with the number of ECM markers we included. Additionally, there are limitations in the appropriate antibodies (carrier-free) that work in FFPE mouse tissue with the antigen retrieval that we use for good, reliable staining of ECM components. As such, we will perform additional immunofluorescence staining in 3D precision cut lung slices to better characterize the CD11b+ population to address comments by both reviewers.

Minor: Terminology Inconsistency- The manuscript uses inconsistent terminology for ECM components and anatomical regions (e.g., adventitial collagen, immune foci, inflammatory zone).

This point was directly addressed above in “Major” points and appears to be a duplicate comment.

ROI Mask Coverage - Statistical insignificance in C57BL/6 ROI mask coverage is not addressed.

The increase in C57BL/6 mice upon DRA treatment in panel A is not “significant” in the modern sense of the word. However, we would argue that stating it is “not significant” would also be a mistake. We prefer to use p values as an inferential measure of significance in combination with measures such as effect size and variance (PMID: 8465801). We find this more useful considering the vast number of mistakes made when interpreting p values (PMID: 18582619). The importance of not purely relying on p values for clinical research has been reviewed recently here (PMID: 39909800).

Whilst we appreciate the reviewer’s requirement for significance, we do not want to make sweeping statements based off of a p value of 0.07, especially in only one experiment. Many papers have been published on the pitfalls of stringently adhering to p

Spelling Error - "Immunte foci" in Figure 4h.

We thank the reviewer for pointing this out and will correct this.

Figure 6g Correlation Issue- The matrix environment correlation plot does not align with expected cell-ECM interactions.

We find it hard to comment on this without more detail of the cell-ECM interactions that the reviewer believes should be occurring. We analysed this in an unbiased way, so we have not forced interactions to appear based on our preconceptions. The regions being analysed in Fig 6g are the resting (PBS) and activated (DRA) airways that contain expected cell populations of airway epithelial cells and a low level of fibroblasts, likely from just under the airway epithelial cells. These cell populations align with AEC-associated matrix, laminin and hyaluronan, and adventitial collagen regions. Perhaps the reviewer is questioning why the airways are associated with adventitial collagens? The reason behind this, is due to adventitial cuff residing adjacent to a proportion of all airways, and hence any ECM associated with the adventitial cuff will likely be included in an airway region. However, as mentioned previously there are limitations to this analysis and we are very likely missing finer details due to issues such as resolution which we have discussed within the point-by-point on numerous occasions, and something we will directly address by adding a limitations section to the discussion of the revised manuscript.

Color Issues in Figures - ColI and ColIII have the same color in Fig. 3a, making interpretation difficult.

We agree with the reviewer on this point. The issue we had here was that Col-I and Col-III strongly overlap in these images, whilst one was green and one yellow the effect was to make them look the same in the final images. We will remake these images with clearer colours that better illustrate differences in Col-I and Col-III expression.

Patch Annotation (Fig. 4i) - The method for defining immune cell patches is unclear.

Patches refers to an approach that is used to identify interconnected groups of similar cell types and is a method that is based off published data (PMID: 35363540). We will add further method details that explains this process to the revised manuscript.

Detailed review: Methods: Animal model is suitable for differential analysis of various mouse strain responses to allergic airway inflammation.

We thank the reviewer for this comment and also agree that the mouse models presented in the manuscript can provide insightful and mechanistic data for investigating human disease.

Deepthresh matrix thresholding: IMCDenoise is sensitive to clusters of staining artefacts (specks). Please explain how DeepThresh via manual thresholding enables staining artefacts removal/detection. Manual ground truth mapping is common however it is not clear how your approach is performing against another tool. How was manual thresholding controlled (several analysts thresholded same image)?

As described in a previous comment this is not the function of DeepThresh. Manual annotation for training data was performed by consensus agreement of four independent researchers. In terms of performance against another tool, we are not aware of another tool which performs this function and hence cannot compare. However, we will add additional data showing the validation metrics for the pipeline to make future comparisons easier.

Antibodies Collagen IV, stains col4a1 - please correct, as isoforms vary throughout tissue. Collagen VI, stains col6a1 - isoforms vary in lung tissue, please state correct isoform throughout the document. Heparan sulfate: Molecular weight? Collagen I - isoform not defined, please state in methods.

Figure 3 d As a resultant of the choice of antibodies against some particular isoforms of ECM molecules associations of cells, compartments are correct yet do not comply with all isoforms. Col4a1 is a basal membrane collagen from blood vessels; the adventitial area and vascular area are high in Col4a1. Other Col4 isoforms are found more frequently in the alveolar regions (col4a5,a6) and the subepithelial membrane. It is of utmost importance to clearly label the correct isoforms throughout the document.

This relates to the comment above made by reviewer 2. As mentioned, we agree with this key point and will provide this information from the respective antibody clones.

However, we are unable to provide details on the molecular weight of heparan sulfate as this will vary depending on location/tissue/condition etc. The antibody recognises 10E4 epitope on HS which is found across a wide variety of tissues and species and will recognise many different sizes of HS and even porcine Heparan. Importantly it is relatively specific, not cross reacting with hyaluronan, keratan sulphate, chondroitin sulphate, or dermatan sulphate which is an issue for certain clones. Whilst the size of the HS is an interesting facet, consideration of changes in sulphation patterns would also be of interest, though these currently cannot be accurately assessed via purely immunostaining-based methodologies and would require more targeted biochemical techniques. In addition to this there are multiple nuances in 10E4 antibody binding (PMID: 15044385 and 11278655) which are interesting, but far beyond the scope of this study. Although captured in the antibody clone information, we will also ensure this is clear in the methods.

In relation to Col4 isoforms specifically, often antibodies for the ECM are limited because of their repeating structures it is hard to generate specific antibodies. For collagen IV there many clones for Col4a1, but no specific clones for Col4a3/col4a5 etc, suitable for use in FFPE tissues and metal conjugation required for IMC. Therefore, we were very limited in what was available to detect them at all. We will bring this up in the discussion as this is an important point, not just for our data, but also for people attempting to replicate this kind of analysis.

Figure 2i: The cell-specific marker expression is in part already confounded by region. So vasculature or resting airways show no "resting" fibroblasts as their annotation is linked to activation (indicated by S100A4 expression). Anatomic locations such as airways with remodelling are termed "activated" to explain morphological differences which is acceptable given the model chosen. However, some cell type are not given an anatomical or morpholocial "resting" nomenclature. Only during activation and through location a cell type may aquire e.g. a nomenclature such as "alveolar fibroblast". The correlation blot 2i should provide this basic information. Please explain.

Our staining approach and analysis have only identified certain activated populations as pointed out by the reviewer. Most of the populations that we have identified as “activated” have been identified primarily only in mice administered DRA. The reason that we have not included “resting” and “activated” populations for all cell types is that these clusters were generated using a clustering algorithm based on the cellular markers used within the study. Each cluster was then simply labelled as best we could, using information from marker expression, published biological data, anatomical location, and sample identity (e.g. PBS or DRA).

A caveat to using IMC and other similar imaging techniques is that we will miss certain “flavours” of cell populations because we simply do not have the markers, or scope to include markers, with which to identify these cells. This is partly a problem of appropriate antibody availability, but also for many populations there are no specific markers identified in the literature/databases. Single cell RNAseq has provided deep segmentation of some of these populations, but we (and others) have found that often these make poor antibody choices at the protein immunostaining level.

We are unsure what the reviewer wants adding to plot 2i. This plot shows the cell cluster contribution to different lisaClust defined tissue regions. Hence the presence of alveolar fibroblasts in the resting and activate alveoli region. However, we will include more discussion on the limitations of markers and identification of specific cell populations in the discussion.

Figure 2h: How do you explain subepithelia to "leak" luminally in C57BL/6 DRA animal?

We assume the reviewer is referring to the overlap of some grey circles though/over the red airway epithelial cells in the C57BL/6 DRA panel of figure 2h. This figure represents individual cells as circles with the centroid of the circle at the centroid of the cell. Cells are rarely perfect circles and, in this case, it has made it seem like the cell is coming through the airway epithelium, when likely it is a longer cell that sits directly under it. In addition to this, these are effectively 2-dimensional section (5um thick) that capture as small portion of the lung anatomy, hence occasionally this can result in unusual tissue structures that make no sense in the confines of a 2D section, but instead correlate with the larger 3D structure.

How is an activated airway possible in a Balb/c PBS animal (same for inflammatory adventitia, alveoli)?

Activated airway simply describes a region that is showing some evidence of activation markers such as RELMα and/or Ym2 etc. PBS itself, as with any other liquid administered into the lungs, will drive a very low level of inflammation, which is why it is used as a control in the animal model. Therefore, it is not surprising that we see a low number of these “activated” cells in PBS animals vice versa for their activated counterparts in DRA treated animals. This is similar for the other regions mentioned.

How is subepithelia adjacent to immune foci and inflammatory adventitia (Balb/c DRA).

We are somewhat confused by this question. We have termed the region “subepithelia” because it is mostly found under the airway epithelial cells. We found that this region expands during DRA treatment and covers areas close to the immune foci and inflammatory adventitia, hence they are next to each other.

As described above, the names of these regions were chosen for simplicity and to communicate its general features. These, regions were identified by detection of nearby regions of cells with similar cellular compositions and the names we a “best fit”.

Text for fig 3c: Here it should be mentioned that a cell is used as a proxy locator to the ECM region.

We apologise that this was unclear for the reviewer. Rather than describing it as using the cell as a proxy locator to the ECM region we find it more accurate to think of it as we are characterizing the matrix environment of the cell i.e. what is the cell close to and what is it far away from. We will make this clearer in the results by changing the name to cellular matrix environment, rather than matrix environment.

Again, in UMAP3b location and ECM molecule a mixed a priori which only can be achieved through proxy loction as in fig 3c or correlation matrix analysis. The UMAP shows ECM molecules in various combinations. Fig3c analysis of anatomic location from images with cell proxies would validate morpho-spatial UMAP annotation. Please make this clear in the manuscript or specify why your approach is superior in its presented format.

We struggled to ascertain what the reviewer was referring to here and what edits they were suggesting to the revised manuscript. However, this comment seems to assume that we have used cellular location as an input to the UMAP in figure 3b, which is untrue. This UMAP (and associated clustering) shows each cell as a dot which is organised based on its distance to the different matrix components. Effectively showing us how different cells cluster based on their cellular matrix environment, with no input of cellular based markers. We are unsure what the reviewer is referring to on line 486 – as they seem to be describing exactly what figure 3c already is (a spatial map of the UMAP clusters on representative images, which shows that a cells matrix environment does seem to show patterns that align with the general lung anatomy).

Finally, the reviewer asks us to specify why our approach is superior, but we are unclear what the alternative approach is.

This methodology is effectively a repurposing of the traditional UMAP and clustering methodology used in many single cell techniques, but instead of applying this to cellular markers we are applying it to a cells matrix environment as quantified by the matrix distances. If the reviewer could clarify this comment we would be happy to revisit it. As mentioned in the previous comment, we will more clearly describe cellular matrix environments in the revised manuscript and this may also help with the confusion.

Fig 3d: The Matrix Cluster names are in part not correct. Subepithelial & Vascular ECM does not correlate with Vasculature in LisaClust Regions. Also ColIV is not AEC associated, yet subepithelial.

Respectfully, we completely disagree with the reviewer on this point. In the heatmap (Fig 3d) the Subepithelial & Vascular matrix environment correlates most strongly with the Vasculature and Subepithelial cells as shown by the stronger green-yellow colour in the corresponding cell of the heatmap.

As mentioned previously in response to another comment by reviewer 2, there could be many reasons that we are not detecting collagen-IV in the AEC associate cell matrix environment. One likely explanation is that this is too fine for the resolution of IMC (1-micron2) or it could be that certain subchains are utilised here that are not recognized by the antibody we managed to optimize. Additionally, AEC-associated matrix environment is comprised of both mouse strains and includes higher representation from DRA treated animals. From our previous work (PMID: 33587776), we have shown that Col-IV expression around the AEC is reduced in DRA versus PBS -treated animals.

No ECM molecule is inflammatory zone associated. Does this indicate cellular density does not allow to distinguish ECM?

This is a great point from the reviewer and their explanation is entirely possibly. As mentioned there are huge limitations in the resolution of IMC and so we are likely missing finer matrix structures. There is a huge recruitment of cells within this environment so it could be that we cannot clearly visualise fine ECM structure through this considering we are also looking at a 5-micron thick 2D tissue section. Additionally, cells maybe degrading the ECM in order to infiltrate into the tissue. This is definitely an interesting point to examine in further detail, but would need to be done with a different methodology. We will aim to look at an ECM molecules and its distribution within the inflammatory zone using 3D precision cut lung slices and also immune-staining of tissue sections to see whether we can better resolve this in a revised manuscript.

Also the term "adventitial collagen" is locating to LisaClust Regions Vasculature, Subepithelial Cells, Resting Airways, Infiltrating Cells, Activated Airways. Adventitial per definition of fig. 2g is around blood vessels extending to airways and around it. The adventitial regions are the ECM rich areas after the fibroblasts (as for blood vessels, PMID: 31522963). The definition used in this study therefore generates morphological overlaps between airways and their basolateral regions and blood vessels. Whilst both morphological regions have an adventitia the Matrix cluster assumes from areas to close by this terminology. As a sensitivity analysis I would suggest to reduce the perimeter around blood vessels to the same borderline as seen in airways. If composition remains similar "adventitial collagen" could be a broader term. Alternatively, if adventitia from airway and blood vessel differ these should be separate terms.

Whilst the adventitial cuff does refer to the region immediately around a blood vessel in the lung, these structures are slightly more nuanced as blood vessels normally travel through the lung in close association with an airway. This is true all the way down to the close association with the capillaries and the alveolar spaces where gas exchange occurs. Indeed, previous publications (PMID: 30824323) have shown that these adventitial cuffs extend out from around the contiguous area around the blood vessel and associated airway and these can expand during inflammation (PMID: 24631179). This region is rich in Collagen-I and Collagen-III, as we have shown in this manuscript and previously (PMID: 33587776).

Whilst we agree that there are likely microanatomical niches within this larger structure, our dataset lacks the resolution to study this in more detail. However, as mentioned above we can include matrix markers in our future IF staining to examine this region in more detail. The adventitial collagen environment described in this manuscript and beyond, are vital “meet and greet” spots for immune cell infiltrating into the lungs (PMID: 30824323) as well as being sites of iBALT formation (PMID: 24631179)

We are unsure what the reviewer means by “…reduce the perimeter around blood vessels to the same borderline as seen in airways.” We have not defined a manual threshold for the border of the airways. These regions were all defined by SNN clustering and not manual segmentation. Whilst this methodology could be developed we do not believe that this dataset has the resolution to answer this question, as mentioned previously.

Fig 4c: Balb/c and C57bl/6 labels are incorrect (see a,b)

We thank the reviewer for noticing this incorrect labelling and will update this.

Fig 4h: Cell type "other is highly present in immune foci and inflammatory adventitia but not further classified and not myeloid. This seems either a difficult definition for myeloid or a significant immune population wasn't stained. How is myeloid defined?

We define myeloid broadly as CD11b+ or alveolar macs. There were certain populations that were not stained, notably T cells. We were unable to have suitable or reliable staining in FFPE tissue with CD90, TCRa/b, CD3e antibodies via IMC. The same was true for Eosinophil markers (SiglecF, Ccr3, EPO, MBP). The additional experiments we will perform for a revised manuscript (using 3D precision cut lung slices and/or IF staining) should shed further light on these cells. Additionally, as we are not limited by the processing requirements of IMC, we can use a wider range of markers.

Fig 4l has a vast variety of marker combinations some being very specific within the staining panel, others subsummarise entire groups of cells. It would be very helpful to know if the lables are specific and exclusive or if larger clusters exist, that they then subdivide into specific groups (e.g. Infiltrating cells: any of CD11b, CD44, Ly6C vs. B-cells or CD11b+Lys6C). This graph would profit also from either using markers or cell types only. Your marker set is very distinct and limited so per definition it is either a neutrophil or a Lys6C+. Please decide, explain and provide the other graph as supplement.

We apologise that this was not clear to the reviewer. Labels are exclusive and represent the clusters that were identified in figure 2 and are at the finest level of detail that we felt we were able to biologically infer from the data. In terms of the reviewer’s first point about infiltrating cells, these are completely separate from the other cell types mentioned. As mentioned in the previous comment line 570, we were simply unable to find working antibodies for some of the common lung populations (a common problem for FFPE sections where antigens are often masked or lost due to fixation and processing) and so are limited to general annotations for these. For the reviewer’s second example of Neutrophils vs Ly6C+ cells, neutrophils were classified by expression of Ly6G, CD11b+, and Ym1 whereas there are many other cell types that express Ly6C, including, but not limited to, dendritic cells, monocytes, eosinophils, and even some T cells.

We believe that the graph in combination with data in Fig 1c and supplementary Fig 1a, already shows what the reviewer is asking for.

Fig 5l and sup Fig4i: There is no graph confirming the statement that Ym1 is produced by macrophages. From the graphs in either of the two panels, The AEC are highly associated with Ym1/2 expression or the activated alveoli. Please explain ad amend.

We assume the reviewer means Fig 5l and sup Fig 5i (as there is no figure sup Fig 4i). Whilst we did not include a graph to show that alveolar macrophages produce Ym1, we did include two references in the text and this has been widely shown in the literature for many years (PMID: 11141507 and 15148607). We are somewhat unclear on the reviewers second point. AEC (airway epithelial cells) can definitely also produce Ym1, though this can often be contentious because of issues with cross-reactivity with its highly homologous sister protein Ym2, which is also produced from airway epithelial cells under Type-2 settings. If the reviewer is referring to AEC (alveolar epithelial cells) then this is true. Activated alveoli are lisaClust regions with lots of alveolar macrophages which was the original statement we made and aligns with sup Fig 5i. Activated alveoli II have less alveolar macrophages and also have less Ym1, which would correlate though there are other cell types which can make Ym1 as well.

Fig 6g: The correlation plots again show that the matrix environment labels are somewhat confounded. Whilst AEC associated makes perfect sense, adventitial collagen only weakly correlates, yet was part of the adventitial mapping. Cell types like AEC are expected however fibroblasts, especially in resting airways as large constituent cell populations. There are not other, myeloid or lymphoid cells associated with these airways, which under activated conditions seems rather odd. From fig6a it is appearant that the lisaClust has ascribed subepithelial regions to distal parts of the airway separated by blood vessel or parenchyma (C57BL/6 and Balb/c DRA). Also blood vessels are in part other cell types or epithelium (B6 PBS). Is the annotation here the reason for this rather confusing result? Please explain and/or amend.

We are again somewhat confused by this comment. Adventitial collagen only weakly correlates because it is not within the airway epithelial cells, instead it is adjacent in the subepithelial region which is shown in Fig 6j. We are unsure exactly what the reviewer is referring to in terms of “adventitial mapping” but are happy to comment on this if the reviewer can clarify what they mean.

We agree with the reviewer that it is somewhat surprising to see so many fibroblasts in the resting and activated airway regions. There is a level of ambiguity here in what lisaClust decides to include in one region vs another. However, what it does show is that there are a large population of fibroblasts around the airway, possibly correlating with peribronchial fibroblasts. We did not observe immune cells in between the airway cells or immediately underneath it. We do not believe this is odd, as from our data it appears that these cells are more likely to be found in the adventitial (including peribronchial as mentioned previously) cuff. Cell are most certainly moving into the airways as shown from the BAL in our previous publication (PMID: 33587776). However, we are unlikely to capture this process in the snapshot of our histology across a relatively small section of the airways covered in our 2D sections.

In regards to the reviewers comment about figure 6a we agree that some of the regions between the airways and blood vessels have been characterised as subepithelia. As mentioned previously we are happy to consider alternative names but have been unable to come up with an alternative that encompasses the cells and spatial region more accurately and clearly., Regardless, the main purpose of these names is to provide simple nomenclature to follow throughout the manuscript and make these types of analyses accessible to all readers. We believe that these are accurately labelled and have provided information about the constituent cell populations that are present within them, making the data and subsequent analysis transparent for others to view and explore. Our data suggests that the adventitial cuff may fulfil multiple roles during DRA-induced inflammation, some of which are more focused on immune cell recruitment and others which may correlate more with the fibroblast rich subepithelial region.

The reviewer is entirely correct to point out that some blood vessels were not entirely annotated. We used vWF to manually separate blood vessels from the adjacent smooth muscle layers, which were not separated by the clustering originally. Notably it appears that veins seem to not separate as well as arteries suggesting another marker (e.g. CD31) may help with this, though we were limited in what we could include as mentioned previously. As this is only a small effect, which we do not have a way to correct, and blood vessels were not the focus of this manuscript, we have left the annotation as it is with raw data included.

__Significance __

Strength Innovative ECM-Immune Interaction Approach- The study integrates extracellular matrix (ECM) phenotyping with immune cell spatial mapping, providing novel insights into allergic airway inflammation Multiplex Imaging Technology - The use of Imaging Mass Cytometry (IMC) allows high-resolution spatial characterization of both cellular and ECM components. Strain Analysis - The inclusion of BALB/c and C57BL/6 mice enables differentiation of strain-specific ECM and immune responses. Deep-Learning-Based ECM Quantification - DeepThresh offers an advanced computational approach for ECM analysis, enhancing accuracy in defining ECM-cell associations. Comprehensive Tissue Classification- LisaClust clustering facilitates detailed segmentation of lung microenvironments, improving understanding of localized tissue remodeling.

Limitations ECM Isoform Inconsistencies - The study lacks precise annotation of ECM isoforms, which affects the accuracy of reported ECM-cell interactions. Ambiguous Spatial Correlations- Some ECM clusters, such as "adventitial collagen," overlap inconsistently with anatomical regions, making interpretation challenging. Unvalidated DeepThresh Method - The manuscript does not provide sufficient validation of DeepThresh's ability to remove staining artifacts. Lack of Supplemental Data- Key activated cell types and regions lack supporting data for classification.

__Advance, gap filled __ Clearly the next step to improve organ compendia such as the lung cell atlas, spatial protein analysis is warranted. scRNA-Seq in particular for ECM molecules is challenging as these molecules are produced in small quantities or have a very slow turn-over. This study has the potential to provide novel deep learning algorithms to include not only cellular markers but consider larger panels of ECM molecules and their spatial orientation in the lung.

__Audience __ The manuscript is interdisciplinary located between advanced image analysis with deep learning methods, fundamental lung biology and single cell analysis. The readership would entice molecular biologists, bioinformaticians and basic disease model scientists. The manuscript would appeal to clinician scientists and a broader audience if human tissue pendants could be provided validating the methods and outcomes.

__Own Expertise __ Translational scientist in the field of chronic lung disease, highly familiar with epithelial cells, mouse models, human cohorts and next generation sequencing and imaging of live single cells.

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Referee #2

Evidence, reproducibility and clarity

Summary

Parkinson and colleagues provided a highly intriguing manuscript on spatial resolution of cell-ECM interaction in mouse models of allergic airway inflammation. They used IMH to analyse two common mouse strains for allergic airway inflammation with a human relevant allergen mix. The study implements a novel technique to better segment tissue stainings (DeepThresh) and modifies existing tools to assess non-cellular seqmentation, ECM or fibrous structures that is. The study identifies region-specific ECM and confirms cellular proximity with canonical cell markers. Furthermore, clear ECM and cellular differences between the two mouse strains are found. The study concludes that this IMH approach is superior to existing methods as it provides a high spatial resolution of ECM protein - cell interaction.

Major:

ECM Isoform Annotation - The manuscript lacks precise annotation of ECM isoforms, particularly for Collagen I, IV, and VI. This impacts the accuracy of reported associations between ECM environments and cellular interactions. Spatial Annotation Consistency- The manuscript inconsistently defines and annotates ECM environments (e.g., adventitial collagen, subepithelial & vascular ECM), leading to confusion in spatial correlation analyses. Lack of Supplemental Data- Activated cell types and regions are not clearly defined, and no supplemental data is provided to verify classifications. DeepThresh Validation - The method for removing staining artifacts via DeepThresh lacks clear validation. Complexity - Overlapping marker definitions (e.g., CD11b+ cells and infiltrating cells) need clarification for accurate immune cell characterization.

Minor:

Terminology Inconsistency- The manuscript uses inconsistent terminology for ECM components and anatomical regions (e.g., adventitial collagen, immune foci, inflammatory zone). ROI Mask Coverage - Statistical insignificance in C57BL/6 ROI mask coverage is not addressed. Figure 3d Labeling- Matrix cluster names do not always match tissue localization. Spelling Error - "Immunte foci" in Figure 4h. Figure 6g Correlation Issue- The matrix environment correlation plot does not align with expected cell-ECM interactions. Color Issues in Figures - ColI and ColIII have the same color in Fig. 3a, making interpretation difficult. Patch Annotation (Fig. 4i) - The method for defining immune cell patches is unclear.

Detailed review:

Methods: Animal model is suitable for differential analysis of various mouse strain responses to allergic airway inflammation.

Deepthresh matrix thresholding: IMCDenoise is sensitive to clusters of staining artefacts (specks). Please explain how DeepThresh via manual thresholding enables staining artefacts removal/detection. Manual ground truth mapping is common however it is not clear how your approach is performing against another tool. How was manual thresholding controlled (several analysts thresholded same image)?

Antibodies Collagen IV, stains col4a1 - please correct, as isoforms vary throughout tissue. Collagen VI, stains col6a1 - isoforms vary in lung tissue, please state correct isoform throughout the document. Heparan sulfate: Molecular weight? Collagen I - isoform not defined, please state in methods.

Figure 3 d As a resultant of the choice of antibodies against some particular isoforms of ECM molecules associations of cells, compartments are correct yet do not comply with all isoforms. Col4a1 is a basal membrane collagen from blood vessels; the adventitial area and vascular area are high in Col4a1. Other Col4 isoforms are found more frequently in the alveolar regions (col4a5,a6) and the subepithelial membrane. It is of utmost importance to clearly label the correct isoforms throughout the document.

Spelling error in figure 4 h (immunte foci)

ROI mask coverage in C57/6 not significant

Activated cell types/region: This definition is not specified and no supplemental data is provided to see which markers classify such areas/cells. Please provide.

Figure 2i: The cell-specific marker expression is in part already confounded by region. So vasculature or resting airways show no "resting" fibroblasts as their annotation is linked to activation (indicated by S100A4 expression). Anatomic locations such as airways with remodelling are termed "activated" to explain morphological differences which is acceptable given the model chosen. However, some cell type are not given an anatomical or morpholocial "resting" nomenclature. Only during activation and through location a cell type may aquire e.g. a nomenclature such as "alveolar fibroblast". The correlation blot 2i should provide this basic information. Please explain.

Figure 2h: How do you explain subepithelia to "leak" luminally in C57BL/6 DRA animal? How is an activated airway possible in a Balb/c PBS animal (same for inflammatory adventitia, alveoli)? How is subepithelia adjacent to immune foci and inflammatory adventitia (Balb/c DRA).

Fig 3a: ColI and ColIII have same colour, this makes images not easy to understand please change. Text for fig 3c: Here it should be mentioned that a cell is used as a proxy locator to the ECM region. Again, in UMAP3b location and ECM molecule a mixed a priori which only can be achieved through proxy loction as in fig 3c or correlation matrix analysis. The UMAP shows ECM molecules in various combinations. Fig3c analysis of anatomic location from images with cell proxies would validate morpho-spatial UMAP annotation. Please make this clear in the manuscript or specify why your approach is superior in its presented format.

Fig 3d: The Matrix Cluster names are in part not correct. Subepithelial & Vascular ECM does not correlate with Vasculature in LisaClust Regions. Also ColIV is not AEC associated, yet subepithelial. No ECM molecule is inflammatory zone associated. Does this indicate cellular density does not allow to distinguish ECM? Also the term "adventitial collagen" is locating to LisaClust Regions Vasculature, Subepithelial Cells, Resting Airways, Infiltrating Cells, Activated Airways. Adventitial per definition of fig. 2g is around blood vessels extending to airways and around it. The adventitial regions are the ECM rich areas after the fibroblasts (as for blood vessels, PMID: 31522963). The definition used in this study therefore generates morphological overlaps between airways and their basolateral regions and blood vessels. Whilst both morphological regions have an adventitia the Matrix cluster assumes from areas to close by this terminology. As a sensitivity analysis I would suggest to reduce the perimeter around blood vessels to the same borderline as seen in airways. If composition remains similar "adventitial collagen" could be a broader term. Alternatively, if adventitia from airway and blood vessel differ these should be separate terms.

Fig 4c: Balb/c and C57bl/6 labels are incorrect (see a,b) Fig 4h: Cell type "other is highly present in immune foci and inflammatory adventitia but not further classified and not myeloid. This seems either a difficult definition for myeloid or a significant immune population wasn't stained. How is myeloid defined?

Fig 4l has a vast variety of marker combinations some being very specific within the staining panel, others subsummarise entire groups of cells. It would be very helpful to know if the lables are specific and exclusive or if larger clusters exist, that they then subdivide into specific groups (e.g. Infiltrating cells: any of CD11b, CD44, Ly6C vs. B-cells or CD11b+Lys6C). This graph would profit also from either using markers or cell types only. Your marker set is very distinct and limited so per definition it is either a neutrophil or a Lys6C+. Please decide, explain and provide the other graph as supplement.

Fig 5l and sup Fig4i: There is no graph confirming the statement that Ym1 is produced by macrophages. From the graphs in either of the two panels, The AEC are highly associated with Ym1/2 expression or the activated alveoli. Please explain ad amend.

Fig 6g: The correlation plots again show that the matrix environment labels are somewhat confounded. Whilst AEC associated makes perfect sense, adventitial collagen only weakly correlates, yet was part of the adventitial mapping. Cell types like AEC are expected however fibroblasts, especially in resting airways as large constituent cell populations. There are not other, myeloid or lymphoid cells associated with these airways, which under activated conditions seems rather odd. From fig6a it is appearant that the lisaClust has ascribed subepithelial regions to distal parts of the airway separated by blood vessel or parenchyma (C57BL/6 and Balb/c DRA). Also blood vessels are in part other cell types or epithelium (B6 PBS). Is the annotation here the reason for this rather confusing result? Please explain and/or amend.

Significance

Strength

Innovative ECM-Immune Interaction Approach- The study integrates extracellular matrix (ECM) phenotyping with immune cell spatial mapping, providing novel insights into allergic airway inflammation Multiplex Imaging Technology - The use of Imaging Mass Cytometry (IMC) allows high-resolution spatial characterization of both cellular and ECM components.<br /> Strain Analysis - The inclusion of BALB/c and C57BL/6 mice enables differentiation of strain-specific ECM and immune responses.<br /> Deep-Learning-Based ECM Quantification - DeepThresh offers an advanced computational approach for ECM analysis, enhancing accuracy in defining ECM-cell associations.<br /> Comprehensive Tissue Classification- LisaClust clustering facilitates detailed segmentation of lung microenvironments, improving understanding of localized tissue remodeling.

Limitations

ECM Isoform Inconsistencies - The study lacks precise annotation of ECM isoforms, which affects the accuracy of reported ECM-cell interactions.<br /> Ambiguous Spatial Correlations- Some ECM clusters, such as "adventitial collagen," overlap inconsistently with anatomical regions, making interpretation challenging.<br /> Unvalidated DeepThresh Method - The manuscript does not provide sufficient validation of DeepThresh's ability to remove staining artifacts.<br /> Lack of Supplemental Data- Key activated cell types and regions lack supporting data for classification.

Advance, gap filled

Clearly the next step to improve organ compendia such as the lung cell atlas, spatial protein analysis is warranted. scRNA-Seq in particular for ECM molecules is challenging as these molecules are produced in small quantities or have a very slow turn-over. This study has the potential to provide novel deep learning algorithms to include not only cellular markers but consider larger panels of ECM molecules and their spatial orientation in the lung.

Audience

The manuscript is interdisciplinary located between advanced image analysis with deep learning methods, fundamental lung biology and single cell analysis. The readership would entice molecular biologists, bioinformaticians and basic disease model scientists. The manuscript would appeal to clinician scientists and a broader audience if human tissue pendants could be provided validating the methods and outcomes.

Own Expertise

Translational scientist in the field of chronic lung disease, highly familiar with epithelial cells, mouse models, human cohorts and next generation sequencing and imaging of live single cells.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

In this study, Parkinson et al. investigated lung extracellular matrix using imaging mass cytometry (IMC) in mouse models. Overall, the paper is well-written, and the data are clear, although major points outlined below need to be addressed. In its current form, the paper appears more like a methods-focused study since, to my understanding, no new biological responses are described. The methods employed are very interesting, particularly the extracellular matrix analysis. However, the scope of the study is quite limited, as all the experiments were performed with mouse samples, which are relatively easy to work with, and the cell organisation is simple compared to humans. The authors do not discuss how this analysis pipeline could be applied to human samples. Furthermore, the entire paper relies on imaging mass cytometry, and additional techniques could have been used to confirm some of the observations, especially given the availability of mouse samples. The introduction mentions the DRA model without providing an explanation of what it involves. Non-specialist readers may not be familiar with this abbreviation, and further clarification should be provided. In the methods section, it is not mentioned whether the lungs were inflated before tissue collection, which is crucial for preserving normal cellular organization. The authors should clarify whether this was performed. Figure 1 provides a brief summary of the methods employed in the study but could be enriched with additional information. In its current state, it does not provide meaningful insights beyond what is described in the methods section. It would be helpful if the authors clarified whether the mice used were adults and whether both male and female animals were included. Additionally, they could present examples of the cell segmentation approach with zoomed-in images at the cellular level to illustrate the analysis. The first set of data in Figure 2 suggests that C57Bl/6 mice did not respond to allergen treatment, as shown by the non-significant increase in cell numbers. The authors should provide evidence that their model induced inflammation through alternative methods, such as assessing eosinophil counts or pathology. The UMAP representation indicates significant overlap between cell clusters, which raises concerns about the accuracy of cell segmentation. For example, the heatmap in Supplementary Figure 1 shows endothelial cells expressing markers such as VWF, aSMA, Vimentin, and PDGFRα, suggesting that the cell cluster may contain a mixture of endothelial cells, vascular smooth muscle cells, and fibroblasts. Examples of segmented cells should be shown to validate this approach. It is unclear what Figure 2e represents. If it is simply to show that certain clusters can be grouped together, such as AEC, AT1, and AT2 as epithelial cells, this could be conveyed in a simpler way. The analysis of extracellular matrix components presented in Figure 3 provides a novel method for studying these acellular structures, which is a challenge in the field. The authors should be commended for their efforts in this area. However, the parameters investigated in Figures 4-6 do not report any novel findings. While IMC appears to work effectively to analyse these parameters simultaneously, the induction of immune foci and changes in tissue organisation following allergen challenges are already well-documented in both mouse models and human samples.

In Figure 5, the authors show a decrease in neutrophil numbers in challenged mice. This is unexpected, as this model is widely known to induce strong neutrophil recruitment. The authors should clarify this finding and investigate whether neutrophil chemoattractants are increased in these samples. When analysing epithelial cells, the authors separate AT1 and AT2 cells based on podoplanin expression. However, data in Supplementary Figure 4b suggest that both cell types express similar levels of podoplanin. The authors do not provide statistical validation for the claim that AT1 cells express higher levels. Additionally, as podoplanin is expressed by various cell types, such as lymphatic endothelial cells, additional markers are required to confirm the identity of AT1 cells. The last set of data in Figure 7 is interesting and shows that immune cells interact with a population of S100a4 fibroblasts. This finding could be expanded further, as CD11b and Ly6C are expressed by a variety of immune cells. The authors should include additional staining to identify the specific cell types involved, such as monocytes, eosinophils, or airway macrophages. Furthermore, the authors should speculate on why these fibroblast regions attract immune cells. Are these regions enriched in chemokines or other factors? The discussion is engaging but focuses more on methodological aspects than new biological insights. Without mechanistic links, it is challenging to draw meaningful biological conclusions.

Significance

The study of Parkinson et al. provides interesting methodological insights into the use of imaging mass cytometry (IMC) to analyse lung architecture following inflammation. The application of multiplex antibody staining will leverage important information related to how tissues are adapting to chronic immune response. Here the authors rely entirely on mouse models for their studies and compared two lines of WT animals and the same allergen model. This limits the scope of the study, additional timepoints, sex or age would have improved the manuscript.

The applicability of this analysis pipeline to human tissue samples is not discussed, which would significantly enhance the impact of the study. Additionally, complementary techniques, such as flow cytometry or immunohistochemistry, could be used to validate the findings and improve reproducibility. A specialised audience of immunology researchers would be interested by the image analysis approach.

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Reply to the reviewers

Reply to the Reviewers

We are very grateful to the reviewers for their time and care in reviewing our manuscript. We have tried to incorporate all of their feedback to the best of our ability, and we feel that this has greatly improved the manuscript.

Reviewer #1

This study provides a strong support for the relationship between replication starting point competition and initial factor concentration. However, some predictive conclusions, such as "the origin of high efficiency may not be activated earlier", are still preliminary. Can the author further clarify the scope of these predictions and any potential mechanism in the discussion part to improve the rigor of this study?

__Response: __In the discussion, we now emphasize the complexity of predicting origin firing time distributions, which are influenced by multiple interrelated factors beyond efficiency alone.

The resolution and accuracy of the model prediction are obvious to all, but the specific generalization ability is still unknown, which makes the further promotion slightly insufficient. Does the author consider conducting additional experiments? To detect the replication time and efficiency in yeast cells with changed levels of key initiation factors (such as Cdc45 or Dpb11). The empirical data can be compared with the model prediction by editing CRISPR gene or manipulating the initial factor abundance through overexpression vector.

__Response: __We fully agree that this would be a very interesting direction, but as this is a theoretical study focused on mathematical modelling, conducting further wet lab experiments would be beyond the scope of this work.

The model currently uses single values for the initiation factor number and recycling rate, though these parameters may vary across cell cycles or under different growth conditions. It is suggested that sensitivity analysis should be added to supplementary materials to explore how the changes of these parameters affect the model output, such as replication time distribution and origin efficiency.

__Response: __Sensitivity analysis of how the model fit and validation is affected by using different recycling rates and initial firing factor counts will be conducted.

While the authors use mean absolute error (MAE) to assess model fit, it is suggested to add other statistical methods, such as root mean square error or correlation analysis, to further evaluate the model's accuracy and robustness. In addition, this model lacks comparison with other studies on fitting yeast replication time, and it is difficult to evaluate the effect of this model compared with other models from the specific performance.

__Response: __We have now included the root mean squared error (RMSE) alongside the mean absolute error (MAE) and R-squared value to compare the simulated replication timing profiles with the experimental data. We agree that we could have been more detailed in comparing our model to other approaches. We have now added a lengthened discussion of this. In some cases, a direct comparison of performance is difficult due to fundamental differences between the approaches, but we have highlighted why this is the case.

Although the code is open, it is suggested to provide specific instructions or examples of the running code in supplementary materials, so as to facilitate reproduction and application by other researchers.

__Response: __The GitHub repository will be updated to enable the running of the entire pipeline. This update will include code for processing replication timing data from Müller et al. (2014) and extracting origin positions from the OriDB. Code will also be provided for writing Beacon Calculus scripts with different parameters and origin firing rates. Instructions on the recommended sequence in which scripts should be executed will also be provided. To enable users to run the model locally on their own computers, a smaller version focused on chromosome 2 will be included in the supplementary information and GitHub repository, along with example input data and expected outputs.

In Figure 2(a), compared with other chromosomes, the fitting effect of chromosome 1 seems to be not good. Has the author ever thought about the reason? In addition, what is the guiding significance of this model in practical applications, such as online services, forecasting tools, or experiments? Can the author give relevant application examples in this regard?

__Response: __Potential explanations for the poorer fit of the replication timing profile for chromosome 1 are now discussed. The y-axis range has also now been set as the same for all subplots in Figure 2a to make the replication timing profiles for each chromosome more easily comparable. In the discussion, we highlight how the intuitive and flexible nature of the model places it as a valuable tool which could be adapted to predict the effect of different perturbations on DNA replication dynamics.

Reviewer #2

In figure 5, the authors demonstrate that replication dynamics are robust to an increase in the number of available firing factors. However, experimental data from strains in which these limiting factors are overexpressed indicate that replication dynamics are substantially altered (e.g. PMID 22081107 and 23562327) since dNTPs become limiting. So the conclusions of the analysis in figure 5 are at best an oversimplification and at worst rather misleading. If adding dNTPs as a factor that becomes limiting only at higher firing factor concentrations is not technically feasible, the authors should be more circumspect in their description and discussion of the results in figure 5.

__Response: __We now discuss the interpretation of the effect of increasing the number of firing factors, given that factors such as dNTP availability are not included in the model.

The analysis of replication dynamics appears to exclude origins within the rDNA, which in the average strain account for ~20-25% of all replication origins in S. cerevisiae depending on the origin list chosen. Ignoring this large number of origins likely has a substantial impact on the model: if rDNA origins are intentionally ignored due to the difficulty of modeling repetitive regions or of having multiple identical origins in the competition model, this should be explicitly addressed in the text.

__Response: __We now emphasize that our model restricts initiation to specific sites and note that some low-efficiency origins, such as those in rDNA, have not been included.

Reviewer #3

Can the authors provide some insight into the model's dependency on the Müller, 2014 replication data set? They initialize and converge to this dataset so this paper's findings are highly contingent on treating this data set as ground truth.

__Response: __In the discussion, we now highlight that, despite the model's reliance on the Müller, 2014 replication data set for fitting, its ability to reproduce other features of DNA replication demonstrates its ability to reflect DNA replication dynamics more broadly.

The authors describe their model as one that simplifies the origin firing mechanisms compared to more complex models. Is there a direct comparison available that can quantify this advantage? Likewise, how does their model compare to a naive discriminative model, such as one that performs peak finding on the replication timing data. For example, the replication fork directionality can be estimated, naively, using a peak finding algorithm. This type of analysis will provide a stronger argument for the usage of their model.

__Response: __Quantitative comparisons between our model and other published models are challenging due to differences in underlying assumptions and metrics used to assess goodness of fit. However, we have now added a discussion addressing these challenges and highlighting how our model's design contrasts with that of other models.

Currently the code is available as supplemental data. Ideally, the code should be available and provided to run the entire pipeline beginning with the initialization of the origin firing program from the Müller, 2014 data set.

__Response: __The GitHub repository will be updated to enable the running of the entire pipeline. This update will include code for processing replication timing data from Müller et al. (2014) and extracting origin positions from the OriDB.

The authors mention that origin firing factors and their recycling time to be the basis of how this model is constructed. While also describing the recycle time as a general timing delay that is dependent on a number of reasons such as diffusion and replisome complex formation. Can the authors discuss the limitations of their model towards this simplification?

__Response: __Limitations of our model's assumptions of constant recycling rates of firing factors are now discussed, as well as our assumption that the firing rates of origins and the maximum number of available firing factors remain constant between simulations.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this paper, the authors create a model of origin replication in yeast using Beacon calculus and a small set of parameters. The model is described as the relationship between origin firing rate and the abundance and recycling of origin firing factors. Using the (Müller, 2014) replication timing data to initialize and fit their model, the authors show that their model recapitulates known replication-related work such as inter-origin distances, replication fork directionality, and origin efficiency. Next, they utilize their model to make predictions that characterize the broader replication program, such as in the quantification of active replication forks, replicons, and replication timing.

Major comments:

Can the authors provide some insight into the model's dependency on the Müller, 2014 replication data set? They initialize and converge to this dataset so this paper's findings are highly contingent on treating this data set as ground truth.

The authors describe their model as one that simplifies the origin firing mechanisms compared to more complex models. Is there a direct comparison available that can quantify this advantage?

Likewise, how does their model compare to a naive discriminative model, such as one that performs peak finding on the replication timing data. For example, the replication fork directionality can be estimated, naively, using a peak finding algorithm. This type of analysis will provide a stronger argument for the usage of their model.

Currently the code is available as supplemental data. Ideally, the code should be available and provided to run the entire pipeline beginning with the initialization of the origin firing program from the Müller, 2014 data set.

The authors mention that origin firing factors and their recycling time to be the basis of how this model is constructed. While also describing the recycle time as a general timing delay that is dependent on a number of reasons such as diffusion and replisome complex formation. Can the authors discuss the limitations of their model towards this simplification?

Minor comments:

The author describes the prediction of 200 active replication forks 22 minutes into S phase. Please discuss why this peak number of active replication forks may have been reached. Is this related to the model configured for the number of firing factors F = 200?

The recycling parameter appears to be very important for this model. A sensitivity analysis of the value of 0.05 would be helpful to understand why this value was chosen.

It would be helpful to understand the convergence of the model better. Can the authors provide insight or a plot to better understand why the convergence parameter alpha was chosen as 1.2?

The authors comment that simulated origin efficiencies were estimated close to zero (6.2%{plus minus}22%). Can the authors comment on the large variability in this estimation (the {plus minus}22%)?

Significance

General Assessment

The strength of the model is in summarizing the origin efficiency firing mechanism into a small set of parameters. This also relates to its limitations. The model asserts that the origin firing depends solely on the abundance and recycling of origin firing factors. This limits the scope of the interpretation of the mechanisms of origin firing compared to more complex models.

Additionally, the model is fit to, and thus, highly dependent on the quality of the Müller, 2014 dataset.

Improvements:

This work can be improved by comparing and contrasting their results to existing models where they argue the advantages of employing a simpler model for origin firing compared to more complex ones they cite (Arbona, 2018; de Moura, 2010; Retkute, 2014; Brümmer, 2010).

While their modeling and dependency on the Müller, 2024 replication timing data may be sufficient, some of the findings can be naively characterized from this data set, such as in replication fork direction and origin firing times. Thus, the authors can argue the strengths of their model by contrasting theirs to more simpler and naive quantifications.

Currently the paper is very descriptive. A nice addition would be to model the effects of Rpd3 deletion which is thought to either have a direct effect on late origins (advancing their time of replication) or an indirect effect via the rDNA locus which may, in the absence of rpd3) act as sink for limiting replication factors. (Vogelauer et al., Mol Cell, 2002; Yoshida et al.,Mol Cell 2014, He et al., PNAS 2022). Specifically, how does titrating the number of active rDNA origins out of the ~150 available rDNA origins impact global origin usage under this model?

Scope:

Audience: Specialized towards groups modeling and studying replication.

Reviewer's field of expertise: Computer science, computational biology, bioinformatics, and general computational modeling

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Berners-Lee et al extend the beacon calculus approach previously developed by the Boemo lab to model the dynamics of Saccharomyces cerevisiae genome duplication at high resolution, based on competition for limiting origin firing factors. The simulations converge to produce a timing profile that closely matches experimentally determined replication dynamics through the genome. In an extension, the authors model how an increase in firing factor availability (assuming abundant dNTPs) would affect replication dynamics and conclude that overall timing would be robust.

Major comments

In figure 5, the authors demonstrate that replication dynamics are robust to an increase in the number of available firing factors. However, experimental data from strains in which these limiting factors are overexpressed indicate that replication dynamics are substantially altered (e.g. PMID 22081107 and 23562327) since dNTPs become limiting. So the conclusions of the analysis in figure 5 are at best an oversimplification and at worst rather misleading. If adding dNTPs as a factor that becomes limiting only at higher firing factor concentrations is not technically feasible, the authors should be more circumspect in their description and discussion of the results in figure 5.

The analysis of replication dynamics appears to exclude origins within the rDNA, which in the average strain account for ~20-25% of all replication origins in S. cerevisiae depending on the origin list chosen. Ignoring this large number of origins likely has a substantial impact on the model: if rDNA origins are intentionally ignored due to the difficulty of modeling repetitive regions or of having multiple identical origins in the competition model, this should be explicitly addressed in the text

Minor comment

Sekedat et al (2010, PMID PMID: 20212525) demonstrated convincingly that replication-fork movement is uniform throughout the genome but are not cited in favor of more recent work.

Significance

This manuscript will be of interest to researchers working on DNA replication dynamics, since the methodology and conclusions could be extended to other genomes for which high-quality replication timing data are available. The technical advance of including limiting firing factor availability is interesting, although the overall utility of these models is perhaps somewhat limited by the need for experimental data on which the model can converge. Extending the model to include known additional factors affecting replication-fork movement and replication timing as outlined above would extend the significance, especially since variations in replication-fork speed are associated with genome instability (e.g. PMID 29950726), differentiation (e.g PMID 35256805) and other biologically important phenomena.

Expertise: molecular biology, high-throughput analysis of DNA replication. I do not have sufficient expertise to evaluate the mathematical model itself.

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Referee #1

Evidence, reproducibility and clarity

Summary

This study develops a high-resolution stochastic model to explore DNA replication timing regulation in Saccharomyces cerevisiae, specifically focusing on competition between replication origins for limited initiation factors. The model, based on "Beacon Calculus," utilizes an iterative optimization process to fit experimental data, successfully reproducing timing, efficiency, and directionality features of genome replication origins. Additionally, the authors use the model to make predictions on replication dynamics under varying initiation factor concentrations, providing new insights into DNA replication processes that have not yet been observed empirically or experimentally.

Major Comments:

1. This study provides a strong support for the relationship between replication starting point competition and initial factor concentration. However, some predictive conclusions, such as "the origin of high efficiency may not be activated earlier", are still preliminary. Can the author further clarify the scope of these predictions and any potential mechanism in the discussion part to improve the rigor of this study?
2. The resolution and accuracy of the model prediction are obvious to all, but the specific generalization ability is still unknown, which makes the further promotion slightly insufficient. Does the author consider conducting additional experiments? To detect the replication time and efficiency in yeast cells with changed levels of key initiation factors (such as Cdc45 or Dpb11). The empirical data can be compared with the model prediction by editing CRISPR gene or manipulating the initial factor abundance through overexpression vector.
3. The model currently uses single values for the initiation factor number and recycling rate, though these parameters may vary across cell cycles or under different growth conditions. It is suggested that sensitivity analysis should be added to supplementary materials to explore how the changes of these parameters affect the model output, such as replication time distribution and origin efficiency.
4. While the authors use mean absolute error (MAE) to assess model fit, it is suggested to add other statistical methods, such as root mean square error or correlation analysis, to further evaluate the model's accuracy and robustness. In addition, this model lacks comparison with other studies on fitting yeast replication time, and it is difficult to evaluate the effect of this model compared with other models from the specific performance.
5. Although the code is open, it is suggested to provide specific instructions or examples of the running code in supplementary materials, so as to facilitate reproduction and application by other researchers.
6. In Figure 2(a), compared with other chromosomes, the fitting effect of chromosome 1 seems to be not good. Has the author ever thought about the reason? In addition, what is the guiding significance of this model in practical applications, such as online services, forecasting tools, or experiments? Can the author give relevant application examples in this regard?

Minor Comments:

1. Suggestions for Improving Figures: Figures 2 and 3: It is suggested that the differences between experimental data and model fitting data should be clearly marked by using more distinctive colors or symbols with different shapes in these figures, so as to help readers quickly distinguish between simulation results and experimental observation results. Density Plot in Figure 4: The current color gradient is dense, making it difficult to differentiate activation distributions for different origins. Consider using a broader color gradient or adding a slight separation between each origin's curve to improve readability.
2. Model Parameter Table: Adding a table in the Methods section or supplementary materials that summarizes the main model parameters (e.g., number of initiation factors, recycling rate, replication speed) and the basis for each parameter's setting would be helpful. This will allow readers to quickly understand the model setup and provide a reference for future researchers who may wish to use or adjust this model.
3. Citation and Description of Experimental Data: Clarify the origin and characteristics of the experimental data used, such as the specific details of the replication timing dataset applied for model fitting, and indicate whether the data represents single-cell or population-averaged measurements. This information will help readers better understand the comparison between the model and actual data.
4. Background and References: In the Introduction, consider adding a brief explanation of "Beacon Calculus" to aid non-specialist readers in understanding the novelty and applicability of this method. Adding foundational references for Beacon Calculus would further help readers appreciate the advantages of this approach. Additionally, in the discussion of the model's suitability for other biological systems, citing some reviews on high-efficiency replication origin analyses would help demonstrate the model's broader applicability.

Significance

1. Significance of the Research:

This study advances our understanding of DNA replication timing regulation in S. cerevisiae and presents a mathematical modeling approach with theoretical importance. By reconstructing a DNA replication timing framework for yeast, the model also provides a foundation that could be adapted for other systems, potentially advancing modeling techniques in genome replication research. 2. Relation to Existing Literature:

This study builds upon prior research on S. cerevisiae DNA replication initiation and proposes a simplified, reproducible model. Compared to more complex mathematical models or large-scale data analyses, this approach is more interpretable and easier to reproduce. The study's predictions on initiation factor concentration effects provide another perspective for future experimental work. 3. Target Audience:

This work will influence researchers studying DNA replication regulation, yeast genomics, and bioinformatics modeling. Additionally, scholars in microbiology and genetics may also benefit from the innovative modeling methods introduced. 4. Reviewer Expertise:

My expertise includes computational biology and bioinformatics, with a professional knowledge in DNA replication origins and bioinformatics modeling.

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Reply to the reviewers

Manuscript number: RC-2024-02824

Corresponding author(s): Rita tewari

1. General Statements [optional]

We wish to thank the reviewers and the Editor for their constructive comments and valuable suggestions to improve our manuscript. We have addressed as far as possible all comments and concerns and we hope that this revised manuscript, with additional new data, will be acceptable for publication. Please find below detailed responses (red text) to all specific points raised by the reviewers

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

• *

We would like to thank all the reviewers for using their valuable time to review our manuscript and to provide constructive comments and suggestions. We have now revised the manuscript taking their comments into consideration; our responses to these comments are detailed below (in red).

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

Minor comments: In the results section (lines 498-499), the authors describe free kinetochores in many cells without associated spindle microtubules. However, some nuclei appear to have kinetochores, as presented in Figure 6. Could the authors clarify how this conclusion was derived using transmission electron microscopy (TEM) without serial sectioning, as this is not explicitly mentioned in the materials and methods?

We observed free kinetochores in the ALLAN-KO parasites with no associated spindle microtubules (see Fig. 6Gh), while kinetochores are attached to spindle microtubules in WT-GFP cells (see Fig. 6Gc). To provide further evidence we analysed additional images and found that ALLAN-KO cells have free kinetochores in the centre of nucleus, unattached to spindle microtubules. We provide some more images clearly showing free kinetochores in these cells (new supplementary Fig. S11).

However, in the ALLAN mutant, this difference is not absolute: in a search of over 50 cells, one example of a cell with a "normal" nuclear spindle and attached kinetochores was observed.

The use of serial sectioning has limitations for examining small structures like kinetochores in whole cells. The limitations of the various techniques (for example, SBF-SEM vs tomography) are highlighted in our previous study (Hair et al 2022; PMID: 38092766), and we consider that examining a population of randomly sectioned cells provides a better understanding of the overall incidence of specific features.

Discussion Section:

Could the authors expand on why SUN1 and ALLAN are not required during asexual replication, even though they play essential roles during male gametogenesis?

We observed no phenotype in asexual blood stage parasites associated with the sun1 and allan gene deletions. Several other Plasmodium berghei gene knockout parasites with a phenotype in sexual stages, for example CDPK4 (PMID: 15137943), SRPK (PMID: 20951971), PPKL (PMID: 23028336) and kinesin-5 (PMID: 33154955) have no phenotype in blood stages, so perhaps this is not surprising. One explanation may be the substantial differences in the mode of cell division between these two stages. Asexual blood stages produce new progeny (merozoites) over 24 hours with closed mitosis and asynchronous karyokinesis during schizogony, while male gametogenesis is a rapid process, completed within 15 min to produce eight flagellated gametes. During male gametogenesis the nuclear envelope must expand to accommodate the increased DNA content (from 1N to 8N) before cytokinesis. Furthermore, male gametogenesis is the only stage of the life cycle to make flagella, and axonemes must be assembled in the cytoplasm to produce the flagellated motile male gametes at the end of the process. Thus, these two stages of parasite development have some very different and specific features.

Lines 611-613 states: "These loops serve as structural hubs for spindle assembly and kinetochore attachment at the nuclear MTOC, separating nuclear and cytoplasmic compartments." Could the authors elaborate on the evidence supporting this statement?

We observed the loops/folds in the nuclear envelope (NE) as revealed by SUN1-GFP and 3D TEM images during male gametogenesis. These folds/loops occur mainly in the vicinity of the nuclear MTOC where the spindles are assembled (as visualised by EB1 fluorescence) and attached to kinetochores (as visualised by NDC80 fluorescence). These loops/folds may form due to the contraction of the spindle pole back to the nuclear periphery, inducing distortion of the NE. Since there is no physical segregation of chromosomes during the three rounds of mitosis (DNA increasing from 1N to 8N), we suggest that these folds provide additional space for spindle and kinetochore dynamics within an intact NE to maintain separation from the cytoplasm (as shown by location of kinesin-8B).

In lines 621-622, the authors suggest that ALLAN may have a broader role in NE remodelling across the parasite's lifecycle. Could they reflect on or remind readers of the finding that ALLAN is not essential during the asexual stage?

ALLAN-GFP is expressed throughout the parasite life cycle but as the reviewer points out, a functional role is more pronounced during male gametogenesis. This does not mean that it has no role at other stages of the life cycle even if there is no obvious phenotype following deletion of the gene during the asexual blood stage. The fact that ALLAN is not essential during the asexual blood stage is noted in lines 628-29.

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

Introduction Line 63: The authors stat: "NE is integral to mitosis, supporting spindle formation, kinetochore attachment, and chromosome segregation..". Seemingly at odds, they also say (Line 69) that 'open' "mitosis is "characterized by complete NE disassembly". The authors could explain better the ideas presented in their quoted review from Dey and Baum, which points out that truly 'open' and 'closed' topologies may not exist and that even in 'open' mitosis, remnants of the NE may help support the mitotic spindle.

We have modified the sentence in which we discuss current opinions about 'open' and 'closed' mitosis. It is believed that there is no complete disassembly of the NE during open mitosis and no completely intact NE during closed mitosis, respectively. In fact, the NE plays a critical role in the different modes of mitosis during MTOC organisation and spindle dynamics. Please see the modified lines 64-71.

Results

Fig 7 is the final figure; but would be more useful upfront.

We have provided a new introductory figure (Fig 1) showing a schematic of conventional /canonical LINC complexes and evidence of SUN protein functions in model eukaryotes and compare them to what is known in apicomplexans.

Fig 1D. The authors generated a C-terminal GFP-tagged SUN1 transfectants and used ultrastructure expansion microscopy (U-ExM) and structured illumination microscopy (SIM) to examine SUN1-GFP in male gametocytes post-activation. The immuno-labelling of SUN1-GFP in these fixed cells appears very different to the live cell images of SUN1-GFP. The labelling profile comprises distinct punctate structures (particularly in the U-ExM images), suggesting that paraformaldehyde fixation process, followed by the addition of the primary and secondary antibodies has caused coalescing of the SUN1-GFP signal into particular regions within the NE.

We agree with the reviewer. Fixation with paraformaldehyde (PFA) results in a coalescence of the SUN1-GFP signal. We have also tried methanol fixation (see below, new Fig. S2), but a similar problem was encountered.

Given these fixation issues, the suggestion that the SUN1-GFP signal is concentrated at the BB/ nuclear MTOC and "enriched near spindle poles" needs further support.

These statements seem at odd with the data for live cell imaging where the SUN1-GFP seems evenly distributed around the nuclear periphery. Can the observation be quantitated by calculating the percentage of BB/ nuclear MTOC structures with associated SUN1-GFP puncta? If not, I am not convinced these data help understand the molecular events.

We agree with the reviewer that whilst the live cell imaging showed an even distribution of SUN1-GFP signal, after fixation with either PFA or methanol, then SUN1-GFP puncta are observed in addition to the peripheral location around the stained DNA (Hoechst) (See the above figure; puncta are indicated by arrows). These SUN1-GFP labelled puncta were observed at the junction of the nuclear MTOC and the basal body (Fig. 2F). Quantification of the distribution showed that these SUN1-GFP puncta are associated with nuclear MTOC in more than 90 % of cells (18 cells examined). Live cell imaging of the dual labelled parasites; SUN1xkinesin-8B (Fig. 2H) and SUN1x EB1 (Fig. 2I) provides further support for the association of SUN1-GFP puncta with BB (kinesin-8B) /nuclear MTOC (EB1).

The authors then generated dual transfectants and examined the relative locations of different markers in live cells. These data are more informative.

The authors state; " ..SUN1-GFP marked the NE with strong signals located near the nuclear MTOCs situated between the BB tetrads". The nuclear MTOCs are not labelled in this experiment. The SUN1-GFP signal between the kinesin-8B puncta is evident as small puncta on regions of NE distortion. I would prefer to not describe this signal as "strong". The signal is stronger in other regions of the NE.

We have modified the sentence on line 213 to accommodate this suggestion.

Line 219. The authors state; "..SUN1-GFP is partially colocalized with spindle poles as indicated by EB1,.. it shows no overlap with kinetochores (NDC80)." The authors should provide an analysis of the level of overlap at a pixel by pixel level to support this statement.

We now provide the overlap at a pixel-by-pixel level for representative images, and we have quantified more cells (n>30), as documented in the new Fig. S4A, which is displayed below. We have also modified the sentence on line 219 to reflect these additions.

The SUN1 construct is C-terminally GFP-tagged. By analogy with human SUN1, the C-terminal SUN domain is expected to be in the NE lumen. That is in a different compartment to EB1, which is located in the nuclear lumen (on the spindle). Thus, the overlap of signal is expected to be minimal.

We agree with the reviewer that the overlap between EB1 and Sun1 signals is expected to be minimal. We have quantified the data and included it in Supplementary Fig. S4A.

Similarly, given that EB1 and NDC80 are known to occupy overlapping locations on the spindle, it seems unlikely that SUN1 can overlap with one and not the other.

We agree with the reviewer's analysis that EB1 and NDC80 occupy overlapping locations on the spindle, although the length of NDC80 is less at the ends of spindles (see below Fig A) as shown in our previous study where we compared the locations of two spindle proteins, ARK2 and EB1, with that of NDC80 (Zeeshan et al, 2022; PMID: 37704606). In the present study we observed that Sun1-GFP partially overlaps with EB1 at the ends of the spindle, but not with NDC80. Please see Fig. B, below.

I note on Line 609, the authors state "Our study demonstrates that SUN1 is primarily localized to the nuclear side of the NE.." As per Fig 7D, and as discussed above, the bulk of the protein, including the SUN1 domain, is located in the space between the INM and the ONM.

We appreciate the reviewer's correction; we have now modified the sentence to indicate that the protein is largely localized in the space between the INM and the ONM on line 617.

Interestingly, as the authors point out, nuclear membrane loops are evident around EB1 and NDC80 focal regions. The data suggests that the contraction of the spindle pole back to the nuclear periphery induces distortion of the NE.

We agree with the reviewer's suggestion that the data indicate that contraction of spindle poles back to the nuclear periphery may induce distortion of the NE.

The author should discuss further the overlap of findings of this study with that from a recent manuscript (https://doi.org/10.1016/j.cels.2024.10.008). That Sayers et al. study identified a complex of SUN1 and ALLC1 as essential for male fertility in P. berghei. Sayers et al. also provide evidence that this complex particulate in the linkage of the MTOC to the NE and is needed for correct mitotic spindle formation during male gametogenesis.

We thank the reviewer for this suggestion. The study by Sayers et al, (2024) was published while our manuscript was under preparation. It was interesting to see that these complementary studies have similar findings about the role of SUN1 and the novel complex of SUN1-ALLAN. Our study contains a more detailed, in-depth analysis both by Expansion and TEM of SUN1. We include additional studies on the role of ALLAN. We discuss the overlap in the findings of the two studies in lines 590-605.

While the work is interesting, the conclusions may need to be tempered. The authors suggestion that in the absence of KASH-domain proteins, the SUN1-ALLAN complex forms a non-canonical LINC complex (that is, a connection across the NE), that "achieves precise nuclear and cytoskeletal coordination".

We have toned down the wording of this conclusion in lines 665-677.

In other organisms, KASH interacts with the C-terminal domain on SUN1, which as mentioned above is located between the INM and ONM. By contrast, ALLAN interacts with the N-terminal domain of SUN1, which is located in the nuclear lumen. The SUN1-ALLAN interaction is clearly of interest, and ALLAN might replace some of the roles of lamins. However, the protein that functionally replaces KASH (i.e. links SUN1 to the ONM) remains unidentified.

We agree with reviewer, and future studies will need to focus on identifying the KASH replacement that links SUN1 to the ONM.

It may also be premature to suggest that the SUN1-ALLAN complex is promising target for blocking malaria transmission. How would it be targeted?

We have deleted the sentence that raised this suggestion.

While the above datasets are interesting and internally consistent, there are two other aspects of the manuscript that need further development before they can usefully contribute to the molecular story.

The authors undertook a transcriptomic analysis of Δsun1 and WT gametocytes, at 8 and 30 min post-activation, revealing moderate changes (~2-fold change) in different genes. GO-based analysis suggested up-regulation of genes involved in lipid metabolism. Given the modest changes, it may not be correct to conclude that "lipid metabolism and microtubule function may be critical functions for gametogenesis that can be perturbed by sun1 deletion." These changes may simply be a consequence of the stalled male gametocyte development.

Following the reviewer's suggestion we have moved these data to the supplementary information (Fig. S5D-I) and toned down their discussion in the results and discussion sections.

The authors have then undertaken a detailed lipid analysis of the Δsun1 and WT gametocytes, before and after activation. Substantial changes in lipid metabolites might not be expected in such a short period of time. And indeed, the changes appear minimal. Similarly, there are only minor changes in a few lipid sub-classes between Δsun1 and WT gametocytes. In my opinion, the data are not sufficient to support the authors conclusion that "SUN1 plays a crucial role, linking lipid metabolism to NE remodelling and gamete formation."

In agreement with the reviewer's comments we have moved these data to supplementary information (Fig. S6) and substantially toned down the conclusions based on these findings.

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

Major comments: My main concern with this manuscript is that the authors do conclude not only that SUN1 is important for spindle formation and basal body segregation, but also that it influences for lipid metabolism and NE dynamics. I don't think the data supports this conclusion, for several reasons listed below. I would suggest to remove this claim from the manuscript or at least tone it down unless more supporting data are provided, in particular showing any change in NE dynamics in the SUN1-KO. Instead I would recommend to focus on the more interesting role of SUN1-ALLAN in bipartite MTOC organisation, which likely explains all observed phenotypes (including those in later stages of the parasite life cycle). In addition, some aspects of the knockout phenotype should be quantified to a bit deeper level.

In more detail:

• The lipidomics analysis is clearly the weakest point of the manuscript: The authors state that there are significant changes in some lipid populations between WT and sun1-KO, and between activated and non-activated cells, yet no statistical analysis is shown and the error bars are quite high compared to only minor changes in the means. For some discussed lipids, the result text does not match the graphs, e.g. PA, where the increase upon activation is more pronounced in the SUN1-KO vs WT (contrary to the text), or MAG, which is reduced in the SUN1-KO vs WT (contrary to the text). I don't see the discussed changes in arachidonic acid levels and myristic acid levels in the data either. Even if the authors find after analysis some statistically significant differences between some groups, they should carefully discuss the biological significance of these differences. As it is, I do not think the presented data warrants the conclusion that deletion of SUN1 changes lipid homeostasis, but rather shows that overall lipid homeostasis is not majorly affected by gametogenesis or SUN1 deletion. As a minor comment, if you decide to keep the lipidomics analysis in the manuscript, please state how many replicates were done.

As detailed above we have moved the lipidomics data to supplementary information (Fig. S6) and substantially toned down the discussion of these data in the results and discussion sections.

• I can't quite follow the logic why the authors performed transcriptomic analysis of the SUN1 and how they chose their time points. Their data up to this point indicate that SUN1 has a structural or coordinating role in the bipartite MTOC during male gametogenesis. Based on that it is rather unlikely that SUN1 KO directly leads to transcriptional changes within the 8 min of exflagellation. Isn't it more likely that transcriptional differences are purely a downstream effect of incomplete/failed gametogenesis? This is particularly true for the comparison at 30 min, which compares a mixture of exflagellated/emerged gametes and zygotes in WT to a mixture of aberrant, arrested gametes in the knockout, which will likely not give any meaningful insight. The by far most significant GO-term is then also nuclear-transcribed mRNA catabolic process, which is likely not related at all to SUN1 function (and the authors do not even comment on this in the main text). I would therefore suggest removing the 30 min data set from this manuscript. As a minor point, I would suggest highlighting some of the top de-regulated gene IDs in the volcano plots and stating their function. Also, please state how you prepared the cells for the transcriptomes and in how many replicates this was done.

As suggested by the reviewer we have removed the 30 min post activation data from the manuscript. We have also moved the rest of the transcriptomics data to supplementary information (Fig. S5) and toned down the presentation of this aspect of the work in the results and discussion sections.

• Live-cell imaging of SUN1-GFP does nicely visualise the NE during gametogenesis, showing a highly dynamic NE forming loops and folds, which is very exciting to see. It would be beneficial to also show a video from the life-cell imaging.

We have now added videos to the manuscript as suggested by the reviewer. Please see the supplementary Videos S1 and S2.

In their discussion, the authors state multiple times that NE dynamics are changed upon SUN1 KO. Yet, they do not provide data supporting this claim, i.e. that the extended loops and folds found in the nuclear envelope during gametogenesis are affected in any way by the knockout of SUN1 or ALLAN. What happens to the NE in absence of SUN1? Are there less loops and folds? In absence of a reliable NE marker this may not be entirely easy to address, but at least some SBF-SEM images of the sun1-KO gametocytes could provide insight.

It was difficult to provide SBF-SEM images as that work is beyond the scope of this manuscript. We will consider this approach in our future work. We re-examined many of our TEM images of SUN1-KO and ALLAN-KO parasites and did find some micrographs showing aberrant nuclear membrane folding ( - I think the exciting part of the manuscript is the cell biological role of SUN1 on male gametogenesis, which could be carved out a bit more by a more detailed phenotyping. Specifically it would be good to quantify

1) if DNA replication to an octoploid state still occurs in SUN1-KO and ALLAN-KO,

DNA replication is not affected in the SUN1-KO and ALLAN-KO mutants: DNA content increases to 8N (data added in Fig. 3J and Fig. S10F).

2) the proportion of anucleated gametes in WT and the KO lines

We have added these data in Fig. 3K and Fig. S10G

3) a quantification of the BB clustering phenotype (in which proportion of cells do the authors see this phenotype). This could be addressed by simple fixed immunofluorescence images of the respective WT/KO lines at various time points after activation (or possibly by reanalysis of the already obtained images) and would really improve the manuscript.

We have reanalysed the BB clustering phenotype and added the quantitative data in Fig. 4E and Fig. S7.

Especially the claim that emerged SUN1-KO gametes lack a nucleus is currently only based on single slices of few TEM cells and would benefit from a more thorough quantification in both SUN1- and ALLAN-Kos

We have examined many microgametes (100+ sections). In WT parasites a small proportion of gametes can appear to lack a nucleus if it does not extend all the way to the apical and basal ends (Hair et al. 2022). However, the proportion of microgametes that appear to lack a nucleus (no nucleus seen in any section) was much higher in the SUN1 mutant. In contrast, this difference was not as clear cut in the ALLAN mutant with a small proportion of intact (with axoneme and nucleus) microgametes being observed.

We have done additional analysis of male gametes, looking for the presence of the nucleus by live cell imaging after DNA staining with Hoechst. Please see the figure below. These data are added in Fig. 3K (for Sun1-KO) and S10G (for Allan-KO).

• The TEM suggests that in the SUN1-KO, kinetochores are free in the nucleus. Are all kinetochores free or do some still associate to a (minor/incorrectly formed) spindle? The authors could address this by tagging NDC80 in the KO lines.

Our observation and quantification of the data indicated that 100% of kinetochores were attached to spindle microtubules and that 0% were unattached kinetochores in the WT parasites. However, the exact opposite was found for the SUN1 mutant with 100% unattached kinetochores and 0% attached. The result was not quite as clear cut in the ALLAN mutant, with 98% unattached and 2% attached. An important observation was the lack of separation of the nuclear poles and any spindle formation. Spindle formation was never or very rarely observed in the mutants.

• Finally, I think it is curious that in contrast to SUN1, ALLAN seems to be less important, with some KO parasite completing the life cycle. Maybe a more detailed phenotyping as above gives some more hints to where the phenotypic difference between the two proteins lies. I would assume some ALLAN-KO cells can still segregate the basal body. Can the authors speculate/discuss in more detail why these two proteins seems to have slightly different phenotypes?

We agree with the reviewer. Overall, the ALLAN-KO has a less prominent phenotype than that of the Sun1-KO. The main difference is that in the ALLAN-KO mutant some basal body segregation can occur, leading to the production of some fertile microgametocytes, and ookinetes, and oocyst formation (Fig. 8). Approximately 5% of oocysts sporulated to release infective sporozoites that could infect mice in bite back experiments and complete the life cycle. In contrast the Sun1-KO mutant made no healthy oocysts, or infective sporozoites, and could not complete the life cycle in bite back experiments. We have analysed the phenotype in detail and provide quantitative data for gametocyte stages by EM and ExM in Figs. 4 and S8 (SUN1) and Figs. 7 and S11 (ALLAN). We have also performed detailed analysis of oocyst and sporozoite stages and included the data in Fig. 3 (SUN1) and S10 (ALLAN).

Based on the location, and functional and interactome data, we think that SUN1 plays a central role in coordinating nucleoplasm and cytoplasmic events as a key component of the nuclear membrane lumen, whereas ALLAN is located in the nucleoplasm. Deleting the SUN1 gene may disrupt the connection between INM and ONM whereas the deletion of ALLAN may affect only the INM.

. Some additional points where the data is not entirely sound yet or could be improved:

• Localisation of SUN1: There seems to be a discrepancy between SUN1-GFP location as observed by live cell microscopy, and by Expansion Microscopy (ExM), similar for ALLAN-GFP. By live-cell microscopy, the SUN1 localisation is much more evenly distributed around the NE, while the localisation in ExM is much more punctuated, and e.g. in Figure 1E seems to be within the nucleus. Do the authors have an explanation for this? Also, in Fig. 1D there are two GFP foci at the cell periphery (bottom left of the image), which I would think are not SUN1-Foci, as they seem to be outside of the cell. Is the antibody specific? Was there a negative control done for the antibody (WT cells stained with GFP antibodies after ExM)?

High resolution SIM and expansion microscopy showed that the SUN1-GFP molecules coalesce to form puncta, in contrast to the more uniform distribution observed by live cell imaging. This apparent difference may be due to a better resolution that could not be achieved by live cell imaging. We agree with the reviewer that the two green foci are outside of the cell. As a negative control we have used WT-ANKA cells (which contain no GFP) and the anti-GFP antibody, which gave no signal. This confirms the specificity of the antibody (please see the new Fig. S3).

• The authors argue that SIM gave unexpected results due to PFA fixation leading to collapse of the NE loops. However, they also fix their ExM cells and their EM cells with PFA and do not observe a collapse, at least from what I see in the two presented images and in the 3D reconstruction. Is there something else different in the sample preparation?

There was no difference in the fixation process for samples examined by SIM and ExM, but we used an anti-GFP antibody in ExM to visualise the SUN1-GFP, while in SIM the images of GFP signal were collected directly after fixation. We used both PFA and methanol as fixative, and both methods showed a coalescing of the SUN1-GFP signal (please see the new Fig. S2 and S3).

Can the authors trace their NE in ExM according to the NHS-Ester signal?

We could trace the NE in the ExM by the NHS-ester signal and observed that the SUN1-GFP signal was largely coincident with the NE (Please see the new Fig. S3B below).

• Fig 2D: It would be good to not just show images of oocysts but actually quantify their size from images. Also, have the authors determined the sporozoite numbers in SUN1-KO?

We have measured oocyst size (data added in new Fig. 3) and added the sporozoite quantification data in Fig. 3D.

• Line 481-483: the authors state that oocyst size is reduced in ALLAN-KO but do not show the data. Please quantify oocyst size or at least show representative images. Also the drastic decrease in sporozoite numbers (Fig. 6D, E) is not mentioned in the text. Please add reference to Fig S7D when talking about the bite back data.

We have added the oocyst size data in Fig. S10. We mention the changes in sporozoite numbers (now shown in Fig. 7D, E), and refer to the bite back data shown in current Fig. 7E.

• Fig S1C, 6C: Both WB images are stitched, but this is not clearly indicated e.g. by leaving a small gap between the lanes. Also please show a loading control along with the western blots. Also there seems to be a (unspecific?) band in the control, running at the same height as Allan-GFP WB. What exactly is the control?

We have provided the original blot showing the bands of ALLAN-GFP and SUN1-GFP. As a positive control, we used an RNA associated protein (RAP-GFP) that is highly expressed in Plasmodium and regularly used in our lab for this purpose.

• Regarding the crossing experiment: The authors conclude from this cross that SUN1 is only needed in males, yet for this conclusion they would need to also show that a cross with a female line does not rescue the phenotype. The authors should repeat the cross with a male-deficient line to really test if the phenotype is an exclusively male phenotype. In addition, line 270-272 states that no oocysts/sporozoites were detected in sun1-ko and nek4-ko parasites. However, the figure 2E shows only oocysts, not sporozoites, and shows also that sun1-ko does form oocysts, albeit dead ones.

We have now performed the experiment of crossing the Sun1-KO parasite line with a male deficient line (Hap2-KO) and added the data in Fig. 3I. We have added images showing sporozoites in oocysts.

• In Fig S1 the authors show that they also generated a SUN1-mCherry line, yet they do not use it in any of the presented experiments (unless I missed it). Would it be beneficial to cross the SUN1-mCherry line with the Allan1-GFP line to test colocalisation (possibly also by expansion microscopy)?

We did generate a SUN1-mCherry line, with the intent to cross ALLAN-GFP and SUN1-mCherry lines and observe the co-location of the proteins. Despite multiple attempts this cross was unsuccessful. This may have been due to their close proximity such that the addition of both GFP and mCherry was difficult to facilitate a proper protein-protein interaction between either of the proteins.

• Line 498: "In a significant proportion of cells" - What was the proportion of cells, and what does significant mean in this context?

Approximately 67% of cells showed the clumping of BBs. We have now added the numbers in Figs. 6H and S11I.

• The authors should discuss a bit more how their work relates to the work of Sayers et al. 2024, which also identified the SUN1-ALLAN complex. The paper is cited, but only very briefly commented on.

We have extended this discussion now in lines 590-605.

Suggestions how to improve the writing and data presentation.

• General presentation of microscopy images: Considering that large parts of the manuscript are based on microscopy data, their presentation could be improved. Single-channel microscopy images would benefit from being depicted in gray scale instead of color, which would make it easier to see the structures and intensities (especially for blue channels).

Whilst we agree with the reviewer, sometimes it is difficult to see the features in the merged images. Therefore, we would like to request to be allowed to retain the colours, which can be easily followed in both individual and merged images.

Also, it would be good to harmonize in which panels arrows are shown (e.g. Fig 1G, where some white arrows are in the SUN1-GFP panel, while others are in the merge panel, but they presumably indicate the same thing.). At the same time, Fig 1H doesn't have any with arrows, even though the figure legend states so.

We apologise for this lack of consistency, and we have now added arrows wherever they are missing to harmonise in the presentations.

Fig 3A and S4 show the same experiment but are coloured in different colours (NHS-Eester in green vs grey scale).

• Are the scale bars of all expansion microscopy images adjusted for the expansion factor?

Yes, the scale bars are adjusted accordingly.

• The figure legends would benefit from streamlining, as they have very different style between figures (eg Fig. 6 which has a concise figure legend vs microscopy figures where figure legends are very long and describe not only the figure but the results)

The figure legends have been streamlined, with removal of the description of results.

• Line 155-156: The text makes it sound like the expression only happens after activation. is that the case? Are these images activated or non-activated gametocytes?

They are expressed before activation, but the signal intensifies after activation. Images from before and after activation of gametocytes have been added in Fig. S1F.

• Line 267: Reference to the original nek4-KO paper missing

This reference is now included.

• Line 301: The reference to Figure 2J seems to be a bit arbitrarily placed. Also, this schematic of lipid metabolism is never discussed in relation to the transcriptomic or lipidomic data.

We have moved these data to supplementary information and modified the text.

• Line 347-349 states that gametes emerged, but the referenced figure shows activated gametocytes before exflagellation.

We have corrected the text to the start of exflagellation.

• Line 588: Spelling mistake in SUN1-domain

Corrected.

• Line 726/731: i missing in anti-GFP

Corrected.

• Line 787-789: statement of scale bar and number of cells imaged is not at the right position in the figure legend.

Moved to right place

• Line 779, 783: "shades of green" should be just "green". Same goes for line 986, 989 with "shades of grey"

Changed.

• Line 974, 976: please correct to WT-GFP and dsun1

Corrected.

• Line 1041, 1044: WT-GFP instead of WTGFP.

Corrected to WT-GFP.

• Fig 1B, D, E, Fig S1G, H: What are the time points of imaging?

We have added the time points to the images in these figures.

• Fig 1D/Line 727: the scale of the scale bar on the inset is missing.

We have added the scale bar.

• Fig 3 E-G and 6H-J: Please indicate total number of cells/images analysed per quantification, either in the graphs themselves or in the figure legend.

We indicate now the number of cells analysed in individual figures and also in Fig. S5C and S8C, respectively.

• Fig 5B: What is NP

Nuclear Pole (NP), also known as the nuclear/acentriolar MTOC (Zeeshan et al 2022; PMID: 35550346).

• Fig S1B/D: The legend states that there is an arrow indicating the band, but there is none.

We have added the arrow.

• Fig S2C: Is the scale bar really the same for the zygote and the ookinete?

We have checked this and used the same for both zygote and ookinete.

• Fig S3C, S7C: which stages was qRT-PCR done on?

Gametocytes activated for 8 min.

• Fig. S3D, S7D: According to the figure legend, three independent experiments were performed. How many mice were used per experiment? It would be good to depict the individual data points instead of the bar graph. For S7D, 3 data points are depicted (one in WT, two in allan-KO), what do they mean?

The bite back experiment was performed using 15-20 mosquitoes infected with WT-GFP and gene knockout lines to feed on one naïve mouse each, in three different experiments. We have now included the data points in the bar diagrams.

• Fig S3: Panel letters E and G are missing

We have updated the lettering in current Fig. S5

• Fig 3D: Please indicate what those boxes are. I presume that these are the insets show in b, e and j, but it is never mentioned. J is not even larger than i. Also, f is quite cropped, it would be good to see the large-scale image it comes from to see where in the nucleus these kinetochores are placed. Were there unbound kinetochores found in WT?

We mention the boxes in the figure legends. It is rare to find unbound kinetochores in WT parasite. We provide large scale and zoomed-in images of free kinetochores in Fig. S8.

• Fig S4: Insets are not mentioned in the figure legend. Please add scale bar to zoom-ins

We now describe the insets in the figure legends and have added scale bars to the zoomed-in images.

• Fig S5A, B: Please indicate which inset belongs to which sub-panel. Where does Ac stem from?

We have now included the full image showing the inset (new Fig. S8).

• Fig S5C and S8C: Change "DNA" to "Nucleus".

We have changed "DNA" to "Nucleus". Now they are Fig. S8K and S11I.

Reviewer #3 (Significance (Required)):

Yet, the statement that SUN1 is also important for lipid homoeostasis and NE dynamics is currently not backed up by sufficient data. I believe that the manuscript would benefit from removing the less convincing transcriptomic and lipidomic datasets and rather focus on more deeply characterising the cell biology of the knockouts. This way, the results would be interesting not only for parasitologists, but also for more general cell biologists.

We have moved the lipidomics and transcriptomics data to supplementary information and toned down the emphasis on these data to make the manuscript more focused on the cell biology and analysis of the genetic KO data.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this manuscript, the authors investigate the function of the protein SUN1, a proposed nuclear envelope protein linking nuclear and cytoplasmic cytoskeleton, during the rapid male gametogenesis of the rodent malaria parasite Plasmodium berghei. They reveal that SUN1 localises to the nuclear envelope (NE) in male and female gametes, and show that the male NE has unexpectedly high dynamics during the rapid process of gametogenesis. Using expansion microscopy, the authors find that SUN1 is enriched at the neck of the bipartite MTOC that links the intranuclear spindle to the basal bodies of the cytoplasmic axonemes. They further show that upon deletion of SUN1, the basal bodies of the eight axonemes fail to segregate, no spindle is formed, and emerging gametes are anucleated, leading to a complete block in transmission. By interactomics they identify a divergent allantoicase-like protein, ALLAN, as a main interaction partner of SUN1 and further show that ALLAN deletion largely phenocopies the effect of SUN1. Overall, the work here reveals a new protein complex important for maintaining the structural integrity of the bipartite MTOC during the rapid rounds of endomitosis in male gametogenesis. In addition, the authors use transcriptomics and lipidomics to further characterise the effects of SUN1 deletion on gametogenesis and conclude that SUN1 is also required for lipid homeostasis and NE dynamics.

Major comments:

My main concern with this manuscript is that the authors do conclude not only that SUN1 is important for spindle formation and basal body segregation, but also that it influences for lipid metabolism and NE dynamics. I don't think the data supports this conclusion, for several reasons listed below. I would suggest to remove this claim from the manuscript or at least tone it down unless more supporting data are provided, in particular showing any change in NE dynamics in the SUN1-KO. Instead I would recommend to focus on the more interesting role of SUN1-ALLAN in bipartite MTOC organisation, which likely explains all observed phenotypes (including those in later stages of the parasite life cycle). In addition, some aspects of the knockout phenotype should be quantified to a bit deeper level.

In more detail:

• The lipidomics analysis is clearly the weakest point of the manuscript: The authors state that there are significant changes in some lipid populations between WT and sun1-KO, and between activated and non-activated cells, yet no statistical analysis is shown and the error bars are quite high compared to only minor changes in the means. For some discussed lipids, the result text does not match the graphs, e.g. PA, where the increase upon activation is more pronounced in the SUN1-KO vs WT (contrary to the text), or MAG, which is reduced in the SUN1-KO vs WT (contrary to the text). I don't see the discussed changes in arachidonic acid levels and myristic acid levels in the data either. Even if the authors find after analysis some statistically significant differences between some groups, they should carefully discuss the biological significance of these differences. As it is, I do not think the presented data warrants the conclusion that deletion of SUN1 changes lipid homeostasis, but rather shows that overall lipid homeostasis is not majorly affected by gametogenesis or SUN1 deletion. As a minor comment, if you decide to keep the lipidomics analysis in the manuscript, please state how many replicates were done.
• I can't quite follow the logic why the authors performed transcriptomic analysis of the SUN1 and how they chose their time points. Their data up to this point indicate that SUN1 has a structural or coordinating role in the bipartite MTOC during male gametogenesis. Based on that it is rather unlikely that SUN1 KO directly leads to transcriptional changes within the 8 min of exflagellation. Isn't it more likely that transcriptional differences are purely a downstream effect of incomplete/failed gametogenesis? This is particularly true for the comparison at 30 min, which compares a mixture of exflagellated/emerged gametes and zygotes in WT to a mixture of aberrant, arrested gametes in the knockout, which will likely not give any meaningful insight. The by far most significant GO-term is then also nuclear-transcribed mRNA catabolic process, which is likely not related at all to SUN1 function (and the authors do not even comment on this in the main text). I would therefore suggest removing the 30 min data set from this manuscript. As a minor point, I would suggest highlighting some of the top de-regulated gene IDs in the volcano plots and stating their function. Also, please state how you prepared the cells for the transcriptomes and in how many replicates this was done.
• Live-cell imaging of SUN1-GFP does nicely visualise the NE during gametogenesis, showing a highly dynamic NE forming loops and folds, which is very exciting to see. It would be beneficial to also show a video from the life-cell imaging. In their discussion, the authors state multiple times that NE dynamics are changed upon SUN1 KO. Yet, they do not provide data supporting this claim, i.e. that the extended loops and folds found in the nuclear envelope during gametogenesis are affected in any way by the knockout of SUN1 or ALLAN. What happens to the NE in absence of SUN1? Are there less loops and folds? In absence of a reliable NE marker this may not be entirely easy to address, but at least some SBF-SEM images of the sun1-KO gametocytes could provide insight.
• I think the exciting part of the manuscript is the cell biological role of SUN1 on male gametogenesis, which could be carved out a bit more by a more detailed phenotyping. Specifically it would be good to quantify 1) if DNA replication to an octoploid state still occurs in SUN1-KO and ALLAN-KO, 2) the proportion of anucleated gametes in WT and the KO lines and 3) a quantification of the BB clustering phenotype (in which proportion of cells do the authors see this phenotype). This could be addressed by simple fixed immunofluorescence images of the respective WT/KO lines at various time points after activation (or possibly by reanalysis of the already obtained images) and would really improve the manuscript. Especially the claim that emerged SUN1-KO gametes lack a nucleus is currently only based on single slices of few TEM cells and would benefit from a more thorough quantification in both SUN1- and ALLAN-KOs
• The TEM suggests that in the SUN1-KO, kinetochores are free in the nucleus. Are all kinetochores free or do some still associate to a (minor/incorrectly formed) spindle? The authors could address this by tagging NDC80 in the KO lines.
• Finally, I think it is curious that in contrast to SUN1, ALLAN seems to be less important, with some KO parasite completing the life cycle. Maybe a more detailed phenotyping as above gives some more hints to where the phenotypic difference between the two proteins lies. I would assume some ALLAN-KO cells can still segregate the basal body. Can the authors speculate/discuss in more detail why these two proteins seems to have slightly different phenotypes?

Minor comments:

Some additional points where the data is not entirely sound yet or could be improved:

• Localisation of SUN1: There seems to be a discrepancy between SUN1-GFP location as observed by live cell microscopy, and by Expansion Microscopy (ExM), similar for ALLAN-GFP. By live-cell microscopy, the SUN1 localisation is much more evenly distributed around the NE, while the localisation in ExM is much more punctuated, and e.g. in Figure 1E seems to be within the nucleus. Do the authors have an explanation for this? Also, in Fig. 1D there are two GFP foci at the cell periphery (bottom left of the image), which I would think are not SUN1-Foci, as they seem to be outside of the cell. Is the antibody specific? Was there a negative control done for the antibody (WT cells stained with GFP antibodies after ExM)? - The authors argue that SIM gave unexpected results due to PFA fixation leading to collapse of the NE loops. However, they also fix their ExM cells and their EM cells with PFA and do not observe a collapse, at least from what I see in the two presented images and in the 3D reconstruction. Is there something else different in the sample preparation? Can the authors trace their NE in ExM according to the NHS-Ester signal?
• Fig 2D: It would be good to not just show images of oocysts but actually quantify their size from images. Also, have the authors determined the sporozoite numbers in SUN1-KO?
• Line 481-483: the authors state that oocyst size is reduced in ALLAN-KO but do not show the data. Please quantify oocyst size or at least show representative images. Also the drastic decrease in sporozoite numbers (Fig. 6D, E) is not mentioned in the text. Please add reference to Fig S7D when talking about the bite back data.
• Fig S1C, 6C: Both WB images are stitched, but this is not clearly indicated e.g. by leaving a small gap between the lanes. Also please show a loading control along with the western blots. Also there seems to be a (unspecific?) band in the control, running at the same height as Allan-GFP WB. What exactly is the control?
• Regarding the crossing experiment: The authors conclude from this cross that SUN1 is only needed in males, yet for this conclusion they would need to also show that a cross with a female line does not rescue the phenotype. The authors should repeat the cross with a male-deficient line to really test if the phenotype is an exclusively male phenotype. In addition, line 270-272 states that no oocysts/sporozoites were detected in sun1-ko and nek4-ko parasites. However, the figure 2E shows only oocysts, not sporozoites, and shows also that sun1-ko does form oocysts, albeit dead ones.
• In Fig S1 the authors show that they also generated a SUN1-mCherry line, yet they do not use it in any of the presented experiments (unless I missed it). Would it be beneficial to cross the SUN1-mCherry line with the Allan1-GFP line to test colocalisation (possibly also by expansion microscopy)?
• Line 498: "In a significant proportion of cells" - What was the proportion of cells, and what does significant mean in this context?
• The authors should discuss a bit more how their work relates to the work of Sayers et al. 2024, which also identified the SUN1-ALLAN complex. The paper is cited, but only very briefly commented on.

Suggestions how to improve the writing and data presentation.

• General presentation of microscopy images: Considering that large parts of the manuscript are based on microscopy data, their presentation could be improved. Single-channel microscopy images would benefit from being depicted in gray scale instead of color, which would make it easier to see the structures and intensities (especially for blue channels). Also, it would be good to harmonize in which panels arrows are shown (e.g. Fig 1G, where some white arrows are in the SUN1-GFP panel, while others are in the merge panel, but they presumably indicate the same thing.). At the same time, Fig 1H doesn't have any with arrows, even though the figure legend states so. Fig 3A and S4 show the same experiment but are coloured in different colours (NHS-Eester in green vs grey scale).
• Are the scale bars of all expansion microscopy images adjusted for the expansion factor?
• The figure legends would benefit from streamlining, as they have very different style between figures (eg Fig. 6 which has a concise figure legend vs microscopy figures where figure legends are very long and describe not only the figure but the results)
• Line 155-156: The text makes it sound like the expression only happens after activation. is that the case? Are these images activated or non-activated gametocytes?
• Line 267: Reference to the original nek4-KO paper missing
• Line 301: The reference to Figure 2J seems to be a bit arbitrarily placed. Also, this schematic of lipid metabolism is never discussed in relation to the transcriptomic or lipidomic data.
• Line 347-349 states that gametes emerged, but the referenced figure shows activated gametocytes before exflagellation.
• Line 588: Spelling mistake in SUN1-domain
• Line 726/731: i missing in anti-GFP
• Line 787-789: statement of scale bar and number of cells imaged is not at the right position in the figure legend.
• Line 779, 783: "shades of green" should be just "green". Same goes for line 986, 989 with "shades of grey"
• Line 974, 976: please correct to WT-GFP and dsun1
• Line 1041, 1044: WT-GFP instead of WTGFP.
• Fig 1B, D, E, Fig S1G, H: What are the time points of imaging?
• Fig 1D/Line 727: the scale of the scale bar on the inset is missing.
• Fig 3 E-G and 6H-J: Please indicate total number of cells/images analysed per quantification, either in the graphs themselves or in the figure legend.
• Fig 5B: What is NP?
• Fig S1B/D: The legend states that there is an arrow indicating the band, but there is none.
• Fig S2C: Is the scale bar really the same for the zygote and the ookinete?
• Fig S3C, S7C: which stages was qRT-PCR done on?
• Fig. S3D, S7D: According to the figure legend, three independent experiments were performed. How many mice were used per experiment? It would be good to depict the individual data points instead of the bar graph. For S7D, 3 data points are depicted (one in WT, two in allan-KO), what do they mean?
• Fig S3: Panel letters E and G are missing
• Fig 3D: Please indicate what those boxes are. I presume that these are the insets show in b, e and j, but it is never mentioned. J is not even larger than i. Also, f is quite cropped, it would be good to see the large-scale image it comes from to see where in the nucleus these kinetochores are placed. Were there unbound kinetochores found in WT?
• Fig S4: Insets are not mentioned in the figure legend. Please add scale bar to zoom-ins
• Fig S5A, B: Please indicate which inset belongs to which sub-panel. Where does Ac stem from?
• Fig S5C and S8C: Change "DNA" to "Nucleus".

Significance

This study uses extensive microscopy and genetics to characterise an unusual SUN1-ALLAN complex and provides new insights into the molecular events during Plasmodium male gametogenesis, especially how the intranuclear events (spindle formation and mitosis) are linked to the extranuclear, cytoplasmic formation of the axonemes. While it could be more extensive, the phenotypic characterisation of the mutants reveals an interesting phenotype, showing that SUN1 and ALLAN are localised to and maintain the neck region of the bipartite MTOC. The authors here confirm and expand the previous knowledge about SUN1 in P. berghei (as published by Sayers et al., 2024), adding more detail to its localisation and dynamics, and further characterise the interaction partner ALLAN. Yet, the statement that SUN1 is also important for lipid homoeostasis and NE dynamics is currently not backed up by sufficient data. I believe that the manuscript would benefit from removing the less convincing transcriptomic and lipidomic datasets and rather focus on more deeply characterising the cell biology of the knockouts. This way, the results would be interesting not only for parasitologists, but also for more general cell biologists.

My expertise lies within the cell biology of malaria parasites, especially during early transmission stages.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #2

Evidence, reproducibility and clarity

This manuscript by Zeeshan et al describes the organisation of SUN1 during the rapid closed mitosis of male Plasmodium gametocytes and the consequences of knockout of the SUN1 gene for male gamete formation and oocyst development.

SUN (Sad1, UNC84-domain) proteins have been shown, in other studies, in other organisms, to be part of a bridging complex (LINC) that links cytoplasm-located structural elements with nuclear structures. They are anchored in the inner nuclear envelope and present a C-terminal SUN domain into the space between nuclear envelope (NE) inner and outer membranes. In humans, the SUN domain interacts with the outer NE-embedded KASH (Klarsicht, ANC-1, Syne Homology)-protein, which in turn binds to the cytoskeletal components, including the centrosome.

Introduction

Line 63: The authors stat: "NE is integral to mitosis, supporting spindle formation, kinetochore attachment, and chromosome segregation..". Seemingly at odds, they also say (Line 69) that 'open' "mitosis is "characterized by complete NE disassembly". The authors could explain better the ideas presented in their quoted review from Dey and Baum, which points out that truly 'open' and 'closed' topologies may not exist and that even in 'open' mitosis, remnants of the NE may help support the mitotic spindle.

Results

Fig 7 is the final figure; but would be more useful upfront. The authors compared the sequence of SUN1, ALLAN, KASH proteins and lamins across apicomplexans, and Arabidopsis and humans. They note that plasmodium has two SUN domain proteins. Plasmodium SUN1 has the same orientation as in human SUN1 with the C-terminal SUN domain into the space between nuclear envelope (NE) inner and outer membranes. In agreement with previous reports, no KASH-like or lamin proteins were identified.

Fig 1D. The authors generated a C-terminal GFP-tagged SUN1 transfectants and used ultrastructure expansion microscopy (U-ExM) and structured illumination microscopy (SIM) to examine SUN1-GFP in male gametocytes post-activation. The immuno-labelling of SUN1-GFP in these fixed cells appears very different to the live cell images of SUN1-GFP. The labelling profile comprises distinct punctate structures (particularly in the U-ExM images), suggesting that paraformaldehyde fixation process, followed by the addition of the primary and secondary antibodies has caused coalescing of the SUN1-GFP signal into particular regions within the NE.

Given these fixation issues, the suggestion that the SUN1-GFP signal is concentrated at the BB/ nuclear MTOC and "enriched near spindle poles" needs further support. These statements seem at odd with the data for live cell imaging where the SUN1-GFP seems evenly distributed around the nuclear periphery. Can the observation be quantitated by calculating the percentage of BB/ nuclear MTOC structures with associated SUN1-GFP puncta? If not, I am not convinced these data help understand the molecular events.

The authors then generated dual transfectants and examined the relative locations of different markers in live cells. These data are more informative.

The authors state; " ..SUN1-GFP marked the NE with strong signals located near the nuclear MTOCs situated between the BB tetrads". The nuclear MTOCs are not labelled in this experiment. The SUN1-GFP signal between the kinesin-8B puncta is evident as small puncta on regions of NE distortion. I would prefer to not describe this signal as "strong". The signal is stronger in other regions of the NE.

Line 219. The authors state; "..SUN1-GFP is partially colocalized with spindle poles as indicated by EB1,.. it shows no overlap with kinetochores (NDC80)." The authors should provide an analysis of the level of overlap at a pixel by pixel level to support this statement.

The SUN1 construct is C-terminally GFP-tagged. By analogy with human SUN1, the C-terminal SUN domain is expected to be in the NE lumen. That is in a different compartment to EB1, which is located in the nuclear lumen (on the spindle). Thus, the overlap of signal is expected to be minimal. Similarly, given that EB1 and NDC80 are known to occupy overlapping locations on the spindle, it seems unlikely that SUN1 can overlap with one and not the other.

I note on Line 609, the authors state "Our study demonstrates that SUN1 is primarily localized to the nuclear side of the NE.." As per Fig 7D, and as discussed above, the bulk of the protein, including the SUN1 domain, is located in the space between the INM and the ONM.

Interestingly, as the authors point out, nuclear membrane loops are evident around EB1 and NDC80 focal regions. The data suggests that the contraction of the spindle pole back to the nuclear periphery induces distortion of the NE.

The authors generate Δsun1 parasites and showed that a functional sun1 gene is required for male gamete formation and subsequent oocyst development.

In a very impressive set of micrographs (Fig 3), the authors used U-ExM and TEM to show that spindle formation is severely disrupted, and BB fail to segregate in Δsun1 gametocytes. Axoneme elongation occurs but the axenomes are inconnected to BBs and nuclear spindles.

The authors undertook immunoprecipitation (IP) experiment using a nanobody that recognises SUN1-GFP in lysates of purified activated gametocytes.

They identified several nuclear pore proteins, as well as the allantoicase-like protein (ALCC1/ ALLAN). They reverse-immunoprecipitated ALLAN-GFP from lysates of activated gametocytes and identified SUN1 and its interactors, DDRGK-domain containing protein and kinesin-15. This is an important finding.

The authors used AlphaFold to predict potential complexes of SUN1 and ALLAN. A complex is predicted between the plasmodium-specific N-terminal domain of SUN1. The authors conclude that ALLAN is located in the nuclear lumen and is involved in linking SUN1 to nuclear components.

The authors generated a line expressing ALLAN-GFP. In activated male gametocytes, ALLAN-GFP rapidly relocates to focal points at the nuclear periphery that correlated with the nuclear MTOCs (spindle poles). This is another important finding.

Δallan mutants exhibit a very similar phenotype to the Δsun1 parasites. Activated male gametocyte exhibited clustered BB, with incomplete segregation and misalignment relative to the nuclear MTOCs. TEM data is consistent with the author's conclusion that "ALLAN is critical for the alignment of spindle microtubules with kinetochores and BB segregation."

Taken together these results are consistent with the suggestion that SUN1 and ALLN proteins play an important structural role in linking the nuclear spindle of P. berghei male gametocytes to the BB and axonemes.

These are important findings. The author should discuss further the overlap of findings of this study with that from a recent manuscript (https://doi.org/10.1016/j.cels.2024.10.008). That Sayers et al. study identified a complex of SUN1 and ALLC1 as essential for male fertility in P. berghei. Sayers et al. also provide evidence that this complex particulate in the linkage of the MTOC to the NE and is needed for correct mitotic spindle formation during male gametogenesis.

While the work is interesting, the conclusions may need to be tempered. The authors suggestion that in the absence of KASH-domain proteins, the SUN1-ALLAN complex forms a non-canonical LINC complex (that is, a connection across the NE), that "achieves precise nuclear and cytoskeletal coordination".

In other organisms, KASH interacts with the C-terminal domain on SUN1, which as mentioned above is located between the INM and ONM. By contrast, ALLAN interacts with the N-terminal domain of SUN1, which is located in the nuclear lumen. The SUN1-ALLAN interaction is clearly of interest, and ALLAN might replace some of the roles of lamins. However, the protein that functionally replaces KASH (i.e. links SUN1 to the ONM) remains unidentified.

It may also be premature to suggest that the SUN1-ALLAN complex is promising target for blocking malaria transmission. How would it be targeted?

While the above datasets are interesting and internally consistent, there are two other aspects of the manuscript that need further development before they can usefully contribute to the molecular story.

The authors undertook a transcriptomic analysis of Δsun1 and WT gametocytes, at 8 and 30 min post-activation, revealing moderate changes (~2-fold change) in different genes. GO-based analysis suggested up-regulation of genes involved in lipid metabolism. Given the modest changes, it may not be correct to conclude that "lipid metabolism and microtubule function may be critical functions for gametogenesis that can be perturbed by sun1 deletion." These changes may simply be a consequence of the stalled male gametocyte development.

The authors have then undertaken a detailed lipid analysis of the Δsun1 and WT gametocytes, before and after activation. Substantial changes in lipid metabolites might not be expected in such a short period of time. And indeed, the changes appear minimal. Similarly, there are only minor changes in a few lipid sub-classes between Δsun1 and WT gametocytes. In my opinion, the data are not sufficient to support the authors conclusion that "SUN1 plays a crucial role, linking lipid metabolism to NE remodelling and gamete formation."

Significance

While the work is interesting, the conclusions may need to be tempered. Datasets are interesting and internally consistent. The aspects of manuscript and conclusion derived from transcriptomic and the lipidomic analysis, however, need further development before they can usefully contribute to the molecular story.

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Referee #1

Evidence, reproducibility and clarity

Summary: The study explores the role of the SUN1-ALLAN complex in Plasmodium berghei, identifying it as a unique mediator of nuclear envelope (NE) remodeling and microtubule-organizing center (MTOC) coordination during the rapid closed mitosis of male gametogenesis. The authors demonstrate that SUN1, a nuclear envelope protein, and ALLAN, a novel allantoicase-like protein, form a non-canonical complex. This complex bridges chromatin and cytoskeletal interactions, compensating for the lack of canonical LINC components like KASH-domain proteins and lamins in Plasmodium. Using lipidomics, mass spectrometry, RNA-seq, and advanced imaging methods like ultrastructure expansion microscopy (U-ExM), they reveal that disruption of this complex results in impaired spindle assembly, basal body segregation, and kinetochore attachment. This leads to defective, anuclear flagellated gametes incapable of fertilization. Furthermore, SUN1 deletion affects lipid metabolism, emphasizing its role in maintaining NE homeostasis. The study sheds light on a highly specialized adaptation for rapid mitotic division in Plasmodium, providing insights into NE and MTOC evolution and identifying potential targets for malaria transmission-blocking strategies.

The authors have utilized an impressive array of techniques, including lipidomics, mass spectrometry, RNA sequencing, and diverse microscopy approaches, to characterize the role of SUN1 deletion during male gametogenesis in Plasmodium.

Minor comments:

In the results section (lines 498-499), the authors describe free kinetochores in many cells without associated spindle microtubules. However, some nuclei appear to have kinetochores, as presented in Figure 6. Could the authors clarify how this conclusion was derived using transmission electron microscopy (TEM) without serial sectioning, as this is not explicitly mentioned in the materials and methods?

Discussion Section:

Could the authors expand on why SUN1 and ALLAN are not required during asexual replication, even though they play essential roles during male gametogenesis? Lines 611-613 states: "These loops serve as structural hubs for spindle assembly and kinetochore attachment at the nuclear MTOC, separating nuclear and cytoplasmic compartments." Could the authors elaborate on the evidence supporting this statement? In lines 621-622, the authors suggest that ALLAN may have a broader role in NE remodeling across the parasite's lifecycle. Could they reflect on or remind readers of the finding that ALLAN is not essential during the asexual stage?

Significance

General assessment:

The introduction is well-constructed, providing a clear and comprehensive overview of the current understanding of closed mitosis in protozoa and how it differs in Plasmodium parasites. The results are presented clearly and without overstatement, allowing readers to follow the logical progression of the study.

The knockout (KO) and rescue experiment for Neck4 was particularly innovative, effectively demonstrating the absence of male gametocytes in the SUN1 KO line.

Impact: This study uncovers how malaria parasites orchestrate one of the fastest cell division processes in biology during male gametogenesis, a critical step for disease transmission. By identifying a novel protein complex, the SUN1-ALLAN axis, that links the nuclear envelope to the machinery organizing cell division, we reveal a unique solution evolved by the parasite to achieve rapid and precise chromosome segregation. This discovery sheds light on how these parasites overcome the lack of proteins commonly found in other organisms, using an entirely distinct strategy to sustain their lifecycle. The findings not only deepen our understanding of the cellular innovations in malaria parasites but also open new avenues for interventions targeting the processes essential for parasite survival and transmission. These insights could contribute to the development of next-generation strategies to combat a disease that continues to impact millions worldwide.

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Reply to the reviewers

Reviewer #1

Evidence, reproducibility and clarity

Summary:

• In this study, authors investigate the impact of pre-membane (prM) and envelope (E) proteins of tick-borne encephalitis virus (TBEV) on viral distribution and tropism, mostly in the brain.*
• To do so, authors use high resolution imaging of whole mouse brain after infection by either LGTV, a low pathogenic orthoflavivirus also transmitted by ticks, TBEV, or TBEV/LGTV chimeric virus where prM and E of TBEV are inserted in a LGTV background.*
• Structural and antigenic characterization of the chimeric virus reveal that it remains a low pathogenic virus exhibiting TBEV structural and antigenic features.*
• Those viruses are then used to infect wt or mavs -/- mice and viral propagation / tropism is explored, revealing that LGTV and LGTVT:prM predominantly infect cerebral cortex while TBEV infects cerebellum.*
• Authors work at characterizing their viruses is nicely done and convincing, showing that LGTVT:prM replicated just like LGTV, and exhibited increased viral spread in cellulo.*
• However LGTVT:prM appear to be less pathogenic in vivo and its brain tropism in mavs -/- mice seems to be similar to wt LGTV virus, stressing the fact that the role of structural proteins prM/E is only modest in TBEV specific tropism to cerebellum.*

Major comments:

• It is stated in the introduction that prior work on LGTV/TBEV chimera have already been done, and that both LGTV and LGTV/TBEV are neuroinvasive and neurovirulent in animal models. In this study, both LGTV and LGTVT:prM fails to establish infection in wt mouse model. Were previous published data on LGTV and derivatives also only performed in mavs, or ifnar deficient mice? The previous studies referred to in the manuscript (ref 21 and 23) are both using wt mice of younger age, 3.5 and 3 weeks respectively. It is known that age influences immune status, and some of the experiments in these previous studies are performed in even younger animals (3 to 8 days suckling mice) likely for this specific reason. The different mice strains in these studies may also influence their susceptibility to infection.

• *While LGTV and LGTVT:prME fails to result in symptomatic infection in wt mice in our study, a certain level of localized infection is likely taking place and the outcome will depend on the immune status of the animals (age/immune deficiencies). What we tried to highlight in the manuscript was that the relative pathogenicity (TBEV/LGTV The fact that the whole "tropism" part of the study is performed in mavs -/- mice limits the impact of the study as escape from innate immune response is central in shaping viral tropism. Authors should advertise more this fact (absent from the abstract) and discuss more the links between LGTV / TBEV and innate immune response (escape mechanisms and NS proteins, implication of prM in controlling MDA5, MAVS)

Thank you for pointing out the lack of clarity. All the tropism studies, figure 4 and 5, were done in adult WT mice infected i.c. to allow the virus to surpass the initial barrier of peripheral immune response and establish infection in the brain. We have now stressed this in the result section and in the relevant figure legends.

Minor comments:

• Figures need some re-working:*

• Figure 1 :

• 1D : only the difference between TBEV and LGTVT:prME is shown. Plotting the difference LGTV / LGTVT:prM would be a nice upgrade.* Thank you for this suggestion. However, as there is no statistical difference between LGTV and ChLGTV in Fig 1D we have maintained the figure as originally made.

• Figure 2 : Numbering in the panels is wrong (2j in the text is 2K, 2H is 2I, ...) and should be corrected. Thank you, this has been corrected in the figure.

• Figure 3 : Route of infection could be added to figure labels for more clarity. Thank you, we have added this to the figure.

• Figure 4A : Labelling the Mock panel with areas of concern in the brain(Cerebrum, Cerebellum, ...) would help a lot readers not familiar with brain anatomy. We agree that adding these labels improves the clarity and accessibility of the figure and have added this to 4A.

• Figure 4 E : images are too small to be convincing. What is staining Iba-1 is not mentioned in the figure legend. Thank you, we have added the explanation that microglia were stained by Iba1 and increased the size of the images in Figure 4. Additionally, co-staining of viral antigens and the neuronal marker UCHL-1 has been added as the new Figure 4E and Iba-1 staining moved to 4F.

Significance

Prior studies already described the generation and characterization of TBEV/LGTV chimeric viruses. * The main addition of this paper to the field is the use of impressive high-resolution imaging of whole mouse brains, to explore viral infection and tropism in the brain. * However, presented data remain mostly descriptive, and experiments are performed in a model that may not be optimal to study tropism. As the ability of the virus to escape type I interferon participates to tropism, the fact that infections are only performed in mavs -/- mice limits the relevance of those findings.

We agree that studying tropism in MAVS-/- mice might be misleading and that is why the whole tropism study was performed in adult WT mice, we have clarified in the text that these data are from WT mice. In addition to the significance of this study in highlighting the respective contribution of structural proteins and the immune response in shaping tropism, this study also provides a __well-characterized chimeric virus __with a safety profile comparable to LGTV while retaining key structural and antigenic features of TBEV, model that has already helped advance studies on flavivirus receptor interactions and structural dynamics.

Reviewer #2

Evidence, reproducibility and clarity

In the manuscript entitled "The influence of the pre-membrane and envelope proteins on structure, * pathogenicity and tropism of tick-borne encephalitis virus" Ebba Rosendal and colleagues present a wealth of data regarding generation and characterisation of a chimeric LGTV virus with TBEV structural proteins, comparing this virus to both LGTV and TBEV across a number of different basic and advanced readouts. They present interesting data regarding the ability of the LGTV-TBEV chimera to spread cell-cell, and the prolonged survival of immunocompromised mice compared with LGTV, which the authors associate with reduced replication in the periphery. As well as an overall increased ability of TBEV to replicate in vitro, and lead to mortality in WT mice in vivo, TBEV was found to be able to infect the cerebellum, whilst this region was rarely infected by LGTV and the chimera. The authors also demonstrate the cross-reactivity of these three viruses via neutralization using serum of TBEV vaccinated individuals.*

General comment: * In general, I am impressed by the amount of work and breadth of techniques included in this manuscript, which I think speaks to the benefit of multidisciplinary collaboration. However, in my opinion, some points are lacking. My primary concerns lie with the in vivo experiments. The comparison of LGTV and the chimera at the same timepoints isn't ideal as the shift in mortality means these animals are at a different stage of disease at different time points. Whilst this is interesting in itself, it leaves questions about viral titres and tropism of i.p. inoculated animals at end points, in addition to the exclusion of serum titre analysis, the strength of discussion regarding peripheral replication and its potential impact on neuroinvasion/virulence is weakened. Further, claims of neuronal infection are made in figure 4 in total absence of a neuron marker. If the authors wish to claim cell-specific tropism, the cell-specific markers must be included. For figures dependent upon fluorescent imaging, further clarification as to what the AU axes indicate would aid in better interpretation of the data, especially regarding comparison of cerebellar layers for TBEV infection (described in more detail in my specific comments). Finally, In general, I think some opportunities are missed to describe the big picture of potential applicability/impact/translatability of the results obtained, especially the conclusions can be expanded to better highlight this.*

Thank you for these very relevant comments and suggestions. In line with these, we have now added a later timepoint (8 days) for LGTV:prME in IPS1-/- mice to better understand the kinetics of the chimeric virus at later time points (Figure 3). Additionally, we have added a neuronal marker in figure 4. The explanation of quantification of the fluorescence data is described in detail in the material and method, where the concept of this arbitrary unit (AU) used for quantification is described.

Specific points: * • Line 67: "It" is a bit of a shaky antecedent - assumedly the authors are referring to tropism, but would be good to state this, as they could also be referring to the underlying mechanisms of pathology. i.e. Tropism is determined by....*

We agree here and have specified this accordingly.

• Line 70 - Low pathogenicity in which species? All? Humans? The sentence refers to mice as there has not been any human clinical case with LGTV. We have added that to the text.

• Line 79 - Strange wording - "and which viral factors influence tropism" is sufficient Corrected accordingly.

• Line 82 - What does "low pathogenic" mean in this context? Good survivability? No clinical signs? We have clarified in the text that this is referring to similarity to the pathogenicity of LGTV.

• Line 95: Good to mention in the text the cell type in which the foci are seen We agree, this information has been added to the main text in addition to the figure legend.

• Line 133 - What is the rationale for the different TBEV strains used? (Kuutsalo-14 here but 93/783 before) We compare the structure of our chimeric virus with the previously published Kuutsalo-14 strain (ref 25). The use of 93/783 in this study is to ensure the same strain of TBEV is used as was used to generate LGTV:prME and to compare the chimeric virus to infectious clones of the parental viruses rescued and passaged in the same way as the chimeric virus itself to ensure differences observed is indeed due to the genetic factors.

• Line 175/Figure 3 - Why these time points and not later ones for the LGTV chimera? I understand the early time points for replication in the periphery, but would also be good to see brain titres around day 14 when the survival of the chimera inoculated mice decreases quite rapidly. Further, imaging at timepoints at which mortality is somewhat comparable (meaning that virus is likely in the brain) would enable additional readouts to characterise neurovirulence such as cell death markers etc. and allow for a more solid comparative characterisation. Thank you for bringing this to our attention. The figure 3E is displaying data for MAVS-/- mice infected with 10^5 FFU, where the some animals meet end-point criteria already around day 7-9. To address this comment, we have added an additional timepoint at day 8 (seven animals) to explore the trend in viral loads in the brain. However, we refrain from analyzing later time points as this would require a high number of starting animals to ensure adequate numbers surviving to e.g. the suggested day 14, which is not in line with RRR.

• Interestingly, there is not significant increase in viral loads of LGTV:prME infected animals between day 6 and 8. In line with this, IF imaging analysis of brains from later end-point animals (day 10-14) has shown limited staining of viral antigen in the brain (data not included in manuscript but could be provided to reviewers if requested). This suggests that inflammation is driving the pathology in these animals rather than uncontrolled viral replication. This has also been noted in the text. The tropism and imaging is done in WT mice infected i.c.. and the time/infectious dose has been adjusted to ensure similar clinical manifestation as presented in supplemental Figure 2A. These mice are then euthanized around day 5-6 and processed for brain imaging, line 189.

• Line 174-182/Figure 3 - Why were serum titres not included in these experiments? These would help to strengthen your argument. (also nice to look at neutralisation in this context, though maybe not essential thanks to your data in figure 2). Viral serum titers have been analyzed previously in MAVS-/- mice in Kurhade et al 2016, and they are high at day 2 and go down to almost detection limit day 4, meaning earlier drop than in peripheral organ and was not included in these experiments. For neutralization, the included time points for the experiments in Figure 3E-H the time points are too short for robust detection of IgG antibody responses.

• Line 183 - Good to overtly state that this is via i.c. inoculation and the justification for use of this route, and that the mice are assumedly WT. I understand LGTV struggles to get to the brain in mice, but is this representative of how neurotropism looks in animals inoculated via a more "natural" route for TBEV? We appreciate the comment and we have clarified that WT mice are i.c. inoculated. Since we wanted to compare the three viruses, we needed to use an inoculation route that is working for all three viruses. While the tropism after peripheral infection of TBEV is a very interesting question, it remains outside the scope of this study as this cannot be compared with LGTV in WT mice.

• Figure 4B - What could account for the large variation seen in the TBEV group? This is a very good question that is difficult to answer. Although these are inbred mice, we have previously seen that there are differences in infection rate between different mice using whole brain imaging (Chotiwan et al 2023).

• Line 200-201 - This image doesn't answer the question of tropism, but contributes to that of microglial activation. A neuronal marker should be included to surmise the cell type infected, rather than using staining for a viral protein to indicate cell morphology/type. Also, the justification for use of the microglial marker over neuronal is lacking, especially as microglia are not mentioned anywhere in the discussion. Also, see suggestion regarding cell death markers above. Thank you for this suggestion we have added a neuronal marker. We have also clarified in the text that we confirm the infection pattern in rhinal cortex with confocal microscopy. Microglia activation has been added to the discussion.

• Line 203/Figure 4E - Are these images quantifiable? Are any differences observed between the viruses? Quantification of microglial activation is sensitive to imaging quality and area of imaging and requires large sample sets to ensure validity in the conclusions. Here we do not observe any clear differences nor claim that the microglia activation is different between the different viral strains.

• Line 210 - Bit strange to mention figure 4D again after figure 4E, and I also couldn't spot reference to figure 4F? Thank you for pointing this out the Figure 4D should be Figure 4E, this has been corrected.

• Are both figures 5A and 5C required for the message you wish to get across? I would suggest either only use 5C or only include the white matter/grey matter comparison for TBEV, in combination with 5A. Thank you we have now removed the mock, LGTV and LGTVT:prME from fig 5C to more clearly communicate the message of difference in infection between GM and WM for TBEV specifically.

• Figure 5D: does the method of quantification you use/the conclusions you arrive at account for cell size/number? The Purkinje cell bodies are very large and the virus signal in these cells looks saturated - however within the granular layer the nuclei are much smaller but have what seem like large foci of NS5 positivity. Though the overall signal is likely much lower, how does relative distribution look when you account for cell size/number or a binary positive/negative quantification? Relatedly, does the primary anti-NS5 antibody have the same affinity for both LGTV and TBEV NS5? The quantification of OPT in figure 5C is not at the level of single cell resolution but rather virus signal over mock. We agree the cells in the cerebellum has different sizes but we do not claim that the Purkinje layer is more infected compared to the granular cell layer, only that Purkinje cells are infected which is relevant in human TBE.

NS5 antibody is raised against a peptide in the TBEV NS5 protein which is highly conserved. The aa identity between TBEV and LGTV is 93%, we have not seen a difference in the staining between the different viruses using this antibody.

• Line 242: Please clarify what you mean by "higher infection" - higher titres? Higher fluorescent signal? We have added "as measured by stronger fluorescent signal" to better explain what we mean with higher infection.

• Line 242: Can you really say anything about replication here? Infection, yes, but the AU readout and lack of multiple time points doesn't allow for much of an insight into replication, especially when TBEV was left out of the comparison in figure 3F, though even this did not look at live virus. We have changed the wording to infected cells.

• Line 269-271: Exactly what I was wondering and maybe worth discussing a bit more - is there appropriate literature that you could cite here? We were unsure about the specific concern raised by the reviewer in this comment and, therefore, have not made any changes. If the reviewer could clarify their request, we would be happy to address it accordingly.

• Line 274-275: Also mosquito borne viruses. See nice paper related to impact of TBEV vaccination on ADE for mosquito borne flaviviruses. Very interesting and would increase the impact of this point. https://doi.org/10.1038/s41467-024-45806-x Thank you for this suggestion we have added this point into the discussion.

• Line 290-291: Are clinical signs associated with cerebellar injury common for TBEV patients? i.e. does this have translatability to human disease and diagnosis? We have now added some information about cerebellum symptoms in human TBE infection to the discussion.

• Line 308 conclusions; Your point about the potential use of the chimera for vaccine research/to understand cross-reactivity is worth reiterating here, and potentially something about "highlighting the role of non-structural proteins on tropism determination" Thank you for these suggestions we have now added these aspects in the conclusions.

• Methods: whilst I realise the statistics are described in the figure legends, it is usually customary to include a short statistics section in the methods to indicate which program was used and why certain statistical tests were chosen, e.g. in figure 1 you use both parametric and non-parametric testing. Thank you for this suggestion. We have added a section describing the statistics in the methods.

Significance

Broad ranging characterisation of a novel chimera which has potential applications for vaccine/cross-reactivity research and highlights a key role of non-structural proteins in the determination of viral fitness and tropism. Some limitations regarding cell-specific tropism and kinetics of neuroinvasion and neurovirulence. Likely of interest for basic researchers from range of disciplines within arbovirology.

• Expertise: arboviruses, imaging, neurovirulence, animal models*
• Limited expertise: in-depth structural biology, therefore my comments on figure 2 are limited.*

Reviewer #3 (Evidence, reproducibility and clarity (Required)): * SUMMARY: The authors generated an LGTV chimeric virus harboring the prM and ectodomain of E from TBEV. Aim of the study is to understand how the virals structural proteins influence the distribution and tropism of the virus in the brain. They solved the atomic structures of LGTV and the chimeric virus demonstrating that the chimeric virus is structurally and antigenically similar to TBEV. In vivo experiments demonstrate that the chimeric virus is less pathogenic than LGTV. Finally using 3D whole brain OPT imaging techniques the authors demonstrate that the three viruses show a similar viral distribution in cerebral cortex with the rhnial cortex being the primary site of cortical infection for all viruses. In general TBEV exhibit higher infection rates and is more widespread in the brain, particularly in cerebellum, compared to LGTV and the chimeric virus. The authors concluded that the distribution and tropism of LGTV and TBEV are not solely dependent on receptor tropism. *

MAJOR COMMENTS: * The conclusions are supported by the data.*

• However, I think the work can be improved if the authors investigate the differences in the antiviral response induced by the chimeric virus compared to LGTV. The authors speculate that the non-structural proteins may play a role in shaping tropism, likely through their immunomodulating role. These data become especially important if you consider that in the experiments of fig 1 the chimeric virus behave similar to the LGTV wt with even an advantage in cell-to-cell spread but in the in vivo experiments with MAVS-/- mice the chimeric virus behave differently, being less pathogenic than LGTV suggesting that the chimeric virus could not escape the antiviral response even in MAVS-/- condition. We thank the reviewer for this suggestion. In line with this we have now added Ifnb1 and Rsad2 RNA levels in different peripheral organs and we see that early on in infection most mice infected with LGTVT:prME show higher upregulation of these genes. These data have been added as a new panel F and G in figure 3.

• Moreover, in the discussion, line 270 the authors speculate that the observed attenuation could also be due to sub-optial interactions between TBEV prM and C and transmembrane domain of LGTV E. I think it is important to explain and justify why they decided to do not include C protein of TBEV in the chimeric virus, as well as the transmembrane domain of E. The rational for not using the C protein of TBEV is that we did not want to reduce the RNA to C interaction which, could affect the packaging or encapsidation. In line with this, previous research on chimeric flaviviruses has shown that exchanging the prM-E proteins are usually well tolerated while exchanging the C-protein may lead to attenuation or even failure to rescue the virus.

• Finally, the authors first used A549 cells for studying the kinetics and viral spread of the chimeric virus in vitro. Than they switch to A549-/- cells for studying structure and antigenicity. The different pathogenicity was assessed in Mavs-/- mice but lastly they used mice WT for the 3D whole brain OPT imaging. I found this discrepancy confusing. The authors should justify, including the explanation in the text, why they switch from WT to A549-/- from experiment to experiment. A549 cells were used in the spread and kinetic study because it is an IFN competent cell type which TBEV and LGTV grows well in. The structural studies were performed in A549 MAVS cells because the lack of MAVS results in higher virus titers. The ability of these cells to produce large amount of virus while grown without serum greatly facilitated the purification protocols for cry-EM and mass spectrometry analysis. This has been highlighted in the text of both the material and method and very briefly in the result.

The pathogenicity with peripheral infection can only be done with MAVS-/- mice as they are more sensitive to LGTV and it is a lethal model. Adult WT mice are resistant to LGTV infection i.p.. As the immune response is important in shaping the tropism, a direct comparison of the viruses is best analyzed in a WT mouse model.

MINOR COMMENTS:

• • Line 96 - "recombinant parental LGTV" and "recombinant TBEV", the word recombinant is misused in the sentence.* We have removed recombinant.

• Line 143-144-145 - I believe the authors are referring to Fig 2I and not 2H as written. Moreover, the authors should clarify if all the experiemtns of fig 2 have been performed in A549-/- cells or only the one of fig 2I All experiments in figure 2 are performed in A549 MAVS-/- as highlighted in the material and methods.

• Line 158 - to be change "Fig 2I" with "fig 2J" Corrected

• Line 159 - as above: fig 2J to be change with figure 2k Corrected

*Significance: *

• The authors designed a chimeric low pathogenic model virus to study the importance of the structural proteins in determing viral tropism and pathogenicity. The strengths of this work is that they combined the use of the chimeric virus with in vivo experiments and 3D whole brain OPT imaging. Integrating together these tools and assays the authors provided an example of complete investigation method for studying neuroinvasive viruses. *

• My field of expertise: virus-host interaction, at molecular level.*

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Referee #3

Evidence, reproducibility and clarity

Summary: The authors generated an LGTV chimeric virus harboring the prM and ectodomain of E from TBEV. Aim of the study is to understand how the virals structural proteins influence the distribution and tropism of the virus in the brain. They solved the atomic structures of LGTV and the chimeric virus demonstrating that the chimeric virus is structurally and antigenically similar to TBEV. In vivo experiments demonstrate that the chimeric virus is less pathogenic than LGTV. Finally using 3D whole brain OPT imaging techniques the authors demonstrate that the three viruses show a similar viral distribution in cerebral cortex with the rhnial cortex being the primary site of cortical infection for all viruses. In general TBEV exhibit higher infection rates and is more widespread in the brain, particularly in cerebellum, compared to LGTV and the chimeric virus. The authors concluded that the distribution and tropism of LGTV and TBEV are not solely dependent on receptor tropism.

Major Comments: The conclusions are supported by the data.

However, I think the work can be improved if the authors investigate the differences in the antiviral response induced by the chimeric virus compared to LGTV. The authors speculate that the non-structural proteins may play a role in shaping tropism, likely through their immunomodulating role. These data become especially important if you consider that in the experiments of fig 1 the chimeric virus behave similar to the LGTV wt with even an advantage in cell-to-cell spread but in the in vivo experiments with MAVS-/- mice the chimeric virus behave differently, being less pathogenic than LGTV suggesting that the chimeric virus could not escape the antiviral response even in MAVS-/- condition.

Moreover, in the discussion, line 270 the authors speculate that the observed attenuation could also be due to sub-optial interactions between TBEV prM and C and transmembrane domain of LGTV E. I think it is important to explain and justify why they decided to do not include C protein of TBEV in the chimeric virus, as well as the transmembrane domain of E.

Finally, the authors first used A549 cells for studying the kinetics and viral spread of the chimeric virus in vitro. Than they switch to A549-/- cells for studying structure and antigenicity. The different pathogenicity was assessed in Mavs-/- mice but lastly they used mice WT for the 3D whole brain OPT imaging. I found this discrepancy confusing. The authors should justify, including the explanation in the text, why they switch from WT to A549-/- from experiment to experiment.

Minor comments:

Line 96 - "recombinant parental LGTV" and "recombinant TBEV", the word recombinant is misused in the sentence.

Line 143-144-145 - I believe the authors are referring to Fig 2I and not 2H as written. Moreover, the authors should clarify if all the experiemtns of fig 2 have been performed in A549-/- cells or only the one of fig 2I

Line 158 - to be change "Fig 2I" with "fig 2J"

Line 159 - as above: fig 2J to be change with figure 2k

Significance

The authors designed a chimeric low pathogenic model virus to study the importance of the structural proteins in determing viral tropism and pathogenicity. The strengths of this work is that they combined the use of the chimeric virus with in vivo experiments and 3D whole brain OPT imaging. Integrating together these tools and assays the authors provided an example of complete investigation method for studying neuroinvasive viruses.

My field of expertise: virus-host interaction, at molecular level.

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Referee #2

Evidence, reproducibility and clarity

In the manuscript entitled "The influence of the pre-membrane and envelope proteins on structure, pathogenicity and tropism of tick-borne encephalitis virus" Ebba Rosendal and colleagues present a wealth of data regarding generation and characterisation of a chimeric LGTV virus with TBEV structural proteins, comparing this virus to both LGTV and TBEV across a number of different basic and advanced readouts. They present interesting data regarding the ability of the LGTV-TBEV chimera to spread cell-cell, and the prolonged survival of immunocompromised mice compared with LGTV, which the authors associate with reduced replication in the periphery. As well as an overall increased ability of TBEV to replicate in vitro, and lead to mortality in WT mice in vivo, TBEV was found to be able to infect the cerebellum, whilst this region was rarely infected by LGTV and the chimera. The authors also demonstrate the cross-reactivity of these three viruses via neutralisation using serum of TBEV vaccinated individuals.

General comment:

In general, I am impressed by the amount of work and breadth of techniques included in this manuscript, which I think speaks to the benefit of multidisciplinary collaboration. However, in my opinion, some points are lacking. My primary concerns lie with the in vivo experiments. The comparison of LGTV and the chimera at the same timepoints isn't ideal as the shift in mortality means these animals are at a different stage of disease at different time points. Whilst this is interesting in itself, it leaves questions about viral titres and tropism of i.p. inoculated animals at end points, in addition to the exclusion of serum titre analysis, the strength of discussion regarding peripheral replication and its potential impact on neuroinvasion/virulence is weakened. Further, claims of neuronal infection are made in figure 4 in total absence of a neuron marker. If the authors wish to claim cell-specific tropism, the cell-specific markers must be included. For figures dependent upon fluorescent imaging, further clarification as to what the AU axes indicate would aid in better interpretation of the data, especially regarding comparison of cerebellar layers for TBEV infection (described in more detail in my specific comments). Finally, In general, I think some opportunities are missed to describe the big picture of potential applicability/impact/translatability of the results obtained, especially the conclusions can be expanded to better highlight this.

Specific points:

• Line 67: "It" is a bit of a shaky antecedent - assumedly the authors are referring to tropism, but would be good to state this, as they could also be referring to the underlying mechanisms of pathology. i.e. Tropism is determined by....
• Line 70 - Low pathogenicity in which species? All? Humans?
• Line 79 - Strange wording - "and which viral factors influence tropism" is sufficient
• Line 82 - What does "low pathogenic" mean in this context? Good survivability? No clinical signs?
• Line 95: Good to mention in the text the cell type in which the foci are seen
• Line 133 - What is the rationale for the different TBEV strains used? (Kuutsalo-14 here but 93/783 before)
• Line 175/Figure 3 - Why these time points and not later ones for the LGTV chimera? I understand the early time points for replication in the periphery, but would also be good to see brain titres around day 14 when the survival of the chimera inoculated mice decreases quite rapidly. Further, imaging at timepoints at which mortality is somewhat comparable (meaning that virus is likely in the brain) would enable additional readouts to characterise neurovirulence such as cell death markers etc. and allow for a more solid comparative characterisation.
• Line 174-182/Figure 3 - Why were serum titres not included in these experiments? These would help to strengthen your argument. (also nice to look at neutralisation in this context, though maybe not essential thanks to your data in figure 2)
• Line 183 - Good to overtly state that this is via i.c. inoculation and the justification for use of this route, and that the mice are assumedly WT. I understand LGTV struggles to get to the brain in mice, but is this representative of how neurotropism looks in animals inoculated via a more "natural" route for TBEV?
• Figure 4B - What could account for the large variation seen in the TBEV group?
• Line 200-201 - This image doesn't answer the question of tropism, but contributes to that of microglial activation. A neuronal marker should be included to surmise the cell type infected, rather than using staining for a viral protein to indicate cell morphology/type. Also, the justification for use of the microglial marker over neuronal is lacking, especially as microglia are not mentioned anywhere in the discussion. Also, see suggestion regarding cell death markers above.
• Line 203/Figure 4E - Are these images quantifiable? Are any differences observed between the viruses?
• Line 210 - Bit strange to mention figure 4D again after figure 4E, and I also couldn't spot reference to figure 4F?
• Are both figures 5A and 5C required for the message you wish to get across? I would suggest either only use 5C or only include the white matter/grey matter comparison for TBEV, in combination with 5A.
• Figure 5D: does the method of quantification you use/the conclusions you arrive at account for cell size/number? The Purkinje cell bodies are very large and the virus signal in these cells looks saturated - however within the granular layer the nuclei are much smaller but have what seem like large foci of NS5 positivity. Though the overall signal is likely much lower, how does relative distribution look when you account for cell size/number or a binary positive/negative quantification? Relatedly, does the primary anti-NS5 antibody have the same affinity for both LGTV and TBEV NS5?
• Line 242: Please clarify what you mean by "higher infection" - higher titres? Higher fluorescent signal?
• Line 242: Can you really say anything about replication here? Infection, yes, but the AU readout and lack of multiple time points doesn't allow for much of an insight into replication, especially when TBEV was left out of the comparison in figure 3F, though even this did not look at live virus.
• Line 269-271: Exactly what I was wondering and maybe worth discussing a bit more - is there appropriate literature that you could cite here?
• Line 274-275: Also mosquito borne viruses. See nice paper related to impact of TBEV vaccination on ADE for mosquito borne flaviviruses. Very interesting and would increase the impact of this point. https://doi.org/10.1038/s41467-024-45806-x
• Line 290-291: Are clinical signs associated with cerebellar injury common for TBEV patients? i.e. does this have translatability to human disease and diagnosis?
• Line 308 conclusions; Your point about the potential use of the chimera for vaccine research/to understand cross-reactivity is worth reiterating here, and potentially something about "highlighting the role of non-structural proteins on tropism determination"
• Methods: whilst I realise the statistics are described in the figure legends, it is usually customary to include a short statistics section in the methods to indicate which program was used and why certain statistical tests were chosen, e.g. in figure 1 you use both parametric and non-parametric testing.

Significance

Broad ranging characterisation of a novel chimera which has potential applications for vaccine/cross-reactivity research and highlights a key role of non-structural proteins in the determination of viral fitness and tropism. Some limitations regarding cell-specific tropism and kinetics of neuroinvasion and neurovirulence. Likely of interest for basic researchers from range of disciplines within arbovirology.

Expertise: arboviruses, imaging, neurovirulence, animal models

Limited expertise: in-depth structural biology, therefore my comments on figure 2 are limited.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this study, authors investigate the impact of pre-membane (prM) and envelope (E) proteins of tick-borne encephalitis virus (TBEV) on viral distribution and tropism, mostly in the brain.

To do so, authors use high resolution imaging of whole mouse brain after infection by either LGTV, a low pathogenic orthoflavivirus also transmitted by ticks, TBEV, or TBEV/LGTV chimeric virus where prM and E of TBEV are inserted in a LGTV background. Structural and antigenic characterization of the chimeric virus reveal that it remains a low pathogenic virus exhibiting TBEV structural and antigenic features. Those viruses are then used to infect wt or mavs -/- mice and viral propagation / tropism is explored, revealing that LGTV and LGTVT:prM predominantly infect cerebral cortex while TBEV infects cerebellum.<br /> Authors work at characterizing their viruses is nicely done and convincing, showing that LGTVT:prM replicated just like LGTV, and exhibited increased viral spread in cellulo. However LGTVT:prM appear to be less pathogenic in vivo and its brain tropism in mavs -/- mice seems to be similar to wt LGTV virus, stressing the fact that the role of structural proteins prM/E is only modest in TBEV specific tropism to cerebellum.

Major comments:

• It is stated in the introduction that prior work on LGTV/TBEV chimera have already been done, and that both LGTV and LGTV/TBEV are neuroinvasive and neurovirulent in animal models. In this study, both LGTV and LGTVT:prM fails to establish infection in wt mouse model. Were previous published data on LGTV and derivatives also only performed in mavs, or ifnar deficient mice?

The fact that the whole "tropism" part of the study is performed in mavs -/- mice limits the impact of the study as escape from innate immune response is central in shaping viral tropism. Authors should advertise more this fact (absent from the abstract) and discuss more the links between LGTV / TBEV and innate immune response (escape mechanisms and NS proteins, implication of prM in controlling MDA5, MAVS)

Minor comments:

Figures need some re-working :

Figure 1 :

1D : only the difference between TBEV and LGTVT:prM is shown. Plotting the difference LGTV / LGTVT:prM would be a nice upgrade.

Figure 2 : Numbering in the panels is wrong (2j in the text is 2K, 2H is 2I, ...) and should be corrected.

Figure 3 : Route of infection could be added to figure labels for more clarity.

Figure 4A : Labelling the Mock pannel with areas of concern in the brain(Cerebrum, Cerebellum, ...) would help a lot readers not familiar with brain anatomy.

Figure 4 E : images are too small to be convincing. What is staining Iba-1 is not mentioned in the figure legend.

Significance

Prior studies already described the generation and characterization of TBEV/LGTV chimeric viruses. The main addition of this paper to the field is the use of impressive high-resolution imaging of whole mouse brains, to explore viral infection and tropism in the brain.

However, presented data remain mostly descriptive, and experiments are performed in a model that may not be optimal to study tropism. As the ability of the virus to escape type I interferon participates to tropism, the fact that infections are only performed in mavs -/- mice limits the relevance of those findings.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

The authors describe a genome-wide CRISPR screen in mouse ES cells to identify factors and genes that regulate positively and negatively FGF/ERK signaling during differentiation. Out of known and potentially novel regulating signals, Mediator subunit Med12 was a strong hit in the screen and it was clearly and extensively shown by that the loss of Med12 results in impaired FGF/ERK signal responsiveness, modulation of mRNA levels and disturbed cell differentiation leading to reduced stem cell plasticity.<br /> This is a very concise and well written manuscript that demonstrates for the first time the important role of Med12 in ES cells and during early cell differentiation. The results support data that had been previously observed in Med12 mouse models and in addition show that Med12 cooperates with various signaling systems to control gene expression during early lineage decision.

We thank the reviewer for their positive evaluation of our work.

Fig. 3 Supp1A-B:<br /> The loci of all three independent Med12 mutant clones and the absence of Med12 should be included. Are all three Med12 loss-of-function mutants?

In the revised version of the manuscript, we have updated the scheme in Fig. 3 Supp 1A to represent both deletions that were obtained with the CRISPR guides used. Both the more common 97 bp deletion as well as the 105 bp deletion that occurred in one clonal line result in a complete loss of the protein on the western blot (Fig. 3 Supp. 1B), suggesting that all mutant clones used for further experiments are loss-of-function mutants.

Minor:<br /> Line 466: Should be Fig. 6F, not 6E.

We have removed this figure panel and the corresponding text in response to the other reviewers' comments.

Reviewer #1 (Significance):

The CRISPR screen identified list of some novel interesting factors that regulate FGF/ERK signaling in ES cells. Med12 was then analyzed in very detail on various levels and under various differentiation conditions, resulting in a complex picture how Med12 controls stem cell plasticity. These data support results observed in mouse models and identified novel regulating mechanisms of Med12.

Reviewer #2 (Evidence, reproducibility and clarity):

In the manuscript "Med12 cooperates with multiple differentiation signals to enhance embryonic stem cell plasticity" Ferkorn and Schröter report on the role of Med12 in mouse embryonic stem cells. The perform an elegant genetic screen to identify regulators of Spry4 in mouse ESCs, screening for mutations that increase and decrease Spr4-reporter expression in serum/LIF conditions. They find that Med12 deletion results in defects in the exit from naïve pluripotency and in PrE-formation upon Gata-TF overexpression. Using scRNAseq experiments they report a reduction in biological noise in Med12 KO cells differentiating towards PrE upon Gata6 OE.

Major points:<br /> 1) The title might not exactly reflect the scientific findings of the manuscript. There is little direct evidence for a decrease in plasticity upon Med12 depletion.

We have changed the title to "Med12 cooperates with multiple differentiation signals to facilitate efficient lineage transitions in embryonic stem cells". In addition, we have toned down claims that Med12 regulates plasticity throughout the manuscript.

2) Fig 1G: From the data provided it is not entirely clear how well screen results can be validated. Did some of the mutants identified in the screen also produce no detectable phenotypes? What would be the phenotype of knocking out an unrelated gene? In other words, are some of the weak phenotypes really showing Spry4 downregulation or are they withing the range of biological variance?

Fluorescence levels in Fig. 1G have been normalized to control wild-type cells (dashed red line). Absence of a detectable phenotype would have resulted in normalized fluorescence values around 1. Fluorescence values of all tested mutants were significantly different from 1, as indicated in the statistical analysis given in the figure legend. Furthermore, H2B-Venus fluorescence of cells transfected with a non-targeting control vector are shown in Fig. 1F, and are not different from that of untransfected control wild-type cells. We have now added an explicit explanation how we normalized the data to the figure legend of Fig. 1G, and hope that this addresses the reviewer's concern.

3) Rescue experiments by re-expressing Med12 in Med12 KO ESCs are missing. Can the differentiation and transcriptional phenotypes be rescued?

We agree with the reviewer that a rescue experiment re-expressing Med12 would be ideal to ensure that the observed phenotypes are specifically due to loss of Med12. However, we could not identify commercially available full-length Med12 cDNA clones. Even though we managed to amplify full-length Med12 cDNA after reverse transcription, we were unable to clone it into expression vectors. These observations suggest that specific properties of the Med12 cds make the construction of expression vectors by conventional means difficult, and solving these issues is beyond the scope of this study.

Throughout the study we used multiple independent clonal lines in multiple experimental readouts and obtained congruent results. The reduced expression of pluripotency genes for example was observed in bulk sequencing of the lines introduced in Fig. 3, and by single-cell sequencing of independently generated _Med12-_mutant GATA6-mCherry inducible lines (Fig. 5 Supp. 1B). We argue that this congruence makes it unlikely that the results are dominated by off-target effects.

4) L365: The subheading "Transitions between embryonic... buffered against loss of Med12" is confusing. The data simply shows that Med12 KOs can still, albeit less efficiently generate PrE upon Gata TF OE. Is there evidence for some active buffering? I think the authors could simply report the data as is, stating that the phenotypes are not a complete block but an impairment of differentiation.

Prompted by the reviewer's comment as well as remarks along similar lines by reviewer #4, we have completely reorganized this section and now present all the analysis pertaining to PrE differentiation in a new figure 4. In the revised text (lines 316 - 378), we refrain from any speculations about possible buffering and simply report the data as is, as suggested by the reviewer.

5) L386: Would it not make more sense to reduce dox concentrations in control cells to equalize Gata6 OE to equalize levels between Med12 KO and controls? A shorter pulse of Gata6 does not really directly address unequal expression levels due to loss of Med12. Different pulse length of OE might have consequences that the authors do not control for. This also impacts scRNAseq experiments which suffer from the same, in my opinion, suboptimal experimental setup. This is a point that needs to be addressed.

We agree with the reviewer that it would have been desirable to equalize GATA6 overexpression levels between wild-type and Med12-mutant cells while keeping induction time the same. In our experience however, reducing the dox concentration is not suitable to achieve this: Rather than reducing transgene expression levels across the board, lower dox concentrations tend to increase the variability within the population - see Fig. 2 in PMID: 16400644 for an example. Since we agree with the reviewer that the setup of the scRNAseq experiment limits our ability to draw conclusion regarding the separation of cell states, we have decided remove these analyses in the revised manuscript. In doing so, we have reorganized the previous figures 5 and 6 into a new single figure 4. This has made the manuscript more concise and allowed us to focus on the main phenotype of the Med12 mutant cells, namely their delayed exit from pluripotency.

6) The reduced transcript number in Med12 KOs is interesting, but how does it come about. Is there indeed less transcriptional activity or is reduced transcript numbers a side effect of slower growth or the different cell states between WT and Med12 mutants. Appropriate experiments to address this should be performed.

To address this point, we have performed EU labeling experiments, to compare RNA synthesis rates between wild-type and Med12-mutant during the exit from pluripotency. These experiments confirmed an increase in the mRNA production upon differentiation for both wild-type and Med12 mutant cells, but the method was not sensitive enough to detect any differences between wild-type and Med12 mutant cells within the same condition. The EU labeling thus supports the notion that overall transcriptional rate increases during differentiation, but leaves open the possibility that reduced mRNA levels in Med12 mutant cells arise from effects other than reduced transcriptional output. These new analyses areshown in Fig. 4 Supp. 3 and described in the main text in lines 373 - 378.

7) I the proposed reduction of biological noise a feature of the PrE differentiation experiments or can it also be observed in epiblast differentiation.

To address this question, we have carried out single-cell measurements of Spry4 and Nanog mRNA numbers to compare transcriptional variability between wild-type and Med12-_mutant cells during epiblast differentiation (new Fig. 3 Supp. 1G, H). These measurements confirmed the differences between genotypes in mean expression levels detected by RNA sequencing. However, this analysis did not reveal strong differences in mRNA number distributions. Furthermore, as discussed in point 6 above, our interpretations of noise levels in the PrE differentiation paradigm could have been influenced by the unequal GATA6 induction times. Finally, reviewer #4 pointed out that 10x genomics scRNAseq is not ideal to compare noise levels when total mRNA content differ between samples, as is the case in our dataset. We therefore decided to tone down our conclusions regarding altered noise levels in _Med12-mutant cells.

8) I cannot follow the authors logic that Med12 loss results in enhanced separation between lineages. How is this experimentally supported.

As discussed in point 6 above, this result could have been influenced by the unequal induction times between wild type and Med12-mutant cells. We have therefore decided to remove this analysis in the revised version of the manuscript.

Minor points:<br /> Fig 3, Supp1 A: What exactly are the black and blue highlighted letters?

The black and blue highlighted letters indicate whether bases are part of an intron or an exon. Exon 7 is now explicitly labelled in the figure, and the meaning of the highlighting is explained in the figure legend.

Reviewer #2 (Significance):

Overall, this is an interesting study. The screen has been performed to a high technical standard and differentiation defects were appropriately analyzed. The manuscript has some weaknesses in investigating the molecular mode of action of Med12 which could be improved to provide more significant insights.

Reviewer #3 (Evidence, reproducibility and clarity):

The authors sought to identify genes important for the transcriptional changes needed during mouse ES cell differentiation. They identified a number of genes and focussed on Med12, as it was the strongest hit from a cluster of Mediator components.

Using knockout ES cells, differentiation assays, bulk and scRNAseq, they clearly show that Med12 is important for transgene activation and for gene activation generally during exit from self-renewal, but it is not specifically influencing differentiation efficacy per se. Rather, cells lacking Med12 display "a reduced ability to react to changing culture conditions" and, by inference, to environmental changes. They conclude that Med12 "contributes to the maintenance of cellular plasticity during differentiation and lineage transitions."

Med12 is a structural component of the kinase module of Mediator, but it is not clear what this study tells us about Mediator function. The authors state that their results contrast with those obtained using a Cdk8 inhibitor, which resulted in increased self-renewal (lines 577-580). I'm not sure where their results show "...that loss of Med12 leads to reduced pluripotency." (lines 579-580). They do not test potency of these cells. There is reduced expression of some pluripotency-associated markers and fewer colonies formed in a plating assay, but these assays to not test cellular potency.

We agree with the reviewer that our RNA sequencing and colony formation assays do not exhaustively test cellular potency. We have therefore changed the wording in the paragraphs that describe these assays and now talk about "reduced pluripotency gene expression" (e.g. lines 20, 228, 461, 512).

While their phenotype certainly appears different from that reported in cells treated with Cdk8 inhibitor, it's not clear to me what to make of it, or what it might tell us about the function of the Mediator Kinase module or of Mediator. That a co-activator is important for gene expression in general, or even for gene activation upon receipt of some signal, is not really surprising.

We believe that reporting differences in the phenotypes obtained with Cdk8 inhibition versus knock-out of Med12 is relevant, because it yields new insight into the different functions that the components of the Mediator kinase module have in pluripotent cells. We have previously discussed possible reasons for these functional differences (discussion line 519 - 528), and further expand on them in the revised manuscript.

Minor points:

It is surprising they don't relate their work to that of Hamilton et al (https://doi.org/10.1038/s41586-019-1732-z) who conclude that differentiation from the ES cell state towards primitive endoderm is compromised without Med24.

Thank you for pointing out this omission. We now cite the work of Hamilton et al., in line 317 (related to new Fig. 4) and 537 - 538 in the discussion.

Stylistic point: please make the separation between paragraphs more obvious. With no indentation or extra spacing between paragraphs it looks like one solid mass of words.

Reviewer #3 (Significance):

There is a lot of careful work here, but I'm not getting a big conclusion here. Perhaps the authors could argue their main points somewhat more stridently and what we've learned beyond this current system.

Prompted by the reviewer's comment, we have re-organized the functional analyses of Med12 function in the manuscript by condensing the previous figures 5 and 6 into a new single figure 4. We have removed all discussions of transcriptional noise and plasticity, and now focus more strongly on the slowed pluripotency transitions as the main phenotype of the Med12 mutant cells. These changes make the manuscript more concise, and we hope that they help to deliver a single, clear message to the reader.

Reviewer #4 (Evidence, reproducibility and clarity):

Fernkorn and Schröter report the results of a screen in mESCs based on modulation of the fluorescent intensity of the Spry4:H2B-Venus reporter. They identify candidate genes that both positively and negatively modulate the expression of the reporter. Amongst those, are several known regulators of the FGF pathway (transcriptional activator of Spry4) that serve as a positive control for the screen. The manuscript focuses on characterisation of Med12, and the authors conclude that Med12 does not specifically affect FGF-targets. Paradoxically, the authors show that based on the expression of key naïve markers Med12 cells show delayed differentiation. Functionally, however, Med12 mutant cells at 48hrs can form less colonies when plated back in naïve conditions (that would normally indicate accelerated differentiation ). The authors conclude that Med12 mutants have "a reduced ability to react to changing culture conditions". Next, they examine the Med12 mutation affects embryonic/extraembryonic differentiation using an inducible Gata6 expression system. They show that transgene induction is slower and dampened in mutant cells and that overall the balance of fates is skewed towards embryonic cells. Finally, they use single cell RNA sequencing and observe differences in the number of mRNAs detected, as well as the separation between clusters in the mutant cells. They conclude that the mutants have reduce transcriptional noise levels.

Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.

Mayor points:

• Med12, transcription levels and noise (Figure 6G, J-L). This is an intriguing observation. The labelling and multiplexing helped resolve many of the issue typically associated with comparing 10x dataset. I have two observations about this analysis:<br /> 1) Clarify how number of mRNA counts per cell is calculated (figure 6F) - the methods only described a value normalised by the total number of counts per cell.

The mRNA counts shown in the figure correspond to the raw number of UMIs detected per cell. We now explicitly state this in the figure legend. Please note that after re-organizing the manuscript, former Fig. 6F has become Fig. 4 Supp. 3A.

I feel this observation is key and has repercussions for the interpretation of the data (see point below) and should be independently validated (although I recognise it's difficult!). Since the authors observed differences in a randomly integrated transgene (iGata experiments), it's possible/likely that the dysregulation of transcription output is more generic. A possible suggestion is measuring global mRNA synthesis and degradation rates, either using inhibitors or by adding modified nucleotides and measuring incorporation rate and loss through pulse/chase labelling.

We have performed an EU labeling experiment to address this point, which is shown in Fig. 4 Supp. 3 and described in the main text in lines 373 - 378 of the revised manuscript. Please refer to our response to reviewer #2, point 6 for a short description of the results.

2) 10x is not the ideal for looking at heterogeneity/noise since it has a low capture efficiency and there are a lot of gaps/zeros in the lower expression range. Therefore, it's simply possible that mutant cells have dampened transcriptional output, meaning lowly expressed genes which in the WT contribute to the apparent heterogeneity (because there is a higher chance of not being captured), are below the 10x detection range in the mutant. This can be seen by plotting the cumulative sum of the mean gene count across each sample - the 50% mark (=mean gene count at 50% detection) reflects a measure of the "capture efficiency" (either because of technical reasons or lower mRNA input). Generally (e.g. also seen across technical repeats), the mean coefficient of variation, entropy and other measures of population heterogeneity directly scale with this "mean gene count at 50% detection", while the cell-cell correlation inversely scales with the "mean gene count at 50% detection". If this scaling relationships are observed for the WT and mutant, then it is impossible to say from the single cell RNA-seq whether the differences in heterogeneity are due to biological or technical reasons. Unfortunately, down-sampling the reads does not generally correct or normalise for this type of technical noise since the technical errors accumulate at every step of sample prep. Of course, it's possible that the technical noise in the RNAseq obfuscates real differences in the level of noise. The failure of mutant cells to re-establish the naïve network certainly suggest there is something going on. Therefore, I suggest performing the analysis of capture efficiency vs CV2 mentioned above and adjusting the discussion accordingly, and potentially perform single molecule FISH of key variable genes at the interface of the two clusters to validate the difference in heterogeneity.

As suggested by the reviewer, we have performed single molecule FISH measurements of variable genes (Fig. 3 Supp. 1 G, H), but these did not provide independent evidence for increased noise levels in Med12 mutant cells. In light of the caveats raised by reviewer #4 when estimating noise levels from 10x scRNAseq data, and the suggestion of reviewer #3 to sharpen the focus of the manuscript, we have decided to remove any strong conclusions about different noise levels between the genotypes. Instead, we focus on the slowed pluripotency transitions as the main phenotype of the Med12 mutant cells to make the manuscript more concise, to deliver a single, clear message.

• Are Oct4 levels affected? Reduction of Oct4 is sufficient to block differentiation (Radzisheuskaya et al. 2013 - PMID: 23629142).

We thank the reviewer for this idea. We measured OCT4 expression levels in single cells via quantitative immunostaining and found that that there is no difference between wild-type and Med12-mutant cells. It is therefore unlikely that lowered OCT4 levels block differentiation in the mutant. These new results are shown in Fig. 5, Supp. 1 D, E.

• Med12 mutants showing transcriptionally delayed differentiation (related to figure 4C). Is this delay also reflected in the expression of formative genes? If I understand correctly, Figure 4C is made from a panel of naïve markers. It would be good to determine if the formative network is equally affected (and in the same direction - suggesting a delay), or if the transcriptional changes speak to a global dysregulation/dampened expression.

Prompted by the reviewer's suggestion, we have extended our analysis of the differentiation delays to genes that are upregulated during differentiation, such as formative genes. Rather than trying to come up with an new set of formative markers to produce a variation of the original Fig. 4C (Fig. 5C in the revised manuscript), we have taken an unbiased approach and extended Fig. 5E with a panel showing the distribution of expression slopes of the 100 most upregulated genes determined as in Fig. 5D. This analysis demonstrates a lower upregulation slope in Med12-mutant cells. This result confirms that both the upregulation and downregulation of genes is less efficient upon the loss of MED12, in line with our conclusion of delayed differentiation.

• Control for the re-plating experiments in 2i/LIF (Figure 4B). Replating in 2iLIF + FBS can have a large selective effect in certain mutant backgrounds (e.g. Nodal mutants) which don't accurately reflect the differentiation status. To exclude such effects, it would be good to repeat the replating assays in serum-free conditions (laminin coating can help with attachment) and include undifferentiated controls to ensure that the mutant doesn't have a clonal disadvantage.

The reason we have included FBS in the re-plating assays is that in our experience, Fgf4-_mutant cells show strongly impaired growth standard in 2i+LIF medium. We anticipate that using laminin coating to help with attachment would not overcome this requirement. We have therefore decided against repeating the re-plating assays. Instead, we state the reason why we used FBS in the main text, and also explicitly acknowledge the reviewers' concern of the risk of selective effects of the FBS and the possible clonal disadvantages of the _Med12 mutant line.

Minor points:<br /> - I found figure 3D and the corresponding text and caption difficult to understand. It is unclear what a "footprint", "relative pathway activity" or "spearman correlation of footprint" mean. Were all the genes listed below Med12 knocked out and sequenced in this study? I suggest re-working and maybe simplifying the text and figure.

We re-worked the description about the pathway analysis and stated more clearly that:

• The footprint is a quantitative measure of the differences in gene expression change of a defined list of target genes between wild-type and perturbation.
• Only the Med12 mutant data is new data produced in this manuscript and all examples below are from Lackner et al., 2021.

We think that a more extensive explanation of the terms "relative pathway activity" and "spearman correlation of footprint" would disturb the flow of the manuscript too much. Therefore, we now cite the original paper just next to the sentence these terms are mentioned.

In figure S1 Sup1 the authors report the dose response of targets to FGF - are those affected in the mutant?

In this manuscript we have not tested if the dose response of FGF target genes changes upon perturbation of Med12. We argue that such an experiment would be beyond the scope of the current manuscript, since - as acknowledged by the reviewer - "Med12 does not specifically affect FGF-targets".

• Similarly, it would be helpful to guide the reader through figure 5H-I and the corresponding text and caption since it's not immediately obvious how the analysis/graphs lead to the conclusion stated.

As a consequence of our reorganization of the manuscript, the original figure 5H-I has been moved to Fig. 4, Supp. 1 in the revised version. The analysis strategy has been described in more detail in one of our previous publications (PMID: 26511924). In keeping with our general decision to make the manuscript more focused and concise, we have decided against further expanding on these data, but instead refer the reader to the original publication.

• Role of Med12 in regulating FGF signalling. There are two observations that seems a bit at odds with the text description and it would be helpful to clarify: "ppERK levels were indistinguishable between wild-type and Med12-mutant lines" (line 222) - 5/6 datapoints show an increase. "[...] overall these results argue against a strong and specific role of Med12 in regulation of FGF target genes." (line 274). If I understood correctly, ~50% of genes are differentially transcribed because of Med12 KO.

To address the reviewers' first question, we have performed a statistical test on the quantifications of the western blots. This test indicates that there is no significant change of ppERK levels upon loss-of MED12, which now stated clearly in the text (line 217).

Second, to clarify why our data argues against a strong and specific role of Med12 in regulation of FGF target genes, we now formulate an expectation (lines 276 - 277): If MED12 specifically regulated FGF target genes, the number of differentially expressed genes would be higher in the wild-type than in the Med12-mutant upon stimulation with FGF. This however is not the case.

• "[...] as well as transitions between different pluripotent states" (line 41) - references missing.

We have added a reference to PMID: 28174249 (line 39).

• Line 447: "differentiation conditions" - it's unclear what it's mean by differentiation and how it relates to the diagram in figure 6A. Are those the 20hr cells? Do the -8h, -4hr and 0hr cells (if I understand the meaning of the diagram) cluster all together?

We now specify in the text that pluripotency conditions refer to cells maintained in 2i + LIF medium, whereas differentiation refers to cells switched to N2B27 after the doxycycline pulse (lines 341 - 342).

• The difference in dynamics of mCherry activation as a consequence of Med12 KO are not apparent from figure 5E. It might be easier to visualise this observation if x-axis was normalised to the starting point plotting "time from start of induction".

We agree with the reviewer that the current alignment has not been optimized to compare GATA6 induction dynamics between wild-type and Med12-mutant cells. If we changed the alignment however, it would not be clear any longer that both genotypes were in N2B27 for the same amount of time before analyzing Epi and PrE differentiation. Since our focus is on the differentiation of the two lineages rather than GATA6-mCherry induction dynamics, we decided to keep the original alignment.

• Figure 3H/I - what does "gene expression changes" and "fold change ratio" mean?

In Fig. 3H, we plot the the fold change of gene expression upon FGF4 stimulation in _Med12-_mutant versus that in wild-type cells; in Fig. 3I we plot the distribution of the ratio of these two fold changes across all genes. To make this strategy clearer, we have changed the axis label in Fig. 3H to "expression fold change upon FGF", to make it consistent with the axis label "fold-change ratio" in Fig. 3I.

• Line 579-580 - please clarify what is meant by "reduced pluripotency".

Prompted by a similar concern raised by reviewer #3, we have changed the wording throughout this paragraph and now talk of "reduced pluripotency gene expression". See also our response to reviewer #3 above.

• Title: "enhance ESC plasticity". not sure enhance is the right word? There is no evidence that the plasticity of cells is affected.

We have changed the title; see also our response to reviewer #2, point 1.

Reviewer #4 (Significance):

Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.

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Referee #4

Evidence, reproducibility and clarity

Fernkorn and Schröter report the results of a screen in mESCs based on modulation of the fluorescent intensity of the Spry4:H2B-Venus reporter. They identify candidate genes that both positively and negatively modulate the expression of the reporter. Amongst those, are several known regulators of the FGF pathway (transcriptional activator of Spry4) that serve as a positive control for the screen. The manuscript focuses on characterisation of Med12, and the authors conclude that Med12 does not specifically affect FGF-targets. Paradoxically, the authors show that based on the expression of key naïve markers Med12 cells show delayed differentiation. Functionally, however, Med12 mutant cells at 48hrs can form less colonies when plated back in naïve conditions (that would normally indicate accelerated differentiation ). The authors conclude that Med12 mutants have "a reduced ability to react to changing culture conditions". Next, they examine the Med12 mutation affects embryonic/extraembryonic differentiation using an inducible Gata6 expression system. They show that transgene induction is slower and dampened in mutant cells and that overall the balance of fates is skewed towards embryonic cells. Finally, they use single cell RNA sequencing and observe differences in the number of mRNAs detected, as well as the separation between clusters in the mutant cells. They conclude that the mutants have reduce transcriptional noise levels.

Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.

Major points:

• Med12, transcription levels and noise (Figure 6G, J-L). This is an intriguing observation. The labelling and multiplexing helped resolve many of the issue typically associated with comparing 10x dataset. I have two observations about this analysis:
• Clarify how number of mRNA counts per cell is calculated (figure 6F) - the methods only described a value normalised by the total number of counts per cell. I feel this observation is key and has repercussions for the interpretation of the data (see point below) and should be independently validated (although I recognise it's difficult!). Since the authors observed differences in a randomly integrated transgene (iGata experiments), it's possible/likely that the dysregulation of transcription output is more generic. A possible suggestion is measuring global mRNA synthesis and degradation rates, either using inhibitors or by adding modified nucleotides and measuring incorporation rate and loss through pulse/chase labelling.
• 10x is not the ideal for looking at heterogeneity/noise since it has a low capture efficiency and there are a lot of gaps/zeros in the lower expression range. Therefore, it's simply possible that mutant cells have dampened transcriptional output, meaning lowly expressed genes which in the WT contribute to the apparent heterogeneity (because there is a higher chance of not being captured), are below the 10x detection range in the mutant. This can be seen by plotting the cumulative sum of the mean gene count across each sample - the 50% mark (=mean gene count at 50% detection) reflects a measure of the "capture efficiency" (either because of technical reasons or lower mRNA input). Generally (e.g. also seen across technical repeats), the mean coefficient of variation, entropy and other measures of population heterogeneity directly scale with this "mean gene count at 50% detection", while the cell-cell correlation inversely scales with the "mean gene count at 50% detection". If this scaling relationships are observed for the WT and mutant, then it is impossible to say from the single cell RNA-seq whether the differences in heterogeneity are due to biological or technical reasons. Unfortunately, down-sampling the reads does not generally correct or normalise for this type of technical noise since the technical errors accumulate at every step of sample prep. Of course, it's possible that the technical noise in the RNAseq obfuscates real differences in the level of noise. The failure of mutant cells to re-establish the naïve network certainly suggest there is something going on. Therefore, I suggest performing the analysis of capture efficiency vs CV2 mentioned above and adjusting the discussion accordingly, and potentially perform single molecule FISH of key variable genes at the interface of the two clusters to validate the difference in heterogeneity.
• Are Oct4 levels affected? Reduction of Oct4 is sufficient to block differentiation (Radzisheuskaya et al. 2013 - PMID: 23629142).
• Med12 mutants showing transcriptionally delayed differentiation (related to figure 4C). Is this delay also reflected in the expression of formative genes? If I understand correctly, Figure 4C is made from a panel of naïve markers. It would be good to determine if the formative network is equally affected (and in the same direction - suggesting a delay), or if the transcriptional changes speak to a global dysregulation/dampened expression.
• Control for the re-plating experiments in 2i/LIF (Figure 4B). Replating in 2iLIF + FBS can have a large selective effect in certain mutant backgrounds (e.g. Nodal mutants) which don't accurately reflect the differentiation status. To exclude such effects, it would be good to repeat the replating assays in serum-free conditions (laminin coating can help with attachment) and include undifferentiated controls to ensure that the mutant doesn't have a clonal disadvantage.

Minor points:

• I found figure 3D and the corresponding text and caption difficult to understand. It is unclear what a "footprint", "relative pathway activity" or "spearman correlation of footprint" mean. Were all the genes listed below Med12 knocked out and sequenced in this study? I suggest re-working and maybe simplifying the text and figure. In figure S1 Sup1 the authors report the dose response of targets to FGF - are those affected in the mutant?
• Similarly, it would be helpful to guide the reader through figure 5H-I and the corresponding text and caption since it's not immediately obvious how the analysis/graphs lead to the conclusion stated.
• Role of Med12 in regulating FGF signalling. There are two observations that seems a bit at odds with the text description and it would be helpful to clarify: "ppERK levels were indistinguishable between wild-type and Med12-mutant lines" (line 222) - 5/6 datapoints show an increase. "[...] overall these results argue against a strong and specific role of Med12 in regulation of FGF target genes." (line 274). If I understood correctly, ~50% of genes are differentially transcribed because of Med12 KO.
• "[...] as well as transitions between different pluripotent states" (line 41) - references missing .
• Line 447: "differentiation conditions" - it's unclear what it's mean by differentiation and how it relates to the diagram in figure 6A. Are those the 20hr cells? Do the -8h, -4hr and 0hr cells (if I understand the meaning of the diagram) cluster all together?
• The difference in dynamics of mCherry activation as a consequence of Med12 KO are not apparent from figure 5E. It might be easier to visualise this observation if x-axis was normalised to the starting point plotting "time from start of induction".
• Figure 3H/I - what does "gene expression changes" and "fold change ratio" mean?
• Line 579-580 - please clarify what is meant by "reduced pluripotency".
• Title: "enhance ESC plasticity". not sure enhance is the right word? There is no evidence that the plasticity of cells is affected.

Significance

Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.

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Referee #3

Evidence, reproducibility and clarity

The authors sought to identify genes important for the transcriptional changes needed during mouse ES cell differentiation. They identified a number of genes and focussed on Med12, as it was the strongest hit from a cluster of Mediator components.

Using knockout ES cells, differentiation assays, bulk and scRNAseq, they clearly show that Med12 is important for transgene activation and for gene activation generally during exit from self-renewal, but it is not specifically influencing differentiation efficacy per se. Rather, cells lacking Med12 display "a reduced ability to react to changing culture conditions" and, by inference, to environmental changes. They conclude that Med12 "contributes to the maintenance of cellular plasticity during differentiation and lineage transitions."

Med12 is a structural component of the kinase module of Mediator, but it is not clear what this study tells us about Mediator function. The authors state that their results contrast with those obtained using a Cdk8 inhibitor, which resulted in increased self-renewal (lines 577-580). I'm not sure where their results show "...that loss of Med12 leads to reduced pluripotency." (lines 579-580). They do not test potency of these cells. There is reduced expression of some pluripotency-associated markers and fewer colonies formed in a plating assay, but these assays to not test cellular potency. While their phenotype certainly appears different from that reported in cells treated with Cdk8 inhibitor, it's not clear to me what to make of it, or what it might tell us about the function of the Mediator Kinase module or of Mediator. That a co-activator is important for gene expression in general, or even for gene activation upon receipt of some signal, is not really surprising.

Minor points:

It is surprising they don't relate their work to that of Hamilton et al (https://doi.org/10.1038/s41586-019-1732-z) who conclude that differentiation from the ES cell state towards primitive endoderm is compromised without Med24.

Stylistic point: please make the separation between paragraphs more obvious. With no indentation or extra spacing between paragraphs it looks like one solid mass of words.

Significance

There is a lot of careful work here, but I'm not getting a big conclusion here. Perhaps the authors could argue their main points somewhat more stridently and what we've learned beyond this current system.

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Referee #2

Evidence, reproducibility and clarity

In the manuscript "Med12 cooperates with multiple differentiation signals to enhance embryonic stem cell plasticity" Ferkorn and Schröter report on the role of Med12 in mouse embryonic stem cells. The perform an elegant genetic screen to identify regulators of Spry4 in mouse ESCs, screening for mutations that increase and decrease Spr4-reporter expression in serum/LIF conditions. They find that Med12 deletion results in defects in the exit from naïve pluripotency and in PrE-formation upon Gata-TF overexpression. Using scRNAseq experiments they report a reduction in biological noise in Med12 KO cells differentiating towards PrE upon Gata6 OE.

Major points:

1. The title might not exactly reflect the scientific findings of the manuscript. There is little direct evidence for a decrease in plasticity upon Med12 depletion.
2. Fig 1G: From the data provided it is not entirely clear how well screen results can be validated. Did some of the mutants identified in the screen also produce no detectable phenotypes? What would be the phenotype of knocking out an unrelated gene? In other words, are some of the weak phenotypes really showing Spry4 downregulation or are they withing the range of biological variance?
3. Rescue experiments by re-expressing Med12 in Med12 KO ESCs are missing. Can the differentiation and transcriptional phenotypes be rescued?
4. L365: The subheading "Transitions between embryonic... buffered against loss of Med12" is confusing. The data simply shows that Med12 KOs can still, albeit less efficiently generate PrE upon Gata TF OE. Is there evidence for some active buffering? I think the authors could simply report the data as is, stating that the phenotypes are not a complete block but an impairment of differentiation.
5. L386: Would it not make more sense to reduce dox concentrations in control cells to equalize Gata6 OE to equalize levels between Med12 KO and controls? A shorter pulse of Gata6 does not really directly address unequal expression levels due to loss of Med12. Different pulse length of OE might have consequences that the authors do not control for. This also impacts scRNAseq experiments which suffer from the same, in my opinion, suboptimal experimental setup. This is a point that needs to be addressed.
6. The reduced transcript number in Med12 KOs is interesting, but how does it come about. Is there indeed less transcriptional activity or is reduced transcript numbers a side effect of slower growth or the different cell states between WT and Med12 mutants. Appropriate experiments to address this should be performed.
7. I the proposed reduction of biological noise a feature of the PrE differentiation experiments or can it also be observed in epiblast differentiation.
8. I cannot follow the authors logic that Med12 loss results in enhanced separation between lineages. How is this experimentally supported.

Minor points:

Fig 3, Supp1 A: What exactly are the black and blue highlighted letters?

Significance

Overall, this is an interesting study. The screen has been performed to a high technical standard and differentiation defects were appropriately analyzed. The manuscript has some weaknesses in investigating the molecular mode of action of Med12 which could be improved to provide more significant insights.

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Referee #1

Evidence, reproducibility and clarity

The authors describe a genome-wide CRISPR screen in mouse ES cells to identify factors and genes that regulate positively and negatively FGF/ERK signaling during differentiation. Out of known and potentially novel regulating signals, Mediator subunit Med12 was a strong hit in the screen and it was clearly and extensively shown by that the loss of Med12 results in impaired FGF/ERK signal responsiveness, modulation of mRNA levels and disturbed cell differentiation leading to reduced stem cell plasticity.<br /> This is a very concise and well written manuscript that demonstrates for the first time the important role of Med12 in ES cells and during early cell differentiation. The results support data that had been previously observed in Med12 mouse models and in addition show that Med12 cooperates with various signaling systems to control gene expression during early lineage decision.

Fig. 3 Supp1A-B:<br /> The loci of all three independent Med12 mutant clones and the absence of Med12 should be included. Are all three Med12 loss-of-function mutants?

Minor:

Line 466: Should be Fig. 6F, not 6E.

Significance

The CRISPR screen identified list of some novel interesting factors that regulate FGF/ERK signaling in ES cells. Med12 was then analyzed in very detail on various levels and under various differentiation conditions, resulting in a complex picture how Med12 controls stem cell plasticity. These data support results observed in mouse models and identified novel regulating mechanisms of Med12.

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Referee #3

Evidence, reproducibility and clarity

This manuscript addresses the important topic of cell-cell junction maturation and mechanical stability, with a specific focus on how mechanotransduction through the Piezo1 channel regulates these processes. The authors present compelling in vivo evidence demonstrating that Piezo1 plays a role in junction stability and barrier function, particularly in aged tissue. The work makes a valuable contribution to our understanding of mechanotransduction in epithelial biology. However, several aspects of the mechanistic model and in vitro experiments require additional development to fully support the authors' conclusions.

Major Strengths:

• The in vivo experiments are well-designed and provide convincing evidence for Piezo1's role in barrier function
• The study identifies an important connection between mechanical sensing and junction maturation

• The age-dependent phenotype provides interesting insights into tissue mechanics

• Areas Requiring Additional Development:

a. Mechanistic Model Definition A major issue is that the central concept of Piezo1 "balancing membrane and cortical tension" requires more precise definition and experimental support. The authors need to clearly explain what this balance means mechanistically and how it is achieved.

b. Localization-Function Discrepancy There is an important inconsistency between the authors' claims about Piezo1's role and its localization: while they conclude that Piezo1 is crucial for mechanical stability, they also show that Piezo1 is not localized at mature junctions. This apparent contradiction needs to be addressed with a clear mechanistic explanation.

c. Quantification and Statistical Analysis Several key conclusions would benefit from more rigorous quantification: - The quantitation of junction maturation in Fig. 1a and 2a should include independent analysis of each experiment rather than pooling cells from multiple experiments - Actin morphology and pMLC2 levels at junctions in Fig. 1 need systematic quantification - Cytoskeletal dynamics and morphological changes in Piezo1-eKO cells (Fig. 2a) require quantification

d. Methodological and Timeline Clarity The analysis methods and temporal aspects of several experiments need better documentation: Analysis Methods:

The quantification method for mature adhesions (used in Figs. 1a, 1e, 1f, 2a) needs clarification. The Methods section states that "The transition from zipper-like adhesions to mature continuous intercellular junctions were quantified manually," but crucial details are missing: - What specific criteria defined a "continuous junction"? - Was this based on complete visibility of the cell perimeter as one junction? - How were cells classified as having continuous versus zipper-like adhesions?

e. The protein intensity quantification at junctions requires methodological clarification. The Methods state "For quantifying intensities at junctions, max projection images were generated, and region of interests (ROIs) were restricted to ZO1-positive junctions." However: - Were ROIs drawn empirically by the user? Or was the ZO-1 signal used to make a mask? - Was there an automated step to determine junctional areas (e.g., intensity threshold)? - Was the analysis blinded? If subjective methods were used, this should be clearly stated and potential variability addressed. 2. Timeline Documentation:

For blebbistatin experiments (Fig. 1e), specify observation timeframes and quantify the extent of accelerated maturation

The hypotonic shock experiment (Fig. 3e) timeline needs clarification: - When were measurements taken relative to Ca2+ switch? - Duration of hypotonic media exposure? - Were there time-dependent effects in cell response? 3. Data Support and Interpretation

a. Several conclusions require additional support or clarification: - The claim about "more dynamic cytoskeletal motion and irregularly shaped" cells (Fig. 2a) is not supported by the provided data. Quantification of dynamics and cell shape are needed to support this conclusion. Cytoskeletal imaging data would also be useful.

b. The interpretation of junctional tension requires revision: - Current conclusions about increased junctional tension are inferred indirectly from vinculin (Fig. 1c) and a18-catenin (Fig. S1a) immunostaining images. - Consider either:

a) Adding direct junctional tension measurements (e.g., optical measurements, PMID 31964776)

b) Limiting claims to well-supported morphological differences and moving tension-related interpretations to the Discussion as speculative elements

c. The description "Analysis of vinculin translocation to intercellular junctions showed reduced levels of vinculin at cell-cell contacts, but abundant vinculin at cell-matrix adhesions (Supplementary Fig. S2a), indicating abnormal build-up of stresses at intercellular junctions of Piezo1-eKO cells" needs revision: - "Build-up" suggests higher tensions in Piezo1-eKO cells, which contradicts impaired adhesion maturation findings. Suggest replacing with "distribution" or "organization" "Intercellular" is used ambiguously to include both cell-cell and cell-matrix adhesions 4. Literature Context:

The discussion should incorporate recent relevant literature on Piezo1's role in tight junction regulation (e.g., PMID 37005489, PMID 33636174, PMID 31409093) 5. Technical Considerations - For localization studies (Fig. 2), using keratinocytes from Piezo1-tdTomato mouse (JAX #029214) would be preferable to heterologously-expressed Piezo1-FLAG, as it would avoid potential artifacts from non-physiological expression levels - Supp Fig. 1b requires additional replicates - The Fig. 3A legend states "Note increase in FLIPPER-TR lifetime indicative of elevated membrane tension in Piezo1-eKO" when the data actually shows the opposite - a decrease in Flipper-TR lifetime indicating lower membrane tension 6. Conceptual and Experimental Clarity Needed Several statements require clearer explanation or additional supporting evidence:

a. Regarding junction maturation mechanisms:

The authors state: "This indicated that formation of belt-like adhesions was associated with initial contractility build-up by actomyosin stress fibers linked to junctions, followed by a switch to parallel actomyosin bundles and reduced contractility at adhesions, while the junctions themselves were stabilized in a stressed state indicated by a strengthened actin-junction link." Each part of this claim needs experimental support: - The "initial contractility build-up by actomyosin stress fibers linked to junctions" needs to be demonstrated - The "switch to parallel actomyosin bundles and reduced contractility at adhesions" requires quantification - The claim about "junctions themselves were stabilized in a stressed state" needs stronger evidence

b. The statement "contact expansion from zippers to a belt requires collaborative regulation of adhesion tension and actomyosin cytoskeleton to lower interfacial tension at the contact" is unclear and needs clarification

c. The claim "Concomitant with emergence of continuous junctions (8h), the stress fibers were replaced by thick actin bundles positioned perpendicularly to junctions (Fig. 1b)" is not clearly supported by the data 7. Regarding experimental interpretation: - In Fig. 1e, the authors claim that 5µM blebbistatin accelerates junction maturation, but this conclusion is not supported by the statistics (p = 0.0784). Additionally, the timeframe of observation and the quantification of maturation speed should be specified - The results section describing Fig. 3 presents seemingly disconnected observations without clear mechanistic links between them, making it difficult to follow the authors' logic and support their conclusions - The mechanism by which both reduced contractility (blebbistatin) and increased membrane tension can accelerate maturation (Fig. 1e, f; and also in Piezo1-eKO Fig. 3d, e) needs explanation. The fact that these interventions also accelerate maturation also in Piezo1-eKO suggests a mechanism independent of Piezo1 which is at odds with their broad conclusion that Piezo1 balances membrane tension and cortical contractility in the maturation process. The precise mechanism of Piezo1's role in sensing membrane and cortex tension requires clarification. - How Piezo1 maintains mechanical stability of mature junctions despite not being localized there needs to be explained 8. Suggested Additional Experiments:

a. Optional: Given the age-dependent tissue stiffness effects proposed by the authors, examining keratinocyte behavior in vitro on substrates of varying stiffness would provide valuable insights

b. Optional: Direct measurements of tension at cell-cell junctions where Piezo1 localizes would help validate the proposed mechanical model 9. Minor Points: - The cell biology sections, particularly descriptions of in vitro experiments, would benefit from a thorough revision to improve precision and clarity. For instance, the Results section describes "Analysis of vinculin translocation to intercellular junctions" when no translocation is actually being studied - Figure legends should clearly indicate what individual data points represent - Several conclusions are overstated. For example, the authors conclude that "Piezo1 controls the maturation process" and that "Piezo1 is required for cell junction maturation into junctional belts" based on Fig. 2. These are exaggerated claims since maturation still progresses in Piezo1's absence, just more slowly. "Regulates" or "modulates" would be more appropriate terminology

In conclusion, while this manuscript presents important findings regarding Piezo1's role in junction maturation and stability, addressing the mechanistic and quantification issues outlined above is essential for supporting the authors' conclusions. The authors have laid groundwork for understanding an important biological process, and addressing these points would help readers better appreciate the significance of their findings.

Significance

General Assessment: This study investigates the critical role of mechanosensing in epithelial barrier formation and maintenance, with a particular focus on Piezo1's contribution to junction maturation and stability. The work's primary strengths lie in its compelling in vivo demonstrations of Piezo1's importance for barrier function, particularly in aged tissue, and its identification of a novel connection between mechanical sensing and junction maturation. The age-dependent phenotype provides valuable insights into tissue mechanics and barrier maintenance. However, the mechanistic understanding of how Piezo1 coordinates these processes requires further development, particularly regarding the proposed balance between membrane and cortical tension.

Advance: This work provides several important advances:

1. First demonstration of Piezo1's role in regulating the maturation of cell-cell junctions from zipper-like to belt-like structures
2. Novel insights into how mechanical forces influence junction maturation through mechanosensitive ion channels
3. Important connection between aging, tissue mechanics, and barrier function
4. Integration of mechanical sensing with junction assembly and stability

The findings extend our understanding of epithelial barrier formation beyond traditional molecular pathways to include mechanotransduction, suggesting new therapeutic possibilities for barrier dysfunction. The age-dependent phenotype is particularly significant as it reveals how mechanical properties of tissue influence barrier maintenance over time.

Audience: This research will be of broad interest to multiple communities:

• Cell biologists studying junction assembly and epithelial organization
• Mechanobiologists interested in force transmission and sensing
• Ion channel researchers interested in the physiological roles of channels
• Aging researchers investigating tissue barrier function
• Bioengineers developing therapeutic strategies for epithelial barriers

The findings have both basic research and translational implications, particularly for understanding and treating age-related barrier dysfunction in epithelia.

Reviewer Expertise: Cell biology, mechanobiology, live cell imaging, quantitative image analysis, ion channels I have sufficient expertise to evaluate all aspects of the manuscript except for the specific age-related physiological changes in mouse skin, which falls outside my area of expertise.

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Referee #2

Evidence, reproducibility and clarity

This manuscript describes the role of the mechanosensitive ion channel Piezo1 in epithelial junction assembly, using Piezo-1-KO primary epidermal keratinocytes in vitro and mouse skin in vivo. The authors conclude that Piezo1 allows balancing of membrane versus cortical tension to stabilize junctions and promote tight juntion (TJ) barrier integrity assembly. The conclusion that Piezo1 has an important function in the formation and maintenance of apical junctions of keratinocytes both in vitro and in vivo is well documented by experiments in WT, KO and rescue cells/tissues where different parameters are carefully measured: protein localization, quantification of mature junctions, membrane tension using the flipper probe, use of the myosin inhibitor blebbistatin, analysis of cortical stiffness by AFM, etc. Although, the physiological relevance and the mechanism through which Piezo operates in young skin are not clear, the authors make reasonable claims, that are not too speculative.

Major comments:

1. The Supplementary Figure 4d (panel d) that is described in the Results section is missing. It supposedly shows that 1 year-old Piezo1-eKO mice diplay an increase in transepidermal water loss, inducating that TJ barrier function is compromised. The Figure legend for the panel is also missing. Please provide the Figure panel and the legend.
2. TJ barrier function depends on claudins, and the loss of claudin-1 leads to transepidermal water loss (please cite the relevant paper from the Tsukita lab). Considering that altered TJ barrier function is observed only in 1-yr old mice (Supplementary Figure to be shown, see point n.1) and not in young mice (Suppl. Fig. 3f-h), the expression pattern of the main claudin isoforms, and especially claudin-1, in the different cell populations (see Suppl. Fig. 3b, or by IF analysis) in young vs old and WT vs KO mice must provided, to provide a mechanistic basis for the observed TJ barrier phenotype. This would help to determine if the phenotype is linked to altered claudin expression or to altered (increased) perijunctional tension.
3. Mechanistically, the authors mention in the Discussion that Piezo1 might act through RhoA signaling. In Rübsam et al 2017 the authors showed that the uppermost viable layer of the skin has increased apical junctional tension, due to anisotropy of AJ distribution which correlates with EGFR activation and localization. In this context, it is important to know if KO of Piezo-1 affects EGFR localization and signaling, and to probe the RhoA pathway using for example the ROCK inhibitor, instead of blebbistatin.

Minor comments:

1. The Methods sections should be improved with additional details. For example, the description of quantification of junctional labeling is vague, and there is often no or little indication in the Legends that specifies number of experiments and junctional segments. In addition, quantification of junctional stainings for specific proteins should be done using a junctional reference marker and not as "absolute" values, because there can be variability of staining between samples and experiments. This is especially important when measuring ZO-1, which is a dual AJ-TJ protein (for example at zipper-like junctions ZO-1 colocalizes with AJ markers). Double labelling with a true TJ marker (occludin or cingulin) and/or a true AJ marker (PLEKHA7, afadin, Ecadherin or a catenin) and quantifying junctional labeling by ratio is highly recommended. This is particularly important when evaluating tension-sensitive epitopes/antigens (alpha-catenin, vinculin, etc)
2. Please use ZO-1 (and ZO-2) consistently, instead of ZO1 (or ZO2), which is completely inaccurate.
3. Plase cite Furuse et al 2002 JCB (see above).
4. Please include statistical data in Figure Legends, specifying the number of separate experiments and number of samples. At least three experiments is recommended.
5. At the end of the introduction the authors mention "putative" occludin-containing TJs. I would delete putative. Epithelial junctions that contain a continuous circumferential linear distribution of occludin/ZO-1/cingulin and form a barrier comply with the definition of a TJs (Citi et al JCS 2024) .
6. Please insert page numbers in the manuscript.

Significance

The notion that mechanosensitive calcium channels contribute to the formation of continuous apical junctions (repair and assembly) was introduced by the Miller lab, using Xenopus oocytes. This manuscript provides a significant conceptual advance, not only by using in vitro and in vivo mouse (mammalian) epidermal keratinocytes as model system, but especially by using Piezo1-KO and rescue experiments, which was not done in the Xenopus model.

This research would be of great interest to cell biologists interested in epithelial differentiation, polarization and junction assembly, and to clinicians that are interested in the molecular basis of skin pathophysiology.

My expertise is in the biochemistry, cell biology and mechanobiology of epithelial junctions. I have used Xenopus embryos, cultured epithelial cells, primary keratinocytes and keratinocyte cell lines and KO mice as model systems. The research of my group focuses on how specific cytoskeletal proteins are organized to transmit forces and are recruited to junctions, and how junctional proteins respond to mechanical force. I have experience in all of the methods described in this paper, except for transepidermal water loss measurement, in situ RNA hybridization and mechanical stretching experiments.

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Referee #1

Evidence, reproducibility and clarity

The studies described in this manuscript investigated the mechanical regulation of tight junction (TJ) maturation in the epidermis using a combination of in vitro and in vivo analysis. The findings indicate that during calcium-induced cell-cell adhesion in keratinocytes, there is an initial build up cortical tension in the actin cytoskeleton, followed by an increase in membrane tension, which is required for formation of mature TJs. The studies also demonstrate that loss of Piezo1 delays TJ maturation via defects in membrane tension. Loss of Piezo1 also impaired epidermal homeostasis and barrier function in aged mice. The authors propose that the balance in forces between the cortex and membrane is essential for TJ assembly and is mediated by Piezo1.

Overall, the studies are carefully designed and executed and provide a clear role for membrane tension and Piezo1 in TJ development, making use of molecular forces sensors, imaging, and chemical and genetic perturbations. However, not all of the conclusions are fully supported by the data, and some key findings require additional quantitative and statistical analysis.

1. The statement at the end of page 5 ("This indicated that formation of belt-like...) is somewhat overinterpreted from the data shown. To draw conclusions about a switch to reduced contractility at adhesions requires more careful spatio-temporal quantification of F-actin and pmyosin beyond the example single cells shown in 1b. It would also help to see the localization of Ecadherin during this process.
2. To avoid confusion, the authors should pay careful attention to terminology and be specific when referring to adherens junctions or TJs, rather than just junctions generally.
3. The labelling of Figure 2b could be clearer. Were the CNL cells also transfected with Piezo1 or mock transfected to control for general effects of transfection? This was not clear from the figure captions.
4. In Figure 2c-g it is not specified which timepoints the images represent, and the qualitative description of changes in localisation require quantification.
5. The importance of Piezo1 in junction maturation is somewhat overstated throughout. While Piezo KO clearly delays TJ maturation, the process can still be completed. In the absence of Piezo1 what triggers the rise in membrane tension? Could there be any compensation from Piezo2?
6. Some of the differences noted are subtle and not strongly significant, such as K6expression, Ca++ induced Piezo1 expression, and F4/80 staining. The conclusions related to these responses should be tempered or qualified.
7. Analysis of the immune infiltration and the suggested inflammatory response in aged mice is fairly preliminary and not well supported by the data. A second marker of macrophages and addition of T cell markers would help clarify the type of immune response. It would also help to describe the localisation of specific immune cells in more detail and include a direct marker of inflammation (e.g. inflammatory cytokines).
8. OPTIONAL: Although not essential for the conclusions of the study, the impact and insight could be improved by providing more analysis of the mechanism for the role of Piezo1. For example, does the build up of cortical tension trigger changes in ion channel signalling, and how does this then regulate membrane tension? Is RhoA or aPKC involved?

Significance

The process by which epithelia assemble and maintain effective barriers is complex and requires precise spatio-temporal regulation. This study provides some new insight into the mechanical regulation of TJ assembly within the epidermis. It builds upon previous work that identified essential biomechanical cross-talk between adherens junctions and TJs and adds some new information on the timings and specific roles of membrane tension and Piezo1. The interplay between cortical and membrane tension is noteworthy, and this mechanism may have important implications in other barrier tissues. A limitation of the study is a lack of mechanistic detail in how the mechanical switch occurs during TJ maturation, including the specific molecules, structures, and interactions with Piezo1.

The study also describes the functional implications, whereby loss of Piezo1 in the mouse disrupts barrier integrity. However, these effects were quite subtle. Barrier homeostasis was only disrupted in aged mice, and in vitro, loss of Piezo1 delayed but did not prevent junction maturation. It is therefore interesting to speculate what other mechanisms may be involved in TJ maturation. A potential limitation here is also a lack of detail in the analysis of the inflammatory and immune response in Piezo KO skin.